# Analysis of the Development Trend and Scheme of Agricultural Electrification Intelligence Based on Big Data Mining and OLAP Tool Analysis Algorithm

Authors: Chao Chen; Jun Fu; Li Zou; Yi Liu; Yakun Sun
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

To transform from a large agricultural country to a strong agricultural country, China must take the road of modernized, automated, and information-based agriculture. With the advantages of high efficiency, low cost, flexibility, stability, and reliability, electrified production is the material and technical basis for agricultural mechanization, automation, industrialization, and management modernization. Big data mining and OLAP tool analysis algorithm has powerful data processing ability and occupies a very important part in various fields. This article analyzes the current situation of agricultural electrification development, electrification development dynamics, and electrification application scope based on big data mining and OLAP tool analysis algorithm, and finally comes up with several suggestions for agricultural electrification development.


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## Body

## 1. Introduction

With the advent of the information age, various data are rapidly expanding and diverse. In the face of the huge amount of complex storable information data, how to analyze and find the key information data resources or information-related knowledge resources that can be used in a practical or meaningful way is another very complex, difficult, and urgent task for human beings. The theory of data and information resources mining has been increasingly and rapidly developed to meet the basic theoretical needs of data and information utilization. Data information resource mining should actually be the study of how human beings can extract all kinds of data information resources and other related information resources that are temporarily hidden and of unknown significance, but are still possible and useful or meaningful in practice, from the massive information data resources that contain a large amount of information and are not quite complete, with a certain amount of noise, fuzzy information, and various random changes [1].OLAP tool analysis algorithm is a data processing tool for big data mining algorithms, which is able to collect the number of initial devices, based on the analysis and processing, and then the initial data, processed into a systematic data model, and then, through the needs of different users of these data, these data are classified thematically to make the huge database more user-friendly retrieval. Finally, through online analysis, the data processing system can perform multidimensional processing. After these processes, all the factors that can influence are fed back to the relevant department to make decisions for him and provide maximum data support. Databases involve many things, not only mathematics, algorithms in mathematics, but also many other fields and disciplines, such as maximum expectation algorithms, correlation analysis algorithms, and neural network algorithms that we are used to using. The scientific application of these algorithms not only allows the database to automatically identify correlations between data, but also to calculate future developments and the laws of things based on the trends of data [2].For China to change from a large agricultural country to a strong agricultural country, it must follow the path of modernized, automated, and information-based agriculture. Whether it is intelligent equipment for multiple operations in large agricultural environments, intelligent control of crop growth environments in facility agriculture, or digital clouds for agricultural information, the various parameters controlled by terminals should be based on the intelligent development posture of agricultural electrification. Agricultural electrification combined with the development of Chinese agriculture focuses on training the talents needed for modernized, automated, and information-based agriculture. With the development of primitive, traditional, and industrial agriculture, the increase of population, and the deterioration of ecological environment, it is unrealistic to improve agricultural electrification intelligent development of agriculture by increasing the arable land area and consuming industrial products (pesticides, fertilizers, fuel, electricity, etc.). With the development of global navigation satellite systems, remote sensing, and crop simulation models, the results of the information technology revolution have been applied to agricultural GPS. Through the combination of intelligent information technology and agricultural machinery, traditional operations such as watering, fertilizing, and weeding are completed mechanically, thus realizing the effective use of agricultural resources and improving the quality and efficiency of the agricultural industry. This agricultural management model is called “fine agriculture,” which reflects the electrification and intelligence of agricultural production, and the development of agricultural production is good through the massive application of mechanization. In 2020, the Electrified Smart Agriculture Consortium organized 50 experts to collect, process, and analyze spatial and temporal data, personal data, and other relevant information to define agricultural electrified intelligence as a management strategy that provides resource utilization, productivity, quality, efficiency, and sustainability of agricultural production by making management decisions based on the laws of change. In the context of information technology development, the development of agricultural electrification intelligence has changed considerably both in the field of research and in practical applications [3].In this article, we analyze the development of agricultural electrification intelligence in China through the K-means algorithm in the algorithm of cluster analysis, the A-priori algorithm in the classical algorithm of association algorithm, and the analysis of OLAP tools to analyze the future development of the program.


## 2. Research Background

### 2.1. Review of Data Mining Algorithms

The latest foreign big data analysis and its mining methods involved in the field of large databases related to the scope and content of research have been very mature and extensive, and have achieved many significant and representative technical results [4]. The ongoing research on how to use knowledge and data systems in database modeling and related services has largely led to a number of fairly significant technical advances with some important representative values so far [5]. The academic results can be summarized and organized as follows: methods such as induction and analysis oriented to the relational attribute features of data are used to find the relational attribute differentiation rules and feature rules of data attributes in relational database models. In order to be able to go deeper into solving the most complex uncertainty problems in the database computation and analysis of Durie’s rough data collection theory, evidence-theoretic models and fuzzy set theory are also applicable, forming a database with an increasingly high degree of extensive and in-depth study of practice models for related issues, and related technical applications [6]. Compared with other similar research and practice projects jointly conducted abroad, the Chinese subject members in China have been late in developing the ability to rapidly analyze and mine network data and to continuously discover and acquire the value of network knowledge. The transformation of some technical research results and the development of research practices and applications are still mostly in the early stage of development or the beginning of the mapping stage. It has not been further developed to form such a force with a more complete and mature technical system [7]. Currently, most of the professionals or researchers engaged in research or development work are concentrated in universities and some research institutes or companies. The most important recent research results are summarized as follows: (1) an in-depth study by the Institute of Systems Engineering of the National Natural Science Foundation of China, Beijing, which shows that the method and its practical applications in the field of knowledge system detection are not clear; (2) an in-depth theoretical study of data cubic algebra; (3) optimization and improvement of the rule-building algorithm of Jilin University, including East China University of Science and Technology, Beijing University, Fudan Zhejiang University, China University of Technology, and the Institute of Mathematics of the Chinese Academy of Sciences efforts; (4) Nanjing University, Sichuan University, and Shanghai Jiaotong University have conducted empirical studies on unstructured data mining problems. (5) The Institute of Computing Technology of the Chinese Academy of Sciences has designed and developed a multiperson data mining tool that can detect various types of anomalies in the local tax system using decision tree algorithms [8].The research content of this article is agricultural electrification intelligent development trend and scheme analysis based on big data mining and OLAP tool analysis algorithm. Therefore, the research on data mining algorithms is mainly focused on the development needs of agricultural electrification intelligence in this article, and other types of algorithms for data mining are not studied in detail.


### 2.2. Overview of OLAP Tool Analysis

In the 1960s, the Federal Exchange proposed the relationship model, which promoted the development of online transaction processing [9]. The main function of online transaction (OLAP tool analysis) processing is to handle basic and scheduled transactions of traditional relational databases. In the 1990s, as society progressed and the amount of data increased, people began to want more information from the data. The concept of online analytical processing (OLAP) was also introduced. OLAP technology is the core of data warehousing and provides decision support to managers mainly through complex analysis. In the past 20 years, stereotactic technology has been rapidly developed and widely used, including OLAP technology source, data warehouse OLAP server, and front-end tools to clean up. OLAP server technology is developed on the basis of foreign countries, of which palo is an open-source project of Baidu in 2017; Baidu developed an interactive SQL data warehouse with high concurrency and low latency, which is the OLAP engine currently used by Baidu. OLAP is currently widely used and has the following applications: one of the research used for invention patents is a dynamic cost estimation method and device for OLAP query engine proposed by CPYU Technology, which collects data volume and field libraries of data tables in data warehouses, while recording historical query SQL and corresponding query time, discrete SQL execution time, and generating labels [10]. The table information, filter fields, analysis fields, time division, table data volume, and analysis field bases of the data tables are also encoded into their respective features, and a classification algorithm is trained based on the extracted features of the data tables to transform the execution plans of the OLAP query engine into feature vectors that are input to a machine learning model for base evaluation to obtain the estimated query times of the data tables. This method and the apparatus enable OLAP query engines to quickly and accurately evaluate the cost of input query SQL and generate optimal execution plans. In addition, with the rapid development of the information age, people’s daily life and work are inseparable from various network information devices and computer technologies. In order to further develop the research of computer-aided information analysis, the technical basis of computer-aided information analysis is established from the highest level of building design to guide the analysis of computer-aided information. OLAP analysis, as a computer-aided information analysis tool, can provide the basis for the application of computer-aided information analysis methods in the fields of database, data storage, and other technologies. Technical frameworks for data collection and analysis, social media analysis, large-scale data analysis, and online analysis are developed and analyzed [11].The research content of this article is the development trend and solution analysis of agricultural electrification intelligence based on big data mining and OLAP tool analysis algorithm. Therefore, the analysis of OLAP tools is also mainly focused on the development needs of agricultural electrification intelligence in this article, according to the needs of this article, and not to carry out a comprehensive study of OLAP tools in a big and comprehensive way.


### 2.3. Review of the Development of Intelligent Agricultural Electrification

Soon after the world’s first power plant was built and operated in New York in 1882, Edison began using electricity for food production, lighting, and irrigation. In the 1920s, the use of AC and transformers enabled the transfer of electricity to large and dispersed agricultural areas, thus creating the conditions for the electrification of agriculture [12]. In the 1930s, the government established the Rural Electrification Organization and passed the Rural Electrification Act to provide credit for agricultural electrification. After the October Revolution, the Soviet Union strengthened its rural energy base with field testing and production of electromechanical equipment. In the 1970s, all collective farms and kibbutzim were largely electrified, and stand-alone multi-engine machines, fertilizers, cultivators, and cotton harvesters appeared. Almost all factories and mills in industrial countries are now connected to the electricity supply. However, the level of agricultural electrification in developing countries is still low and uneven. In the 1930s, river networks in southern China used small boats to transport combustion generators to power water pumps and food processing equipment. After the founding of New China, agricultural electrification gradually took place throughout the country. Agricultural energy consumption in 1966 was 5.9 × 109 kW, 81.4 times higher than in 1949. In 1985, agricultural electricity consumption (excluding rural public electricity) was 5,089 × 1010 kilowatts, and rural electricity consumption was 76.8 kilowatts per capita per year, with an average of 35 kilowatts per hectare of land per year. In addition to large national grids and large power plants, the development of agricultural electrification includes large hydroelectric projects in rural areas and small towns financed by municipalities and farmers. The state should provide adequate subsidies and support [13]. In 1983, 100 small hydropower plants with abundant resources and a certain energy base were selected as national pilots for the development of agricultural electrification and rural electrification. In 1985, the agricultural electricity consumption of these 100 countries was 3.8 × 109 kWh, and the per capita electricity consumption was 115 kW. At the end of 1985, 55,754 small hydropower stations were built in rural areas, generating 3,802 MW of electricity. In the same year, 90% of urban households, 70% of rural households and 63% of rural households were supplied with electricity. In the 1980s, more than 500 agricultural energy projects were technically implemented, covering all production processes and most production factors in all sectors of agriculture and all aspects of people’s lives. Electrification of all fixed farms, such as rural industry, food industry, water supply, irrigation, refrigeration, heating, and ventilation. Electrification of horticulture also has a successful experience: high electrification of greenhouses creates artificial climate and environmental conditions suitable for plant growth. The processes of an electrified farm are water supply, feeding, ventilation, lighting, heating, milking, disinfection, etc. Automation and maintenance of good and economical nutrition ensure consistency and high productivity of animal products. Fielding and motorization fielding operations are difficult to carry out, mainly in the areas of alarms, control systems, condition control, equipment demonstration, and automated driving. However, the circular nozzles are fully electric and highly automated. One person can take care of several units and irrigate thousands of hectares. In rural life, electrification has begun in areas such as lighting, radio, telephone, movies, and television. At present, with the improvement of rural living standards, various types of urban household appliances are gradually introduced for use by villagers [14].


## 2.1. Review of Data Mining Algorithms

The latest foreign big data analysis and its mining methods involved in the field of large databases related to the scope and content of research have been very mature and extensive, and have achieved many significant and representative technical results [4]. The ongoing research on how to use knowledge and data systems in database modeling and related services has largely led to a number of fairly significant technical advances with some important representative values so far [5]. The academic results can be summarized and organized as follows: methods such as induction and analysis oriented to the relational attribute features of data are used to find the relational attribute differentiation rules and feature rules of data attributes in relational database models. In order to be able to go deeper into solving the most complex uncertainty problems in the database computation and analysis of Durie’s rough data collection theory, evidence-theoretic models and fuzzy set theory are also applicable, forming a database with an increasingly high degree of extensive and in-depth study of practice models for related issues, and related technical applications [6]. Compared with other similar research and practice projects jointly conducted abroad, the Chinese subject members in China have been late in developing the ability to rapidly analyze and mine network data and to continuously discover and acquire the value of network knowledge. The transformation of some technical research results and the development of research practices and applications are still mostly in the early stage of development or the beginning of the mapping stage. It has not been further developed to form such a force with a more complete and mature technical system [7]. Currently, most of the professionals or researchers engaged in research or development work are concentrated in universities and some research institutes or companies. The most important recent research results are summarized as follows: (1) an in-depth study by the Institute of Systems Engineering of the National Natural Science Foundation of China, Beijing, which shows that the method and its practical applications in the field of knowledge system detection are not clear; (2) an in-depth theoretical study of data cubic algebra; (3) optimization and improvement of the rule-building algorithm of Jilin University, including East China University of Science and Technology, Beijing University, Fudan Zhejiang University, China University of Technology, and the Institute of Mathematics of the Chinese Academy of Sciences efforts; (4) Nanjing University, Sichuan University, and Shanghai Jiaotong University have conducted empirical studies on unstructured data mining problems. (5) The Institute of Computing Technology of the Chinese Academy of Sciences has designed and developed a multiperson data mining tool that can detect various types of anomalies in the local tax system using decision tree algorithms [8].The research content of this article is agricultural electrification intelligent development trend and scheme analysis based on big data mining and OLAP tool analysis algorithm. Therefore, the research on data mining algorithms is mainly focused on the development needs of agricultural electrification intelligence in this article, and other types of algorithms for data mining are not studied in detail.


## 2.2. Overview of OLAP Tool Analysis

In the 1960s, the Federal Exchange proposed the relationship model, which promoted the development of online transaction processing [9]. The main function of online transaction (OLAP tool analysis) processing is to handle basic and scheduled transactions of traditional relational databases. In the 1990s, as society progressed and the amount of data increased, people began to want more information from the data. The concept of online analytical processing (OLAP) was also introduced. OLAP technology is the core of data warehousing and provides decision support to managers mainly through complex analysis. In the past 20 years, stereotactic technology has been rapidly developed and widely used, including OLAP technology source, data warehouse OLAP server, and front-end tools to clean up. OLAP server technology is developed on the basis of foreign countries, of which palo is an open-source project of Baidu in 2017; Baidu developed an interactive SQL data warehouse with high concurrency and low latency, which is the OLAP engine currently used by Baidu. OLAP is currently widely used and has the following applications: one of the research used for invention patents is a dynamic cost estimation method and device for OLAP query engine proposed by CPYU Technology, which collects data volume and field libraries of data tables in data warehouses, while recording historical query SQL and corresponding query time, discrete SQL execution time, and generating labels [10]. The table information, filter fields, analysis fields, time division, table data volume, and analysis field bases of the data tables are also encoded into their respective features, and a classification algorithm is trained based on the extracted features of the data tables to transform the execution plans of the OLAP query engine into feature vectors that are input to a machine learning model for base evaluation to obtain the estimated query times of the data tables. This method and the apparatus enable OLAP query engines to quickly and accurately evaluate the cost of input query SQL and generate optimal execution plans. In addition, with the rapid development of the information age, people’s daily life and work are inseparable from various network information devices and computer technologies. In order to further develop the research of computer-aided information analysis, the technical basis of computer-aided information analysis is established from the highest level of building design to guide the analysis of computer-aided information. OLAP analysis, as a computer-aided information analysis tool, can provide the basis for the application of computer-aided information analysis methods in the fields of database, data storage, and other technologies. Technical frameworks for data collection and analysis, social media analysis, large-scale data analysis, and online analysis are developed and analyzed [11].The research content of this article is the development trend and solution analysis of agricultural electrification intelligence based on big data mining and OLAP tool analysis algorithm. Therefore, the analysis of OLAP tools is also mainly focused on the development needs of agricultural electrification intelligence in this article, according to the needs of this article, and not to carry out a comprehensive study of OLAP tools in a big and comprehensive way.


## 2.3. Review of the Development of Intelligent Agricultural Electrification

Soon after the world’s first power plant was built and operated in New York in 1882, Edison began using electricity for food production, lighting, and irrigation. In the 1920s, the use of AC and transformers enabled the transfer of electricity to large and dispersed agricultural areas, thus creating the conditions for the electrification of agriculture [12]. In the 1930s, the government established the Rural Electrification Organization and passed the Rural Electrification Act to provide credit for agricultural electrification. After the October Revolution, the Soviet Union strengthened its rural energy base with field testing and production of electromechanical equipment. In the 1970s, all collective farms and kibbutzim were largely electrified, and stand-alone multi-engine machines, fertilizers, cultivators, and cotton harvesters appeared. Almost all factories and mills in industrial countries are now connected to the electricity supply. However, the level of agricultural electrification in developing countries is still low and uneven. In the 1930s, river networks in southern China used small boats to transport combustion generators to power water pumps and food processing equipment. After the founding of New China, agricultural electrification gradually took place throughout the country. Agricultural energy consumption in 1966 was 5.9 × 109 kW, 81.4 times higher than in 1949. In 1985, agricultural electricity consumption (excluding rural public electricity) was 5,089 × 1010 kilowatts, and rural electricity consumption was 76.8 kilowatts per capita per year, with an average of 35 kilowatts per hectare of land per year. In addition to large national grids and large power plants, the development of agricultural electrification includes large hydroelectric projects in rural areas and small towns financed by municipalities and farmers. The state should provide adequate subsidies and support [13]. In 1983, 100 small hydropower plants with abundant resources and a certain energy base were selected as national pilots for the development of agricultural electrification and rural electrification. In 1985, the agricultural electricity consumption of these 100 countries was 3.8 × 109 kWh, and the per capita electricity consumption was 115 kW. At the end of 1985, 55,754 small hydropower stations were built in rural areas, generating 3,802 MW of electricity. In the same year, 90% of urban households, 70% of rural households and 63% of rural households were supplied with electricity. In the 1980s, more than 500 agricultural energy projects were technically implemented, covering all production processes and most production factors in all sectors of agriculture and all aspects of people’s lives. Electrification of all fixed farms, such as rural industry, food industry, water supply, irrigation, refrigeration, heating, and ventilation. Electrification of horticulture also has a successful experience: high electrification of greenhouses creates artificial climate and environmental conditions suitable for plant growth. The processes of an electrified farm are water supply, feeding, ventilation, lighting, heating, milking, disinfection, etc. Automation and maintenance of good and economical nutrition ensure consistency and high productivity of animal products. Fielding and motorization fielding operations are difficult to carry out, mainly in the areas of alarms, control systems, condition control, equipment demonstration, and automated driving. However, the circular nozzles are fully electric and highly automated. One person can take care of several units and irrigate thousands of hectares. In rural life, electrification has begun in areas such as lighting, radio, telephone, movies, and television. At present, with the improvement of rural living standards, various types of urban household appliances are gradually introduced for use by villagers [14].


## 3. Research Methods and Materials

Firstly, the research of this article is the analysis of the development trend and scheme of agricultural electrification intelligence based on big data mining and OLAP tool analysis algorithm. Secondly, the graphical data presented in this article are not entirely provided by the relevant statistical agencies, and this article should be used to predict and calculate the relevant data through such mining and analysis. Finally, the research scope of this article is the whole country, not a village or a town, so it is not just simple statistics, but also clustering and frequent set mining.


### 3.1. Basic Concepts

#### 3.1.1. The Meaning of Data Mining Algorithms

Data information mining research is also often translated and understood as big data knowledge exploration research and information data information mining, and research in databases must begin with research found in knowledge systems, which is an important step in scientific research. Data retrieval is often seen as the process of automatically retrieving large amounts of valuable information hidden in a specific way in a large number of information systems. Data information mining systems are also usually associated with other computer science systems that utilize data statistics and analysis, automatic information retrieval, machine learning, expert systems, pattern recognition, online information analysis models, and information processing technology methods to assist in achieving the above system goals. A data mining algorithm system is a methodological and computational technology approach to data mining that provides a system of algorithmic data mining models that can be created based on existing data. In order to achieve the creation of a data mining model, the algorithm must first analyze what kind of data the system needs to provide and look for specific types of patterns and trends. Data mining algorithms combine expertise in modern machine deep learning, pattern recognition, statistics, database principles, and computer artificial intelligence systems to obtain knowledge in a large amount of information and provide a reference for decision-making [15].


#### 3.1.2. Types of Data Mining Algorithms

Data mining algorithms are divided into two categories in the general direction: supervised learning and unsupervised learning, and supervised learning and unsupervised learning are further divided into several major categories [16], as shown in Figure 1.Figure 1
Classification of data mining algorithms.Several important data mining algorithms are described and analyzed below.(1) Theory of Clustering Analysis. Data clustering (data clustering) is a multivariate statistical analysis method, using the idea of similar clustering, we can find useful information by classifying samples or indicators. The objects considered in a data cluster are usually a considerable number of samples that can be appropriately classified according to their characteristics. This classification should not be based on existing patterns, nor should it be based on existing patterns that are not known in advance. Data clusters evolved from taxonomy. In ancient taxonomy, qualitative classification was generally performed using historical experience and quantitative classification was rarely performed using mathematical tools. In the modern sense, data clustering arose after the introduction of multivariate analysis for numerical classification. Clustering is the process of classifying data into different categories or groups based on their attributes. Objects belonging to the same type or group have strong similarities, but they are not significantly different from each other. Objects belonging to the same type or group have very strong similarities, but there are no significant differences between them. These datasets cover many fields, including mathematics, computer science, statistics, and economics. Data clustering is also widely used in many fields, and clustering techniques have been developed in different application areas [17].(2) Theory of Association Analysis. Association rules are used to analyze and determine the degree of association between different variables or people in a database, as well as to develop models of customer purchasing behavior, such as purchasing peripherals on a desktop computer. Contact rules can provide valuable links between different portfolios of items from different business activities. The rule formulation process associated with a given operation can be described in a service database: for a database with a given problem, the problem is to eliminate the minimum support and trust in order to find the corresponding communication rules [18].(3) The Process of Data Mining Algorithms. The process of the data mining algorithm to mine data is given as follows: first, the external data are organized, followed by the preprocessing work on the data; then, the data information is formed; after the main algorithmic procedures and formulas of data mining, the analysis is processed, and finally, the calculation results of data mining are formed and given to display, as shown in Figure 2.Figure 2
Data mining algorithm flowchart.


#### 3.1.3. OLAP Tool

OLAP is a multidimensional data analysis and capture tool that allows operators to analyze data from multiple perspectives. By importing factual data from agricultural and agricultural production data warehouses into the OLAP server, facts, tables, and correlations in the database are used to build star models. The OLAP tool is an important application in data warehousing systems. It supports complex analytical processes, especially for decision-makers and implementers. It provides decision-makers with intuitive and comprehensive search results by processing complex queries containing large amounts of data quickly and flexibly according to the needs of analysts, enabling them to accurately track the business situation of the company, understand the needs of the objects, and develop the right procedures [19]. In this article, data mining, model building, data simulation, and scale analysis are performed with emphasis on the OLAP tool for analysis, and the specific process conforms to the tool flow, as shown in Figure 3.Figure 3
Flowchart of OLAP data analysis.


#### 3.1.4. Electrification of Agriculture

Electrification of agriculture means extensive use of electricity in agriculture. The main components of agricultural modernization include the use of electricity to power agricultural mechanization; the use of electrical appliances for heating, heating, cooling, and lighting; and the use of electronic devices (e.g., computers) for production management. Electrochemical manufacturing has the advantages of high efficiency, low cost, flexibility, stability, and reliability. It is the material and technical base for agricultural mechanization, automation, industrialization, and management modernization. Agricultural electrification is a sign that agricultural productivity has improved to a certain level. In the 1940s, agricultural electrification developed rapidly. In the 1970s, it entered a new stage of development after electronic technology was widely used in agricultural production management. Agricultural electrification is also called rural electrification. It refers to the extensive development of rural power grids and their transmission to rural areas in order to improve agricultural production and farmers’ living standards. Agricultural electrification is essential for agricultural mechanization, such as the use of electric tractors for farming, electric harvesters for harvesting, electric pumps for irrigation, electric scissors for repairing trees and lawns between operations, and electric milking machines for milking [20].


### 3.2. Main Formulas

#### 3.2.1. K-Means Algorithm Model

The k-mean clustering algorithm is an iterative solution clustering analysis algorithm, whose steps are predividing the data into K groups, then randomly selecting K objects as the initial cluster centers, then calculating the distance between each object and each seed cluster center, and assigning each object to the cluster center nearest to it. The clustering centers and the objects assigned to them represent a cluster. For each sample assigned, the cluster centers are recalculated based on the existing objects in the cluster. This process is repeated until a termination condition is met. The termination conditions can be that no (or a minimum number of) objects are reassigned to different clusters, no (or a minimum number of) cluster centers change again, and the error sum of squares is locally minimized.Clustering is a process of classifying and organizing members of a dataset that are similar in some way. Clustering is a technique for discovering this intrinsic structure, and clustering techniques are often referred to as unsupervised learning.The k-means clustering is the best known algorithm for dividing clusters, and its simplicity and efficiency make it the most widely used of all clustering algorithms. Given a collection of data points and a desired number of clustersk, with k specified by the user, the k-mean algorithm iteratively partitions the data into k clusters based on some distance function.One of the three most important features of K-cluster theory is that the structure of the cluster model itself should be guaranteed to be as compact as possible, with clusters as independent as possible. The specific steps of the K-means algorithm are as follows:(1)Input.k,datan.SelectingK points as cluster centers, c0=data0,…,ck−1=datak−1.Repeat the calculation until all theci variation values are less than the given threshold.


#### 3.2.2. The A-Priori Algorithm Model Can Be Divided into Two Steps

The A-priori algorithm is one of the most influential algorithms for mining frequent itemsets of Boolean association rules, which was proposed by Rakesh Agrawal and Ramakrishnan Skrikant. It uses an iterative method called layer-by-layer search, where k-itemsets are used to explore (k + 1)-itemsets. First, the set of frequent 1-itemsets is found. This set is denoted as L1. L1 is used to find the set L2 of frequent 2-itemsets, while L2 is used to find L2 and so on until no k-itemsets can be found. One database scan is required for each Lk. To improve the efficiency of frequent itemset generation layer by layer, an important property called A-priori property is used to compress the search space. The operation theorem is that first, all nonempty subsets of frequent itemsets must also be frequent, and second, all parents of nonfrequent itemsets are nonfrequent.Step 1: Linking.To find,lk a lk−1 candidate set is generated by k linking CK with itself. Assume that l1 with l2 is lk−1 the set of items in the set.lij is li the item set j of items. For l11=l21∩…∩l1k−1=l2k−1 example, l1 can l2 be connected with (3-4).Step 2: Prune K a subset of all items.The process is as follows.Input: databaseD minimum support min−sup threshold (3-5)(2)begin,L1=1,fork=2;lk−1≠∅do,CK=Apriori−genlk−1,CT=subsetck,t,lkck,c.count,ReturnLI∪L2…∪Lm.


## 3.1. Basic Concepts

### 3.1.1. The Meaning of Data Mining Algorithms

Data information mining research is also often translated and understood as big data knowledge exploration research and information data information mining, and research in databases must begin with research found in knowledge systems, which is an important step in scientific research. Data retrieval is often seen as the process of automatically retrieving large amounts of valuable information hidden in a specific way in a large number of information systems. Data information mining systems are also usually associated with other computer science systems that utilize data statistics and analysis, automatic information retrieval, machine learning, expert systems, pattern recognition, online information analysis models, and information processing technology methods to assist in achieving the above system goals. A data mining algorithm system is a methodological and computational technology approach to data mining that provides a system of algorithmic data mining models that can be created based on existing data. In order to achieve the creation of a data mining model, the algorithm must first analyze what kind of data the system needs to provide and look for specific types of patterns and trends. Data mining algorithms combine expertise in modern machine deep learning, pattern recognition, statistics, database principles, and computer artificial intelligence systems to obtain knowledge in a large amount of information and provide a reference for decision-making [15].


### 3.1.2. Types of Data Mining Algorithms

Data mining algorithms are divided into two categories in the general direction: supervised learning and unsupervised learning, and supervised learning and unsupervised learning are further divided into several major categories [16], as shown in Figure 1.Figure 1
Classification of data mining algorithms.Several important data mining algorithms are described and analyzed below.(1) Theory of Clustering Analysis. Data clustering (data clustering) is a multivariate statistical analysis method, using the idea of similar clustering, we can find useful information by classifying samples or indicators. The objects considered in a data cluster are usually a considerable number of samples that can be appropriately classified according to their characteristics. This classification should not be based on existing patterns, nor should it be based on existing patterns that are not known in advance. Data clusters evolved from taxonomy. In ancient taxonomy, qualitative classification was generally performed using historical experience and quantitative classification was rarely performed using mathematical tools. In the modern sense, data clustering arose after the introduction of multivariate analysis for numerical classification. Clustering is the process of classifying data into different categories or groups based on their attributes. Objects belonging to the same type or group have strong similarities, but they are not significantly different from each other. Objects belonging to the same type or group have very strong similarities, but there are no significant differences between them. These datasets cover many fields, including mathematics, computer science, statistics, and economics. Data clustering is also widely used in many fields, and clustering techniques have been developed in different application areas [17].(2) Theory of Association Analysis. Association rules are used to analyze and determine the degree of association between different variables or people in a database, as well as to develop models of customer purchasing behavior, such as purchasing peripherals on a desktop computer. Contact rules can provide valuable links between different portfolios of items from different business activities. The rule formulation process associated with a given operation can be described in a service database: for a database with a given problem, the problem is to eliminate the minimum support and trust in order to find the corresponding communication rules [18].(3) The Process of Data Mining Algorithms. The process of the data mining algorithm to mine data is given as follows: first, the external data are organized, followed by the preprocessing work on the data; then, the data information is formed; after the main algorithmic procedures and formulas of data mining, the analysis is processed, and finally, the calculation results of data mining are formed and given to display, as shown in Figure 2.Figure 2
Data mining algorithm flowchart.


### 3.1.3. OLAP Tool

OLAP is a multidimensional data analysis and capture tool that allows operators to analyze data from multiple perspectives. By importing factual data from agricultural and agricultural production data warehouses into the OLAP server, facts, tables, and correlations in the database are used to build star models. The OLAP tool is an important application in data warehousing systems. It supports complex analytical processes, especially for decision-makers and implementers. It provides decision-makers with intuitive and comprehensive search results by processing complex queries containing large amounts of data quickly and flexibly according to the needs of analysts, enabling them to accurately track the business situation of the company, understand the needs of the objects, and develop the right procedures [19]. In this article, data mining, model building, data simulation, and scale analysis are performed with emphasis on the OLAP tool for analysis, and the specific process conforms to the tool flow, as shown in Figure 3.Figure 3
Flowchart of OLAP data analysis.


### 3.1.4. Electrification of Agriculture

Electrification of agriculture means extensive use of electricity in agriculture. The main components of agricultural modernization include the use of electricity to power agricultural mechanization; the use of electrical appliances for heating, heating, cooling, and lighting; and the use of electronic devices (e.g., computers) for production management. Electrochemical manufacturing has the advantages of high efficiency, low cost, flexibility, stability, and reliability. It is the material and technical base for agricultural mechanization, automation, industrialization, and management modernization. Agricultural electrification is a sign that agricultural productivity has improved to a certain level. In the 1940s, agricultural electrification developed rapidly. In the 1970s, it entered a new stage of development after electronic technology was widely used in agricultural production management. Agricultural electrification is also called rural electrification. It refers to the extensive development of rural power grids and their transmission to rural areas in order to improve agricultural production and farmers’ living standards. Agricultural electrification is essential for agricultural mechanization, such as the use of electric tractors for farming, electric harvesters for harvesting, electric pumps for irrigation, electric scissors for repairing trees and lawns between operations, and electric milking machines for milking [20].


## 3.1.1. The Meaning of Data Mining Algorithms

Data information mining research is also often translated and understood as big data knowledge exploration research and information data information mining, and research in databases must begin with research found in knowledge systems, which is an important step in scientific research. Data retrieval is often seen as the process of automatically retrieving large amounts of valuable information hidden in a specific way in a large number of information systems. Data information mining systems are also usually associated with other computer science systems that utilize data statistics and analysis, automatic information retrieval, machine learning, expert systems, pattern recognition, online information analysis models, and information processing technology methods to assist in achieving the above system goals. A data mining algorithm system is a methodological and computational technology approach to data mining that provides a system of algorithmic data mining models that can be created based on existing data. In order to achieve the creation of a data mining model, the algorithm must first analyze what kind of data the system needs to provide and look for specific types of patterns and trends. Data mining algorithms combine expertise in modern machine deep learning, pattern recognition, statistics, database principles, and computer artificial intelligence systems to obtain knowledge in a large amount of information and provide a reference for decision-making [15].


## 3.1.2. Types of Data Mining Algorithms

Data mining algorithms are divided into two categories in the general direction: supervised learning and unsupervised learning, and supervised learning and unsupervised learning are further divided into several major categories [16], as shown in Figure 1.Figure 1
Classification of data mining algorithms.Several important data mining algorithms are described and analyzed below.(1) Theory of Clustering Analysis. Data clustering (data clustering) is a multivariate statistical analysis method, using the idea of similar clustering, we can find useful information by classifying samples or indicators. The objects considered in a data cluster are usually a considerable number of samples that can be appropriately classified according to their characteristics. This classification should not be based on existing patterns, nor should it be based on existing patterns that are not known in advance. Data clusters evolved from taxonomy. In ancient taxonomy, qualitative classification was generally performed using historical experience and quantitative classification was rarely performed using mathematical tools. In the modern sense, data clustering arose after the introduction of multivariate analysis for numerical classification. Clustering is the process of classifying data into different categories or groups based on their attributes. Objects belonging to the same type or group have strong similarities, but they are not significantly different from each other. Objects belonging to the same type or group have very strong similarities, but there are no significant differences between them. These datasets cover many fields, including mathematics, computer science, statistics, and economics. Data clustering is also widely used in many fields, and clustering techniques have been developed in different application areas [17].(2) Theory of Association Analysis. Association rules are used to analyze and determine the degree of association between different variables or people in a database, as well as to develop models of customer purchasing behavior, such as purchasing peripherals on a desktop computer. Contact rules can provide valuable links between different portfolios of items from different business activities. The rule formulation process associated with a given operation can be described in a service database: for a database with a given problem, the problem is to eliminate the minimum support and trust in order to find the corresponding communication rules [18].(3) The Process of Data Mining Algorithms. The process of the data mining algorithm to mine data is given as follows: first, the external data are organized, followed by the preprocessing work on the data; then, the data information is formed; after the main algorithmic procedures and formulas of data mining, the analysis is processed, and finally, the calculation results of data mining are formed and given to display, as shown in Figure 2.Figure 2
Data mining algorithm flowchart.


## 3.1.3. OLAP Tool

OLAP is a multidimensional data analysis and capture tool that allows operators to analyze data from multiple perspectives. By importing factual data from agricultural and agricultural production data warehouses into the OLAP server, facts, tables, and correlations in the database are used to build star models. The OLAP tool is an important application in data warehousing systems. It supports complex analytical processes, especially for decision-makers and implementers. It provides decision-makers with intuitive and comprehensive search results by processing complex queries containing large amounts of data quickly and flexibly according to the needs of analysts, enabling them to accurately track the business situation of the company, understand the needs of the objects, and develop the right procedures [19]. In this article, data mining, model building, data simulation, and scale analysis are performed with emphasis on the OLAP tool for analysis, and the specific process conforms to the tool flow, as shown in Figure 3.Figure 3
Flowchart of OLAP data analysis.


## 3.1.4. Electrification of Agriculture

Electrification of agriculture means extensive use of electricity in agriculture. The main components of agricultural modernization include the use of electricity to power agricultural mechanization; the use of electrical appliances for heating, heating, cooling, and lighting; and the use of electronic devices (e.g., computers) for production management. Electrochemical manufacturing has the advantages of high efficiency, low cost, flexibility, stability, and reliability. It is the material and technical base for agricultural mechanization, automation, industrialization, and management modernization. Agricultural electrification is a sign that agricultural productivity has improved to a certain level. In the 1940s, agricultural electrification developed rapidly. In the 1970s, it entered a new stage of development after electronic technology was widely used in agricultural production management. Agricultural electrification is also called rural electrification. It refers to the extensive development of rural power grids and their transmission to rural areas in order to improve agricultural production and farmers’ living standards. Agricultural electrification is essential for agricultural mechanization, such as the use of electric tractors for farming, electric harvesters for harvesting, electric pumps for irrigation, electric scissors for repairing trees and lawns between operations, and electric milking machines for milking [20].


## 3.2. Main Formulas

### 3.2.1. K-Means Algorithm Model

The k-mean clustering algorithm is an iterative solution clustering analysis algorithm, whose steps are predividing the data into K groups, then randomly selecting K objects as the initial cluster centers, then calculating the distance between each object and each seed cluster center, and assigning each object to the cluster center nearest to it. The clustering centers and the objects assigned to them represent a cluster. For each sample assigned, the cluster centers are recalculated based on the existing objects in the cluster. This process is repeated until a termination condition is met. The termination conditions can be that no (or a minimum number of) objects are reassigned to different clusters, no (or a minimum number of) cluster centers change again, and the error sum of squares is locally minimized.Clustering is a process of classifying and organizing members of a dataset that are similar in some way. Clustering is a technique for discovering this intrinsic structure, and clustering techniques are often referred to as unsupervised learning.The k-means clustering is the best known algorithm for dividing clusters, and its simplicity and efficiency make it the most widely used of all clustering algorithms. Given a collection of data points and a desired number of clustersk, with k specified by the user, the k-mean algorithm iteratively partitions the data into k clusters based on some distance function.One of the three most important features of K-cluster theory is that the structure of the cluster model itself should be guaranteed to be as compact as possible, with clusters as independent as possible. The specific steps of the K-means algorithm are as follows:(1)Input.k,datan.SelectingK points as cluster centers, c0=data0,…,ck−1=datak−1.Repeat the calculation until all theci variation values are less than the given threshold.


### 3.2.2. The A-Priori Algorithm Model Can Be Divided into Two Steps

The A-priori algorithm is one of the most influential algorithms for mining frequent itemsets of Boolean association rules, which was proposed by Rakesh Agrawal and Ramakrishnan Skrikant. It uses an iterative method called layer-by-layer search, where k-itemsets are used to explore (k + 1)-itemsets. First, the set of frequent 1-itemsets is found. This set is denoted as L1. L1 is used to find the set L2 of frequent 2-itemsets, while L2 is used to find L2 and so on until no k-itemsets can be found. One database scan is required for each Lk. To improve the efficiency of frequent itemset generation layer by layer, an important property called A-priori property is used to compress the search space. The operation theorem is that first, all nonempty subsets of frequent itemsets must also be frequent, and second, all parents of nonfrequent itemsets are nonfrequent.Step 1: Linking.To find,lk a lk−1 candidate set is generated by k linking CK with itself. Assume that l1 with l2 is lk−1 the set of items in the set.lij is li the item set j of items. For l11=l21∩…∩l1k−1=l2k−1 example, l1 can l2 be connected with (3-4).Step 2: Prune K a subset of all items.The process is as follows.Input: databaseD minimum support min−sup threshold (3-5)(2)begin,L1=1,fork=2;lk−1≠∅do,CK=Apriori−genlk−1,CT=subsetck,t,lkck,c.count,ReturnLI∪L2…∪Lm.


## 3.2.1. K-Means Algorithm Model

The k-mean clustering algorithm is an iterative solution clustering analysis algorithm, whose steps are predividing the data into K groups, then randomly selecting K objects as the initial cluster centers, then calculating the distance between each object and each seed cluster center, and assigning each object to the cluster center nearest to it. The clustering centers and the objects assigned to them represent a cluster. For each sample assigned, the cluster centers are recalculated based on the existing objects in the cluster. This process is repeated until a termination condition is met. The termination conditions can be that no (or a minimum number of) objects are reassigned to different clusters, no (or a minimum number of) cluster centers change again, and the error sum of squares is locally minimized.Clustering is a process of classifying and organizing members of a dataset that are similar in some way. Clustering is a technique for discovering this intrinsic structure, and clustering techniques are often referred to as unsupervised learning.The k-means clustering is the best known algorithm for dividing clusters, and its simplicity and efficiency make it the most widely used of all clustering algorithms. Given a collection of data points and a desired number of clustersk, with k specified by the user, the k-mean algorithm iteratively partitions the data into k clusters based on some distance function.One of the three most important features of K-cluster theory is that the structure of the cluster model itself should be guaranteed to be as compact as possible, with clusters as independent as possible. The specific steps of the K-means algorithm are as follows:(1)Input.k,datan.SelectingK points as cluster centers, c0=data0,…,ck−1=datak−1.Repeat the calculation until all theci variation values are less than the given threshold.


## 3.2.2. The A-Priori Algorithm Model Can Be Divided into Two Steps

The A-priori algorithm is one of the most influential algorithms for mining frequent itemsets of Boolean association rules, which was proposed by Rakesh Agrawal and Ramakrishnan Skrikant. It uses an iterative method called layer-by-layer search, where k-itemsets are used to explore (k + 1)-itemsets. First, the set of frequent 1-itemsets is found. This set is denoted as L1. L1 is used to find the set L2 of frequent 2-itemsets, while L2 is used to find L2 and so on until no k-itemsets can be found. One database scan is required for each Lk. To improve the efficiency of frequent itemset generation layer by layer, an important property called A-priori property is used to compress the search space. The operation theorem is that first, all nonempty subsets of frequent itemsets must also be frequent, and second, all parents of nonfrequent itemsets are nonfrequent.Step 1: Linking.To find,lk a lk−1 candidate set is generated by k linking CK with itself. Assume that l1 with l2 is lk−1 the set of items in the set.lij is li the item set j of items. For l11=l21∩…∩l1k−1=l2k−1 example, l1 can l2 be connected with (3-4).Step 2: Prune K a subset of all items.The process is as follows.Input: databaseD minimum support min−sup threshold (3-5)(2)begin,L1=1,fork=2;lk−1≠∅do,CK=Apriori−genlk−1,CT=subsetck,t,lkck,c.count,ReturnLI∪L2…∪Lm.


## 4. Results and Discussion

### 4.1. Analysis of the Current Status of Agricultural Electrification Development

Although this article analyzes from a national scale, a study from only four major agricultural and rural consuming provinces (Heilongjiang, Inner Mongolia, Shandong, and Henan) can yield what is needed for this study. Moreover, the four places are typical and representative, and national forecasts and calculations can be made from the study here. Therefore, it is more relevant to consider the four major agricultural and rural power consumption provinces for the current situation analysis.According to the big data algorithm plus OLAP tool analysis algorithm for agricultural electrification intelligent development trend analysis, the study investigated the electrification data analysis of four major agricultural provinces in China as follows:(1)
Agriculture and rural energy consumption patterns, primary chemical energy, electricity, and other clean energy consumption structure for four major agricultural provinces are given as follows: in Heilongjiang Province, primary chemical energy consumption accounted for 65.6%, and electricity and other clean energy accounted for 34.4%; in Shandong Province, primary chemical energy consumption accounted for 73.6%, and electricity and other clean energy accounted for 26.4%; in Henan Province, primary chemical energy consumption accounted for 74.7%, and electricity and other clean energy accounted for 25.3%.In Inner Mongolia Autonomous Region, primary chemical energy consumption accounted for 75.8%, and electricity and other clean energy accounted for 24.2%; it can be seen from the data that the primary energy consumption in agriculture and rural areas accounted for a high proportion of all, but instead, electricity and other clean energy consumption occupy a small share, so it can be seen that the utilization rate of agricultural and rural electrification is relatively low, and no corresponding pattern has been formed to occupy a certain share, as shown in Figure4.(2)
Agricultural and rural electric energy and other clean energy consumption structure, electricity, and other clean energy consumption structure year by year for four major agricultural provinces are given as follows: In Heilongjiang Province in 2016, electricity and other clean energy accounted for 15.3% and 20.5% in 2018, 25.7% in 2020, and 34.4% in 2022, respectively; in Shandong Province in 2016, electricity and other clean energy accounted for 14.9% and 19.7% in 2018, 24.5% in 2020, and 26.4% in 2022, respectively; In Henan Province 2016, electricity and other clean energy accounted for 14.5% and 18.9% in 2018, 23.3% in 2020, and 25.3% in 2022, respectively; In Inner Mongolia Autonomous Region in 2016, electricity and other clean energy accounted for 14.1% and 18.1% in 2018, 22.1% in 2020, and 24.2% in 2022, respectively; the analysis of the data shows that the consumption of clean energy such as electricity in the four major provinces is increasing year by year, which shows that the use of clean energy such as electricity in agriculture and rural areas has increased, and the development trend is good, as shown in Figure5.(3)
In the four major agricultural provinces, the proportion of residents in agriculture and rural areas who have used electricity to the total number of residents is as follows: Heilongjiang Province has used electricity in agriculture and rural areas to 98.3% of the total number of residents; Shandong Province has used electricity in agriculture and rural areas to 99.1% of the total number of residents; Henan Province has used electricity in agriculture and rural areas to 99.2% of the total number of residents; Inner Mongolia Autonomous Region in Heilongjiang Province has used electricity to 96.7% of the total number of residents in agriculture and rural areas, as shown in Figure6.(4)
In the four major agricultural provinces, the year-by-year comparison of the number of agricultural machinery in agriculture and rural areas is as follows: Heilongjiang Province had 15,300 agricultural machines in 2016; 16,205 agricultural machines in 2018; 17,110 agricultural machines in 2020; and 18,015 agricultural machines in 2022; Shandong Province had 15,760 agricultural machines in 2016; 17,197 agricultural machines in 2018; and 18,634 in 2020; and 20,071 in 2022. Henan Province had 14,500 in 2016; 15,189 in 2018; 15,878 in 2020; and 16,567 in 2022; Inner Mongolia Autonomous Region had 14,100 in 2016; 15,181 in 2018; and 16262 in 2020; and 17343 in 2022; from the analysis of the data, it can be seen that the number of rural agricultural machinery in the four major provinces is increasing year by year, which shows that the degree of agricultural rural electrification increased, and the development trend is good, as shown in Figure7.(5)
In the four major agricultural provinces, the year-by-year comparison of agricultural rural electricity consumption is as follows: Heilongjiang Province used 252,000 degrees in 2016; 253,860 degrees in 2018; 255,720 degrees in 2020; and 257,580 degrees in 2022. Shandong Province used 246,800 degrees in 2016 and 253,890 degrees in 2018; and the electricity consumption of Henan Province was 241,600 degrees in 2016; 253,920 degrees in 2018; 266,240 degrees in 2020; and 278,560 degrees in 2022. The electricity consumption of Inner Mongolia Autonomous Region was 246,400 degrees in 2016; 253,950 degrees in 2018; 253,950 degrees in 2020; and 278,560 degrees in 2022. The analysis of the data shows that the electricity consumption in agriculture and rural areas of the four major provinces is increasing year by year, which shows that the degree of electrification in agriculture and rural areas has increased and the development trend is good, as shown in Figure8.Figure 4
Comparison of the share of agricultural rural energy consumption patterns in the four major provinces.Figure 5
Four major provinces of agriculture and rural areas of electricity and other clean energy consumption patterns compared year by year.Figure 6
Proportion of residents in agriculture and rural areas who have used electricity to the total number of all residents.Figure 7
The number of agricultural rural farm machinery in the four major provinces compared year by year.Figure 8
Comparison of electricity consumption in agriculture and rural areas of four major provinces year by year.To sum up, the use of clean energy such as electricity is increasing in rural areas, and the rapid development of the agricultural electrification process is a sign of the development of agricultural productivity to a certain level, which can improve agricultural production and raise the living standard of farmers, and agricultural electrification is crucial to agricultural mechanization.


### 4.2. Analysis of Results

#### 4.2.1. Analysis of the Development Impetus of Electrification

(1) Agricultural Modernization and New Urbanization Construction Promote the Growth of Electricity Demand. Agricultural modernization includes irrigation and drainage, crop drying, agricultural machinery operations, facilities, agriculture and grain storage, and transportation. The relevant equipment and facilities are still mainly diesel and internal combustion engines, and with the promotion of new rural construction and the orderly development of urbanization, the rural power grid structure should be further optimized to significantly improve the level of agricultural and rural electrification.(2) Conservation Farming and Carbon Emission Reduction, and Promoting the Electrification of Agricultural Machinery. Compared with traditional energy sources, electrification is characterized by precision control, simple structure, high energy efficiency, low carbon content, and environmental friendliness. Batteries, electric motors, and electricians have developed battery charging and discharging technologies that improve operational precision and save seeds and fertilizers. Agricultural equipment powered by electrical equipment can reduce the risk of pollution; over-reliance on fossil energy can reduce the emission of carbon dioxide and pollutants.(3) Clean Heating Should be Extended to Rural Areas. With the implementation of a clean heating system, the level of clean heating technology is improving and the cost of equipment is significantly reduced. In view of the low energy-saving rate of rural buildings, the investment and operating costs of clean heaters are still high, and clean heating technology is applied to specific areas, oriented to applications, and gradually promoted and applied.(4) The Development of Distributed Power Supply in Rural Areas Has a Lot of Room. Rural land idle resources are huge. With the further improvement of the rural power grid structure, distributed wind and solar energy can be used for rural production and life, housing, plant quarantine and water conservancy, and the common construction and development.(5) Mobile Energy Has Become a Powerful Supplement to Agricultural Production. In recent years, the power grid company has completed the work of “good connectivity” and “good village connectivity” to achieve higher efficiency in farmers’ production and daily use of electricity, and more than 10 farmers have transformed the use of high-quality irrigation plants. However, the economic resources are only used for farm irrigation, with limited impact on crops, fertilizers, crop protection, and harvesting. As electricity demand increases in the future, it is unlikely that agricultural production will expand the coverage of the Connected model. Batteries are like mobile power and need to be explored in time and space at 2.5%.


#### 4.2.2. Technical Analysis of the Evolution of Electrification

Carbon neutrality and emission reduction in the energy system will play a leading role in achieving peak carbon emissions. As energy uses shifts, the share of new energy sources in the energy system is increasing. The instability and intermittency of new energy production requires adaptation of the system. Agricultural production and rural livelihoods are cost-sensitive, and the low marginal cost of new energy sources can be leveraged through mechanisms. Therefore, flexible resources need to be stimulated to adjust their ability to adapt deeply to new energy fluctuations and turbulence.The electromechanical machine has high power control and relatively fixed operating hours for agricultural machinery. She usually works during the day during the busy season and can be housed in cooperative farming equipment, communities, or farmers’ homes at night. Through long time charging and battery spacing, the charging capacity and charging time can be intelligently controlled according to the rural grid load pattern and regional substation load speed, giving full play to the controllability of the grid and regulating the charging equipment accordingly and reducing the final cost through power supply support services.The power components of electromechanical machinery can be used as distributed backup energy sources, and agricultural production is seasonal in nature. Generally, the same farm machinery has a working cycle of only 7–14 days, and different farm machinery has a maximum of two months a year. The power battery of the electromechanical machinery can make full use of the spare time to play the role of the battery. According to incomplete statistics, the total capacity of agricultural machinery in Jilin Province has exceeded 100 million units (kilowatt). The ability of the rural grid to participate in power distribution will be important if it is replaced with appropriate power lines as a decentralized backup energy source during calm periods.The processing, storage, and transportation of agricultural products all have a demand for energy. Related equipment can streamline the workflow, optimize energy consumption time, and effectively reduce energy consumption. At the same time, longer-term energy orders and strategies can be implemented for the same type of equipment in the same area to participate in regional energy supply and reduce peak fluctuations.With the emergence of new urbanization, the traditional rural heating model of coal-fired stoves or coal-fired furnaces is expected to shift to biomass, electricity, and clean heating. Small thermal power plants using renewable energy and environmentally friendly heaters will change the existing thermal connections and energy consumption in rural areas to some extent. The energy demand is effectively met, while 2.5% of the district heating demand is met.


#### 4.2.3. Electrification Application Scope Analysis

Based on application scenarios, future efforts to promote agricultural and rural electrification are likely to focus on agricultural plantations, agricultural machinery, and electric heating.(1)
Alternative methods of agricultural energy production include mainly greenhouse curtains and electric lights. These technologies include primarily equipment used to manufacture electric curtains and put out agricultural greenhouses. This technology is efficient and convenient, reduces labor intensity, shortens holding time, extends lighting time, and increases crop yield. Electrically assisted lighting technology is a technology that uses a certain energy spectrum for greenhouse growth and development. This technology has the advantages of reasonable uniform lighting, stable light output, energy saving, and environmental protection.(2)
Electric motor farm machinery is widely used in corn planting, harvesting, transportation, and field protection, such as electric seeders, three-wheeled machinery, and self-spraying. Compared with traditional agricultural machinery, electric agricultural machinery has the advantages of high control accuracy, simple structure, energy saving, and low carbon and environmental performance, which can reduce the disturbance of agricultural equipment to the land and unnecessary agricultural waste such as fertilizer. At the same time, the battery, electric motor, electric control, and power battery charging technology have matured and have the conditions for application in agricultural machinery engineering.(3)
Rural energy heating mainly coal consumption is dominated by cogeneration and remains at a high level throughout the year. In order to eliminate this phenomenon, power companies are actively working to increase the use of energy generated by electric heat. According to the conversion process and process method, electric heat technology can be divided into direct heating, thermal storage, and heat pumps. The combined thermal efficiency of electric heating technology can reach more than 80%, which is much higher than other heating methods and has the characteristics of energy saving, emission reduction, and efficient operation.


## 4.1. Analysis of the Current Status of Agricultural Electrification Development

Although this article analyzes from a national scale, a study from only four major agricultural and rural consuming provinces (Heilongjiang, Inner Mongolia, Shandong, and Henan) can yield what is needed for this study. Moreover, the four places are typical and representative, and national forecasts and calculations can be made from the study here. Therefore, it is more relevant to consider the four major agricultural and rural power consumption provinces for the current situation analysis.According to the big data algorithm plus OLAP tool analysis algorithm for agricultural electrification intelligent development trend analysis, the study investigated the electrification data analysis of four major agricultural provinces in China as follows:(1)
Agriculture and rural energy consumption patterns, primary chemical energy, electricity, and other clean energy consumption structure for four major agricultural provinces are given as follows: in Heilongjiang Province, primary chemical energy consumption accounted for 65.6%, and electricity and other clean energy accounted for 34.4%; in Shandong Province, primary chemical energy consumption accounted for 73.6%, and electricity and other clean energy accounted for 26.4%; in Henan Province, primary chemical energy consumption accounted for 74.7%, and electricity and other clean energy accounted for 25.3%.In Inner Mongolia Autonomous Region, primary chemical energy consumption accounted for 75.8%, and electricity and other clean energy accounted for 24.2%; it can be seen from the data that the primary energy consumption in agriculture and rural areas accounted for a high proportion of all, but instead, electricity and other clean energy consumption occupy a small share, so it can be seen that the utilization rate of agricultural and rural electrification is relatively low, and no corresponding pattern has been formed to occupy a certain share, as shown in Figure4.(2)
Agricultural and rural electric energy and other clean energy consumption structure, electricity, and other clean energy consumption structure year by year for four major agricultural provinces are given as follows: In Heilongjiang Province in 2016, electricity and other clean energy accounted for 15.3% and 20.5% in 2018, 25.7% in 2020, and 34.4% in 2022, respectively; in Shandong Province in 2016, electricity and other clean energy accounted for 14.9% and 19.7% in 2018, 24.5% in 2020, and 26.4% in 2022, respectively; In Henan Province 2016, electricity and other clean energy accounted for 14.5% and 18.9% in 2018, 23.3% in 2020, and 25.3% in 2022, respectively; In Inner Mongolia Autonomous Region in 2016, electricity and other clean energy accounted for 14.1% and 18.1% in 2018, 22.1% in 2020, and 24.2% in 2022, respectively; the analysis of the data shows that the consumption of clean energy such as electricity in the four major provinces is increasing year by year, which shows that the use of clean energy such as electricity in agriculture and rural areas has increased, and the development trend is good, as shown in Figure5.(3)
In the four major agricultural provinces, the proportion of residents in agriculture and rural areas who have used electricity to the total number of residents is as follows: Heilongjiang Province has used electricity in agriculture and rural areas to 98.3% of the total number of residents; Shandong Province has used electricity in agriculture and rural areas to 99.1% of the total number of residents; Henan Province has used electricity in agriculture and rural areas to 99.2% of the total number of residents; Inner Mongolia Autonomous Region in Heilongjiang Province has used electricity to 96.7% of the total number of residents in agriculture and rural areas, as shown in Figure6.(4)
In the four major agricultural provinces, the year-by-year comparison of the number of agricultural machinery in agriculture and rural areas is as follows: Heilongjiang Province had 15,300 agricultural machines in 2016; 16,205 agricultural machines in 2018; 17,110 agricultural machines in 2020; and 18,015 agricultural machines in 2022; Shandong Province had 15,760 agricultural machines in 2016; 17,197 agricultural machines in 2018; and 18,634 in 2020; and 20,071 in 2022. Henan Province had 14,500 in 2016; 15,189 in 2018; 15,878 in 2020; and 16,567 in 2022; Inner Mongolia Autonomous Region had 14,100 in 2016; 15,181 in 2018; and 16262 in 2020; and 17343 in 2022; from the analysis of the data, it can be seen that the number of rural agricultural machinery in the four major provinces is increasing year by year, which shows that the degree of agricultural rural electrification increased, and the development trend is good, as shown in Figure7.(5)
In the four major agricultural provinces, the year-by-year comparison of agricultural rural electricity consumption is as follows: Heilongjiang Province used 252,000 degrees in 2016; 253,860 degrees in 2018; 255,720 degrees in 2020; and 257,580 degrees in 2022. Shandong Province used 246,800 degrees in 2016 and 253,890 degrees in 2018; and the electricity consumption of Henan Province was 241,600 degrees in 2016; 253,920 degrees in 2018; 266,240 degrees in 2020; and 278,560 degrees in 2022. The electricity consumption of Inner Mongolia Autonomous Region was 246,400 degrees in 2016; 253,950 degrees in 2018; 253,950 degrees in 2020; and 278,560 degrees in 2022. The analysis of the data shows that the electricity consumption in agriculture and rural areas of the four major provinces is increasing year by year, which shows that the degree of electrification in agriculture and rural areas has increased and the development trend is good, as shown in Figure8.Figure 4
Comparison of the share of agricultural rural energy consumption patterns in the four major provinces.Figure 5
Four major provinces of agriculture and rural areas of electricity and other clean energy consumption patterns compared year by year.Figure 6
Proportion of residents in agriculture and rural areas who have used electricity to the total number of all residents.Figure 7
The number of agricultural rural farm machinery in the four major provinces compared year by year.Figure 8
Comparison of electricity consumption in agriculture and rural areas of four major provinces year by year.To sum up, the use of clean energy such as electricity is increasing in rural areas, and the rapid development of the agricultural electrification process is a sign of the development of agricultural productivity to a certain level, which can improve agricultural production and raise the living standard of farmers, and agricultural electrification is crucial to agricultural mechanization.


## 4.2. Analysis of Results

### 4.2.1. Analysis of the Development Impetus of Electrification

(1) Agricultural Modernization and New Urbanization Construction Promote the Growth of Electricity Demand. Agricultural modernization includes irrigation and drainage, crop drying, agricultural machinery operations, facilities, agriculture and grain storage, and transportation. The relevant equipment and facilities are still mainly diesel and internal combustion engines, and with the promotion of new rural construction and the orderly development of urbanization, the rural power grid structure should be further optimized to significantly improve the level of agricultural and rural electrification.(2) Conservation Farming and Carbon Emission Reduction, and Promoting the Electrification of Agricultural Machinery. Compared with traditional energy sources, electrification is characterized by precision control, simple structure, high energy efficiency, low carbon content, and environmental friendliness. Batteries, electric motors, and electricians have developed battery charging and discharging technologies that improve operational precision and save seeds and fertilizers. Agricultural equipment powered by electrical equipment can reduce the risk of pollution; over-reliance on fossil energy can reduce the emission of carbon dioxide and pollutants.(3) Clean Heating Should be Extended to Rural Areas. With the implementation of a clean heating system, the level of clean heating technology is improving and the cost of equipment is significantly reduced. In view of the low energy-saving rate of rural buildings, the investment and operating costs of clean heaters are still high, and clean heating technology is applied to specific areas, oriented to applications, and gradually promoted and applied.(4) The Development of Distributed Power Supply in Rural Areas Has a Lot of Room. Rural land idle resources are huge. With the further improvement of the rural power grid structure, distributed wind and solar energy can be used for rural production and life, housing, plant quarantine and water conservancy, and the common construction and development.(5) Mobile Energy Has Become a Powerful Supplement to Agricultural Production. In recent years, the power grid company has completed the work of “good connectivity” and “good village connectivity” to achieve higher efficiency in farmers’ production and daily use of electricity, and more than 10 farmers have transformed the use of high-quality irrigation plants. However, the economic resources are only used for farm irrigation, with limited impact on crops, fertilizers, crop protection, and harvesting. As electricity demand increases in the future, it is unlikely that agricultural production will expand the coverage of the Connected model. Batteries are like mobile power and need to be explored in time and space at 2.5%.


### 4.2.2. Technical Analysis of the Evolution of Electrification

Carbon neutrality and emission reduction in the energy system will play a leading role in achieving peak carbon emissions. As energy uses shifts, the share of new energy sources in the energy system is increasing. The instability and intermittency of new energy production requires adaptation of the system. Agricultural production and rural livelihoods are cost-sensitive, and the low marginal cost of new energy sources can be leveraged through mechanisms. Therefore, flexible resources need to be stimulated to adjust their ability to adapt deeply to new energy fluctuations and turbulence.The electromechanical machine has high power control and relatively fixed operating hours for agricultural machinery. She usually works during the day during the busy season and can be housed in cooperative farming equipment, communities, or farmers’ homes at night. Through long time charging and battery spacing, the charging capacity and charging time can be intelligently controlled according to the rural grid load pattern and regional substation load speed, giving full play to the controllability of the grid and regulating the charging equipment accordingly and reducing the final cost through power supply support services.The power components of electromechanical machinery can be used as distributed backup energy sources, and agricultural production is seasonal in nature. Generally, the same farm machinery has a working cycle of only 7–14 days, and different farm machinery has a maximum of two months a year. The power battery of the electromechanical machinery can make full use of the spare time to play the role of the battery. According to incomplete statistics, the total capacity of agricultural machinery in Jilin Province has exceeded 100 million units (kilowatt). The ability of the rural grid to participate in power distribution will be important if it is replaced with appropriate power lines as a decentralized backup energy source during calm periods.The processing, storage, and transportation of agricultural products all have a demand for energy. Related equipment can streamline the workflow, optimize energy consumption time, and effectively reduce energy consumption. At the same time, longer-term energy orders and strategies can be implemented for the same type of equipment in the same area to participate in regional energy supply and reduce peak fluctuations.With the emergence of new urbanization, the traditional rural heating model of coal-fired stoves or coal-fired furnaces is expected to shift to biomass, electricity, and clean heating. Small thermal power plants using renewable energy and environmentally friendly heaters will change the existing thermal connections and energy consumption in rural areas to some extent. The energy demand is effectively met, while 2.5% of the district heating demand is met.


### 4.2.3. Electrification Application Scope Analysis

Based on application scenarios, future efforts to promote agricultural and rural electrification are likely to focus on agricultural plantations, agricultural machinery, and electric heating.(1)
Alternative methods of agricultural energy production include mainly greenhouse curtains and electric lights. These technologies include primarily equipment used to manufacture electric curtains and put out agricultural greenhouses. This technology is efficient and convenient, reduces labor intensity, shortens holding time, extends lighting time, and increases crop yield. Electrically assisted lighting technology is a technology that uses a certain energy spectrum for greenhouse growth and development. This technology has the advantages of reasonable uniform lighting, stable light output, energy saving, and environmental protection.(2)
Electric motor farm machinery is widely used in corn planting, harvesting, transportation, and field protection, such as electric seeders, three-wheeled machinery, and self-spraying. Compared with traditional agricultural machinery, electric agricultural machinery has the advantages of high control accuracy, simple structure, energy saving, and low carbon and environmental performance, which can reduce the disturbance of agricultural equipment to the land and unnecessary agricultural waste such as fertilizer. At the same time, the battery, electric motor, electric control, and power battery charging technology have matured and have the conditions for application in agricultural machinery engineering.(3)
Rural energy heating mainly coal consumption is dominated by cogeneration and remains at a high level throughout the year. In order to eliminate this phenomenon, power companies are actively working to increase the use of energy generated by electric heat. According to the conversion process and process method, electric heat technology can be divided into direct heating, thermal storage, and heat pumps. The combined thermal efficiency of electric heating technology can reach more than 80%, which is much higher than other heating methods and has the characteristics of energy saving, emission reduction, and efficient operation.


## 4.2.1. Analysis of the Development Impetus of Electrification

(1) Agricultural Modernization and New Urbanization Construction Promote the Growth of Electricity Demand. Agricultural modernization includes irrigation and drainage, crop drying, agricultural machinery operations, facilities, agriculture and grain storage, and transportation. The relevant equipment and facilities are still mainly diesel and internal combustion engines, and with the promotion of new rural construction and the orderly development of urbanization, the rural power grid structure should be further optimized to significantly improve the level of agricultural and rural electrification.(2) Conservation Farming and Carbon Emission Reduction, and Promoting the Electrification of Agricultural Machinery. Compared with traditional energy sources, electrification is characterized by precision control, simple structure, high energy efficiency, low carbon content, and environmental friendliness. Batteries, electric motors, and electricians have developed battery charging and discharging technologies that improve operational precision and save seeds and fertilizers. Agricultural equipment powered by electrical equipment can reduce the risk of pollution; over-reliance on fossil energy can reduce the emission of carbon dioxide and pollutants.(3) Clean Heating Should be Extended to Rural Areas. With the implementation of a clean heating system, the level of clean heating technology is improving and the cost of equipment is significantly reduced. In view of the low energy-saving rate of rural buildings, the investment and operating costs of clean heaters are still high, and clean heating technology is applied to specific areas, oriented to applications, and gradually promoted and applied.(4) The Development of Distributed Power Supply in Rural Areas Has a Lot of Room. Rural land idle resources are huge. With the further improvement of the rural power grid structure, distributed wind and solar energy can be used for rural production and life, housing, plant quarantine and water conservancy, and the common construction and development.(5) Mobile Energy Has Become a Powerful Supplement to Agricultural Production. In recent years, the power grid company has completed the work of “good connectivity” and “good village connectivity” to achieve higher efficiency in farmers’ production and daily use of electricity, and more than 10 farmers have transformed the use of high-quality irrigation plants. However, the economic resources are only used for farm irrigation, with limited impact on crops, fertilizers, crop protection, and harvesting. As electricity demand increases in the future, it is unlikely that agricultural production will expand the coverage of the Connected model. Batteries are like mobile power and need to be explored in time and space at 2.5%.


## 4.2.2. Technical Analysis of the Evolution of Electrification

Carbon neutrality and emission reduction in the energy system will play a leading role in achieving peak carbon emissions. As energy uses shifts, the share of new energy sources in the energy system is increasing. The instability and intermittency of new energy production requires adaptation of the system. Agricultural production and rural livelihoods are cost-sensitive, and the low marginal cost of new energy sources can be leveraged through mechanisms. Therefore, flexible resources need to be stimulated to adjust their ability to adapt deeply to new energy fluctuations and turbulence.The electromechanical machine has high power control and relatively fixed operating hours for agricultural machinery. She usually works during the day during the busy season and can be housed in cooperative farming equipment, communities, or farmers’ homes at night. Through long time charging and battery spacing, the charging capacity and charging time can be intelligently controlled according to the rural grid load pattern and regional substation load speed, giving full play to the controllability of the grid and regulating the charging equipment accordingly and reducing the final cost through power supply support services.The power components of electromechanical machinery can be used as distributed backup energy sources, and agricultural production is seasonal in nature. Generally, the same farm machinery has a working cycle of only 7–14 days, and different farm machinery has a maximum of two months a year. The power battery of the electromechanical machinery can make full use of the spare time to play the role of the battery. According to incomplete statistics, the total capacity of agricultural machinery in Jilin Province has exceeded 100 million units (kilowatt). The ability of the rural grid to participate in power distribution will be important if it is replaced with appropriate power lines as a decentralized backup energy source during calm periods.The processing, storage, and transportation of agricultural products all have a demand for energy. Related equipment can streamline the workflow, optimize energy consumption time, and effectively reduce energy consumption. At the same time, longer-term energy orders and strategies can be implemented for the same type of equipment in the same area to participate in regional energy supply and reduce peak fluctuations.With the emergence of new urbanization, the traditional rural heating model of coal-fired stoves or coal-fired furnaces is expected to shift to biomass, electricity, and clean heating. Small thermal power plants using renewable energy and environmentally friendly heaters will change the existing thermal connections and energy consumption in rural areas to some extent. The energy demand is effectively met, while 2.5% of the district heating demand is met.


## 4.2.3. Electrification Application Scope Analysis

Based on application scenarios, future efforts to promote agricultural and rural electrification are likely to focus on agricultural plantations, agricultural machinery, and electric heating.(1)
Alternative methods of agricultural energy production include mainly greenhouse curtains and electric lights. These technologies include primarily equipment used to manufacture electric curtains and put out agricultural greenhouses. This technology is efficient and convenient, reduces labor intensity, shortens holding time, extends lighting time, and increases crop yield. Electrically assisted lighting technology is a technology that uses a certain energy spectrum for greenhouse growth and development. This technology has the advantages of reasonable uniform lighting, stable light output, energy saving, and environmental protection.(2)
Electric motor farm machinery is widely used in corn planting, harvesting, transportation, and field protection, such as electric seeders, three-wheeled machinery, and self-spraying. Compared with traditional agricultural machinery, electric agricultural machinery has the advantages of high control accuracy, simple structure, energy saving, and low carbon and environmental performance, which can reduce the disturbance of agricultural equipment to the land and unnecessary agricultural waste such as fertilizer. At the same time, the battery, electric motor, electric control, and power battery charging technology have matured and have the conditions for application in agricultural machinery engineering.(3)
Rural energy heating mainly coal consumption is dominated by cogeneration and remains at a high level throughout the year. In order to eliminate this phenomenon, power companies are actively working to increase the use of energy generated by electric heat. According to the conversion process and process method, electric heat technology can be divided into direct heating, thermal storage, and heat pumps. The combined thermal efficiency of electric heating technology can reach more than 80%, which is much higher than other heating methods and has the characteristics of energy saving, emission reduction, and efficient operation.


## 5. Conclusion

This article analyzes the current situation of agricultural electrification development, the dynamics of electrification development, and the application scope of electrification based on big data mining and OLAP tool analysis algorithm, and finally puts forward several suggestions for agricultural electrification development in order to make corresponding contributions in the research of this field.(1)
To actively promote rural revitalization, protect land, and establish a modern agricultural industrial system, production system, and operation system, it is necessary to further develop rural energy distribution capacity, promote the development of new energy sources, and establish a rural energy supply and application system focusing on electricity. New energy and agriculture and rural electrification are mutually beneficial, but development faces serious obstacles. The high cost of batteries is the main obstacle to the development of agricultural electrification.(2)
Uniform standards for agricultural machinery batteries and innovative “recycling” application system. The development of electromechanical engineering is still in its infancy, and the relevant standard system is not yet perfect. Therefore, it is necessary to design the top level and establish a unified standard system of shape, size, energy supplement, and communication protocol. For different types of agricultural machinery only for specific seasonal characteristics, a standardized structure system is adopted to facilitate the high adaptability and rapid utilization of agricultural machinery power batteries in the agricultural machinery season and achieve the recycling and cost increase of various mechanical energy components.(3)
Promoting the development of agricultural electrification, exploring the business model of agricultural equipment battery sharing, and providing technical support for battery disassembly and centralized charging in the agricultural battery recycling application system. On this basis, we can develop and explore the business model of “sharing,” that is, renting out electricity, batteries, or electromechanical equipment to farmers in the process of farming to obtain basic income in the form of rent; through the centralized supervision and participation in the production of agricultural batteries, the consumption of new energy will greatly improve the supervisory capacity of the energy system. At the same time, “electromechanical segregation” reduces the purchase cost of agricultural machinery, reduces the exchange of electricity, and reduces the operating cost of agricultural machinery by 3.5%.(4)
Development and sales. Participation in the regulation of power supply and heat supply can reduce costs. Battery-type electric boiler has strong regulation ability and advanced technology. Through intensive management, it can provide cost-effective power supply. Participation in power management can further reduce operating costs and better meet the new demand for urban heating. Combined with the development of decentralized power supply conditions in the region, rural and land resources are fully considered to reduce the cost of decentralized power supply and promote the development of decentralized power supply.



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*Source: 1614939-2022-09-16.xml*