# Design of Moral Education Management System for Higher Vocational Students Based on Multisource Sensing Data Fusion

Authors: Hui Guo
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

Based on the specific circumstances of colleges and universities, this study presents the moral education management system and leverages multisource sensor data and other technologies to develop an information system in line with the moral education management of modern college students. Using multisource sensor data fusion and learning data definition, the fused education data is expressed in a standardized way, resulting in a common and standard data interchange format. This foundation is used to build a shareable and reusable data management system that enables data sharing and interchange across multiple heterogeneous data sources, aids the intelligent education system in obtaining more comprehensive and complete recorded data, and improves data sharing timeliness. As a result, learning behavior analysis results are more objective, immediate, and accurate, allowing the intelligent education system’s response to be more prompt and intelligent. This study, which is based on multisource sensor data fusion, also discusses platform selection, system architecture, database design, and methods and suggestions for overcoming critical challenges and can be used as a reference for other university information management systems with vast amounts of data.


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## Body

## 1. Introduction

Higher education is responsible not only for the transmission of knowledge but also for the establishment of moral education and the cultivation of socialist builders and successors with socialist core values and comprehensive development of moral, intellectual, physical, and aesthetic qualities [1, 2]. Many schools have established moral education management systems to assist higher vocational students in developing a correct worldview, outlook on life, and values, developing correct scientific moral cognition, moral behavior, legal consciousness, learning behavior, and interpersonal behavior, and quantifying the results of moral education [3, 4]. China has witnessed the rapid development of higher vocational education, particularly since the Ministry of Education launched a special project on higher vocational expansion in 2019 to meet the growing demand for technical skill talents for industrial upgrading and economic restructuring and to develop higher vocational education as a strategic move to relieve current employment pressures and solve the shortage of highly skilled talents. The moral education management system based on traditional IT technology of centralized data storage + relational database appears to be inadequate in terms of data retrieval storage and efficiency as the number of higher vocational students in China has increased on a larger scale, and the amount of data to be handled has dramatically increased, and the moral education management system based on traditional IT technology of centralized data storage + relational database appears to be inadequate in terms of data retrieval storage and efficiency [5, 6].In this context, this paper designs a moral education management system for college students based on multisource sensing data to solve the problem of big data storage and retrieval.


## 2. Difficulties of Moral Education Management in Higher Vocational Institutions

It focuses on improving the impact and degree of moral education, encouraging students to develop accurate conceptions that can regulate their behavior, establishing professional moral quality, patriotic spirit, and having a serious and responsible attitude toward their own behavior. [7, 8]. Currently, understudies commonly neglect discipline during moral education management in many institutions, and executives’ ethical instruction techniques are in reverse. They are unable to assist guardians and endeavors in establishing an acceptable upright training improvement climate for understudies, which may result in a decreased moral schooling board influence, particularly throughout the development of organizational innovation. The attributes of understudy moral training, board rules, and other reasonable exploitation of organizational innovation are not included in higher professional foundations [9, 10]. Especially during the development of network technology, higher vocational colleges do not use network technology and software technology reasonably with the characteristics and laws of students’ moral management, and it is difficult to enhance the management effect when the technology is backward.Most higher education institutions also struggle with moral education management, as they are unable to update various data information and moral education management contents in real time and accurately, making it difficult to improve moral education management effectiveness through high-quality and orderly means. The following is a reasonable proposal for an information-based and data-driven stage for moral training executives and the construction of ethical schooling the board framework, with the goal of increasing the impact of moral training executives.


## 3. Data Fusion Analysis of Multisource Heterogeneous Education Data

### 3.1. Analysis of Multisource Data Fusion Methods

Data fusion was first applied in the military field, which is a multilevel and multifaceted data processing process [11, 12]. It is used to process multisource information and naturally identify, join, correspond, gauge, and blend data, basically to accomplish more precise position deduction and personality assessment and afterward make an opportune and complete assessment of war zone conditions, danger level, and significance level. It was later applied and developed in many fields such as sensors, geospatial and intelligence analysis, and so on. Especially, in the Internet era, multisource data fusion has gradually become an important research direction in the field of big data. Through multisource sensing data fusion, the cross-corroboration of multisource information can be realized; mutual compensation of data information can be achieved; and the amount of data can be effectively reduced to obtain definite data and deep semantic knowledge [13, 14]. The biology of training vast amounts of data is progressively being shaped during the age of computerized reasoning education. The massive amount of training data has multisource heterogeneous attributes. The multisource information combination strategy offers another approach for resolving the sharing and interoperability of vast amounts of training data in the modern day, as well as a potentially valuable viewpoint for creating reusable and shared instruction data models.The common multisource data fusion methods are mainly data-level fusion, feature-level fusion, and decision-level fusion. Data-level fusion is the lowest level of data fusion, which is to directly correlate and fuse the original data after simple preprocessing and only after fusion for data feature extraction. Feature-level fusion is to first extract the data features and then correlate and fuse the data. By comparing these three data fusion methods, it can be seen that although data-level fusion can retain the maximum extent of the original. Decision-level fusion is to first make decisions for each data source, then correlate and fuse these decisions, and finally obtain an overall consistent decision result. Although decision-level fusion has high fault tolerance and timeliness, it is a data fusion with specific decision needs as the starting point, and it is difficult to make specific decisions for data fusion in the face of the complex and changing educational big data environment in the era of AI education [15]. The feature-level fusion, on the other hand, may provide immediacy while simultaneously providing the maximum amount of idiosyncratic information needed for decision-making, and its fusion outcomes are also quite accurate. The feature-level fusion method fits well with the requirements of educational big data analysis in the era of artificial intelligence education since educational big data does not have the same high fusion accuracy as picture data.


### 3.2. Extraction of Shared Data Features of Heterogeneous Multisource Education Data

In order to improve the data reuse after data fusion, the multisource education data fusion method in this paper is to perform feature-level data fusion by extracting the data sharing characteristics of each heterogeneous data source. The process of extracting shared data characteristics is in essence the process of extracting learning context characteristics from the learning behavior data generated by learners from each heterogeneous data source. Throughout the previous descriptions of information characteristics of learning contexts, different scholars have classified contextual information characteristics into different types from different perspectives. The following are relevant ones.According to Dey et al. [16], contextual information includes explicitly perceived contextual information such as location, time, and surroundings, as well as implicitly perceived contextual information such as social relationships, habits, consumption levels and preferences, and so on. Kwilinski et al. [17] divided the context into three major aspects: user context, environmental context, and application context, among which user context includes an activity. Chaaya et al. [18] divided the context into user context, environment context, and application context, where user context includes activity, location, and description; environment context includes time, brightness, temperature, weather, resources, and other contexts; and application context includes function, maintenance, energy, and other contexts. Mattila et al. [19] divided the contextual information into three categories: natural environment, device environment, and user environment. From the user-centered perspective, Sarker [20] divided the contextual information into computing scenarios, user contexts, physical contexts, temporal contexts, and social contexts. The learning data from different data sources in the era of AI education have obvious spatial and temporal characteristics and the learning data from different data sources.The learning data from different data sources in the era of AI education have obvious spatial and temporal characteristics, and the learning interaction of learners cannot be supported by devices. Based on the above contextual information characteristics analysis, the article extracts the shared data characteristics of each heterogeneous data source into five dimensions: learner context, time context, location context, device context, and event context, through statistical analysis of research samples (see Figure1).Figure 1
Pie chart of the proportion of four heterogeneous data sources.


### 3.3. Feature-Level Data Fusion Based on Shared Data Characteristics

The above five dimensions of learning context information characteristics well characterize the shared data characteristics of each heterogeneous data source, and through these five shared data characteristics, the real learning life scenarios of learners in each heterogeneous data source can be accurately described, and then the seamless connection between heterogeneous data can be well realized. These five shared data features represent five data dimensions, which can be combined to form a real learning scene of learners: “learner context + time context + location context + device context + event context learning scene,” which describes “learners, when, where, based on what device, and what they did.”  Based on these five data dimensions, the feature-level data fusion method is used to fuse educational data. The feature-level data fusion mainly goes through a hierarchical extraction of the semantic features of each data dimension, hierarchical semantic feature-level data fusion, and cross-dimensional and cross-layered correlated semantic feature-level data fusion.The main goal of hierarchical semantic feature extraction for each data dimension [21] is to extract the semantic features of these five different data dimensions hierarchically, determine the semantic attributes of each data dimension, and determine the multilayer semantic logical relationships of each data dimension semantically at the same level and at the upper and lower levels. For example, the semantic attributes of the time dimension can be separated into broad semantic information about working days and vacations, and working days can be differentiated into semantic information about classroom study time and self-study time at varying levels of granularity.The main goal of highlight-level information combining progressive semantics is to entangle instructional data from several heterogeneous data sources. The comparative fine-grained combination technique is used to build scene information, as represented by the progressive semantic order and element-level information combination. These data can accurately depict students’ learning characteristics, eliminate irregularity and overt repetition within data structures, and perform a similar semantic agglomeration at a similar granularity.


## 3.1. Analysis of Multisource Data Fusion Methods

Data fusion was first applied in the military field, which is a multilevel and multifaceted data processing process [11, 12]. It is used to process multisource information and naturally identify, join, correspond, gauge, and blend data, basically to accomplish more precise position deduction and personality assessment and afterward make an opportune and complete assessment of war zone conditions, danger level, and significance level. It was later applied and developed in many fields such as sensors, geospatial and intelligence analysis, and so on. Especially, in the Internet era, multisource data fusion has gradually become an important research direction in the field of big data. Through multisource sensing data fusion, the cross-corroboration of multisource information can be realized; mutual compensation of data information can be achieved; and the amount of data can be effectively reduced to obtain definite data and deep semantic knowledge [13, 14]. The biology of training vast amounts of data is progressively being shaped during the age of computerized reasoning education. The massive amount of training data has multisource heterogeneous attributes. The multisource information combination strategy offers another approach for resolving the sharing and interoperability of vast amounts of training data in the modern day, as well as a potentially valuable viewpoint for creating reusable and shared instruction data models.The common multisource data fusion methods are mainly data-level fusion, feature-level fusion, and decision-level fusion. Data-level fusion is the lowest level of data fusion, which is to directly correlate and fuse the original data after simple preprocessing and only after fusion for data feature extraction. Feature-level fusion is to first extract the data features and then correlate and fuse the data. By comparing these three data fusion methods, it can be seen that although data-level fusion can retain the maximum extent of the original. Decision-level fusion is to first make decisions for each data source, then correlate and fuse these decisions, and finally obtain an overall consistent decision result. Although decision-level fusion has high fault tolerance and timeliness, it is a data fusion with specific decision needs as the starting point, and it is difficult to make specific decisions for data fusion in the face of the complex and changing educational big data environment in the era of AI education [15]. The feature-level fusion, on the other hand, may provide immediacy while simultaneously providing the maximum amount of idiosyncratic information needed for decision-making, and its fusion outcomes are also quite accurate. The feature-level fusion method fits well with the requirements of educational big data analysis in the era of artificial intelligence education since educational big data does not have the same high fusion accuracy as picture data.


## 3.2. Extraction of Shared Data Features of Heterogeneous Multisource Education Data

In order to improve the data reuse after data fusion, the multisource education data fusion method in this paper is to perform feature-level data fusion by extracting the data sharing characteristics of each heterogeneous data source. The process of extracting shared data characteristics is in essence the process of extracting learning context characteristics from the learning behavior data generated by learners from each heterogeneous data source. Throughout the previous descriptions of information characteristics of learning contexts, different scholars have classified contextual information characteristics into different types from different perspectives. The following are relevant ones.According to Dey et al. [16], contextual information includes explicitly perceived contextual information such as location, time, and surroundings, as well as implicitly perceived contextual information such as social relationships, habits, consumption levels and preferences, and so on. Kwilinski et al. [17] divided the context into three major aspects: user context, environmental context, and application context, among which user context includes an activity. Chaaya et al. [18] divided the context into user context, environment context, and application context, where user context includes activity, location, and description; environment context includes time, brightness, temperature, weather, resources, and other contexts; and application context includes function, maintenance, energy, and other contexts. Mattila et al. [19] divided the contextual information into three categories: natural environment, device environment, and user environment. From the user-centered perspective, Sarker [20] divided the contextual information into computing scenarios, user contexts, physical contexts, temporal contexts, and social contexts. The learning data from different data sources in the era of AI education have obvious spatial and temporal characteristics and the learning data from different data sources.The learning data from different data sources in the era of AI education have obvious spatial and temporal characteristics, and the learning interaction of learners cannot be supported by devices. Based on the above contextual information characteristics analysis, the article extracts the shared data characteristics of each heterogeneous data source into five dimensions: learner context, time context, location context, device context, and event context, through statistical analysis of research samples (see Figure1).Figure 1
Pie chart of the proportion of four heterogeneous data sources.


## 3.3. Feature-Level Data Fusion Based on Shared Data Characteristics

The above five dimensions of learning context information characteristics well characterize the shared data characteristics of each heterogeneous data source, and through these five shared data characteristics, the real learning life scenarios of learners in each heterogeneous data source can be accurately described, and then the seamless connection between heterogeneous data can be well realized. These five shared data features represent five data dimensions, which can be combined to form a real learning scene of learners: “learner context + time context + location context + device context + event context learning scene,” which describes “learners, when, where, based on what device, and what they did.”  Based on these five data dimensions, the feature-level data fusion method is used to fuse educational data. The feature-level data fusion mainly goes through a hierarchical extraction of the semantic features of each data dimension, hierarchical semantic feature-level data fusion, and cross-dimensional and cross-layered correlated semantic feature-level data fusion.The main goal of hierarchical semantic feature extraction for each data dimension [21] is to extract the semantic features of these five different data dimensions hierarchically, determine the semantic attributes of each data dimension, and determine the multilayer semantic logical relationships of each data dimension semantically at the same level and at the upper and lower levels. For example, the semantic attributes of the time dimension can be separated into broad semantic information about working days and vacations, and working days can be differentiated into semantic information about classroom study time and self-study time at varying levels of granularity.The main goal of highlight-level information combining progressive semantics is to entangle instructional data from several heterogeneous data sources. The comparative fine-grained combination technique is used to build scene information, as represented by the progressive semantic order and element-level information combination. These data can accurately depict students’ learning characteristics, eliminate irregularity and overt repetition within data structures, and perform a similar semantic agglomeration at a similar granularity.


## 4. Hardware Design

### 4.1. Establishing Multisource Data Signal Sensor

To construct a multisource information signal sensor, first, the design of the multisource information signal sensor is investigated. The information stream connection point is planned by the presentation necessities of the sign detecting framework and the sensor engineering. The sensor structure is planned through the information stream structure, and the system of information keen examination is illustrated. The information keen examination multisource information signal sensor is set as a various leveled design to store and preprocess the multisource information signals. The control information signal streams to the multisource information handling layer and is yielded at the handling layer after information checking and stockpiling. The sensor data flow interface is thus constructed, as shown in Figure2.Figure 2
Diagram of sensor data flow interface.According to the sensor data flow interface analysis, it can be seen that the data signal flows to the multisource data processing layer and the sensor data is delivered through the cloud server. Therefore, there are mandatory requirements for multisource data signal sensors to be able to transmit data signals accurately. Accordingly, the packet loss rate of the data state signal is calculated, and the data transmission rate is monitored. The source data transmission reference standard is developed according to the data state of the application layer, as shown in Figure3.Figure 3
Columnar scatter diagram of reference standard value of source data transmission.The data sensing content is determined based on the developed data state to source data transmission standard and the reference value, and the sensing content is analyzed in parallel with the data sending delay to form a multisource data signal sensor structure, as shown in Figure4.Figure 4
Multisource data signal sensor structure schematic.By forming a multisource data signal sensor, the multisource data signal is preprocessed and the data signal features are extracted. The data intelligent analysis results for multisource data fusion are stored, and the data content is updated in time, so as to achieve the purpose of judging the intelligent analysis of data delay demodulation.


### 4.2. Building Data Intelligent Analysis Circuit

In order to ensure the data intelligent analysis rate and better storage of machine learning data, the load of the data intelligent analysis circuit is adjusted. The historical data analysis output process of the intelligent circuit is long, so the circuit update timing is constructed, as shown in Figure5.Figure 5
Circuit update timing.According to the circuit update timing diagram, the data types transmitted by the circuit are intelligently classified, and the data types transmitted by the circuit are summarized. The data intelligent analysis circuit is constructed according to the circuit transmission data type. In order to ensure that the influx of data listening packets corresponding to the data intelligent analysis does not exceed the load range of the data intelligent analysis circuit [22], the size of the data analysis packet needs to be calculated in advance with the following formula:(1)A=A1+A2+A3,S=∑i=1nSi,Ti=SA,where the first three values of the parameter in the packet are A1, A2, A3; n is the length of the first packet of data transmission; and Si is the packet transmission byte of the data intelligent analysis circuit. Using the size of the data analysis packet, the load fluctuation range during the packet influx is calculated as follows:(2)Y=5q∫i−1i+1li4di,where q is the standard value of line load and li is the length of the ith packet, the load fluctuation range is established based on the data vector, and the line type of the data intelligent analysis circuit is selected accordingly to ensure the safety of data transmission.The data intelligent analysis circuit is used to intelligently analyze the data fused from multiple sources and share the transmission pressure of the main data transmission line. And, in the range of data vector load fluctuation, determine the byte fluctuation range of data transmission, while avoiding the influx of data packets overload caused by data line short circuit.


## 4.1. Establishing Multisource Data Signal Sensor

To construct a multisource information signal sensor, first, the design of the multisource information signal sensor is investigated. The information stream connection point is planned by the presentation necessities of the sign detecting framework and the sensor engineering. The sensor structure is planned through the information stream structure, and the system of information keen examination is illustrated. The information keen examination multisource information signal sensor is set as a various leveled design to store and preprocess the multisource information signals. The control information signal streams to the multisource information handling layer and is yielded at the handling layer after information checking and stockpiling. The sensor data flow interface is thus constructed, as shown in Figure2.Figure 2
Diagram of sensor data flow interface.According to the sensor data flow interface analysis, it can be seen that the data signal flows to the multisource data processing layer and the sensor data is delivered through the cloud server. Therefore, there are mandatory requirements for multisource data signal sensors to be able to transmit data signals accurately. Accordingly, the packet loss rate of the data state signal is calculated, and the data transmission rate is monitored. The source data transmission reference standard is developed according to the data state of the application layer, as shown in Figure3.Figure 3
Columnar scatter diagram of reference standard value of source data transmission.The data sensing content is determined based on the developed data state to source data transmission standard and the reference value, and the sensing content is analyzed in parallel with the data sending delay to form a multisource data signal sensor structure, as shown in Figure4.Figure 4
Multisource data signal sensor structure schematic.By forming a multisource data signal sensor, the multisource data signal is preprocessed and the data signal features are extracted. The data intelligent analysis results for multisource data fusion are stored, and the data content is updated in time, so as to achieve the purpose of judging the intelligent analysis of data delay demodulation.


## 4.2. Building Data Intelligent Analysis Circuit

In order to ensure the data intelligent analysis rate and better storage of machine learning data, the load of the data intelligent analysis circuit is adjusted. The historical data analysis output process of the intelligent circuit is long, so the circuit update timing is constructed, as shown in Figure5.Figure 5
Circuit update timing.According to the circuit update timing diagram, the data types transmitted by the circuit are intelligently classified, and the data types transmitted by the circuit are summarized. The data intelligent analysis circuit is constructed according to the circuit transmission data type. In order to ensure that the influx of data listening packets corresponding to the data intelligent analysis does not exceed the load range of the data intelligent analysis circuit [22], the size of the data analysis packet needs to be calculated in advance with the following formula:(1)A=A1+A2+A3,S=∑i=1nSi,Ti=SA,where the first three values of the parameter in the packet are A1, A2, A3; n is the length of the first packet of data transmission; and Si is the packet transmission byte of the data intelligent analysis circuit. Using the size of the data analysis packet, the load fluctuation range during the packet influx is calculated as follows:(2)Y=5q∫i−1i+1li4di,where q is the standard value of line load and li is the length of the ith packet, the load fluctuation range is established based on the data vector, and the line type of the data intelligent analysis circuit is selected accordingly to ensure the safety of data transmission.The data intelligent analysis circuit is used to intelligently analyze the data fused from multiple sources and share the transmission pressure of the main data transmission line. And, in the range of data vector load fluctuation, determine the byte fluctuation range of data transmission, while avoiding the influx of data packets overload caused by data line short circuit.


## 5. Software Design

### 5.1. Acquisition of Multisource Intelligent Sensing Data Signals

The multisource data signal sensor is used to capture the multisource intelligent sensing data signal. To begin, control the information procurement and transmission layer by prehandling multisource clever detecting information, control astute investigation guidelines through signal prehandling, and use the association between the application layer of multisource shrewd detecting information to accomplish correspondence associations by prehandling signal calculation. In addition, use the top PC to manage the data transmission arrangement and enter the first phase of the inborn rationale data prehandling application layer.The computer control data of multi-source sensitive detection information is processed in a decentralized manner, and the basic principles of detection signals are summarized. The detection information is classified and troubleshooting according to the information types transmitted by circuits. Decide on the troubleshooting information’s question area right away and inquire about and count the multisource shrewd detecting information as indicated by the consensus for the ensuing investigation. Accordingly, we get the multisource intelligent sensing data termination matching report and truncate the report to organize the termination matching report of circuit transmission data types, as shown in Figure6.Figure 6
Matching data distribution diagram of different circuit transmission data types.When the data type termination mismatch is known to exist in the matching result, the termination data types of the termination matching report are excluded, and the matching circuit data types are collated, and the set of multisource intelligent sensing data signal acquisition categories [23] is set as Ci (i = 1, 2, …, C) according to the matching result, and the set of data categories with the total number of Ni is screened, and for some of the screened data sets, the initial entropy value is calculated by the following formula:(3)Pt=NiCilog2NiCi,SI=∑t=1cPt,SI=∑t=1cNiCilog2NiCi,where t is the sensing data signal acquisition time and Pt is the circuit matching termination power. The initial entropy value of the screened data set is used to determine the clustering center of the multisourced intelligent sensing data, and the collection interval of the sensing data is calculated as follows:(4)U=SI−m¯i2,di,j=U,di,j=U=SI−m¯i2,where mi is the clustering center of the data set with k as sample points. By determining the acquisition interval of sensing data, the multisource intelligent sensing data signal is acquired; the multisource data signal is structured and processed within the interval; and the multisource data signal structured analysis algorithm is designed for the structured processing.


### 5.2. Designing Multisource Data Signal Structuring Analysis Algorithm

According to the multisource intelligent sensing data signal, the structured analysis algorithm of the multisource data signal is designed. First, set the analysis procedure of the multisource data signal structured analysis algorithm.Use the multisource data downstream to self-check and divide the data attribute structure; reason the multisource data signal structure in reverse by linking relationships [24]; when there is reverse logical feedback, extract the data signal and carry out structured analysis; and at this time, the expression of linking relationship is as follows:(5)Zi=12z12+z22+⋯+zi2.By modifying the multisource data sensing signal, the relationship matrix of the multisource data signal structure is obtained, and the formula is as follows:(6)Mi=z11z12…z1iz21z22…z2i⋮⋮…⋮zn1zn2…zni.Through the relationship matrix of the multisource data signal structure, establish the multisource data signal link relationship, label the signal data through the link relationship, accordingly control the position of the multisource data signal in the data signal structure, analyze the multisource data signal through link jumping, and read the content of the multisource data signal to analyze the multisource data signal structure.


### 5.3. Complete Data Intelligent Analysis

Use multisource data blend and format data touchy identification project. Then, by adding data perusing cooperation focuses, the servers at the two terminations of the data overview are related to facilitating the client study data. At last, the data limit is given; the wise examination method of data handling is taken on; the data keen application layer is gathered and organized; and the data examination results are shown by the last connection report. Information investigation shows content, information smart examination upgrading, and wise examination of multisource information, according to the conclusion association report. The content of the multisource data signal is exported according to the predetermined inner logic, and the preset processing logic is exported by independently evaluating the self-tested data type when the overall analysis progress decompression result of the data package appears. Simultaneously, data kinds that do not match the end connection report are separated, and the background compressed file data signal is used to match the end connection report upload once more, completing the intelligent data analysis. So far, the design of the sensor data intelligent analysis system based on multisource data fusion is completed.


## 5.1. Acquisition of Multisource Intelligent Sensing Data Signals

The multisource data signal sensor is used to capture the multisource intelligent sensing data signal. To begin, control the information procurement and transmission layer by prehandling multisource clever detecting information, control astute investigation guidelines through signal prehandling, and use the association between the application layer of multisource shrewd detecting information to accomplish correspondence associations by prehandling signal calculation. In addition, use the top PC to manage the data transmission arrangement and enter the first phase of the inborn rationale data prehandling application layer.The computer control data of multi-source sensitive detection information is processed in a decentralized manner, and the basic principles of detection signals are summarized. The detection information is classified and troubleshooting according to the information types transmitted by circuits. Decide on the troubleshooting information’s question area right away and inquire about and count the multisource shrewd detecting information as indicated by the consensus for the ensuing investigation. Accordingly, we get the multisource intelligent sensing data termination matching report and truncate the report to organize the termination matching report of circuit transmission data types, as shown in Figure6.Figure 6
Matching data distribution diagram of different circuit transmission data types.When the data type termination mismatch is known to exist in the matching result, the termination data types of the termination matching report are excluded, and the matching circuit data types are collated, and the set of multisource intelligent sensing data signal acquisition categories [23] is set as Ci (i = 1, 2, …, C) according to the matching result, and the set of data categories with the total number of Ni is screened, and for some of the screened data sets, the initial entropy value is calculated by the following formula:(3)Pt=NiCilog2NiCi,SI=∑t=1cPt,SI=∑t=1cNiCilog2NiCi,where t is the sensing data signal acquisition time and Pt is the circuit matching termination power. The initial entropy value of the screened data set is used to determine the clustering center of the multisourced intelligent sensing data, and the collection interval of the sensing data is calculated as follows:(4)U=SI−m¯i2,di,j=U,di,j=U=SI−m¯i2,where mi is the clustering center of the data set with k as sample points. By determining the acquisition interval of sensing data, the multisource intelligent sensing data signal is acquired; the multisource data signal is structured and processed within the interval; and the multisource data signal structured analysis algorithm is designed for the structured processing.


## 5.2. Designing Multisource Data Signal Structuring Analysis Algorithm

According to the multisource intelligent sensing data signal, the structured analysis algorithm of the multisource data signal is designed. First, set the analysis procedure of the multisource data signal structured analysis algorithm.Use the multisource data downstream to self-check and divide the data attribute structure; reason the multisource data signal structure in reverse by linking relationships [24]; when there is reverse logical feedback, extract the data signal and carry out structured analysis; and at this time, the expression of linking relationship is as follows:(5)Zi=12z12+z22+⋯+zi2.By modifying the multisource data sensing signal, the relationship matrix of the multisource data signal structure is obtained, and the formula is as follows:(6)Mi=z11z12…z1iz21z22…z2i⋮⋮…⋮zn1zn2…zni.Through the relationship matrix of the multisource data signal structure, establish the multisource data signal link relationship, label the signal data through the link relationship, accordingly control the position of the multisource data signal in the data signal structure, analyze the multisource data signal through link jumping, and read the content of the multisource data signal to analyze the multisource data signal structure.


## 5.3. Complete Data Intelligent Analysis

Use multisource data blend and format data touchy identification project. Then, by adding data perusing cooperation focuses, the servers at the two terminations of the data overview are related to facilitating the client study data. At last, the data limit is given; the wise examination method of data handling is taken on; the data keen application layer is gathered and organized; and the data examination results are shown by the last connection report. Information investigation shows content, information smart examination upgrading, and wise examination of multisource information, according to the conclusion association report. The content of the multisource data signal is exported according to the predetermined inner logic, and the preset processing logic is exported by independently evaluating the self-tested data type when the overall analysis progress decompression result of the data package appears. Simultaneously, data kinds that do not match the end connection report are separated, and the background compressed file data signal is used to match the end connection report upload once more, completing the intelligent data analysis. So far, the design of the sensor data intelligent analysis system based on multisource data fusion is completed.


## 6. Create a Standardized Platform for Moral Education Management

Higher vocational institutions can create a standardized platform for moral education management through network technology and software technology, which can be used to enhance the standardization of students’ speech and behavior and play a certain restraint so that students can form a correct concept of compliance with discipline.


### 6.1. Construction of a Standardized Platform for Behavior

In the process of designing the platform, the “moral education reward and punishment point system” should be used as the basic part so that all moral education personnel can use the platform to make the adjustment to students’ behavior scores while visualizing and quantifying students’ behavior. The software allows moral staff to change student behavior scores, add or delete points, and administer consequences. To improve the binding force of student management, software technology should be used to push the content of behavior record data for students once a day during the platform’s design so that students can consciously regulate their own behavior, discipline their own behavior, abide by the law, and play a good moral education management role through the platform.


### 6.2. Designing an Interactive Platform

Higher vocational institutions can use network technology to create interactive platforms for moral education management, including parents’ and teachers’ ends, where parents and teachers can discuss moral education topics and communicate with one another, and students’ ends, where students can understand the behavior and information of excellent students in mobile terminals, thereby increasing the influence of all students unintentionally.Students can learn about the behavior and information of good students in mobile terminals and enhance the behavior management ability and self-management ability of all students under the influence of good students’ role models [25]. Higher education institutions also need to pay attention to the standardization of moral education activities in the platform and use the database system to store students’ daily information and then release it on the platform to enhance students’ self-confidence in moral education learning and make them have the pride of standardizing themselves. In terms of the platform’s normative release of various information, it should also pay attention to the normative release of school recruitment data information and enterprise recruitment data information; provide students with employment information, professional ethics information, job work rules and regulations information, and so on; and design corresponding modules to guide students to understand the professional skills they need to master for future job work and the ethical norms and rules and regulations they need to abide by, so as to cultivate students’ moral qualities. The module is designed to guide students to understand the professional skills, moral codes, and rules and regulations that need to be observed in their future jobs, so as to cultivate their moral and professional qualities.


## 6.1. Construction of a Standardized Platform for Behavior

In the process of designing the platform, the “moral education reward and punishment point system” should be used as the basic part so that all moral education personnel can use the platform to make the adjustment to students’ behavior scores while visualizing and quantifying students’ behavior. The software allows moral staff to change student behavior scores, add or delete points, and administer consequences. To improve the binding force of student management, software technology should be used to push the content of behavior record data for students once a day during the platform’s design so that students can consciously regulate their own behavior, discipline their own behavior, abide by the law, and play a good moral education management role through the platform.


## 6.2. Designing an Interactive Platform

Higher vocational institutions can use network technology to create interactive platforms for moral education management, including parents’ and teachers’ ends, where parents and teachers can discuss moral education topics and communicate with one another, and students’ ends, where students can understand the behavior and information of excellent students in mobile terminals, thereby increasing the influence of all students unintentionally.Students can learn about the behavior and information of good students in mobile terminals and enhance the behavior management ability and self-management ability of all students under the influence of good students’ role models [25]. Higher education institutions also need to pay attention to the standardization of moral education activities in the platform and use the database system to store students’ daily information and then release it on the platform to enhance students’ self-confidence in moral education learning and make them have the pride of standardizing themselves. In terms of the platform’s normative release of various information, it should also pay attention to the normative release of school recruitment data information and enterprise recruitment data information; provide students with employment information, professional ethics information, job work rules and regulations information, and so on; and design corresponding modules to guide students to understand the professional skills they need to master for future job work and the ethical norms and rules and regulations they need to abide by, so as to cultivate students’ moral qualities. The module is designed to guide students to understand the professional skills, moral codes, and rules and regulations that need to be observed in their future jobs, so as to cultivate their moral and professional qualities.


## 7. Improve the Modules of Moral Education Management in Higher Vocational Institutions

So far, school personnel, plan operation staff, and board teachers have waived the authority to make adjustments to teachers and class educators when most professional undergraduates disobey these rules. This shows that tutors’ and head teachers’ management data is burdensome and chaotic, and they are unable to sort out the behavior data of substitute pupils in an ideal and correct manner, implying that they lack multisensor data. Simultaneously, they find it difficult to truly summarize and interpret behavioral data (a type of multisensor data), reducing the influence of alternative data on executives and making them unable to engage in certain actions. It is difficult to summarize and deal with it effectively, which leads to a decrease in the effectiveness of student information management and the inability to play the role of behavior management and moral management. In view of this situation, it is suggested that important departments of schools and colleges use data innovation and programming innovation to improve the volume and quality of multisensor data, take their own moral training as the board module, and set up special modules for educators, managers, experts, and coordination factor instructors, as shown in Figure7. Simultaneously, employees from all departments should gradually record replacement behavior data and violation data (a sort of multisensor data) in accordance with module prerequisites so that managers’ moral counseling may accurately comprehend the conduct of various substitutes. With a single click, the software may retrieve information about a student’s behavior. As a result, behavior control and moral education management may be carried out effectively using in-depth mining of multisensor data, highlighting the role and benefits of information technology and digital moral education management.Figure 7
Curve of teaching personnel, management personnel, technicians, and logistics personnel applying information technology and software technology.


### 7.1. Designing Moral Education Management System of Home-School Cooperation

The fundamental measure for moral education management of college students in higher education institutions is home-school cooperation. It not only can improve the moral education management level with parental support but also can encourage college students to govern their own behavior and build good moral quality and superb quality in all aspects. As a result, it is suggested that higher education institutions strengthen home-school cooperation and establish a moral education management system in terms of home-school cooperation by using network technology and software technology to record students’ performance data and moral education data in real time, allowing moral education managers to improve the moral education effect during parent-teacher collaboration.


### 7.2. Designing a Module to Record Students’ Growth Information in Real Time

It is suggested that higher vocational institutions design modules that can record students’ growth information in real time on the platform and summarize the data information of the modules into the database system to facilitate the implementation of moral education management. Many parents are too busy to provide moral education for their children, particularly for some residential students, and parents are unable to communicate effectively with students, and there is little time for communication between parents and teachers, so they are unable to fully comprehend their children’s daily performance and behavior. During the design of the moral education management platform, it is necessary to include a module for recording students’ growth information, as well as the input of students’ daily behavior and learning data so that parents can grasp and understand their children’s situation through the module and then learn how to conduct moral education in the limited time they have with them under the guidance of teachers’ communication.


### 7.3. Designing the Module of Real-Time Feedback Information

Higher vocational institutions can apply network technology and software systems to design real-time feedback modules for home-school cooperation in the moral education management platform, in which parents are required to communicate with school moral education managers and give feedback on students’ behavior. Parents can get real-time student behavior information on the platform, and if communication with teachers is not effective, they can leave messages for the school on the platform, and the school’s moral education management department can provide parents with fast and accurate feedback to help parents clarify how to regulate student behavior and solve behavior problems, forming an interactive feedback model. At present, some parents cannot actively communicate with schools and moral education management personnel because they do not recognize the importance of moral education. In this case, higher education institutions should explain to parents that students’ bad behavioral habits are linked to family education and the growing environment to some extent and that once moral education is neglected in families, it is difficult for higher education institutions to complete related education work independently. In this approach, a huge quantity of feedback information can be offered in the moral education management platform with the help and understanding of parents, and moral education management can be carried out efficiently to improve the effectiveness of home-school cooperation.


### 7.4. Designing Moral Education Management System of School-Enterprise Cooperation

Higher vocational colleges and universities will lead students to study vocational skills, job work practice operation knowledge, and technology to enhance the development of students’ vocational ability through school-enterprise cooperation in the process of fostering talents. During this time, it is suggested that higher vocational institutions design a moral education management system of school-enterprise cooperation in a moral education platform and collaborate with enterprises to cultivate students’ professional moral quality and professional cultivation, in order to cultivate composite talents with professional skills, innovation ability, moral quality, and a sense of social responsibility with the help of good moral education management.


#### 7.4.1. Designing the Moral Education Module of “Craftsmanship”

Therefore, in the process of cooperation between higher education institutions and enterprises, the moral education module of “craftsmanship” should be designed, in which it is clear that the enterprises need to provide students with professional skills, innovation, moral quality, and responsibility through craftsmen. Therefore, in the process of cooperation between higher education institutions and enterprises, the moral education module of “craftsmanship” should be designed, in which it is clear that enterprises need to lead students to learn and get in touch with more professional ethical behaviors and comply with rules and regulations through technicians with craftsmanship so that students can improve their behavioral concepts and have good moral quality and professionalism under the influence of technicians’ subtle influence.


#### 7.4.2. Designing the Moral Education Module of Enterprise Intuitive Management

When designing moral education modules with advanced technology, higher education institutions should also pay attention to designing modules for enterprises to manage moral education intuitively so that enterprises can complete the task of moral education management for students in the process of cooperation with higher education institutions. In the module, a database of students’ behavioral information should be set up, in which students’ data on rewards and punishments in school, professional ability, and career potential should be entered so that companies can provide students with moral management according to various information. At the same time, enterprises can also select outstanding talents to work in enterprise positions by understanding students’ situation in the module system, which not only can improve the employment rate of students’ talents but also can meet the talent demand of enterprises, achieving a win-win situation.


#### 7.4.3. Designing Moral Education Modules in Employment

It is suggested that higher vocational colleges and enterprises should jointly study how to design employment modules and guidance platforms for students, especially for college students who are confused about the direction of employment, and should set up relevant employment guidance modules and management systems for them in a targeted way. Firstly, in the module, a system introducing information about different employment enterprises should be designed for students so that students can know the job information, work information, and skill requirement information of future employment enterprises through mobile terminals, and employment enterprises can also know students’ information in the platform so that the management mode of employment guidance can be formed in the case of two-way selection of students and enterprises’ employment. Secondly, in the digital platform of moral education management, it is also necessary to set up a moral education management module for fixed-term internships, a moral education management module for practical tutorials, a moral education management module for employment information, a moral education management module for registration interview, a moral education management module for the presigning list, and so on. In this way, the quality of moral education management can be promoted with the help and support of various modules.


## 7.1. Designing Moral Education Management System of Home-School Cooperation

The fundamental measure for moral education management of college students in higher education institutions is home-school cooperation. It not only can improve the moral education management level with parental support but also can encourage college students to govern their own behavior and build good moral quality and superb quality in all aspects. As a result, it is suggested that higher education institutions strengthen home-school cooperation and establish a moral education management system in terms of home-school cooperation by using network technology and software technology to record students’ performance data and moral education data in real time, allowing moral education managers to improve the moral education effect during parent-teacher collaboration.


## 7.2. Designing a Module to Record Students’ Growth Information in Real Time

It is suggested that higher vocational institutions design modules that can record students’ growth information in real time on the platform and summarize the data information of the modules into the database system to facilitate the implementation of moral education management. Many parents are too busy to provide moral education for their children, particularly for some residential students, and parents are unable to communicate effectively with students, and there is little time for communication between parents and teachers, so they are unable to fully comprehend their children’s daily performance and behavior. During the design of the moral education management platform, it is necessary to include a module for recording students’ growth information, as well as the input of students’ daily behavior and learning data so that parents can grasp and understand their children’s situation through the module and then learn how to conduct moral education in the limited time they have with them under the guidance of teachers’ communication.


## 7.3. Designing the Module of Real-Time Feedback Information

Higher vocational institutions can apply network technology and software systems to design real-time feedback modules for home-school cooperation in the moral education management platform, in which parents are required to communicate with school moral education managers and give feedback on students’ behavior. Parents can get real-time student behavior information on the platform, and if communication with teachers is not effective, they can leave messages for the school on the platform, and the school’s moral education management department can provide parents with fast and accurate feedback to help parents clarify how to regulate student behavior and solve behavior problems, forming an interactive feedback model. At present, some parents cannot actively communicate with schools and moral education management personnel because they do not recognize the importance of moral education. In this case, higher education institutions should explain to parents that students’ bad behavioral habits are linked to family education and the growing environment to some extent and that once moral education is neglected in families, it is difficult for higher education institutions to complete related education work independently. In this approach, a huge quantity of feedback information can be offered in the moral education management platform with the help and understanding of parents, and moral education management can be carried out efficiently to improve the effectiveness of home-school cooperation.


## 7.4. Designing Moral Education Management System of School-Enterprise Cooperation

Higher vocational colleges and universities will lead students to study vocational skills, job work practice operation knowledge, and technology to enhance the development of students’ vocational ability through school-enterprise cooperation in the process of fostering talents. During this time, it is suggested that higher vocational institutions design a moral education management system of school-enterprise cooperation in a moral education platform and collaborate with enterprises to cultivate students’ professional moral quality and professional cultivation, in order to cultivate composite talents with professional skills, innovation ability, moral quality, and a sense of social responsibility with the help of good moral education management.


### 7.4.1. Designing the Moral Education Module of “Craftsmanship”

Therefore, in the process of cooperation between higher education institutions and enterprises, the moral education module of “craftsmanship” should be designed, in which it is clear that the enterprises need to provide students with professional skills, innovation, moral quality, and responsibility through craftsmen. Therefore, in the process of cooperation between higher education institutions and enterprises, the moral education module of “craftsmanship” should be designed, in which it is clear that enterprises need to lead students to learn and get in touch with more professional ethical behaviors and comply with rules and regulations through technicians with craftsmanship so that students can improve their behavioral concepts and have good moral quality and professionalism under the influence of technicians’ subtle influence.


### 7.4.2. Designing the Moral Education Module of Enterprise Intuitive Management

When designing moral education modules with advanced technology, higher education institutions should also pay attention to designing modules for enterprises to manage moral education intuitively so that enterprises can complete the task of moral education management for students in the process of cooperation with higher education institutions. In the module, a database of students’ behavioral information should be set up, in which students’ data on rewards and punishments in school, professional ability, and career potential should be entered so that companies can provide students with moral management according to various information. At the same time, enterprises can also select outstanding talents to work in enterprise positions by understanding students’ situation in the module system, which not only can improve the employment rate of students’ talents but also can meet the talent demand of enterprises, achieving a win-win situation.


### 7.4.3. Designing Moral Education Modules in Employment

It is suggested that higher vocational colleges and enterprises should jointly study how to design employment modules and guidance platforms for students, especially for college students who are confused about the direction of employment, and should set up relevant employment guidance modules and management systems for them in a targeted way. Firstly, in the module, a system introducing information about different employment enterprises should be designed for students so that students can know the job information, work information, and skill requirement information of future employment enterprises through mobile terminals, and employment enterprises can also know students’ information in the platform so that the management mode of employment guidance can be formed in the case of two-way selection of students and enterprises’ employment. Secondly, in the digital platform of moral education management, it is also necessary to set up a moral education management module for fixed-term internships, a moral education management module for practical tutorials, a moral education management module for employment information, a moral education management module for registration interview, a moral education management module for the presigning list, and so on. In this way, the quality of moral education management can be promoted with the help and support of various modules.


## 7.4.1. Designing the Moral Education Module of “Craftsmanship”

Therefore, in the process of cooperation between higher education institutions and enterprises, the moral education module of “craftsmanship” should be designed, in which it is clear that the enterprises need to provide students with professional skills, innovation, moral quality, and responsibility through craftsmen. Therefore, in the process of cooperation between higher education institutions and enterprises, the moral education module of “craftsmanship” should be designed, in which it is clear that enterprises need to lead students to learn and get in touch with more professional ethical behaviors and comply with rules and regulations through technicians with craftsmanship so that students can improve their behavioral concepts and have good moral quality and professionalism under the influence of technicians’ subtle influence.


## 7.4.2. Designing the Moral Education Module of Enterprise Intuitive Management

When designing moral education modules with advanced technology, higher education institutions should also pay attention to designing modules for enterprises to manage moral education intuitively so that enterprises can complete the task of moral education management for students in the process of cooperation with higher education institutions. In the module, a database of students’ behavioral information should be set up, in which students’ data on rewards and punishments in school, professional ability, and career potential should be entered so that companies can provide students with moral management according to various information. At the same time, enterprises can also select outstanding talents to work in enterprise positions by understanding students’ situation in the module system, which not only can improve the employment rate of students’ talents but also can meet the talent demand of enterprises, achieving a win-win situation.


## 7.4.3. Designing Moral Education Modules in Employment

It is suggested that higher vocational colleges and enterprises should jointly study how to design employment modules and guidance platforms for students, especially for college students who are confused about the direction of employment, and should set up relevant employment guidance modules and management systems for them in a targeted way. Firstly, in the module, a system introducing information about different employment enterprises should be designed for students so that students can know the job information, work information, and skill requirement information of future employment enterprises through mobile terminals, and employment enterprises can also know students’ information in the platform so that the management mode of employment guidance can be formed in the case of two-way selection of students and enterprises’ employment. Secondly, in the digital platform of moral education management, it is also necessary to set up a moral education management module for fixed-term internships, a moral education management module for practical tutorials, a moral education management module for employment information, a moral education management module for registration interview, a moral education management module for the presigning list, and so on. In this way, the quality of moral education management can be promoted with the help and support of various modules.


## 8. Conclusion

The primary goal of higher vocational education is to build moral education, and moral education plays a significant role in daily classroom activities. Building a reusable and shareable data management system to standardize multisource heterogeneous education data in the artificial intelligence education environment and achieve high sharing of multisource sensing data is one of the urgent problems to be solved in the development of education in the era of artificial intelligence education.In this paper, the design of a moral education management system is done in the context of the specific circumstances of higher education schools, and an information system based on the moral education management of modern college students is built using multisource sensor data and other technologies. The fused education data is described in a standardized way through multisource sensing data fusion combined with learning data specification, resulting in a common and standard data exchange format, on which a shareable and reusable data management system is built to realize data sharing and exchange among various heterogeneous data sources, allowing the intelligent education system to obtain more comprehensive and complete record data to enhance. The immediacy and intelligence of the intelligent education system reaction are enhanced by the timeliness of data exchange, which makes the outcomes of learning behavior analysis more objective, timely, and correct. This paper also includes recommendations for platform selection, system architecture, database design, and other difficulties, as well as methodologies and suggestions for resolving significant issues, all of which can be useful for other university information management systems with enormous data volumes.



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*Source: 7986472-2022-08-17.xml*