CN107609835B - Power grid manpower configuration application system and method - Google Patents

Abstract

The invention discloses a power grid manpower configuration application system and method, and relates to the field of power information systems. The system and the method do not need manual interference, multi-source data are calculated, counted and integrated through tools such as ETL (extract transform load), data copying and the like, data such as real-time and non-real-time data acquisition and the like are accessed into a data warehouse and are stored in a large amount, high-level application of a data processing layer is supported, and accurate analysis is independently carried out on post matching of electric power system personnel and accurate analysis and prediction is carried out on the flow of the electric power system human resources; and (4) carrying out accurate management on manpower resources, training arrangement and the like. The technical problem that manpower resource data are inconsistent under the setting of complex mechanisms of a power grid system in the prior art is solved, the defect that the manpower resources of the power system are qualitatively distributed and guided to work only by experts or leading experience in the prior art is overcome, an accurate and convenient application tool is provided for manpower management of the power system, and meanwhile, the manpower cost of the power system is greatly saved.

Description

An application system and method for power grid manpower allocation

technical field

The invention relates to the field of electric power information systems, in particular to an application system and method for power grid manpower configuration.

Background technique

With the continuous progress of my country's economic undertakings, my country's electric power enterprises have also entered a new stage of development. The so-called "three collections and five majors" system is to build a system of large-scale operation, large-scale planning, large-scale maintenance, large-scale marketing and large-scale construction, and carry out relatively intensive management of human, financial, material and other resources of electric power enterprises, so as to comprehensively improve the quality of power enterprises. management, economic efficiency and service level.

As the core of the enterprise's organizational competitiveness, the post personnel of the enterprise play a vital role in the deployment and optimization of the post personnel of the power enterprise. Taking an electric power company in X province as an example, human resources management covers more than 80,000 employees (by the end of 2016) including all employees, rural power workers, labor dispatch workers and other employees. However, traditional human resource management often uses the attendance system to manage the attendance of employees commuting to and from work and the personnel of various departments. It lacks detailed statistics and analysis of talent conditions. For the analysis and management of human resources with a large volume and strong professionalism in the power system Often powerless, there is a lack of one-stop accurate analysis tools such as power staffing, talent demand for comprehensive analysis and forecasting, which leads to a waste of time and energy for a large number of human management personnel, and increases costs.

For example, Chinese patent CN105787706A discloses a human resource management system based on a network platform. The human resource management system based on a network platform includes an organization management module, a personnel information management module, a recruitment management module, a labor contract module, an attendance management module, a welfare Management module and salary management module. The invention provides a human resource management system based on a network platform, which can reduce manpower in the conventional human resource management of small and medium-sized enterprises. Work such as professional job transfer and job optimization cannot be realized, and technical problems such as manpower analysis and job matching in the huge power system cannot be solved.

The electric power enterprise has a huge system, a large number of professionals, different human resource management methods and systems used by different branches, and inconsistent human resource information data in the whole system. The existing technology lacks a unified solution for the human resource data under the complex organization setting of the power grid system. This leads to a serious waste of talents in the whole system, and the low degree of matching of personnel and positions leads to a waste of costs. In view of this, the overall allocation of manpower in the power system can only rely on experience, rules or post-event adjustments and conventional human resource management systems. It is difficult to achieve accurate analysis of electric power human resources, optimal allocation of electric power professionals, and comprehensive analysis and forecasting of talent needs. It is easy to cause short-sightedness, and it is difficult to rationally allocate positions and personnel from the overall perspective of the strategic development of electric power companies.

Contents of the invention

Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a power grid manpower configuration application system and method. After the configuration is set, no manual intervention is required to independently and accurately analyze the position matching of power system personnel and the flow of human resources in the power system. Accurate analysis and prediction; accurate management of human resources, training arrangements, etc. During the matching process, the knowledge sample database is continuously updated and improved, which saves labor costs and improves the rationality and accuracy of power grid human resource management.

In order to achieve the above purpose, the present invention proposes a grid manpower configuration application system, the system includes the following modules,

(1) Data source module: the collected personnel basic information data, including multiple data information systems used by power systems such as ERP human resource centralized deployment database, state grid recruitment platform, state grid quota system, human resource management and control system, etc.

(2) Data integration module: use extraction, conversion, and loading methods to unify the database formats of multiple systems into the power system manpower configuration analysis and management database. Use ETL tools to extract data to the data analysis platform on a monthly basis, and monitor indicators such as data quality and system resource utilization, memory usage, database space usage, and system average response time.

(3) Knowledge base module: Obtain job flow data through the human resources management system, combine basic personnel data, establish positions and professional knowledge bases, update personnel characteristics and job descriptions in the data in the knowledge base in a timely manner, according to the categories in the knowledge base Determine the post configuration rules for power grid personnel based on historical data.

(4) Data processing module: According to the distribution of data, select an appropriate data range to process the data, which is to standardize and characterize the processed data. Determine the support and confidence of each item in each personnel flow data through the Apriori algorithm. Taking each piece of data flowing between personnel positions as a sample, and education, age, gender, professional technical qualifications, and skill levels as personnel characteristics, the original data is converted into sample data related to the position.

(5) Data center analysis module: the data center analysis module is a core module of a power grid manpower configuration application system provided by the present invention, which is used to connect with other modules, through real-time collection of personnel basic information, personnel characteristics and flow data , on the basis of satisfying the stable management of the power grid, to realize the optimal allocation of human resources in the power system. Complete staffing analysis, talent demand forecast analysis, staff training forecast and analysis, etc. Optimize, adjust and control the demand for post personnel on different time scales to achieve the overall balance of power human resources and the lean management of the overall power grid.

This module starts from the needs of various professionals in the power grid, shortage of personnel, and actual employment, combined with basic information on organizational units and employees, and according to information such as units, ages, and job classifications, and analyzes the flow of professionals to build a model. Conduct in-depth analysis and forecasts on the needs of various professionals (talent flow), staffing situation, retirement situation, and employment rationality.

(6) Data display module: Display query results and visual display analysis results. From the perspective of personnel professional analysis, it shows the needs and changing trends of various professionals in the future, and compares and analyzes the age structure, education structure, and post-level institutions of various units. Carry out visual display, and display the allocation of various professionals according to unit category, personnel category, age structure, education structure, professional technical qualifications, skill level and job level, and predict the future demand for personnel to provide auxiliary support for the company's decision-making level.

(7) Instruction issuing module: used for issuing instructions.

Among them, the data source module, data integration module, knowledge base module, data processing module, and data display module are all connected with the data center analysis module.

Preferably, the data source module: collect basic personnel information data including personnel age, gender, major, highest education level, job category, job level, skill level, professional title, entry time, working years, affiliation unit, said department, etc.

The present invention proposes an application method for manpower configuration of a power grid, which includes the following steps,

Step 1: Collect basic personnel information data through the data source module of the power grid manpower configuration system. The basic personnel information data collected by the data source module includes personnel age, gender, major, highest education level, job category, job level, skill level, professional title, and entry time , working years, affiliated units, and departments;

Step 2: Use ETL tools to extract data on a monthly basis to the power system manpower allocation analysis and management database, and monitor data quality and system resource utilization indicators;

Step 3: Obtain job flow data through the human resource management system, combine basic personnel data, establish a job and professional knowledge base, update personnel characteristics and job descriptions in the data in the knowledge base in a timely manner, and determine according to the categories and historical data in the knowledge base The post allocation rules for power grid personnel;

Step 4: According to the distribution of the data, select an appropriate data range to process the data, which is to standardize and characterize the processed data. Determine the support and confidence of each item in each personnel flow data through the Apriori algorithm.

Step 5: Set the confidence threshold;

Step 6: Extract representative itemsets from frequent itemsets, analyze personnel characteristics and typical examples of job matching;

Step 7: Establish a sequence model for the flow of personnel between positions, and use time as the baseline to analyze the flow trend of personnel between positions;

Step 8: Conduct staffing analysis based on unit category, personnel category, age structure, education structure, professional technical qualifications, skill level, and job level information, and predict the trend of staffing by job level;

Step 9: Enter basic personnel information;

Step 10: Issue a notification according to the rules.

Preferably, the data center analysis module of the power grid manpower allocation application system mines the potential information in the personnel flow data between positions, uses the artificial neural network algorithm to establish a matching model for the position characteristics and personnel characteristics, and reasonably performs position matching for personnel, Accurately forecast job headcount. The application system and method for manpower configuration of the power grid, by analyzing and summarizing historical personnel flow information and historical data of each position, analyzing the characteristics of the positions, and statistically analyzing the characteristic data closely related to the basic information of personnel, grasps the general laws and regulations of the talent requirements of each position. special case. Through analysis of variance, cross-validation and other methods to test the frequency of personnel turnover under different educational backgrounds, different majors, and different skill levels, the output results are the distribution characteristics of the number of positions in different dimensions, and the major categories or categories of positions with significant differences in professional distribution. The middle category provides support for grasping the general law of the distribution characteristics of personnel positions as a whole. According to the fluctuation of indicators, the standard of normal turnover between positions is formulated. For posts with normal flow, each indicator will fluctuate within a reasonable range. By analyzing the posts that have not undergone personnel changes within a certain period of time, the standard for normal personnel flow status of the post is established. Firstly, the input data is the index data of the normal flow state, and the concentrated distribution interval of each index is studied through data mining algorithms such as correlation analysis. The output result is the reasonable value range of each index in the normal operating state. Before the loss of personnel, the position will show certain precursory characteristics in terms of operating indicators. By constructing a relationship model between various types of personnel outflows, inflow types and operating indicators, the relationship between the characteristics of job indicators and the occurrence of personnel changes is studied to identify personnel. Precursor characteristics of circulation.

Preferably, the data center analysis module of the power grid manpower allocation application system predicts and analyzes the flow trend of power grid personnel, by mining the potential information in the personnel flow data between positions and units, and using the correlation analysis algorithm to analyze Establish a forecasting model for inflow and outflow according to the different flow characteristics and personnel characteristics of the company, rationally allocate talents between positions and units, and accurately predict the number of positions. Through the analysis and summary of historical personnel flow information, historical data of various positions and inter-unit mobility, analysis of flow characteristics, statistical analysis of characteristic data closely related to basic personnel information, grasp the general rules and special cases of talent demand for various positions and units. Through analysis of variance, cross-validation and other methods to test the frequency of personnel turnover under different educational backgrounds, different majors, and different skill levels, the output results are the distribution characteristics of the number of positions in different dimensions, and the major categories or categories of positions with significant differences in professional distribution. The middle category provides support for grasping the general law of the distribution characteristics of personnel positions as a whole. According to the fluctuation of indicators, the standard of normal turnover between positions is formulated. For positions with normal flow, each index will fluctuate within a reasonable range. By analyzing the positions and units that have not undergone personnel changes within a certain period of time, the standard for normal personnel flow status is established. Firstly, the input data is the index data of the normal flow state, and the concentrated distribution interval of each index is studied through data mining algorithms such as correlation analysis. The output result is the reasonable value range of each index in the normal operating state. Before the loss of personnel, the position will show certain precursory characteristics in terms of operating indicators. By constructing a relationship model between various types of personnel outflows, inflow types and operating indicators, the relationship between the characteristics of job indicators and the occurrence of personnel changes is studied to identify personnel. Precursor characteristics of circulation.

Preferably, the data center analysis module of the power grid manpower configuration application system can analyze and match the power manpower training. This method combines different characteristics of power grid personnel with training content and frequency, and regularly organizes and arranges training for personnel , through the interaction between the human resource management and control system and the power grid personnel training system, the human resource management system can automatically arrange collaborative arrangements for different personnel, so that the conventional personnel training organization can be developed into an automatic personnel training allocation, according to the characteristics of personnel positions and work experience According to the accumulation and different needs of personnel capabilities, the correlation analysis between personnel and job characteristics and training content is carried out to find the potential connection between personnel and training content. This method overcomes the shortcomings of the existing methods that only rely on the qualitative distribution of experts or leadership experience. Starting from the factors that affect the training effect of personnel in different positions, it fully applies the correlation analysis method in carding statistics, and conducts a large number of personnel basic information and training data. The analysis and calculation have greatly improved the theoretical support of the method, and can provide relevant data references for different units, departments, positions, and personnel.

Beneficial effects of the present invention: the method solves the technical problem of inconsistency of human resource data under the complex organization setting of the power grid system in the prior art, and overcomes the shortcomings of the prior art that only rely on the experience of experts or leaders to qualitatively allocate and guide the work of human resources in the power system, Starting from the factors that affect the ability of personnel in power system positions, analyzing a large number of personnel flow data can provide accurate one-stop analysis and application systems and methods for different units, departments, positions, and personnel in the power system, and carry out Visual display. It not only provides accurate and convenient application tools for power system manpower management, but also greatly saves power system manpower costs.

Description of drawings

Fig. 1 is a structural frame diagram of a grid manpower configuration application system provided by the present invention.

Fig. 2 is a flow chart of a method for allocating electric manpower posts.

Figure 3 is a flow chart of a method for predicting the flow of electric power and manpower

Fig. 4 is a flowchart of an analysis method for job training of electric power manpower.

Detailed ways

The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

Example 1

As shown in Fig. 1, the present invention provides a kind of grid manpower allocation application system, and this system comprises:

(1) Data source module: the collected personnel basic information data, including multiple data information systems used by power systems such as ERP human resource centralized deployment database, state grid recruitment platform, state grid quota system, human resource management and control system, etc. The collected personnel basic information data include personnel age, gender, major, highest education level, job category, job level, skill level, professional title, entry time, working years, affiliation unit, said department, etc.

(2) Data integration module: use extraction, conversion, and loading methods to unify the database formats of multiple systems into the power system manpower configuration analysis and management database. Preferably, ETL tools are used to extract data to the data analysis platform on a monthly basis, and monitor data quality and system resource utilization indicators.

Furthermore, this module mainly performs calculation statistics and data integration for historical business data such as staffing information, personal data, education information, organization information, job information, employee statistics, and professional information in the data source, and realizes it through tools such as ETL and data replication. Extract and collect structured data (relational database records), semi-structured data (logs, emails, etc.) Structured data (files, video, audio, network data streams, etc.), and at the same time, realize real-time and non-real-time collection of data and other data access to the data warehouse (MPP). At the same time, it is responsible for the storage of massive data. For all data types and diverse computing needs, it is characterized by massive-scale storage and fast query and reading. It stores all kinds of data extracted and collected from the data integration stage, and supports advanced applications in the data processing layer. .

Usually, unstructured data is stored in a distributed file system, semi-structured data is stored in a columnar database or a key-value database, and structured data is stored in a row-based database. Data storage with high real-time performance and high computing performance requirements In-memory database or real-time database. Provides computing functions such as stream computing, batch computing, memory computing, and query computing for diverse big data, allowing query and computing of distributed stored data files or memory data. Based on the batch computing component, it realizes regular data preprocessing and forms a wide table to support subsequent data mining analysis. The method of extracting, converting and loading is used to unify the formats of multiple system databases into the power system manpower configuration analysis and management database.

(3) Knowledge base module: Obtain job flow data through the human resources management system, combine basic personnel data, establish positions and professional knowledge bases, update personnel characteristics and job descriptions in the data in the knowledge base in a timely manner, according to the categories in the knowledge base Determine the post configuration rules for power grid personnel based on historical data.

(4) Data processing module: According to the distribution of data, select an appropriate data range to process the data, which is to standardize and characterize the processed data. Determine the support and confidence of each item in each personnel flow data through the Apriori algorithm. Taking each piece of data flowing between personnel positions as a sample, and education, age, gender, professional technical qualifications, and skill levels as personnel characteristics, the original data is converted into sample data related to the position.

(5) Data center analysis module: the data center analysis module is a core module of a power grid manpower configuration application system provided by the present invention, which is used to connect with other modules, through real-time collection of personnel basic information, personnel characteristics and flow data , on the basis of satisfying the stable management of the power grid, to realize the optimal allocation of human resources in the power system. Complete staffing analysis, talent demand forecast analysis, staff training forecast and analysis, etc. Optimize, adjust and control the demand for post personnel on different time scales to achieve the overall balance of power human resources and the lean management of the overall power grid.

(6) Data display module: Display query results and visual display analysis results. From the perspective of personnel professional analysis, it shows the needs and changing trends of various professionals in the future, and compares and analyzes the age structure, education structure, and post-level institutions of various units. Carry out visual display, and display the allocation of various professionals according to unit category, personnel category, age structure, education structure, professional technical qualifications, skill level and job level, and predict the future demand for personnel to provide auxiliary support for the company's decision-making level.

(7) Instruction issuing module: used for issuing instructions.

Example 2

As shown in Figure 2, the present invention provides a method for manpower allocation in power grids. In this method, the potential information in the personnel flow data between positions is excavated, and the artificial neural network algorithm is used to establish a matching model for the characteristics of the positions and the characteristics of the personnel, so that the personnel can be reasonably allocated. Perform job matching and accurately predict the number of jobs. By analyzing and summarizing the historical personnel flow information and historical data of each position, analyzing the characteristics of the positions, and statistically analyzing the characteristic data closely related to the basic information of the personnel, we can grasp the general rules and special cases of the talent demand of each position. Through analysis of variance, cross-validation and other methods to test the frequency of personnel turnover under different educational backgrounds, different majors, and different skill levels, the output results are the distribution characteristics of the number of positions in different dimensions, and the major categories or categories of positions with significant differences in professional distribution. The middle category provides support for grasping the general law of the distribution characteristics of personnel positions as a whole. According to the fluctuation of indicators, the standard of normal turnover between positions is formulated. For posts with normal flow, each indicator will fluctuate within a reasonable range. By analyzing the posts that have not undergone personnel changes within a certain period of time, the standard for normal personnel flow status of the post is established. Firstly, the input data is the index data of the normal flow state, and the concentrated distribution interval of each index is studied through data mining algorithms such as correlation analysis. The output result is the reasonable value range of each index in the normal operating state. Before the loss of personnel, the position will show certain precursory characteristics in terms of operating indicators. By constructing a relationship model between various types of personnel outflows, inflow types and operating indicators, the relationship between the characteristics of job indicators and the occurrence of personnel changes is studied to identify personnel. Precursor characteristics of circulation.

The method specifically includes the following steps:

Step 1: Collect basic personnel information data through the data source module of the power grid manpower configuration system. The basic personnel information data collected by the data source module includes personnel age, gender, major, highest education level, job category, job level, skill level, professional title, and entry time , working years, affiliation, department, etc.

Step 2: Unify the formats of multiple system databases into the power system manpower configuration analysis and management database by means of extraction, conversion, and loading. Preferably, ETL tools are used to extract data to the data analysis platform on a monthly basis, and indicators such as data quality, system resource utilization, memory utilization, database space utilization, and system average response time are monitored.

Step 3: Obtain job flow data through the human resource management system, combine basic personnel data, establish a job and professional knowledge base, update personnel characteristics and job descriptions in the data in the knowledge base in a timely manner, and determine according to the categories and historical data in the knowledge base The post allocation rules for power grid personnel;

Step 4: According to the distribution of the data, select an appropriate data range to process the data, which is to standardize and characterize the processed data. Determine the support and confidence of each item in each personnel flow data through the Apriori algorithm. Taking each piece of data flowing between personnel positions as a sample, education, age, gender, professional technical qualifications, and skill levels as personnel characteristics, convert the original data into sample data related to the position;

Step 5: Set the confidence threshold, and regularly extract the characteristics of personnel such as major, skill level, education, professional and technical qualifications and the rules for large, medium, and small job configurations according to the rules of the sample data;

Step 6: Extract representative itemsets from frequent itemsets, analyze personnel characteristics and typical examples of job matching;

Step 7: Establish a sequence model for the flow of personnel between positions, and use time as the baseline to analyze the flow trend of personnel between positions;

Step 8: Conduct staffing analysis based on unit category, personnel category, age structure, education structure, professional technical qualifications, skill level, and job level information, and predict the trend of staffing by job level;

Step 9: Enter basic personnel information;

Step 10: Issue a notification according to the rules.

Example 3

As shown in Figure 3, the present invention provides a method for predicting the flow trend of electric power manpower. The method mines the potential information in the personnel flow data between positions and units, and adopts the correlation analysis algorithm to analyze the different flow characteristics and personnel characteristics in each position and unit. Establish a prediction model for inflow and outflow, rationally allocate talents between positions and units, and accurately predict the number of positions. Through the analysis and summary of historical personnel flow information, historical data of various positions and inter-unit mobility, analysis of flow characteristics, statistical analysis of characteristic data closely related to basic personnel information, grasp the general rules and special cases of talent demand for various positions and units. Through analysis of variance, cross-validation and other methods to test the frequency of personnel turnover under different educational backgrounds, different majors, and different skill levels, the output results are the distribution characteristics of the number of positions in different dimensions, and the major categories or categories of positions with significant differences in professional distribution. The middle category provides support for grasping the general law of the distribution characteristics of personnel positions as a whole. According to the fluctuation of indicators, the standard of normal turnover between positions is formulated. For positions with normal flow, each index will fluctuate within a reasonable range. By analyzing the positions and units that have not undergone personnel changes within a certain period of time, the standard for normal personnel flow status is established. Firstly, the input data is the index data of the normal flow state, and the concentrated distribution interval of each index is studied through data mining algorithms such as correlation analysis. The output result is the reasonable value range of each index in the normal operating state. Before the loss of personnel, the position will show certain precursory characteristics in terms of operating indicators. By constructing a relationship model between various types of personnel outflows, inflow types and operating indicators, the relationship between the characteristics of job indicators and the occurrence of personnel changes is studied to identify personnel. Precursor characteristics of circulation.

Example 4

As shown in Figure 4, the present invention provides an analysis method for electric power manpower training, which combines different characteristics of power grid personnel with training content and frequency, regularly organizes and arranges personnel training, and communicates with the power grid through the human resource management and control system. The personnel training system interacts, and the human resource management system can automatically arrange collaborative arrangements for different personnel, so that the conventional personnel training organization can be developed into an automatic personnel training allocation. Relevant analysis of personnel and job characteristics and training content, looking for potential connections between personnel and training content. This method overcomes the shortcomings of the existing methods that only rely on the qualitative distribution of experts or leadership experience. Starting from the factors that affect the training effect of personnel in different positions, it fully applies the correlation analysis method in carding statistics, and conducts a large number of personnel basic information and training data. The analysis and calculation have greatly improved the theoretical support of the method, and can provide relevant data references for different units, departments, positions, and personnel. The present invention includes the following functions:

●Measuring the per capita training situation of the power grid in the past year, helping the human resources management department to formulate reasonable training hours, so as to better exert the training effect without affecting the daily work of personnel;

●Arrange corresponding training courses according to the different characteristics of each personnel to maximize the training effect, and formulate training courses that meet their characteristics;

●Analyze the relationship between training courses and personnel performance, and help managers judge the effectiveness of training on actual work.

●Display the impact of the previous quarter or year training on the next quarter or year enterprise benefits;

The power grid personnel training organization based on correlation analysis is based on the training plan, meets the constraints of each unit and post personnel training and assessment methods, and aims to maximize the training effect. The method specifically includes the following steps:

Step 1: Obtain basic personnel information, including major, position, skill level, professional title, education, etc., through the basic personnel information data collected by the human resources management and control system;

Step 2: Obtain personnel training data through the human resources management system, and combine personnel basic data to determine the training content and results of power grid personnel;

Step 3: According to the distribution of data, select an appropriate data range to process the data, which is to standardize and characterize the processed data. Determine the support and confidence of each item in each personnel flow data through the Apriori algorithm. Taking each piece of personnel training data as a sample, education, age, gender, professional technical qualifications, and skill levels as personnel characteristics, convert the original data into training-related sample data;

Step 4: Set the confidence threshold and extract the rules for personnel training;

Step 5: Extract representative itemsets from frequent itemsets, analyze personnel characteristics and typical examples of job matching;

Step 6: Establish a sequence model for the flow of training content among personnel between positions, and use time as the baseline to analyze the training trend of personnel;

Step 7: Establish the analysis standard of matching degree of personnel training;

Step 8: Enter basic personnel information;

Step 9: Issue the training notice according to the rules.

Claims (1)

1. A power grid manpower configuration method is characterized in that a data source module, a data integration module, a knowledge base module, a data processing module, a data center analysis module, a data display module and an instruction issuing module which are contained in a power grid manpower configuration application system are adopted to complete personnel configuration analysis, talent demand prediction analysis, personnel training prediction and analysis;

the method comprises the following steps:

the method comprises the following steps: the method comprises the steps that basic information data of personnel collected by a data source module of the power grid manpower configuration application system are utilized, and the basic information data of the personnel collected by the data source module comprise the age, the sex, the specialty, the highest academic calendar, the post category, the post grade, the skill grade, the job title, the working time, the working age, the affiliated unit and the affiliated department of the personnel;

step two: extracting data to a power system manpower configuration analysis and management database by using an ETL tool monthly, and monitoring data quality and system resource utilization rate indexes;

step three: acquiring post flow data through a human resource management system, establishing a post and professional knowledge base by combining basic data of personnel, updating personnel characteristics and post description in the data in the knowledge base in time, and determining post configuration rules of power grid personnel according to categories and historical data in the knowledge base;

step four: selecting a proper data range to process the data according to the distribution condition of the data, standardizing and characterizing the processed data, and determining the support degree and the confidence degree of each item in the flow data of each person through an Apriori algorithm;

step five: setting a confidence threshold;

step six: extracting a representative item set in the frequent item set, and analyzing the characteristic of personnel and a typical sample matched with a post;

step seven: establishing a sequence mode of the flow capacity of the personnel between the posts, and analyzing the flow trend of the personnel between the posts by taking time as a base line;

step eight: carrying out personnel configuration analysis according to the unit category, personnel category, age structure, academic structure, professional technical qualification, skill level and job level information, and carrying out personnel configuration trend prediction according to the job level;

step nine: inputting basic information of personnel;

step ten: issuing a notice according to a rule;

the method for completing the talent demand prediction analysis comprises the following technical scheme:

s1, establishing inflow and outflow condition prediction models for different flow characteristics and personnel characteristics in each post and unit by adopting an association analysis algorithm, reasonably configuring talents among the posts and the units, and accurately predicting the number of the posts;

s2, analyzing and summarizing historical personnel flow information and flow historical data among all posts and units, analyzing flow characteristics, and carrying out statistical analysis on characteristic data closely related to basic information of personnel to master general rules and special cases of talents requirements of all posts and units;

s3, checking the flow occurrence frequency of the personnel under different academic calendars, different specialties and different skill levels by using a variance analysis and cross validation method;

s4, establishing a standard of normal circulation between index fluctuation posts;

s5, analyzing staff loss precursor characteristics, researching the relation between post index characteristics and staff change occurrence by constructing a relation model between outflow and inflow types of various staff and operation indexes, and identifying staff circulation precursor characteristics;

the method for analyzing, matching and managing the electric power and manpower training comprises the following steps:

the method comprises the following steps: acquiring basic information of personnel, including professions, posts, skill levels, titles, academic calendars and the like, through basic information data of the personnel acquired by a human resource management and control system;

step two: acquiring personnel training data through a human resource management system, and determining training contents and results of power grid personnel by combining personnel basic data;

step three: selecting a proper data range to process the data according to the distribution condition of the data, wherein the data is standardized and characterized; determining the support degree and the confidence degree of each item in each personnel flow data through an Apriori algorithm; taking each piece of data of the training of the personnel as a sample, and taking a study history, age, gender, professional technical qualification and skill level as characteristics of the personnel, and converting the original data into sample data related to the training;

step four: setting a confidence threshold value and extracting rules for personnel training;

step five: extracting a representative item set in the frequent item set, and analyzing the characteristic of personnel and a typical sample matched with a post;

step six: establishing a sequence mode of training content flowing of personnel between posts, and analyzing the training trend of the personnel by taking time as a base line;

step seven: establishing an analysis standard of personnel training matching degree;

step eight: inputting basic information of personnel;

step nine: and issuing a training notice according to rules.

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