# Optimization Simulation of Supply-Side Structure of Agricultural Economy Based on Big Data Analysis in Data Sharing Environment

Authors: Yuan Sui; Jizhu Zhao
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

Big data is transforming how people live their lives, and the widespread use of information technology has tremendously aided in the development and deployment of big data technologies. The use of big data technology can lower agricultural production and distribution costs, increase agricultural modernization’s effectiveness, and support the transformation and upgrading of the agricultural economy. Big data technology has garnered a lot of attention as a burgeoning technological area. Big data has revolutionised the service model for “agriculture, rural areas, and farmers” and given agricultural economy supply-side structure optimization new life. In the age of big data, we should reexamine the way agricultural economic information is currently released, propose more scientific techniques for doing so, and improve the effectiveness of implementation across the board. In light of the background of big data, this research investigates and evaluates the supply-side structure of the agricultural sector. The big data method is combined in this work to explore and analyse it. This study shows that the supply-side structure of the agricultural economy, when seen against the backdrop of big data, has a definite impact, with an impact as high as 56.56%. This essay lays the groundwork for future agricultural economy research and development.


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## Body

## 1. Introduction

Modern society is a society with rapid message expansion and iteration. With the uninterrupted expansion of science and skill and the enhancement of message circulation, big data skill is the high-tech product of this message rapid expansion era. At present, the technical value of big data skill is mainly reflected in providing accurate marketing and expansion for a big number of social work groups and service enterprises. Relevant enterprises can use big data skill to reduce manufacture and operation costs and provide technical assistance for improving work Eff, developing new products, and making decisions. The amount of data has multiplied over the past few years as a result of the government and businesses’ increased willingness to invest in the budget to build their own messaging tools and gather and analyse more data. This is due to the rapid increase in the capacity, speed, and intelligence of calculate and storage resources as well as the sharp decrease in prices. In today’s message-based society, individuals have gotten used to working, having fun, and living their lives anytime on the Internet and mobile devices. Therefore, our demand for “Internet of Things” and “cloud calculate” arises at the historic moment. And in this message explosion environment—we usually conduct online social networking, go shopping on JD.COM, Taobao, Tmall, and other websites; blog; make phone calls; send text messages; and so on, which will generate a huge amount of message—because of such a big environment, the data we need to process is also huge. Big data refers to a new data asset with high data, high capacity, and various message values. This kind of data asset needs to be dealt with by a new processing mode in order to optimize processing and correct judgment. The optimization of ag supply-side structure is to optimize the distribution of ag resources according to the market demand and to ensure the balance between consumption demand and supply. Under the setting of the vigorous expansion of big data skill, big data has osmosis into all walks of life, and its connection with agriculture is also closer. The combination of agriculture with big data, Internet of Things, and other technologies will better merge the data of ag manufacture, processing, circulation, and other links; give full play to the decision-making guiding role of data; boost the integration of agriculture with more industries; and build a high-quality and efficient ag economic chain with merged manufacture and marketing and message sharing. In recent years, the price of ag products fluctuates greatly, and the unsalable phenomenon of ag products occurs almost every year. Apart from objective factors, it is mainly related to the asymmetry of market message and the imbalance between supply and demand [1]. Macroscopically, the 12th Five-Year Plan period is the best period in the history of China’s ag and rural economic expansion. The ag manufacture has increased year after year, and farmers’ income has continued to increase, making great contributions to stabilizing growth, adjusting structure, benefiting people’s livelihood, and preventing risks.Release of messages is an essential measure to safeguard the public’s access to information, participation, and oversight. It is also an essential institutional guarantee to promote the idea of “transparent government.” The relevant message issues in the current agricultural expansion should be fully analysed as part of the work being done on “agriculture, rural areas, and farmers,” and at the same time, by modifying the message release mechanism and website, we can more successfully develop a set of scientific data message resource sharing systems. Humans have entered the era of digitalization, which is characterised by ever-closer communication and a rapid expansion of messaging skill. Every day, enormous amounts of data are generated in every region of the world. These data continue to flow over time and develop into big data that people are familiar with. A large-scale data collection that cannot be stored, managed, or analysed using conventional methods is referred to as “big data.” Big data is characterised by its size, rapidity, diversity, worth, authenticity, etc. Big data provides a wealth of information that is extremely important for advancing social, technical, and scientific progress [2].In the study of this article, a variety of study ways are used to study and analyse it. In the related study and analysis of big data skill, several model diagrams and algorithm formulas are established to study and analyse it. In the study on the supply side of ag income, a corresponding data map is established to analyse it. The blaze new trails of this sheet:(1)
In this sheet, the big data skill is combined to study the ag income(2)
Based on big data skill, the sheet studies and analyses the supply-side structure optimization of ag income


## 2. Related Work

The era of big data has arrived as a result of the quick advancement of computer network technology. Governments, academics, and the international business community are all interested in big data. Globally speaking, big data presents both potential and difficulties. Large-scale data has a lot to say. Our lives can be changed by effective mastery and use of these data. Big data, cloud computing, artificial intelligence, and other technologies are being used in an increasing number of businesses due to the continuous expansion of message skill. Advanced analytical abilities give managers sound bases for decision-making, which improves the improvement of manufacturing efficiency while ensuring product quality. The use of big data-related skills in agricultural economic management for analysis and prediction can help pertinent businesses get the message out in a timely manner, further optimize company plans, and support greater expansion. With the interaction of Internet skill, computer skill, and cloud calculate skill, tens of millions of sensors and huge data generated by human-computer interaction can be saved and utilised. Because of the huge amount of data and complicated data structure, the previous data analysis and processing skill cannot handle such huge data and cannot make good use of these massive data, so the big data skill is born. After entering the 21st century, due to the uninterrupted expansion and advancement of Internet skill, cloud calculate skill, and sensor skill, all kinds of data are exploding, and these huge amounts of data can be stored and analysed and utilised on the basis of the support of storage skill and cloud calculate skill. Under this setting, big data skill is born.In the study, Huang thinks that big data skill can bring some negative effects to human beings as well as positive effects [3]. At present, it is in a vital period of the expansion of big data skill. Analysing the value and diffusion mode of big data skill from the perspective of technological philosophy is helpful to solve various problems in the app of big data skill and understand the value of big data itself. Kai believes that big data is a huge new wealth given to us by the times, which can help the global income wake up. Therefore, 2013 is also called the first year of big data. Since then, big data has slowly changed from a concept to a new capital that exists in reality and can be used, and it shows a trend of influencing human life in all aspects [4]. Governments and enterprises have already thought of the necessity of using big data skill to analyse and achieve effective results, so all countries have begun to seize the opportunities in the era of big data. Chen believes that we are more easily controlled by data in the era of rapid expansion of big data skill, and data may control us in a mixed way [5]. Due to the popularity of the Internet, our privacy protection has been threatened to a certain extent, and the rapid expansion of big data skill may further deepen this threat. Besides the threat to privacy protection, big data skill can also suppress people’s instinct. “The predictability of nature is regarded as the only key to unlock the secrets of the world. The predictability of nature is regarded as the plunder of all human efforts by the real world. At the same time, people’s thoughts are solidified into simple predictive thoughts.” That is to say, human beings measure and think about everything with utilitarian standards. Jing suggested that data sharing, gathering, and use not only assist decision-makers in the size and field of farms but also cover other points in this field. Farmers and businesses have put a lot of effort and money into this. Farmers now confront a variety of opportunities and problems, such as managing their data, enhancing manufacturing efficiency, and enhancing the agricultural environment. Accessing data and figuring out how to use it better are challenges [6]. Building ag benchmark data, conducting research on the real-time collection skill of ag products message, developing complex intelligent model analysis system, and establishing visual early warning service platform will become crucial trends of ag products monitoring and early warning expansion in the future, according to Wen and Song’s proposal [7]. The concepts of semisupervised learning and reinforcement learning were introduced by Zhi-Jun and Yuan. The input data is recognised and classed in the semisupervised learning mode, which is mostly utilised for predictive analysis. In order to organise the data effectively, the algorithm module first determines the internal structure of the data. It then primarily uses classification and regression algorithms, which are variations of well-known supervised learning algorithms like the Laplacian support vector machine algorithm [8].


## 3. Study and Analysis of Big Data Skill

### 3.1. Introduction and Analysis of Big Data Skill

Big data skill is a complete application of technologies such as parallel computing, artificial intelligence, natural language processing, predictive analysis, data mining, statistical analysis, data storage, and other technologies. It is the newest trend in data engineering app skill. Data by its very nature exists and arises objectively in both the natural world and human society. More and more messages from nature and human society can be mapped and collected into the message system by technical means as human cognition and message collection and analysis methods continue to progress. Big data skill is the most sophisticated skill in this industry. By analysing and studying data nature, people may uncover and master all kinds of rules of human society and nature. A plethora of personal messages can be found in big data. We can effectively assess people’s personal preferences and even privacy through combined analysis, which also reveals their behavioural patterns and makes the message about personal privacy more accessible. An important topic in the next big data study is how to improve the ability of data collecting and better preserve individual privacy. The two components that make up the analysis and processing of large data are distributed parallel expertise and the algorithm and model that are produced from parallel data mining in a cloud computing environment. The app mode is employed for processing and analysis in the big data platform’s app skill. In addition, various app standards and technical standards such as app processing services of big data platform, various interfaces of app processing of big data platform, and various safety standards and specifications of big data platform apps are established. Big data keeps flowing, generating, and developing rapidly, and now, it has become a social resource with great value. The generation, analysis, storage, sharing, detection, and consumption of data form a serial network of big data, which constitutes the organic ecological structure system of big data. Different needs can be generated in each link, and the different needs of each link boost the innovation of theory, skill, and way. In order to fully utilise and tap into the enormous potential and value of big data skill, big data skill and social app are combined. This allows big data to be related to policy and business decisions, reorganise and quickly utilise all types of strategic resources, and make messages generate value-added content. Platforms for processing large data can be categorised into batch, streaming, interactive, and picture data processing platforms depending on the type of data they are dealing with [9, 10]. In order to understand them, appropriate model diagrams are created, as seen in Figures 1 and 2.Figure 1
Big data processing model diagram.Figure 2
Multiprocessing model diagram.Big data skill has great potential value. It is necessary to conduct a profound analysis and study on big data skill and discover its hidden huge functions and values. For example, big data skill can play a huge role in policy making and business decision-making, and it can also penetrate into other fields such as medical care and education, thus exerting its own value. In order to exert the good effects of big data skill and improve humankind’s quality of life, after researching the value of big data skill, apply it to all facets of social life. Therefore, it is crucial to research the importance of big data expertise. The skill of data analysis entails comparing data items, which are broken down into four categories: analysis base, analysis objects, analysis Eff, and analysis hardware requirements. The challenge with using large data analysis objects is that their input analysis data may be lacking, inaccurate, or contain extraneous information. These unstructured data indicate that big data analysis of the object is more challenging, but this analysis process may also yield other unanticipated benefits. Regarding the foundation of analysis, in the real world, data is unstructured, which means that it might take the shape of video, photographs, signals produced by movement, or other types of data. It differs significantly from the conventional relational database in that all of these can be utilised for big data research. Statistical analysis, machine learning, data mining, natural language processing, knowledge and reasoning, and other technical approaches in the field of artificial intelligence, in addition to the classic BI talent, offer extensive and varied analysis ways for big data. Machine learning is the foundation of many natural language processing methods. Apps that are commonly used in this area of expertise include electronic legal detection and linguistic emotion analysis based on social media. Fraud detection, text categorization, message retrieval and filtering, text conversion systems, machine translation, and other applications are examples of further apps [11, 12].


### 3.2. Big Data Algorithm and Study and Analysis Based on Agriculture

While big data skills speed up and simplify social interactions, they also increase the threats to data security, which are mostly seen in technological security, content security, fragile management, and other areas. Data mining is the process of extracting hidden messages and knowledge from a large amount of incomplete, noisy, fuzzy, and random practical app data that people do not know in advance but that may be valuable. It is a series of technologies that extract patterns from large data sets by broadly utilising statistics and machine learning techniques in database management systems. It is the comprehensive use of statistics, database skill, and artificial intelligence talent. The enormous volume of data in the big data era, however, cannot be handled by traditional data processing methods. As a result, it is important to process data from various levels of computers using new technologies. In ag large data, the message is more erratic and accompanied by noise. If handled improperly, it will turn into message junk. Deep learning and machine learning are essential to efficiently evaluate and use huge data. Machine learning is the practise of using algorithms to study data laws and forecast outcomes. Making robots capable of unsupervised learning, supervised learning, and reinforcement learning is the goal of this project. In comparison to shallow learning, deep learning is the extension of the neural network technique in machine learning. In the field of ag big data, crop pest forecasting system using deep learning skill is widely used. Because there are many influencing conditions, the target data is extremely complex and changeable, and there are many elements involved in the ag system, the traditional crop pest forecasting system based on database and single learning algorithm has low forecasting accuracy and unstable performance [13, 14]. Therefore, in the study of ag data, the corresponding data tables are established to study and analyse them, as shown in Tables 1 and 2.Table 1
Ag data analysis.
Serial numberWeatherTemperatureSuitable planting1Fine37.5No2Cloudy33.5Yes3Rain25.5No4Cloudy32.5YesTable 2
Ag data analysis.
Serial numberWeatherTemperatureSuitable planting1Fine38.5No2Cloudy28.8Yes3Rain24.3No4Fine36.2No5Rain23.5NoData mining needs data preparation at first. Data selection means selecting the data suitable for data mining from different heterogeneous data sources, and then preprocess the data such as denoising and duplication elimination. Data selection and preprocessing in data mining is the core of data preparation. For the data sample set with a known sample number, the corresponding algorithm formula is established for analysis and study, as shown in(1)EX=−∑i=1kuilogui,whereui=xi/x, the message entropy of the sample set is also called average uncertainty, which is also called prior entropy. It means the message content of the data set X and the selection of classification attributes is determined according to the message content. The probability can be obtained from the sample subset as shown in
(2)uij=xijx1j+x2j+,⋯+xk−1j.The subset message entropy is shown in(3)EXj=−∑j=1kuijlog2uij.According to the message entropy of the sample set divided by its attributes, it can be obtained as shown in(4)EH=−∑j=1lx1j+x2j+,⋯+xk−1j+xkjxEXj.The message gain is calculated as shown in(5)GXH=EX−EH.It is represented by a subset of samples based on the attribute values of its set as shown in(6)X=X1,X2,⋯XI−1,XI.Among them, the genera are shown in(7)Xj=j=1,2,...1,(8)Yi=i=1,2,⋯k.According to the data attributes in the above table, the message entropy can be obtained as shown in(9)EX=−∑I=1Kuilogu,(10)EH=−∑i=1Kuilog2ui.Scholars are also wary of big data skill. As an emerging skill, big data skill not only brings positive effects to people’s social life but also inevitably brings negative consequences. Therefore, we must be alert to the possible risks of big data skill and conduct in-depth and profound study on it [15, 16].


## 3.1. Introduction and Analysis of Big Data Skill

Big data skill is a complete application of technologies such as parallel computing, artificial intelligence, natural language processing, predictive analysis, data mining, statistical analysis, data storage, and other technologies. It is the newest trend in data engineering app skill. Data by its very nature exists and arises objectively in both the natural world and human society. More and more messages from nature and human society can be mapped and collected into the message system by technical means as human cognition and message collection and analysis methods continue to progress. Big data skill is the most sophisticated skill in this industry. By analysing and studying data nature, people may uncover and master all kinds of rules of human society and nature. A plethora of personal messages can be found in big data. We can effectively assess people’s personal preferences and even privacy through combined analysis, which also reveals their behavioural patterns and makes the message about personal privacy more accessible. An important topic in the next big data study is how to improve the ability of data collecting and better preserve individual privacy. The two components that make up the analysis and processing of large data are distributed parallel expertise and the algorithm and model that are produced from parallel data mining in a cloud computing environment. The app mode is employed for processing and analysis in the big data platform’s app skill. In addition, various app standards and technical standards such as app processing services of big data platform, various interfaces of app processing of big data platform, and various safety standards and specifications of big data platform apps are established. Big data keeps flowing, generating, and developing rapidly, and now, it has become a social resource with great value. The generation, analysis, storage, sharing, detection, and consumption of data form a serial network of big data, which constitutes the organic ecological structure system of big data. Different needs can be generated in each link, and the different needs of each link boost the innovation of theory, skill, and way. In order to fully utilise and tap into the enormous potential and value of big data skill, big data skill and social app are combined. This allows big data to be related to policy and business decisions, reorganise and quickly utilise all types of strategic resources, and make messages generate value-added content. Platforms for processing large data can be categorised into batch, streaming, interactive, and picture data processing platforms depending on the type of data they are dealing with [9, 10]. In order to understand them, appropriate model diagrams are created, as seen in Figures 1 and 2.Figure 1
Big data processing model diagram.Figure 2
Multiprocessing model diagram.Big data skill has great potential value. It is necessary to conduct a profound analysis and study on big data skill and discover its hidden huge functions and values. For example, big data skill can play a huge role in policy making and business decision-making, and it can also penetrate into other fields such as medical care and education, thus exerting its own value. In order to exert the good effects of big data skill and improve humankind’s quality of life, after researching the value of big data skill, apply it to all facets of social life. Therefore, it is crucial to research the importance of big data expertise. The skill of data analysis entails comparing data items, which are broken down into four categories: analysis base, analysis objects, analysis Eff, and analysis hardware requirements. The challenge with using large data analysis objects is that their input analysis data may be lacking, inaccurate, or contain extraneous information. These unstructured data indicate that big data analysis of the object is more challenging, but this analysis process may also yield other unanticipated benefits. Regarding the foundation of analysis, in the real world, data is unstructured, which means that it might take the shape of video, photographs, signals produced by movement, or other types of data. It differs significantly from the conventional relational database in that all of these can be utilised for big data research. Statistical analysis, machine learning, data mining, natural language processing, knowledge and reasoning, and other technical approaches in the field of artificial intelligence, in addition to the classic BI talent, offer extensive and varied analysis ways for big data. Machine learning is the foundation of many natural language processing methods. Apps that are commonly used in this area of expertise include electronic legal detection and linguistic emotion analysis based on social media. Fraud detection, text categorization, message retrieval and filtering, text conversion systems, machine translation, and other applications are examples of further apps [11, 12].


## 3.2. Big Data Algorithm and Study and Analysis Based on Agriculture

While big data skills speed up and simplify social interactions, they also increase the threats to data security, which are mostly seen in technological security, content security, fragile management, and other areas. Data mining is the process of extracting hidden messages and knowledge from a large amount of incomplete, noisy, fuzzy, and random practical app data that people do not know in advance but that may be valuable. It is a series of technologies that extract patterns from large data sets by broadly utilising statistics and machine learning techniques in database management systems. It is the comprehensive use of statistics, database skill, and artificial intelligence talent. The enormous volume of data in the big data era, however, cannot be handled by traditional data processing methods. As a result, it is important to process data from various levels of computers using new technologies. In ag large data, the message is more erratic and accompanied by noise. If handled improperly, it will turn into message junk. Deep learning and machine learning are essential to efficiently evaluate and use huge data. Machine learning is the practise of using algorithms to study data laws and forecast outcomes. Making robots capable of unsupervised learning, supervised learning, and reinforcement learning is the goal of this project. In comparison to shallow learning, deep learning is the extension of the neural network technique in machine learning. In the field of ag big data, crop pest forecasting system using deep learning skill is widely used. Because there are many influencing conditions, the target data is extremely complex and changeable, and there are many elements involved in the ag system, the traditional crop pest forecasting system based on database and single learning algorithm has low forecasting accuracy and unstable performance [13, 14]. Therefore, in the study of ag data, the corresponding data tables are established to study and analyse them, as shown in Tables 1 and 2.Table 1
Ag data analysis.
Serial numberWeatherTemperatureSuitable planting1Fine37.5No2Cloudy33.5Yes3Rain25.5No4Cloudy32.5YesTable 2
Ag data analysis.
Serial numberWeatherTemperatureSuitable planting1Fine38.5No2Cloudy28.8Yes3Rain24.3No4Fine36.2No5Rain23.5NoData mining needs data preparation at first. Data selection means selecting the data suitable for data mining from different heterogeneous data sources, and then preprocess the data such as denoising and duplication elimination. Data selection and preprocessing in data mining is the core of data preparation. For the data sample set with a known sample number, the corresponding algorithm formula is established for analysis and study, as shown in(1)EX=−∑i=1kuilogui,whereui=xi/x, the message entropy of the sample set is also called average uncertainty, which is also called prior entropy. It means the message content of the data set X and the selection of classification attributes is determined according to the message content. The probability can be obtained from the sample subset as shown in
(2)uij=xijx1j+x2j+,⋯+xk−1j.The subset message entropy is shown in(3)EXj=−∑j=1kuijlog2uij.According to the message entropy of the sample set divided by its attributes, it can be obtained as shown in(4)EH=−∑j=1lx1j+x2j+,⋯+xk−1j+xkjxEXj.The message gain is calculated as shown in(5)GXH=EX−EH.It is represented by a subset of samples based on the attribute values of its set as shown in(6)X=X1,X2,⋯XI−1,XI.Among them, the genera are shown in(7)Xj=j=1,2,...1,(8)Yi=i=1,2,⋯k.According to the data attributes in the above table, the message entropy can be obtained as shown in(9)EX=−∑I=1Kuilogu,(10)EH=−∑i=1Kuilog2ui.Scholars are also wary of big data skill. As an emerging skill, big data skill not only brings positive effects to people’s social life but also inevitably brings negative consequences. Therefore, we must be alert to the possible risks of big data skill and conduct in-depth and profound study on it [15, 16].


## 4. Ag Study Based on Big Data

### 4.1. Supply-Side Analysis of Ag Income under the Setting of Big Data

Big data plays a vital role in the distribution of ag economic factors. It can not only realize the digitalization and modernization of ag manufacture but also realize the deep integration of ag income with other fields, and boost the change and upgrading of ag income. The app of big data skill can break through the limitation of time and space, capture the latest market message in time, realize the message interconnection in all aspects of ag manufacture, effectively link various agriculture-related resources, and avoid the loss caused by message asymmetry. At the same time, an agriculture-related e-commerce platform can be set up to release message such as ag products sales, display, and transaction matching, so as to facilitate the quick connection between the two trading parties through the mobile network, and use big data skill to merge message, optimize resource distribute according to changes in consumer demand, and then reduce the circulation cost of each link. The combination of big data skill and ag income has boosted the scale and standardization of ag manufacture, especially the rapid transmission of message based on the Internet, which helps to cultivate new ag business entities, such as ag enterprises and planting experts. Different from the small-scale peasant income, these new subjects need to grasp the market input and output and supply demand in time, build a new industrial system with the help of new technologies such as big data, and use the e-commerce platform to realize the rapid circulation of products. It can be said that big data skill is the driving force of ag supply-side structure optimization. The consumption of ag products continues to grow rigidly, the consumption structure is accelerated and upgraded, and the demand for diversification and branding is more obvious. Consider the implementation of the comprehensive two-child policy, the acceleration of urbanization, rural poverty alleviation and the improvement of residents’ income level. The structure of ag manufacture costs has changed and the rising factors have increased, and the prices of foreign ag products in China are relatively weak. The corn planting area has been greatly reduced, and the inventory pressure has been effectively released. With the vigorous promotion of corn structure adjustment in “Sickle Bend” area, the corn planting area will be greatly reduced in the next five years, the contradiction of phased oversupply will be effectively solved, and corn destocking will be obvious. During the “13th Five-Year Plan” period, China’s ag manufacture costs will rise steadily, and the cost structure will undergo vital changes. Apart from the rigid increase in material input, labour, and land costs, the costs of plant and animal pest control, maintenance of modern breeding tool, waste treatment and ag product processing, packaging, sales, and brand building will also increase significantly [17, 18]. Therefore, in the study, the corresponding data graphs are established to analyse and study them, as shown in Figures 3–5.Figure 3
Big data ag economic analysis chart.Figure 4
Analysis of supply-side impact of ag income based on big data.Figure 5
Ag economic analysis chart.Figure4 shows that under the setting of big data, the supply-side structure of ag income has a certain impact, and the impact is as high as 56.56%. For a long time, China’s ag sector has been out of touch with supply. The app of big data skill has provided impetus for the supply-side reform of ag income, and initially formed a new ag service pattern. However, there are still many problems and challenges in the app of big data. The app of big data skill in agriculture has given birth to new service modes such as ag product traceability and Internet finance, and has also achieved certain app benefits. This year’s No.1 Document of the Central Committee proposed to speed up the construction of ag data investigation and analysis system. By giving full play to ag monitoring and early warning, strengthening ag outlook and message release, we can effectively grasp the dynamics of ag supply-side reform, optimize and adjust the manufacture structure, and realize the dynamic matching with the supply side, which plays an vital role in promoting the structural reform of ag supply side. In every stage from planting, manufacture, processing to circulation of ag products, setting up message traceability system and making manufacture and operation plans according to market changes can avoid losses caused by blindly following the trend, avoid mistakes caused by inaccurate decision-making, and make all links known and controllable. Under the setting of big data, ag modernization must rely on intelligent science and skill; have satellite navigation systems, sensors, and network equipment; and use big data analysis software to accurately judge and analyse all kinds of message, so as to realize the refined management of the whole process from manufacture to sales and improve scale benefits. The manufacture areas of ag products are more concentrated, the scale operation is accelerated, and the characteristics of local manufacture and global supply are prominent. During the “Thirteenth Five-Year Plan” period, especially with the rapid expansion of a series of multibilateral free trade zones between China and ASEAN, Singapore, New Zealand, Chile, South Korea, and Australia and the construction of the belt and road initiative, the import source countries of resource-intensive ag products such as oil, fruit, meat, and dairy products will also gradually expand from the traditional countries of America, Australia, and Southeast Asia to countries in Central Asia, Australia, and Europe, showing a pattern of “double increase” in variety and sources [19, 20].


### 4.2. Ag Economic Analysis Based on Big Data

The work of “agriculture, rural areas, and farmers” has a very direct impact on China’s social and economic expansion. In the process of related work, we should establish a fair and open working image so that the ag sector’s own working level can be further improved and guaranteed. At the same time, by improving the level of ag economic message release, the image and credibility of the current Chinese government can be better established and protected. Looking at China’s current ag market environment, the problems of message release exist objectively and cause some waste of resources distribution and message errors, which are not conducive to the healthy expansion of China’s ag income. As a vital foundation of modern market income, message itself has a very direct impact on the distribute of market resources. The effective expansion of ag economic release management can further guarantee the overall technical level and Eff of ag manufacture. At present, the level of ag manufacture is constantly improving, and the actual manufacture behaviour can be more effectively controlled in the process of skill app of related ag income. In terms of ag products sales, the traditional circulation mode from producers, wholesalers, and logistics enterprises to ag supermarkets leads to a too long circulation chain and high marketing cost. This not only causes consumers to pay higher fees but also greatly increases the risk of ag products marketing, which makes it difficult for farmers to get benefits. The broad masses of farmers can obtain relevant advanced ag technical guidance message through the platform released by ag income. In the process of obtaining economic message, the technical level of farmers themselves has been further improved and developed, and at the same time, more scientific ag manufacture guidance can be obtained, which is very vital for improving the output and quality, and also helps to reduce the ag manufacture cost. Therefore, in the study, corresponding data graphs are established to analyse and study them, as shown in Figures6 and 7.Figure 6
Big data ag analysis figure.Figure 7
Data map of big data affecting ag income.Figure7 shows that under the setting of big data, the ag income is also developing and has a certain impact on the income, which is about 45.63%. The importance of data app in the era of big data is self-evident, and mastering core data is the key to improving competitiveness. The government attaches great importance to the app of big data, especially the agriculture-related departments advocate the open sharing of big data, requiring all localities to establish data sharing platforms as soon as possible, eliminate the isolated island of ag data message, and realize the uninterrupted optimization of data resources. Including ag departments as representatives of governments at all levels, study institutions such as government-funded study institutes and related Internet enterprises. The supply of big data skill is essentially a question about how to allocate scarce resources reasonably. In the market income, the distribute of resources usually pays attention to Eff, and resources are allocated with maximum Eff. However, big data skill has its particularity, with the characteristics of “quasi-public goods”, strong public welfare in consumption, free rides for farmers, high construction and maintenance costs, etc. Based on this, if these quasipublic goods only follow the principle of market distribute, it will cause a shortage of supply. Generally, the government should participate in the supply of public goods and quasi-public goods, and the same is true for big data skill. The characteristics of ag big data can be summarized in the following aspects: first, from the perspective of its field, it gradually expands to colleagues in related upstream and downstream industries, taking the ag field as the core, and integrating the data of macroeconomic setting, including statistical accurate data, import, and export quantity data, stable price data, manufacture flow and data, and accurate meteorological data. Secondly, from the geographical point of view, it mainly takes the data of China as the core and further draws lessons from the international ag data and serves as an effective reference. This data includes not only the data of China but also the data of provinces and cities, and the data of prefectures and cities cannot be ignored. These data will provide effective reference for accurate regional study. The app of big data skill needs massive data to support it, which requires close cooperation and data exchange among all walks of life involved in ag manufacture. However, in the current environment, there are still some bottlenecks in message sharing. For example, some enterprises think that crop growth data, business data, etc. Belong to enterprise privacy, so they report false message when sharing data, which leads to the deviation of the final analysis results. Some enterprises have leaked the private message of farmers and enterprises because of inadequate message security protection measures, resulting in message security problems, and stopped message sharing in order to rectify the problems. Ag science and skill play an inestimable role in the expansion of rural income, and big data skill can be used as an effective helper for the promotion of ag science and skill, which enables relevant scientific researchers to collect meteorological message and planting message from all over the country in time, so as to provide reference opinions for farmers’ manufacture and planting in time. Accurate data analysis can further increase the output rate of ag products. Under the setting of more and more mature ag informatization construction, farmers can have a more timely and in-depth understanding of the market operation status and the demand for various crops, thus enhancing farmers’ market awareness. At the same time, farmers can also learn ag-related scientific knowledge on the Internet, get the needed knowledge according to the results of big data analysis, and boost ag expansion by improving their own quality.


## 4.1. Supply-Side Analysis of Ag Income under the Setting of Big Data

Big data plays a vital role in the distribution of ag economic factors. It can not only realize the digitalization and modernization of ag manufacture but also realize the deep integration of ag income with other fields, and boost the change and upgrading of ag income. The app of big data skill can break through the limitation of time and space, capture the latest market message in time, realize the message interconnection in all aspects of ag manufacture, effectively link various agriculture-related resources, and avoid the loss caused by message asymmetry. At the same time, an agriculture-related e-commerce platform can be set up to release message such as ag products sales, display, and transaction matching, so as to facilitate the quick connection between the two trading parties through the mobile network, and use big data skill to merge message, optimize resource distribute according to changes in consumer demand, and then reduce the circulation cost of each link. The combination of big data skill and ag income has boosted the scale and standardization of ag manufacture, especially the rapid transmission of message based on the Internet, which helps to cultivate new ag business entities, such as ag enterprises and planting experts. Different from the small-scale peasant income, these new subjects need to grasp the market input and output and supply demand in time, build a new industrial system with the help of new technologies such as big data, and use the e-commerce platform to realize the rapid circulation of products. It can be said that big data skill is the driving force of ag supply-side structure optimization. The consumption of ag products continues to grow rigidly, the consumption structure is accelerated and upgraded, and the demand for diversification and branding is more obvious. Consider the implementation of the comprehensive two-child policy, the acceleration of urbanization, rural poverty alleviation and the improvement of residents’ income level. The structure of ag manufacture costs has changed and the rising factors have increased, and the prices of foreign ag products in China are relatively weak. The corn planting area has been greatly reduced, and the inventory pressure has been effectively released. With the vigorous promotion of corn structure adjustment in “Sickle Bend” area, the corn planting area will be greatly reduced in the next five years, the contradiction of phased oversupply will be effectively solved, and corn destocking will be obvious. During the “13th Five-Year Plan” period, China’s ag manufacture costs will rise steadily, and the cost structure will undergo vital changes. Apart from the rigid increase in material input, labour, and land costs, the costs of plant and animal pest control, maintenance of modern breeding tool, waste treatment and ag product processing, packaging, sales, and brand building will also increase significantly [17, 18]. Therefore, in the study, the corresponding data graphs are established to analyse and study them, as shown in Figures 3–5.Figure 3
Big data ag economic analysis chart.Figure 4
Analysis of supply-side impact of ag income based on big data.Figure 5
Ag economic analysis chart.Figure4 shows that under the setting of big data, the supply-side structure of ag income has a certain impact, and the impact is as high as 56.56%. For a long time, China’s ag sector has been out of touch with supply. The app of big data skill has provided impetus for the supply-side reform of ag income, and initially formed a new ag service pattern. However, there are still many problems and challenges in the app of big data. The app of big data skill in agriculture has given birth to new service modes such as ag product traceability and Internet finance, and has also achieved certain app benefits. This year’s No.1 Document of the Central Committee proposed to speed up the construction of ag data investigation and analysis system. By giving full play to ag monitoring and early warning, strengthening ag outlook and message release, we can effectively grasp the dynamics of ag supply-side reform, optimize and adjust the manufacture structure, and realize the dynamic matching with the supply side, which plays an vital role in promoting the structural reform of ag supply side. In every stage from planting, manufacture, processing to circulation of ag products, setting up message traceability system and making manufacture and operation plans according to market changes can avoid losses caused by blindly following the trend, avoid mistakes caused by inaccurate decision-making, and make all links known and controllable. Under the setting of big data, ag modernization must rely on intelligent science and skill; have satellite navigation systems, sensors, and network equipment; and use big data analysis software to accurately judge and analyse all kinds of message, so as to realize the refined management of the whole process from manufacture to sales and improve scale benefits. The manufacture areas of ag products are more concentrated, the scale operation is accelerated, and the characteristics of local manufacture and global supply are prominent. During the “Thirteenth Five-Year Plan” period, especially with the rapid expansion of a series of multibilateral free trade zones between China and ASEAN, Singapore, New Zealand, Chile, South Korea, and Australia and the construction of the belt and road initiative, the import source countries of resource-intensive ag products such as oil, fruit, meat, and dairy products will also gradually expand from the traditional countries of America, Australia, and Southeast Asia to countries in Central Asia, Australia, and Europe, showing a pattern of “double increase” in variety and sources [19, 20].


## 4.2. Ag Economic Analysis Based on Big Data

The work of “agriculture, rural areas, and farmers” has a very direct impact on China’s social and economic expansion. In the process of related work, we should establish a fair and open working image so that the ag sector’s own working level can be further improved and guaranteed. At the same time, by improving the level of ag economic message release, the image and credibility of the current Chinese government can be better established and protected. Looking at China’s current ag market environment, the problems of message release exist objectively and cause some waste of resources distribution and message errors, which are not conducive to the healthy expansion of China’s ag income. As a vital foundation of modern market income, message itself has a very direct impact on the distribute of market resources. The effective expansion of ag economic release management can further guarantee the overall technical level and Eff of ag manufacture. At present, the level of ag manufacture is constantly improving, and the actual manufacture behaviour can be more effectively controlled in the process of skill app of related ag income. In terms of ag products sales, the traditional circulation mode from producers, wholesalers, and logistics enterprises to ag supermarkets leads to a too long circulation chain and high marketing cost. This not only causes consumers to pay higher fees but also greatly increases the risk of ag products marketing, which makes it difficult for farmers to get benefits. The broad masses of farmers can obtain relevant advanced ag technical guidance message through the platform released by ag income. In the process of obtaining economic message, the technical level of farmers themselves has been further improved and developed, and at the same time, more scientific ag manufacture guidance can be obtained, which is very vital for improving the output and quality, and also helps to reduce the ag manufacture cost. Therefore, in the study, corresponding data graphs are established to analyse and study them, as shown in Figures6 and 7.Figure 6
Big data ag analysis figure.Figure 7
Data map of big data affecting ag income.Figure7 shows that under the setting of big data, the ag income is also developing and has a certain impact on the income, which is about 45.63%. The importance of data app in the era of big data is self-evident, and mastering core data is the key to improving competitiveness. The government attaches great importance to the app of big data, especially the agriculture-related departments advocate the open sharing of big data, requiring all localities to establish data sharing platforms as soon as possible, eliminate the isolated island of ag data message, and realize the uninterrupted optimization of data resources. Including ag departments as representatives of governments at all levels, study institutions such as government-funded study institutes and related Internet enterprises. The supply of big data skill is essentially a question about how to allocate scarce resources reasonably. In the market income, the distribute of resources usually pays attention to Eff, and resources are allocated with maximum Eff. However, big data skill has its particularity, with the characteristics of “quasi-public goods”, strong public welfare in consumption, free rides for farmers, high construction and maintenance costs, etc. Based on this, if these quasipublic goods only follow the principle of market distribute, it will cause a shortage of supply. Generally, the government should participate in the supply of public goods and quasi-public goods, and the same is true for big data skill. The characteristics of ag big data can be summarized in the following aspects: first, from the perspective of its field, it gradually expands to colleagues in related upstream and downstream industries, taking the ag field as the core, and integrating the data of macroeconomic setting, including statistical accurate data, import, and export quantity data, stable price data, manufacture flow and data, and accurate meteorological data. Secondly, from the geographical point of view, it mainly takes the data of China as the core and further draws lessons from the international ag data and serves as an effective reference. This data includes not only the data of China but also the data of provinces and cities, and the data of prefectures and cities cannot be ignored. These data will provide effective reference for accurate regional study. The app of big data skill needs massive data to support it, which requires close cooperation and data exchange among all walks of life involved in ag manufacture. However, in the current environment, there are still some bottlenecks in message sharing. For example, some enterprises think that crop growth data, business data, etc. Belong to enterprise privacy, so they report false message when sharing data, which leads to the deviation of the final analysis results. Some enterprises have leaked the private message of farmers and enterprises because of inadequate message security protection measures, resulting in message security problems, and stopped message sharing in order to rectify the problems. Ag science and skill play an inestimable role in the expansion of rural income, and big data skill can be used as an effective helper for the promotion of ag science and skill, which enables relevant scientific researchers to collect meteorological message and planting message from all over the country in time, so as to provide reference opinions for farmers’ manufacture and planting in time. Accurate data analysis can further increase the output rate of ag products. Under the setting of more and more mature ag informatization construction, farmers can have a more timely and in-depth understanding of the market operation status and the demand for various crops, thus enhancing farmers’ market awareness. At the same time, farmers can also learn ag-related scientific knowledge on the Internet, get the needed knowledge according to the results of big data analysis, and boost ag expansion by improving their own quality.


## 5. Conclusions

In a word, as a big ag country, China’s ag income is an vital guarantee for economic advance. With the uninterrupted expansion of China’s income, it has become an inevitable trend to introduce message skill into ag economic management. This requires relevant personnel to change their concepts, constantly improve their technical level, apply big data skill to ag economic management better and faster, merge and analyse ag manufacture and management data, and provide decision-making basis for relevant enterprises and farmers. Relevant enterprises should realize data sharing under the premise of complete message security measures and constantly improve their service quality. Since the beginning of the century, new scientific words such as cloud calculate and big data have appeared frequently. People need to look at these new things objectively while understanding and learning these new technologies. Generally speaking, in the era of big data, the construction and improvement of ag economic message release mechanism should combine the new features of the current era and introduce new technologies and new ideas, so as to ensure that its message release mechanism is more scientific and effective and at the same time provide effective support for the practice of message release requirements. In the transition period of the new normal of China’s economic expansion, the integration of big data and ag income has boosted the comprehensive integration of advanced skill into the “three rural” fields and has become a force that cannot be ignored to boost the optimization of the supply-side structure of ag income.



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*Source: 1497357-2022-09-09.xml*