High-quality full employment distribution prediction system and method

Abstract

The invention discloses a high-quality full employment distribution prediction system and method, and relates to the technical field of employment prediction. The system comprises a data integration platform module, a prediction model construction module, a group identification and occupational planning module, a labor flow prediction module, an occupational development trend prediction module and an employment difficulty group monitoring and early warning module. An advanced big data analysis and machine learning technology is applied to construct an innovative prediction model, and the model can process and analyze large-scale multi-dimensional data including industry classification, occupational transition, educational background distribution and skill requirements, can comprehensively reflect the dynamic change of an employment market in real time, and has a good application prospect. Unstructured employment data is deeply processed by means of a machine learning technology, employment types are divided into four types of high-quality full employment, full but non-high-quality employment, insufficient employment and unemployment, and unemployment risk groups are identified, so that more accurate employment prediction and personalized occupational planning suggestions are provided, and the employment prediction efficiency is improved. Scientific decision-making basis is provided for the government and enterprises, and timely strategy adjustment is made.

Description

Technical Field

[0001] The present invention relates to the technical field of employment forecasting, and in particular to a high-quality full employment distribution forecasting system and method. Background Art

[0002] Driven by economic globalization and the revolution in information technology, urbanization has become a significant trend in global development. The urbanization process and industrial transformation have not only brought about population concentration and changes in economic structure, but also triggered profound changes in the employment market. As the main engine of economic growth, the stability and quality of employment in cities are directly related to the overall well-being and sustainable development of society. However, the complexity and uncertainty of urban employment issues have also increased, which requires us to have more efficient forecasting and management capabilities. Existing statistical and empirical methods can no longer meet this demand. An innovative forecasting system and method is urgently needed to improve forecasting accuracy and efficiency and provide a scientific basis for decision-making by governments and enterprises.

[0003] However, the existing urban employment forecasting methods mainly rely on traditional statistical analysis and empirical judgment, and the existing forecasting models are mostly based on simple regression analysis and trend extrapolation of historical data, and the processing and analysis capabilities of unstructured data are poor. They lack the support of advanced big data analysis and machine learning technologies, and it is difficult to reflect the multi-dimensional dynamic changes of the employment market in real time. Especially in the face of a new round of scientific and technological revolution and industrial transformation, as well as industry changes and occupational transformations caused by public health emergencies, the forecast results are often seriously lagging behind. In identifying high-quality fully employed, fully but not high-quality employed, underemployed and unemployed groups, traditional methods are often based on a single statistical indicator, ignoring the comprehensive consideration of multi-dimensional information, making it difficult to accurately identify unemployed and unemployed risk groups, and unable to provide effective career planning advice for individuals and groups. In addition, they lack the ability to predict labor mobility, and fail to fully consider the impact of changes in urban industrial structure, occupational structure, festivals and industry cycles on labor mobility, making it difficult for the forecast results to play an effective role in practical applications.

[0004] In summary, the existing technology lags behind in the prediction model of high-quality full employment distribution prediction, resulting in insufficient processing of unstructured employment data, insufficient model accuracy, limited prediction range, and lack of prediction of labor mobility and career development trends. These problems seriously restrict the actual application effect of the employment prediction system. Therefore, there is an urgent need for a new high-quality full employment distribution prediction model system and method that can accurately identify group needs and has foresight. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and to provide a high-quality full employment distribution prediction system and method, which can integrate high-frequency and long-term employment data from various fields in the city, use big data analysis and machine learning technology, build a prediction model, and realize accurate prediction and dynamic management of the city's employment status.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a high-quality full employment distribution prediction system includes the following components:

[0007] Data integration platform module: Integrate high-frequency, long-term, multi-structured employment data from various fields in the city, and use high-frequency data to build a data set, including structured data on industry classification and industry changes, occupation classification and occupation changes, education distribution and skill distribution, as well as unstructured data on recruitment advertisements and job descriptions, job search forums, discussion groups and community interactions, and news and reports related to the job market, corporate recruitment, and career development, so as to establish a comprehensive data analysis platform;

[0008] Prediction model building module: Utilize big data analysis and machine learning technology, adopt support vector machine technology to build innovative prediction models, and integrate real-time data update functions;

[0009] Group identification and career planning module: Based on machine learning technology, it trains and identifies the multi-dimensional information of individuals and groups, including basic information, education level, household registration, family members, income status, assets and liabilities, migration status, health status and work status, and identifies high-quality fully employed, fully but not high-quality employed, underemployed and unemployed groups;

[0010] Labor flow prediction module: Establish a labor coordination mechanism between inflow and outflow areas, and use machine learning technology to predict the labor flow patterns of different industries, sectors, occupations, skills, and tasks in the city based on changes in the city's industrial structure, occupational structure, festivals, and industry cycles;

[0011] Career development trend prediction module: Identify the probability of each occupation being replaced by artificial intelligence, and predict future career development trends and labor force dynamics;

[0012] Monitoring and early warning module for groups with difficulty finding employment: establish a dynamic monitoring and early warning platform for groups with difficulty finding employment, obtain real-time information on the situation of the labor force with difficulty finding employment and the labor force at risk of unemployment, and identify potential risks of unemployment and falling back into poverty.

[0013] Furthermore, the data integration platform module collects data from various sources, namely government departments, enterprises, schools, medical institutions, and social security institutions, covering multi-dimensional information on industry classification, career changes, educational background and skill requirements, urban economic development level, industrial structure, labor protection, and public employment policies, and uses a variety of prediction methods to process missing values ​​in the identified data. For data with a large proportion of missing values, records containing missing values ​​can be deleted; for data with a small proportion of missing values, the weighted moving average method is used to fill in the missing values, that is, x i The data point at time ti in the set, N is the time window length of the moving average, w i The weight of the i-th time point is stored in an efficient data warehouse and database, and the data from different sources are merged to record the source, update frequency, format and field meaning of the data to provide data support for the prediction model.

[0014] Furthermore, the prediction model building module uses Z-score standardization in big data analysis technology to convert data into a unified scale, pre-processes the data before model building, eliminates the influence of feature scale, makes all features within a similar scale range, accelerates the convergence speed of the model, and makes the numerical ranges of different features similar. The algorithm formula is: x is the original data feature value, μ is the mean value of the feature data, σ is the standard deviation of the feature, and z is the standardized data.

[0015] Furthermore, the prediction model building module uses the support vector machine SVN in the machine learning algorithm to establish a prediction model to predict the distribution of high-quality full employment in the city, and predicts future employment status based on historical employment data of workers, enterprises and governments and related influencing factors, namely labor demand, industry classification, occupation classification, and education distribution. The specific operation process is as follows:

[0016] Data preparation: Use standardized data to select important characteristic variables related to high-quality full employment, clarify the prediction target, and determine the dependent variable y, that is, the high-quality full employment index, employment rate, and unemployment rate. Extract the original features of the data, such as vectorization of text data, feature extraction of time series data, etc., and collect independent variable data x related to the dependent variable, that is, the relevant characteristics of workers, enterprises, and governments; Data set division: Divide the data set into training set, validation set, and test set in a ratio of 70:20:10;

[0017] Support vector machine model construction: The optimization problem of support vector regression is w is the weight vector, is a slack variable, C is a penalty coefficient used to balance model complexity and training error, n is the number of samples, and its constraints are: Among them, y i is the target value of the ith sample, x i is the eigenvector of the i-th sample, ε is the allowable error;

[0018] Model evaluation: Use the validation data set to evaluate the generalization ability of the model, and use the mean square error (MSE) to calculate the performance index of the model;

[0019] Model optimization and deployment: Based on the model evaluation results, the model is optimized, including adding and deleting features, adjusting model parameters, using regularization methods to prevent overfitting, and the optimized model is deployed in actual applications for prediction.

[0020] Furthermore, the prediction model evaluates the performance of the model through the mean square error (MSE), which measures the degree of difference between the model prediction value and the true value, and is achieved by calculating the average of the squares of all prediction errors, that is, is the predicted value of the ith sample, n is the number of samples, is the actual value of the ith sample, and by introducing the penalty term of model complexity and limiting the value range of model parameters through L1 regularization, the model is prevented from overfitting on the training data, that is: MSE(θ) is the mean square error loss function, θ j is the jth model parameter, m is the number of model parameters, λ is the regularization strength coefficient, and the trained support vector machine model is used to predict new data to obtain the prediction results, and decision support is provided based on the prediction results, that is, to formulate policies and optimize resource allocation.

[0021] Furthermore, the group identification and career planning module collects multi-dimensional data including individual basic information, education level, family situation, skill level, health status, employment status, migration experience and work experience, and takes features that help distinguish different employment statuses, namely age, gender, education level, years of work experience, and skill certificates, and uses the Gaussian mixture model clustering algorithm in machine learning technology to identify employment groups with similar characteristics, understand the needs of different groups, and thus provide more accurate career planning services. The process is as follows:

[0022] Construct a Gaussian mixture model (GMM): Establish the probability density function of GMM: where K is the number of Gaussian components, is the probability density function of the kth Gaussian component of the d-dimensional data point x, π k is the mixing coefficient of the kth Gaussian component, satisfying And 0≤π k ≤1;

[0023] Select the optimal number of clusters: Train multiple GMM models, each with a different number of components, and then calculate the corresponding AIC and BIC values. Use the AIC and BIC criteria to evaluate the model, and select the number of clusters with the minimum AIC or BIC value as the optimal number of clusters;

[0024] GMM model fitting: Use the standardized data to fit the GMM model. The fitting process is performed using the EM (Expectation-Maximization) algorithm, which includes the E step and the M step. The E step calculates the posterior probability of each data point belonging to each Gaussian cost. The M step updates the model parameters, including the mixing coefficients Mean vector Covariance matrix The EM algorithm continuously updates the parameter θ = {π k ,μ k ,∑ k}, so that the GMM log-likelihood function Maximization,After these steps, GMM is used to cluster the employment data;

[0025] Result analysis and career planning suggestions: Based on the clustering results, individuals and groups are divided into different categories, and the characteristics of each category, namely educational background, skill level, and career interests, are analyzed to provide targeted career planning suggestions for individuals and groups in different categories.

[0026] Furthermore, the labor mobility prediction module collects various feature data related to labor mobility, namely age, education level, work experience, industry, and determines the target label through the decision tree in the machine learning algorithm, and determines the best feature for node splitting by calculating the information gain, that is, first calculating the empirical entropy of the data set Where H(D) represents the entropy of the dataset D, K is the number of categories, and |C k| is the number of samples belonging to category k, |D| is the total number of samples in data set D, and the empirical conditional entropy H(D|A) of feature A on data set D is calculated, and the information gain is calculated, that is, g(D,A)=H(D)-H(D|A). The feature with the largest information gain is selected as the split feature of the current node. By repeating this process, a complete decision tree structure is gradually constructed. Therefore, when faced with new individual labor data, the structure is gradually deduced along the branches of the decision tree according to its various features until it reaches the leaf node, and a prediction result of whether the individual labor will flow is obtained.

[0027] Furthermore, the career development trend prediction module collects data related to the career, including historical employment data, changes in skill requirements, technological progress information, educational background, and salary trends, extracts features related to career development trends, namely, industry growth rate, degree of automation, complexity of required skills, and education level requirements, and uses a linear regression-based prediction model combined with the output of the prediction model construction module to predict the demand and supply relationship in the future labor market, analyze the matching degree between labor skills and market demand, and use logistic regression technology to evaluate the probability of occupations being replaced, namely, Y is the binary variable of the occupation being replaced, X is the data feature, and β is the model parameter. The prediction results are interpreted to provide career planning and training suggestions for governments, enterprises, and individuals.

[0028] On the other hand, a high-quality full employment distribution prediction method, the specific steps of the method are:

[0029] S100, Data Collection and Processing: Collect employment-related data from multiple sources, including industry information, occupation information, educational background, skill requirements, and demographic data, integrate the data into a unified data integration platform module, clean the data, and remove invalid and erroneous information;

[0030] S200, feature engineering and model building: extract key features from the integrated data, namely industry growth rate, skill demand index, and education level distribution, create a prediction model, and divide the data set into training set, validation set, and test set, train the model, and adjust parameters;

[0031] S300, Group Identification and Career Planning: Use the trained model to identify the status of different employment groups, and provide customized career development advice and planning for each group based on the identification results;

[0032] S400, Labor Mobility Forecast: Use models to predict the mobility trends of labor between different regions and industries, and analyze the impact of holidays and industry cycles on labor mobility;

[0033] S500, career development trend forecast: predict the risk of occupations being replaced by automation and artificial intelligence, analyze future changes in occupational demand, and predict long-term trends in career development;

[0034] S600, Monitoring and early warning of groups with difficulty finding employment: Establish a dynamic monitoring and early warning platform for groups with difficulty finding employment, monitor these groups in real time, including early warning of rising unemployment rates, and analyze potential unemployment risks;

[0035] S700, Result Analysis and Feedback: Interpret the forecast results and provide feedback to the government, enterprises and the public to assist in decision-making and resource allocation. Regularly collect new data and update the data integration platform to adapt to changes in the job market.

[0036] Furthermore, the S600 dynamic monitoring and early warning platform for groups with difficulties in finding employment collects data from various sources, including government statistics, social security records, job search websites, recruitment advertisements, and educational institution reports, and divides employment risks into the following categories based on information on the employment market, job seekers, and labor supply and demand: macroeconomic risks, industry risks, enterprise risks, risks of mismatch between skills and demand, demographic risks, risks of unbalanced regional development, seasonal risks, and risks of emergencies.

[0037] Compared with the prior art, the high-quality full employment distribution prediction system and method has the following beneficial effects:

[0038] 1. The present invention integrates high-frequency and long-term employment data from various fields in the city and uses advanced big data analysis and machine learning technologies to construct an innovative prediction model. This model can process and analyze large-scale multi-dimensional data, including industry classification, occupational changes, educational distribution and skill requirements, and can comprehensively and real-time reflect the dynamic changes in the employment market, thereby providing more accurate employment forecasts, providing scientific decision-making basis for governments and enterprises, and making timely strategic adjustments.

[0039] 2. With the help of machine learning technology, the employment distribution prediction system of the present invention can deeply analyze the multi-dimensional information of individuals and groups, including educational background, work experience, and health status, so as to identify groups with different employment status. For groups with high-quality full employment, full but not high-quality employment, underemployed and unemployed, the system can provide personalized career planning suggestions to help them improve their skills, achieve career transformation, and promote high-quality full employment.

[0040] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 The following is a flowchart of the operation of a high-quality full employment distribution forecasting system.

[0043] Figure 2 Flowchart of a high-quality full-employment distribution forecasting method. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Embodiment 1

[0046] This embodiment provides a high-quality full employment distribution forecasting system. By integrating high-frequency and long-term employment data from various fields in the city and using advanced big data analysis and machine learning techniques, it deeply explores the complex relationships and trends behind the data, thereby achieving accurate forecasts of urban employment conditions, significantly improving the accuracy and timeliness of forecast results, and providing a more reliable scientific basis for government decision-making.

[0047] In the specific implementation, first of all, through the data integration platform module, we collected detailed employment-related data from all corners of the city, including but not limited to government departments, enterprises, educational institutions, medical and health departments, and social security systems. The data covered multi-level information on industry classification and changes, occupational types and their development, education level distribution, and skill requirements. In the data integration stage, we faced the problem of missing data. By analyzing the missing ratio, for large-proportion missing values, we chose to delete the records containing these missing values; and for small-proportion missing values, we adopted the strategy of average filling, that is, To maintain the integrity of the data set, the processed data is stored in an efficient data warehouse, and metadata such as the source of the data, update frequency, and field meaning are recorded, providing a solid foundation for subsequent data analysis and model building.

[0048] Then, the Z-score standardization method in big data analysis technology is used to convert the data to a unified scale. The algorithm formula is: In order to eliminate the scale differences between different features and accelerate the model training process, support vector machine SVN is used to establish the prediction model and define the mathematical form of the model, that is, the optimization problem of support vector regression is The constraints are: By fitting the regression relationship between independent variables (education level, industry category, skill requirements) and dependent variables (employment rate, unemployment rate), the future employment status is predicted. Before building the model, the data is standardized and features highly correlated with the dependent variables are selected. The data is divided into a training set and a test set. The former is used for model training and the latter is used to verify the generalization ability of the model. The mean square error is used to evaluate the model performance: In order to prevent overfitting, L1 regularization is introduced: The model complexity is penalized, and finally the model parameters are optimized and deployed to practical applications for prediction.

[0049] Subsequently, in the group identification and career planning module, machine learning technology, especially the Gaussian mixture model clustering algorithm, was used to identify employment groups with similar characteristics. Based on the multi-dimensional data of individual age, gender, education, years of work experience and skill certificates, the probability density function of GMM was established: Train multiple GMM models, each using a different number of components, and then calculate the corresponding AIC and BIC values. Use the AIC and BIC criteria to evaluate the model, select the number of clusters corresponding to the minimum AIC and BIC values ​​as the optimal number of clusters, and use the standardized data to fit the GMM model. The fitting process is performed using the EM (Expectation-Maximization) algorithm, which includes the E step and the M step. The E step calculates the posterior probability of each data point belonging to each Gaussian cost. The M step updates the model parameters, including the mixing coefficients Mean vector Covariance matrix The EM algorithm continuously updates the parameter θ = {π k ,μ k ,∑ k}, so that the GMM log-likelihood function Maximization,After these steps, GMM is used to cluster the employment data.

[0050] Finally, the labor mobility prediction module collects various feature data related to labor mobility through decision trees, namely age, education level, work experience, industry, and determines the target label. It determines the best feature for node splitting by calculating information gain, that is, first calculating the empirical entropy of the data set. It identifies potential areas of labor surplus or shortage. At the same time, the career development trend prediction module analyzes the possibility of occupations being replaced by artificial intelligence and predicts the long-term trends of career development. These prediction results provide valuable decision-making basis for governments, enterprises and individuals, guide career planning and training directions, and promote the healthy and stable development of the employment market.

[0051] In summary, this embodiment demonstrates how to achieve high-quality distribution prediction of full employment by integrating multi-source data, building prediction models, identifying employment groups and predicting labor mobility and occupational trends. This system not only improves the accuracy of employment forecasts, but also provides scientific guidance for policy making and resource allocation. It is an important tool for modern urban employment management and planning.

[0052] Embodiment 2

[0053] This embodiment aims to elaborate on the specific application and implementation process of a high-quality full employment distribution prediction method. This method integrates high-frequency and long-term employment data from various fields in the city, combines big data analysis and machine learning technology, and constructs a prediction model to achieve accurate prediction and dynamic monitoring of the city’s employment status. The implementation process will be described in detail in different stages below.

[0054] First, enter the data collection and processing stage (S100), collect employment-related data from a variety of sources such as government departments, enterprises, educational institutions, medical and health departments, and social security systems, including multi-level information on industry classification and changes, occupational types and their development, education level distribution, and skill requirements. Clean the data and remove invalid and erroneous information to ensure the quality and consistency of the data set. Missing values ​​are properly handled, and records with a large proportion of missing values ​​are deleted, while small proportions of missing values ​​are filled with the average value to maintain the integrity of the data set. The data are stored in efficient data warehouses and databases, and metadata such as the source of the data, update frequency, format, and field meaning are recorded to provide a solid foundation for subsequent data analysis and model building.

[0055] Then, the feature engineering and model building stage (S200) is entered to extract key features from the integrated data, such as industry growth rate, skill demand index, and education level distribution, create a prediction model, and divide the data set into a training set, a validation set, and a test set. The training set is used for model training, the validation set is used for model tuning, and the test set is used to finally evaluate the generalization ability of the model. In the model building process, the Z-score standardization in big data analysis technology is used to convert the data into a unified scale, eliminate the influence of the feature scale, and make all features within a similar scale range, accelerate the model training process and improve the prediction accuracy. By fitting the regression relationship between the independent variable and the dependent variable, the future employment status is predicted. After the model is trained, the model performance is evaluated by the mean square error MSE to ensure that the difference between the model prediction value and the actual value is minimized, and L1 regularization is used to prevent overfitting and optimize the model parameters.

[0056] Subsequently, we enter the group identification and career planning stage (S300), using machine learning technology, especially clustering algorithms, to identify employment groups with similar characteristics, collect multi-dimensional data including individual basic information, education level, family situation, health status and work experience, select features that help distinguish different employment statuses, including age, gender, education level, years of work experience and skill certificates, and use the Gaussian mixture model clustering algorithm in machine learning technology to identify employment groups with similar characteristics, understand the needs of different groups, and divide individuals and groups into different categories. We analyze the characteristics of each category, namely educational background, skill level, and career interests. This process helps us understand the needs of different groups and provide targeted career planning suggestions for individuals and groups of different categories, helping them improve their skills, achieve career transformation, and promote high-quality full employment.

[0057] Next, enter the labor mobility prediction stage (S400), by analyzing and collecting historical and real-time labor mobility data, including population migration, industry employment changes, skill training records, economic activity indicators, integrating external factor data, namely policy changes, economic cycles, seasonal factors, and the impact of major events, extracting key features related to labor mobility, such as industry growth rate, regional economic development level, occupational skill requirements, and education level distribution, and using information gain to build a decision tree to capture the changing patterns of labor inflows and outflows over time, including seasonal fluctuations and long-term trends, predict future labor mobility trends, and identify potential labor surplus or shortage areas and industries by analyzing the prediction results, so as to provide suggestions for the reasonable allocation of labor for governments, enterprises, and individuals.

[0058] Secondly, enter the career development trend prediction stage (S500), collect career-related data, including historical employment data, changes in skill requirements, technological progress information, educational background, and salary trends, and extract features related to career development trends, namely industry growth rate, degree of automation, complexity of required skills, and education level requirements. Based on the linear regression prediction model in the machine learning algorithm and combined with the output of the prediction model building module, predict the demand and supply relationship in the future labor market, analyze the matching degree between labor skills and market demand, evaluate the probability of occupation being replaced, and provide career planning and training suggestions for governments, enterprises, and individuals to help them cope with the long-term trend of career development.

[0059] Finally, we enter the stage of monitoring and early warning of groups with difficulty finding employment (S600). We establish a dynamic monitoring and early warning platform to monitor groups with difficulty finding employment in real time, including early warning of rising unemployment rates, analyze potential unemployment risks, and collect data from various sources, including government statistics, social security records, job search websites, recruitment advertisements, and reports from educational institutions. Based on information on the employment market, job seekers, and labor supply and demand, we divide employment risks into multiple aspects, including macroeconomic risks, industry risks, enterprise risks, risks of mismatch between skills and demand, demographic risks, risks of unbalanced regional development, seasonal risks, and risks of emergencies. Through real-time monitoring and early warning, we can take measures in advance to reduce the size of groups with difficulty finding employment and prevent the emergence of social instability factors.

[0060] In addition, according to the result analysis and feedback stage (S700), the forecast results are interpreted and fed back to the government, enterprises and the public to assist decision-making and resource allocation. By regularly collecting new data and updating the data integration platform, the system can adapt to changes in the employment market and continuously optimize the forecast effect. This stage ensures the closed-loop operation of the forecast system, so that the forecast results can be transformed into practical actions and promote the healthy and stable development of the employment market. Through this series of steps, we have not only improved the accuracy of employment forecasts, but also provided scientific guidance for policy formulation and resource allocation, becoming an important tool for modern urban employment management and planning.

[0061] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A high-quality full employment distribution prediction system, characterized in that: The system consists of: data integration platform module, prediction model building module, group identification and career planning module, labor flow prediction module, career development trend prediction module, and employment difficulty group monitoring and early warning module; The data integration platform module integrates high-frequency, long-term, multi-structured employment data in various fields in the city, and uses high-frequency data to construct a data set, including structured data on industry classification and industry change, occupation classification and occupation change, education distribution and skill distribution, as well as unstructured data on recruitment advertisements and job descriptions, job search forums, discussion groups and community interactions, and news and reports related to the job market, corporate recruitment, and career development, so as to establish a comprehensive data analysis platform; The prediction model building module uses big data analysis and machine learning technology, adopts support vector machine technology to build an innovative prediction model, and incorporates real-time data update function; The group identification and career planning module: based on machine learning technology, it is trained and identified according to the basic information of individuals and groups, education level, household registration, family members, income status, assets and liabilities, migration status, health status and work status, and identifies high-quality fully employed, full but not high-quality employed, underemployed and unemployed groups; The labor flow prediction module establishes a labor docking and coordination mechanism for the inflow and outflow areas, and uses machine learning technology to predict the labor flow patterns of different industries, sectors, occupations, skills, and tasks in the city based on changes in the city's industrial structure, occupational structure, festivals, and industry cycles; The occupational development trend prediction module: identifies the probability of each occupation being replaced by artificial intelligence, and predicts future occupational development trends and labor force dynamic change trends; The module for monitoring and early warning of groups with difficulties in finding employment establishes a dynamic monitoring and early warning platform for groups with difficulties in finding employment, obtains real-time information on the labor force with difficulties in finding employment and the labor force at risk of unemployment, and identifies potential risks of unemployment and falling back into poverty.

2. A high-quality full employment distribution prediction system according to claim 1, characterized in that: The data integration platform module collects data from various sources, namely government departments, enterprises, schools, medical institutions, and social security institutions, covering multi-dimensional information on industry classification, career changes, educational background and skill requirements, urban economic development level, industrial structure, labor protection, and public employment policies. It uses a variety of prediction methods to process and identify missing values ​​in data. For data with a small proportion of missing values, records containing missing values ​​can be deleted; for data with a large proportion of missing values, the weighted moving average method is used to fill in the missing values. x i The data point at time ti in the set, N is the time window length of the moving average, w i The weight of the i-th time point is stored in an efficient data warehouse and database, and the data from different sources are merged to record the source, update frequency, format and field meaning of the data to provide data support for the prediction model.

3. A high-quality full employment distribution prediction system according to claim 1, characterized in that: The prediction model building module uses Z-score standardization in big data analysis technology to convert data into a unified scale, pre-processes the data before model building, eliminates the influence of feature scale, makes all features within a similar scale range, accelerates the convergence speed of the model, and makes the numerical ranges of different features similar. The algorithm formula is: x is the original data feature value, μ is the mean value of the feature data, σ is the standard deviation of the feature, and z is the standardized data.

4. A high-quality full employment distribution prediction system according to claim 1, characterized in that: The prediction model building module uses the support vector machine SVN in the machine learning algorithm to establish a prediction model to predict the distribution of high-quality full employment in the city, and predicts future employment status based on historical employment data of workers, enterprises and governments and related influencing factors, namely labor demand, industry classification, occupation classification, and education distribution. The specific operation process is as follows: Data preparation: Use standardized data to select important characteristic variables related to high-quality full employment, clarify the prediction target, and determine the dependent variable y, that is, the high-quality full employment index, employment rate, and unemployment rate. Extract the original features of the data, such as vectorization of text data and feature extraction of time series data, and collect independent variable data x related to the dependent variable, that is, the relevant characteristics of workers, enterprises, and governments; Dataset division: The dataset is divided into training set, validation set and test set in a ratio of 70:20:10; Support vector machine model construction: The optimization problem of support vector regression is w is the weight vector, is a slack variable, C is a penalty coefficient used to balance model complexity and training error, n is the number of samples, and its constraints are: Among them, y i is the target value of the ith sample, x i is the eigenvector of the i-th sample, ε is the allowable error; Model evaluation: Use the validation data set to evaluate the generalization ability of the model, and use the mean square error (MSE) to calculate the performance index of the model; Model optimization and deployment: Based on the model evaluation results, the model is optimized, including adding and deleting features, adjusting model parameters, using regularization methods to prevent overfitting, and the optimized model is deployed in actual applications for prediction.

5. A high-quality full employment distribution prediction system according to claim 4, characterized in that: The mean square error (MSE) is a performance indicator of the evaluation model, which measures the difference between the model prediction value and the true value. It is achieved by calculating the average of the squares of all prediction errors, that is, is the predicted value of the ith sample, n is the number of samples, is the actual value of the ith sample, and by introducing the penalty term of model complexity and limiting the value range of model parameters through L1 regularization, the model is prevented from overfitting on the training data, that is: MSE(θ) is the mean square error loss function, θ j is the jth model parameter, m is the number of model parameters, λ is the regularization strength coefficient, and the trained support vector machine model is used to predict new data to obtain the prediction results, and decision support is provided based on the prediction results, that is, to formulate policies and optimize resource allocation.

6. A high-quality full employment distribution prediction system according to claim 3, characterized in that: The group identification and career planning module collects multi-dimensional data including individual basic information, education level, family situation, skill level, health status, employment status, migration experience and work experience, and takes features that help distinguish different employment statuses, namely age, gender, education level, years of work experience, and skill certificates, and uses the Gaussian mixture model clustering algorithm in machine learning technology to identify employment groups with similar characteristics, understand the needs of different groups, and thus provide more accurate career planning services. The process is as follows: Construct a Gaussian mixture model (GMM): Establish the probability density function of GMM: where K is the number of Gaussian components, is the probability density function of the kth Gaussian component of the d-dimensional data point x, π k is the mixing coefficient of the kth Gaussian component, satisfying And 0≤π k ≤1; Select the optimal number of clusters: Train multiple GMM models, each with a different number of components, and then calculate the corresponding AIC and BIC values. Use the AIC and BIC criteria to evaluate the model, and select the number of clusters with the minimum AIC or BIC value as the optimal number of clusters. GMM model fitting: Use the standardized data to fit the GMM model. The fitting process is performed using the EM (Expectation-Maximization) algorithm, which includes the E step and the M step. The E step calculates the posterior probability of each data point belonging to each Gaussian cost. The M step updates the model parameters, including the mixing coefficients Mean vector Covariance matrix The EM algorithm continuously updates the parameter θ = {π k ,μ k ,∑ k }, so that the GMM log-likelihood function Maximization,After these steps, GMM is used to cluster the employment data; Result analysis and career planning suggestions: Based on the clustering results, individuals and groups are divided into different categories, and the characteristics of each category, namely educational background, skill level, and career interests, are analyzed to provide targeted career planning suggestions for individuals and groups in different categories.

7. A high-quality full employment distribution prediction system according to claim 1, characterized in that: The labor flow prediction module collects various feature data related to labor flow, namely age, education level, work experience, industry, and determines the target label through the decision tree in the machine learning algorithm, and determines the best feature for node splitting by calculating the information gain, that is, first calculating the empirical entropy of the data set Where H(D) represents the entropy of the dataset D, K is the number of categories, and |C k | is the number of samples belonging to category k, |D| is the total number of samples in data set D, and the empirical conditional entropy H(D|A) of feature A on data set D is calculated, and the information gain is calculated, that is, g(D,A)=H(D)-H(D|A). The feature with the largest information gain is selected as the split feature of the current node. By repeating this process, a complete decision tree structure is gradually constructed. Therefore, when faced with new individual labor data, the structure is gradually deduced along the branches of the decision tree according to its various features until it reaches the leaf node, and a prediction result of whether the individual labor will flow is obtained.

8. A high-quality full employment distribution prediction system according to claim 7, characterized in that: The career development trend prediction module collects data related to the career, including historical employment data, changes in skill requirements, technological progress information, educational background, and salary trends, and extracts features related to career development trends, namely, industry growth rate, degree of automation, complexity of required skills, and education level requirements. The prediction model based on linear regression is combined with the output of the prediction model construction module to predict the demand and supply relationship of the future labor market, analyze the matching degree between labor skills and market demand, and use the logistic regression technology in the machine learning algorithm to evaluate the probability of occupation being replaced, namely Y is the binary variable of the occupation being replaced, X is the data feature, and β is the model parameter. The prediction results are interpreted to provide career planning and training suggestions for governments, enterprises, and individuals.

9. A high-quality full employment distribution prediction method, characterized in that: The specific steps of this method are: S100, Data Collection and Processing: Collect employment-related data from multiple sources, including industry information, occupation information, educational background, skill requirements, and demographic data, integrate the data into a unified data integration platform module, clean the data, and remove invalid and erroneous information; S200, feature engineering and model building: extract key features from the integrated data, namely industry growth rate, skill demand index, and education level distribution, create a prediction model, and divide the data set into training set, validation set, and test set, train the model, and adjust parameters; S300, Group Identification and Career Planning: Use the trained model to identify the status of different employment groups, and provide customized career development advice and planning for each group based on the identification results; S400, Labor Mobility Forecast: Use models to predict the mobility trends of labor between different regions and industries, and analyze the impact of holidays and industry cycles on labor mobility; S500, career development trend forecast: predict the risk of occupations being replaced by automation and artificial intelligence, analyze future changes in occupational demand, and predict long-term trends in career development; S600, Monitoring and early warning of groups with difficulty finding employment: Establish a dynamic monitoring and early warning platform for groups with difficulty finding employment, monitor these groups in real time, including early warning of rising unemployment rates, and analyze potential unemployment risks; S700, Result Analysis and Feedback: Interpret the forecast results and provide feedback to the government, enterprises and the public to assist in decision-making and resource allocation. Regularly collect new data and update the data integration platform to adapt to changes in the job market.

10. A high-quality full employment distribution prediction method according to claim 9, characterized in that: The S600 dynamic monitoring and early warning platform for groups with difficulties finding employment collects data from various sources, including government statistics, social security records, job search websites, recruitment advertisements, and educational institution reports, and divides employment risks into the following categories based on information on the job market, job seekers, and labor supply and demand: macroeconomic risks, industry risks, enterprise risks, risks of mismatch between skills and demand, demographic risks, risks of unbalanced regional development, seasonal risks, and risks of sudden events.

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