# Construction of Dynamic Balance Model of Supply and Demand in Labor Market under Flexible Employment

Authors: Meng He
Journal: Security and Communication Networks
 (2022)

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## Abstract

The dynamic balance of supply and demand in the labor market is a key issue at present. The change of employment quality is relatively complex. The current dynamic balance model of supply and demand cannot obtain high-precision evaluation results of employment quality and meet the requirements of practical application. Therefore, a dynamic balance model of labor market supply and demand under flexible employment is constructed. Big data network is used to set data acquisition channels, complete data collection, unify data sample format, formulate data processing flow, and obtain processed data samples. The correlation analysis method in big data analysis technology is used to complete the analysis of college graduation employment. This paper analyzes the relevant research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, normalizes the index data, then uses the grey correlation method to determine the weight value of the employment quality evaluation index, and uses the fuzzy c-means algorithm to establish the dynamic balance model of supply and demand in the labor market. The experimental results show that the designed method can better balance the supply-demand relationship in the labor market and has a good effect.


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## Body

## 1. Introduction

Employment is an important indicator to measure a country’s economic situation. In recent years, with the large-scale enrollment of colleges and universities, the number of college students has increased year by year, and the total number of college graduates has increased several times in the past [1, 2]. College graduates have become one of the largest employment groups in various countries. Their employment situation is not only related to the vital interests of graduates and their families but also related to social development and the progress of national reform and opening up. The analysis of the dynamic balance between supply and demand in the labor market is very important for the national economic development and the macro control of the employment market. The formulation of future employment regulation based on flexible employment is an important means to maintain the standardization of the national employment market [3, 4]. With the continuous increase of employment, the pressure of employment is increasing. There are often hundreds of people competing for a job, so employment is facing great pressure. For a university, the quality of employment is not only an important goal of the development of higher education but also an important indicator to measure the goal of talent training, and the dynamic balance of supply and demand in the labor market is very important to the change trend of employment quality.The dynamic balance of supply and demand in the traditional labor market is mainly carried out through the experience of some experts. Due to the simplification of expert information and certain limitations of knowledge, it is unable to describe the information of employment quality, resulting in large deviation in the evaluation of employment quality. Relevant scholars have conducted in-depth research on this. Reference [5] puts forward the integration model of school resources and labor market. In the past half century, the relationship between school resources and student labor market results has been a controversial topic among economists. The publication of the U.S. Census in 1940 was the first time that the issue of income was raised, which enabled people to study this relationship more carefully among people born in the early 20th century. Children living in Georgia in 1910 were linked to their adult response to the 1940 Census and school income indicators at the regional level. Georgia is attractive as a case study because state school fund allocation rules provide a seemingly exogenous source of school district income changes. Reference [6] puts forward the labor market effect model of minimum wage and immigration. The minimum wage level in American states is nonlinear. Through the coexistence of federal and state regulations, we investigate how the popularity of minimum wage affects the impact of immigration on the labor market. In the states with lower minimum wages in the United States, immigration has a more negative impact on the wages and employment of local workers in skill units in Dingzhou. This result is very effective for using immigration and state effective minimum wage as tools and implementing the difference method. It compares the U.S. states where the effective minimum wage is completely determined by the federal minimum wage throughout the consideration period (2000–2013) with those that have never been so. Therefore, the important role of the minimum wage in reducing the adverse impact of low skilled migrants on the labor market was emphasized. Because the employment quality is related to many factors, these dynamic balance models of labor market supply and demand cannot well consider the influence of objective and human factors of employment quality, and it is difficult to accurately evaluate the employment quality network. Based on the above research, this paper puts forward the construction of the dynamic balance model of labor market supply and demand under flexible employment and obtains the effectiveness conclusion through experiments.


## 2. Analysis of College Graduation Employment under Big Data Technology

Big data technology needs a new processing mode to have stronger decision-making power, insight and discovery power, and process optimization ability to adapt to massive, high growth rate and diversified information assets. The advantage of big data technology is that it can master huge data information and professionally process these meaningful data. The generation of big data technology aims at its management. The labor market can combine real-time data flow analysis with historical relevant data, and then big data can analyze and find the supply and demand dynamic balance model they need to help predict and prevent future operation interruption and performance problems. Big data can be used to understand the trend of using the dynamic balance model of supply and demand, so as to deepen big data’s insight into the dynamic balance of supply and demand in the labor market. As far as the employment situation of college graduates is concerned, the number of college graduates is huge every year. It is impossible to accurately analyze the huge data group by using ordinary data acquisition and processing methods [7–9]. Therefore, the relevance of big data technology and big data network are used to complete the data collection and processing process, and the analysis of college graduation employment is completed according to the processing results.The above process (Figure1) is used to complete the design of employment analysis method and pay attention to the integrity of data collection and the standardization of data integration in the design, so as to provide the basis for data analysis.Figure 1
Analysis process of employment status.


### 2.1. Data Collection of College Graduation and Employment

The data collection of college graduation employment status is completed by using the big data network, and the data extraction is completed by using the college employment information data special line [9]. A big data network is built for graduation employment, the collection and separate storage of employment information in colleges and universities are completed, and the data collected by the data network is used to exchange data with the public database or big data platform between colleges and universities, so as to enrich the data content of the data network. The information mining database in the big data network is built and the information processing is integrated in the database [10, 11]. Corresponding data samples are formed and summarized. Most of the employment information is obtained and exchanged through special lines. In this process, many channels need to be used, including government public data release, data disclosure of colleges and universities, data statistics of xuexin.com, number of graduates’ employment agreements, and reporting location information. Some information acquisition channels are shown in Figure 2.Figure 2
Channels for obtaining employment information after college graduation.The above channels are scientifically applied to complete information acquisition, the same table name is set and it is saved in the database by collecting a large amount of graduates’ information from other systems in other ways, and basic data on graduation and employment status of colleges and universities is provided in combination with the basic employment information obtained by the special line [12–14].


### 2.2. Statistics and Processing of Employment Data

In the process of university graduation employment analysis, the employment information obtained from big data network and information channels will be counted and processed for the next analysis.In this process, based on the data items in the database, the data items based on employment status are more complex. The basic items of data items are used to describe the employment situation and direction of graduates [15, 16]. The data item design needs to include a large amount of graduates’ personal information. In order to ensure that the processed information meets the design, the content contained in the data item is set. See Table 1 for details.Table 1
Data item content setting.
Information name settingInformation name settingNameFlat typeSexWagesAgePositionMajorJob siteSchoolJob orientationGradeFuture developmentFamilyLocationAfter the above data items are set, the information in the database is processed and integrated. Data preprocessing is completed through data cleaning, data integration, data transformation, and data protocol [17–19]. The specific information data preprocessing process is shown in Figure 3.Figure 3
Information data preprocessing process.In the preprocessing process, pay attention to data transformation, integrate multiple data sources in the database into one data form, and apply data standardization to clear invalid and wrong data in the database to ensure the effectiveness of data samples.


### 2.3. Analysis of Employment Status

Through the preprocessing results of the above information data, the university graduation employment status analysis is realized by using big data analysis technology. In addition to big data analysis technology, the analysis process also needs corresponding equipment to realize the analysis [20, 21]. The internal information of the database is obtained through the server. A multicore processor is installed in the analysis equipment to realize high operation analysis of data. This equipment is used to complete the acquisition of database information and correlation analysis in big data and complete the research on employment [22]. The factor analysis method of correlation analysis is used to study its situation. The data with high correlation in the database constitute a basic analysis structure, that is, common analysis factors. Using this factor, we can get the linear combination of data association; namely,(1)Gl=Fb×α1M1+α2M2+α3M3+...+αnMn.In formula (1), Fb represents the original information data, M1,M2,...,Mn represents the common analysis factor in the data, and α1,α2,α3,...,αn represents the special analysis factor [23]. On the basis of formula (1), we can see the relationship between the analysis factor and the amount of information in the process of employment analysis. The expression is(2)B=CV+θ.In formula (2), CV represents the variable matrix and θ represents the relationship coefficient. The above formula is used to complete the analysis of college graduation employment information.


### 2.4. Evaluation of Employment Quality Evaluation Index System

In addition to the objective statistical data of employment rate, the indicators to measure the employment quality of college graduates must also evaluate the indicators of graduates’ supply-demand ratio, salary level, employment structure, social recognition, and so on.(1)
Employment rate: quantitative change is the basis of qualitative change, which is the inevitable result of quantitative change. Employment rate can reflect the phased “quantity” of college graduates’ employment and is the basic index in the employment quality evaluation index system. Especially under the background of increasing employment difficulty and competitive pressure of college graduates, the employment rate can intuitively reflect the social needs and employment competitiveness of college graduates.In this index system, the general “employment rate” index is expanded from two observation points: one-time employment rate and vehicle end employment rate. This is because the secondary employment rate only reflects the phased employment results, which is difficult to fully reflect the overall situation of graduates’ employment. Increasing the year-end employment rate can make the short term and long term of the quantitative index of employment rate, which tends to be reasonable. At the same time, it also makes some qualitative amendments to the quantitative index of employment rate.(2)
Graduate supply-demand ratio: graduate supply-demand ratio refers to the ratio of the total number of graduates in the current year to the total demand of social employers for graduates of a school. It can generally reflect the recognition of employers for graduates of the school. As we all know, in the process of graduate employment every year, there will be graduates from some schools and majors in short supply, while some schools and colleges will have oversupply of graduates in the industry.(3)
Salary level: the salary level of graduates in a school reflects the current return level of graduates’ learning (human capital) investment in the school. The difference of graduates’ salary level between different colleges and universities can effectively reflect graduates’ employment competitiveness, their own value, and their ability to contribute to society and enterprises.(4)
Employment structure: the employment structure of graduates includes the nature of graduates’ employment units, employment regions, independent entrepreneurship rate, and other indicators. Its difference can effectively reflect the employment quality of graduates.(5)
Social recognition: social satisfaction with graduates in all aspects can become not only an important indicator for colleges and universities to understand the social popularity of graduates but also an important parameter to improve the quality of talent training.


### 2.5. Design of Employment Quality Evaluation Index System

The construction of employment quality evaluation index system is to analyze some factors affecting its change, find the change law of employment quality, and estimate and evaluate the future change trend of employment quality [24–26]. The employment quality is related to many factors, including external factors, objective factors, and subjective influence. These factors are expressed as indicators in the evaluation of employment quality. Therefore, it is necessary to establish an index system that can describe the change characteristics of employment quality [27, 28]. According to the construction principle of employment quality evaluation index system, the established employment quality evaluation index system is shown in Figure 4.Figure 4
Evaluation index system of employment quality.The construction principles of employment quality evaluation index system are as follows:(1)
The principle of comprehensiveness and systematization. On the one hand, the index system should include the main factors affecting the employment quality; there should be a certain logical relationship among the indexes in the index system, so as to systematically evaluate the employment quality of graduates.(2)
The principle of combining qualitative and quantitative indicators. Due to the complexity of the evaluation problem, some factors are difficult to quantify, so qualitative indicators and quantitative indicators are used together. By setting the weight value of each factor indicator, we can not only realize the quantitative analysis of qualitative indicators but also make qualitative analysis of the data of quantitative indicators, so as to systematically and objectively reflect the employment quality of graduates, as well as the connotation and essence of quantity.(3)
In order to make the evaluation results applicable to colleges and universities at different levels and facilitate the horizontal comparison between colleges and universities and the vertical comparison of colleges and universities themselves, it is necessary to design a unified evaluation index system so that the constructed index system can be compared horizontally and vertically.(4)
The principle of independence and operability. The constructed index system should have a unified classification benchmark, each index has a clear connotation, the extensions of the index do not overlap and overlap with each other and have a certain degree of independence. At the same time, in order to ensure the effective implementation of the evaluation, it is necessary to make the methods used by the evaluation index system easy to understand and operate, there should not be many indicators, and it should be easy to collect and have strong operability.The main characteristics of the employment quality evaluation index system are professional counterpart and job stability.


## 2.1. Data Collection of College Graduation and Employment

The data collection of college graduation employment status is completed by using the big data network, and the data extraction is completed by using the college employment information data special line [9]. A big data network is built for graduation employment, the collection and separate storage of employment information in colleges and universities are completed, and the data collected by the data network is used to exchange data with the public database or big data platform between colleges and universities, so as to enrich the data content of the data network. The information mining database in the big data network is built and the information processing is integrated in the database [10, 11]. Corresponding data samples are formed and summarized. Most of the employment information is obtained and exchanged through special lines. In this process, many channels need to be used, including government public data release, data disclosure of colleges and universities, data statistics of xuexin.com, number of graduates’ employment agreements, and reporting location information. Some information acquisition channels are shown in Figure 2.Figure 2
Channels for obtaining employment information after college graduation.The above channels are scientifically applied to complete information acquisition, the same table name is set and it is saved in the database by collecting a large amount of graduates’ information from other systems in other ways, and basic data on graduation and employment status of colleges and universities is provided in combination with the basic employment information obtained by the special line [12–14].


## 2.2. Statistics and Processing of Employment Data

In the process of university graduation employment analysis, the employment information obtained from big data network and information channels will be counted and processed for the next analysis.In this process, based on the data items in the database, the data items based on employment status are more complex. The basic items of data items are used to describe the employment situation and direction of graduates [15, 16]. The data item design needs to include a large amount of graduates’ personal information. In order to ensure that the processed information meets the design, the content contained in the data item is set. See Table 1 for details.Table 1
Data item content setting.
Information name settingInformation name settingNameFlat typeSexWagesAgePositionMajorJob siteSchoolJob orientationGradeFuture developmentFamilyLocationAfter the above data items are set, the information in the database is processed and integrated. Data preprocessing is completed through data cleaning, data integration, data transformation, and data protocol [17–19]. The specific information data preprocessing process is shown in Figure 3.Figure 3
Information data preprocessing process.In the preprocessing process, pay attention to data transformation, integrate multiple data sources in the database into one data form, and apply data standardization to clear invalid and wrong data in the database to ensure the effectiveness of data samples.


## 2.3. Analysis of Employment Status

Through the preprocessing results of the above information data, the university graduation employment status analysis is realized by using big data analysis technology. In addition to big data analysis technology, the analysis process also needs corresponding equipment to realize the analysis [20, 21]. The internal information of the database is obtained through the server. A multicore processor is installed in the analysis equipment to realize high operation analysis of data. This equipment is used to complete the acquisition of database information and correlation analysis in big data and complete the research on employment [22]. The factor analysis method of correlation analysis is used to study its situation. The data with high correlation in the database constitute a basic analysis structure, that is, common analysis factors. Using this factor, we can get the linear combination of data association; namely,(1)Gl=Fb×α1M1+α2M2+α3M3+...+αnMn.In formula (1), Fb represents the original information data, M1,M2,...,Mn represents the common analysis factor in the data, and α1,α2,α3,...,αn represents the special analysis factor [23]. On the basis of formula (1), we can see the relationship between the analysis factor and the amount of information in the process of employment analysis. The expression is(2)B=CV+θ.In formula (2), CV represents the variable matrix and θ represents the relationship coefficient. The above formula is used to complete the analysis of college graduation employment information.


## 2.4. Evaluation of Employment Quality Evaluation Index System

In addition to the objective statistical data of employment rate, the indicators to measure the employment quality of college graduates must also evaluate the indicators of graduates’ supply-demand ratio, salary level, employment structure, social recognition, and so on.(1)
Employment rate: quantitative change is the basis of qualitative change, which is the inevitable result of quantitative change. Employment rate can reflect the phased “quantity” of college graduates’ employment and is the basic index in the employment quality evaluation index system. Especially under the background of increasing employment difficulty and competitive pressure of college graduates, the employment rate can intuitively reflect the social needs and employment competitiveness of college graduates.In this index system, the general “employment rate” index is expanded from two observation points: one-time employment rate and vehicle end employment rate. This is because the secondary employment rate only reflects the phased employment results, which is difficult to fully reflect the overall situation of graduates’ employment. Increasing the year-end employment rate can make the short term and long term of the quantitative index of employment rate, which tends to be reasonable. At the same time, it also makes some qualitative amendments to the quantitative index of employment rate.(2)
Graduate supply-demand ratio: graduate supply-demand ratio refers to the ratio of the total number of graduates in the current year to the total demand of social employers for graduates of a school. It can generally reflect the recognition of employers for graduates of the school. As we all know, in the process of graduate employment every year, there will be graduates from some schools and majors in short supply, while some schools and colleges will have oversupply of graduates in the industry.(3)
Salary level: the salary level of graduates in a school reflects the current return level of graduates’ learning (human capital) investment in the school. The difference of graduates’ salary level between different colleges and universities can effectively reflect graduates’ employment competitiveness, their own value, and their ability to contribute to society and enterprises.(4)
Employment structure: the employment structure of graduates includes the nature of graduates’ employment units, employment regions, independent entrepreneurship rate, and other indicators. Its difference can effectively reflect the employment quality of graduates.(5)
Social recognition: social satisfaction with graduates in all aspects can become not only an important indicator for colleges and universities to understand the social popularity of graduates but also an important parameter to improve the quality of talent training.


## 2.5. Design of Employment Quality Evaluation Index System

The construction of employment quality evaluation index system is to analyze some factors affecting its change, find the change law of employment quality, and estimate and evaluate the future change trend of employment quality [24–26]. The employment quality is related to many factors, including external factors, objective factors, and subjective influence. These factors are expressed as indicators in the evaluation of employment quality. Therefore, it is necessary to establish an index system that can describe the change characteristics of employment quality [27, 28]. According to the construction principle of employment quality evaluation index system, the established employment quality evaluation index system is shown in Figure 4.Figure 4
Evaluation index system of employment quality.The construction principles of employment quality evaluation index system are as follows:(1)
The principle of comprehensiveness and systematization. On the one hand, the index system should include the main factors affecting the employment quality; there should be a certain logical relationship among the indexes in the index system, so as to systematically evaluate the employment quality of graduates.(2)
The principle of combining qualitative and quantitative indicators. Due to the complexity of the evaluation problem, some factors are difficult to quantify, so qualitative indicators and quantitative indicators are used together. By setting the weight value of each factor indicator, we can not only realize the quantitative analysis of qualitative indicators but also make qualitative analysis of the data of quantitative indicators, so as to systematically and objectively reflect the employment quality of graduates, as well as the connotation and essence of quantity.(3)
In order to make the evaluation results applicable to colleges and universities at different levels and facilitate the horizontal comparison between colleges and universities and the vertical comparison of colleges and universities themselves, it is necessary to design a unified evaluation index system so that the constructed index system can be compared horizontally and vertically.(4)
The principle of independence and operability. The constructed index system should have a unified classification benchmark, each index has a clear connotation, the extensions of the index do not overlap and overlap with each other and have a certain degree of independence. At the same time, in order to ensure the effective implementation of the evaluation, it is necessary to make the methods used by the evaluation index system easy to understand and operate, there should not be many indicators, and it should be easy to collect and have strong operability.The main characteristics of the employment quality evaluation index system are professional counterpart and job stability.


## 3. Dynamic Equilibrium Model of Labor Market Supply and Demand

### 3.1. Determining the Weight of the Dynamic Balance Model of Supply and Demand in Labor Market by Grey Correlation Degree Method

Grey correlation degree method is a kind of grey system analysis method. It is a method to measure the correlation degree between factors according to the similarity or difference of development trend between factors. The grey correlation degree method is a multivariable processing technology, which can describe the relationship between variables and systems. For the dynamic balance model of labor market supply and demand in this paper, it is to find the correlation degree between the evaluation index and the evaluation of employment quality [29, 30]. The grey correlation degree method analyzes the employment quality evaluation index, establishes the corresponding statistical data of the employment quality evaluation index, and transforms it into a geometric curve. The closer the geometric curve is, the greater the correlation degree of the employment quality evaluation index is [31, 32]. The construction process of the dynamic balance model of labor market supply and demand under flexible employment is as follows:Step 1: select the reference sequence of the employment quality evaluation index combination according to the employment quality evaluation objectives specifically as follows:(3)Ql=q1,q2,⋯,qn.In formula (3), qn represents the number of evaluation indicators.Step 2: collect employment quality evaluation index data as samples specifically:(4)Q1l,Q2l,...,Qml=q11,q21,⋯,qmnq12,q22,⋯,qm2⋮⋮⋮⋮q1n,q2n,⋯,qmn.In formula (4), m represents the number of employment quality evaluation samples.Step 3: due to the different dimensions of the employment quality evaluation indicators, the index values vary greatly in the order of magnitude, which will affect the efficiency of employment quality evaluation [33]. Therefore, in order to improve the efficiency of employment quality evaluation, this paper adopts the extreme value method of formula (5) to deal with the dimensionless treatment of employment quality evaluation indicators. The dimensionless treatment result is shown in formula (6):(5)El′h=elh−eminemax−emin,(6)Q1l′,Q2l′,...,Qml′=q1′1,q1′1,⋯,qm′nq1′2,q2′2,⋯,qm′2⋮⋮⋮⋮q1′n,q2′n,⋯,qm′n.Step 4: calculate the reference sequence of employment quality evaluation index combination and compare the maximum and minimum values of absolute difference and obtain the grey correlation coefficient as follows:(7)Rlh=minq0h−qlh+ρ×maxq0h−qlhq0h−qlh+ρ×maxq0h−qlh.In formula (7), ρ is expressed as the resolution coefficient.Step 5: calculate the comprehensive relevance value of the employment quality evaluation index as follows:(8)rl=1n∑h=1nRlh.Step 6: sort the employment quality evaluation indicators according to the comprehensive correlation value, remove some indicators with relatively small comprehensive correlation value, reduce the number of employment quality evaluation indicators, and reduce the complexity of employment quality evaluation.


### 3.2. Fuzzy Mean Clustering Algorithm to Establish and Optimize the Dynamic Balance Model of Labor Market Supply and Demand

Fuzzy mean clustering algorithm is the most widely used algorithm in many fuzzy clustering algorithms. The membership of each sample point to all class centers is obtained by optimizing the objective function, so as to determine the class of sample points and achieve the purpose of automatic classification of sample data. Mean clustering algorithm is a data classification method, which divides different data into multiple types. However, mean clustering algorithm has some limitations. Some scholars introduced fuzzy theory on the basis of mean clustering algorithm and produced fuzzy mean clustering algorithm [34, 35]. The basic working idea of fuzzy mean clustering algorithm is as follows: first, design the objective function of classification, then design certain fuzzy rules, calculate the distance between all samples and clustering center, and finally determine the attribution category of each sample according to the distance. The specific steps to optimize the dynamic balance model of labor market supply and demand are as follows:Assuming thatEd represents only one state of the cluster evolution driving force system, R represents the entropy of the system, and WGH represents the extreme of entropy; based on the information obtained by W∂, entropy is used to measure the concentration trend of industrial cluster evolution, which is expressed by(9)FG=Ed×R×WGHλj.In formula (9), λj represents different states of industrial cluster evolution.Assuming that the proportion of enterprises that choose labor division strategy in the cluster isκl and the proportion of enterprises that choose to complete labor division strategy independently is l, the following equation is used to express the income of labor division of cluster enterprises:(10)USx=κl×ϖ×lps.In formula (10), ϖ represents the average income of cluster enterprises and ps represents the expected income.Assuming thatvh represents the relationship between the subjects of industrial clusters, Rth represents the evolution path of industrial clusters, and ϕQ represents the subjects of clusters with limited information, the driving force mechanism between labor and industrial clusters is obtained by using the fusion game theory of(11)Dq=ϕQ×Rthvh×∂η×Εn.In formula (11), ∂η indicates that enterprises, universities, scientific research institutions, government agencies, and intermediaries in the industrial cluster are interrelated in the process of labor division, and Εn indicates the ability of the overall labor division of the cluster. To realize the dynamic balance model of labor market supply and demand under flexible employment, the specific steps are as follows:Step 1: establish an employment quality evaluation index system and collect corresponding employment quality evaluation index data.Step 2: the grade of employment quality is divided into five grades: excellent, good, medium, qualified, and unqualified, which are described by 1∼5, respectively.Step 3: analyze the employment quality evaluation index data by experts, determine the corresponding level of employment quality, and establish the sample data for the level evaluation of employment quality.Step 4: use the grey correlation method to determine the weight of the employment quality evaluation index, set a certain threshold, and remove the index whose weight is less than the threshold.Step 5: process the sample data of the grade evaluation of employment quality according to the important indicators retained by the grey correlation method to obtain the learning sample set.Step 6: for the sample data without knowing the employment quality level, the established dynamic balance model of labor market supply and demand is used for analysis to obtain the corresponding level. According to the learning sample set, the fuzzy mean clustering algorithm is used to construct the dynamic balance model of labor market supply and demand under flexible employment.


## 3.1. Determining the Weight of the Dynamic Balance Model of Supply and Demand in Labor Market by Grey Correlation Degree Method

Grey correlation degree method is a kind of grey system analysis method. It is a method to measure the correlation degree between factors according to the similarity or difference of development trend between factors. The grey correlation degree method is a multivariable processing technology, which can describe the relationship between variables and systems. For the dynamic balance model of labor market supply and demand in this paper, it is to find the correlation degree between the evaluation index and the evaluation of employment quality [29, 30]. The grey correlation degree method analyzes the employment quality evaluation index, establishes the corresponding statistical data of the employment quality evaluation index, and transforms it into a geometric curve. The closer the geometric curve is, the greater the correlation degree of the employment quality evaluation index is [31, 32]. The construction process of the dynamic balance model of labor market supply and demand under flexible employment is as follows:Step 1: select the reference sequence of the employment quality evaluation index combination according to the employment quality evaluation objectives specifically as follows:(3)Ql=q1,q2,⋯,qn.In formula (3), qn represents the number of evaluation indicators.Step 2: collect employment quality evaluation index data as samples specifically:(4)Q1l,Q2l,...,Qml=q11,q21,⋯,qmnq12,q22,⋯,qm2⋮⋮⋮⋮q1n,q2n,⋯,qmn.In formula (4), m represents the number of employment quality evaluation samples.Step 3: due to the different dimensions of the employment quality evaluation indicators, the index values vary greatly in the order of magnitude, which will affect the efficiency of employment quality evaluation [33]. Therefore, in order to improve the efficiency of employment quality evaluation, this paper adopts the extreme value method of formula (5) to deal with the dimensionless treatment of employment quality evaluation indicators. The dimensionless treatment result is shown in formula (6):(5)El′h=elh−eminemax−emin,(6)Q1l′,Q2l′,...,Qml′=q1′1,q1′1,⋯,qm′nq1′2,q2′2,⋯,qm′2⋮⋮⋮⋮q1′n,q2′n,⋯,qm′n.Step 4: calculate the reference sequence of employment quality evaluation index combination and compare the maximum and minimum values of absolute difference and obtain the grey correlation coefficient as follows:(7)Rlh=minq0h−qlh+ρ×maxq0h−qlhq0h−qlh+ρ×maxq0h−qlh.In formula (7), ρ is expressed as the resolution coefficient.Step 5: calculate the comprehensive relevance value of the employment quality evaluation index as follows:(8)rl=1n∑h=1nRlh.Step 6: sort the employment quality evaluation indicators according to the comprehensive correlation value, remove some indicators with relatively small comprehensive correlation value, reduce the number of employment quality evaluation indicators, and reduce the complexity of employment quality evaluation.


## 3.2. Fuzzy Mean Clustering Algorithm to Establish and Optimize the Dynamic Balance Model of Labor Market Supply and Demand

Fuzzy mean clustering algorithm is the most widely used algorithm in many fuzzy clustering algorithms. The membership of each sample point to all class centers is obtained by optimizing the objective function, so as to determine the class of sample points and achieve the purpose of automatic classification of sample data. Mean clustering algorithm is a data classification method, which divides different data into multiple types. However, mean clustering algorithm has some limitations. Some scholars introduced fuzzy theory on the basis of mean clustering algorithm and produced fuzzy mean clustering algorithm [34, 35]. The basic working idea of fuzzy mean clustering algorithm is as follows: first, design the objective function of classification, then design certain fuzzy rules, calculate the distance between all samples and clustering center, and finally determine the attribution category of each sample according to the distance. The specific steps to optimize the dynamic balance model of labor market supply and demand are as follows:Assuming thatEd represents only one state of the cluster evolution driving force system, R represents the entropy of the system, and WGH represents the extreme of entropy; based on the information obtained by W∂, entropy is used to measure the concentration trend of industrial cluster evolution, which is expressed by(9)FG=Ed×R×WGHλj.In formula (9), λj represents different states of industrial cluster evolution.Assuming that the proportion of enterprises that choose labor division strategy in the cluster isκl and the proportion of enterprises that choose to complete labor division strategy independently is l, the following equation is used to express the income of labor division of cluster enterprises:(10)USx=κl×ϖ×lps.In formula (10), ϖ represents the average income of cluster enterprises and ps represents the expected income.Assuming thatvh represents the relationship between the subjects of industrial clusters, Rth represents the evolution path of industrial clusters, and ϕQ represents the subjects of clusters with limited information, the driving force mechanism between labor and industrial clusters is obtained by using the fusion game theory of(11)Dq=ϕQ×Rthvh×∂η×Εn.In formula (11), ∂η indicates that enterprises, universities, scientific research institutions, government agencies, and intermediaries in the industrial cluster are interrelated in the process of labor division, and Εn indicates the ability of the overall labor division of the cluster. To realize the dynamic balance model of labor market supply and demand under flexible employment, the specific steps are as follows:Step 1: establish an employment quality evaluation index system and collect corresponding employment quality evaluation index data.Step 2: the grade of employment quality is divided into five grades: excellent, good, medium, qualified, and unqualified, which are described by 1∼5, respectively.Step 3: analyze the employment quality evaluation index data by experts, determine the corresponding level of employment quality, and establish the sample data for the level evaluation of employment quality.Step 4: use the grey correlation method to determine the weight of the employment quality evaluation index, set a certain threshold, and remove the index whose weight is less than the threshold.Step 5: process the sample data of the grade evaluation of employment quality according to the important indicators retained by the grey correlation method to obtain the learning sample set.Step 6: for the sample data without knowing the employment quality level, the established dynamic balance model of labor market supply and demand is used for analysis to obtain the corresponding level. According to the learning sample set, the fuzzy mean clustering algorithm is used to construct the dynamic balance model of labor market supply and demand under flexible employment.


## 4. Experimental Test

In order to analyze the advantages of the dynamic balance model of labor market supply and demand under flexible employment, simulation experiments are carried out. The specific configuration of the experimental test environment is shown in Table2.Table 2
Specific configuration of test environment.
Configuration parameterSpecific settingsCPUAMD Speed Dragon 200 GEMemoryXPG F1 DDR4 2666 16 GBHard diskSamsung 860 EVO 250 GB SATANetwork card100 MOperating systemWindowsProgramming environmentPythonThe historical data of the dynamic balance of supply and demand in the labor market are collected as the test object. Under flexible employment, the employment quality evaluation results adopt the 100-point system to normalize the employment quality so that they are in the range of 0∼1. A total of 500 historical pieces of data of employment quality are obtained. The normalized data are shown in Table3.Table 3
Employment quality results after normalization.
Data sequencex1x2x3x4…Employment quality grade10.210.330.340.56…220.600.310.570.53…130.680.600.450.66…240.260.420.350.63…350.450.590.580.53…460.380.640.510.51…470.330.310.290.20…280.220.590.460.35…590.610.450.510.62…1…………………1000.360.630.220.35…2It can be seen from Table3 that the normalized employment quality result level is high, which can effectively complete the dynamic balance of supply and demand in the labor market. According to the results of employment quality after normalization, it is found that the evolving labor structure has brought vitality to the form of employment and improved employment efficiency. From the perspective of urban and rural areas, this paper calculates the impact of the evolution of the two main bodies on the labor market. It is known that the labor market in rural areas is dominated by agricultural related posts, and the labor market in urban areas is dominated by service industry. Therefore, this effect value is calculated through the dynamic balance model of labor market supply and demand. The model uses the production function to assume that the return to scale remains unchanged:(12)Qt=Ft×Wtu×Gtv.In formula (12), Qt represents the total output of the supply and demand dynamics of the labor market; Ft represents the labor force factor that plays a role in growth; Wtu represents labor quality; Gtv indicates the labor force put into use. It is known that, under the condition of constant remuneration, u+v=1.When the assumption of constant return is true, the value of outputu elasticity is derived by using the above formula regression. Then solve the differential of formula (12) and divide both sides by Gt at the same time to obtain the growth rate of posts with dynamic balance between supply and demand in the labor market:(13)Dd=dQtQt−udWtWt−vdGtGt.The output elasticity and other relevant data are substituted into formula (13) to obtain the value of growth rate. According to this value, the post growth rate of dynamic balance between supply and demand in the labor market under the influence of the evolution of labor structure from 2011 to 2020 is calculated. The results are shown in Table 4.Table 4
Dynamic balance between supply and demand in labor market post growth rate/%.
Particular yearSupplyDemandSupply + demandGrowth rate20111.610.432.04—20121.721.813.531.4920132.643.546.182.6520142.774.437.201.0220153.154.888.030.8320164.348.1612.504.4720175.7911.2717.064.5620185.8615.5421.404.3420198.1220.2228.346.9420206.2316.3522.58−5.76According to the calculation results in Table4, the dynamic balance between supply and demand in the labor market and the growth rate of posts increase year by year, from 1.49% in 2012 to 6.94% in 2019, indicating that a good labor structure promotes the demand for posts. At the beginning of 2020, due to the influence of force majeure factors, some industries went bankrupt and liquidated, and the labor market tilted, which affected the job growth rate of that year, with a negative growth of −5.76%. Based on the above results, it can be seen that, without considering the impact of emergencies, the optimized labor industrial structure will gradually improve the development of the dynamic balance model of labor market supply and demand in each region. Based on the above results, the change trend of the evolution effect of labor structure in different time periods is drawn, as shown in Figure 5.Figure 5
Change trend of evolution effect of labor structure.According to the results shown in Figure5, the evolution effect of labor structure in the first two stages is less than 0.5%, so the growth rate of this industry is at a relatively low level. After entering the third and fourth stages of development, the evolution effect of labor force structure at this time increases to more than 2.5%, and the industrial growth rate also increases substantially, indicating that, after the labor structure continues to expand the scale and improve the quality, the commercial circulation industry also expands the scale, continuously improving the economic level of the whole region, thus improving the consumption level of rural and urban areas. It also promotes the optimization and upgrading of the industry.Select the fuzzy c-means algorithm to test the changes of the dynamic balance of supply and demand in the labor market, and set the time series as(14)Pt=PtT+PtB.In formula (14), PtT and PtB represent trend component and periodic variation component, respectively. By default, the sequence is the superposition of two subparts, and the trend sequence and periodic variation sequence are obtained by minimizing the loss function. The general calculation formula of this function is(15)S=∑i=1TPt−PtT2+μ∑i=1T−1Pt+1T−PtT2.In formula (15), μ represents a constant. Since the research object is annual data, the value of μ is set to 100. Using the above formula to obtain the sequence value, according to the calculation results, the change curve of labor force and trade circulation industrial structure is obtained. The results are shown in Figure 6.Figure 6
Variation curve of labor force and trade circulation industrial structure.According to the curve changes in Figure6, it can be seen that the component curves of the variation trend are symmetric, indicating that the two structural variations in a long period of time are in completely opposite directions. Among them, the variation trend of the labor force structure before 2010 has increased. However, the trend of industrial structure variation has been increasing, and the growth effect is more obvious. Based on the above results, it can be seen that the adjustment of labor distribution structure is slow at this stage, while the optimization process of industrial structure is fast, so the former lags behind the latter. From the perspective of the change range, the change range of labor supply and demand changes with the growth of time, indicating that, in the development process, with the improvement of the marketization process, they are closer and closer to the economic level of the current development stage.To sum up, the dynamic balance model of labor market supply and demand under flexible employment has a higher result level of employment quality after normalization, which can effectively complete the dynamic balance of labor market supply and demand. The dynamic balance between supply and demand in the labor market and the increase of post growth rate year by year show that a good labor structure promotes the demand for posts. After the labor structure continues to expand the scale and improve the quality, the commercial circulation industry also expands the scale, which continuously improves the economic level of the whole region and promotes the optimization and upgrading of the industry. With the growth of time, the change range of labor supply and demand has changed, indicating that, in the development process, with the improvement of the marketization process, they are closer and closer to the economic level of the current development stage. The research method has good performance and can balance the relationship between supply and demand in the labor market.


## 5. Conclusion

(1)
After normalization, the result grade of employment quality is high, which can effectively complete the dynamic balance of supply and demand in the labor market. According to the results of employment quality after normalization, it is found that the evolving labor structure has brought vitality to the form of employment and improved employment efficiency.(2)
The dynamic balance between supply and demand in the labor market and the increase of post growth rate year by year show that a good labor structure promotes the demand for posts.(3)
After the labor structure continues to expand the scale and improve the quality, the commercial circulation industry also expands the scale, which continuously improves the economic level of the whole region, improves the consumption level of rural and urban areas, and promotes the optimization and upgrading of the industry.(4)
With the growth of time, the change range of labor supply and demand has changed, indicating that, in the development process, with the improvement of the marketization process, they are closer and closer to the economic level of the current development stage.



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*Source: 4933239-2022-03-18.xml*