CN107609835B - Power grid manpower configuration application system and method - Google Patents
Power grid manpower configuration application system and method Download PDFInfo
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- CN107609835B CN107609835B CN201710628637.9A CN201710628637A CN107609835B CN 107609835 B CN107609835 B CN 107609835B CN 201710628637 A CN201710628637 A CN 201710628637A CN 107609835 B CN107609835 B CN 107609835B
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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
Technical Field
The invention relates to the field of power information systems, in particular to a power grid manpower configuration application system and a power grid manpower configuration application method.
Background
With the continuous progress of the economic business of China, the electric power enterprises of China also step into a new development stage. The system of 'three sets and five sets' is a system for constructing large operation, large planning, large overhaul, large marketing and large construction, and carries out relatively intensive management on resources such as people, properties, objects and the like of the power enterprise, so that the management, economic benefit and service level of the power enterprise are comprehensively improved.
The enterprise position personnel are used as the organization competitiveness core of the enterprise, and the allocation optimization of the position personnel of the power enterprise plays a vital role in enterprise development. Taking only a certain X electric power saving company as an example, human resource management covers more than 8 thousands of people, agricultural and electric power workers, labor dispatching workers and other employees (by the end of 2016). In the traditional human resource management, an attendance system is often used to manage attendance of workers on duty and personnel of each department, careful statistical analysis on talent conditions is lacked, the analysis and management of human resource states with huge volume and strong speciality of an electric power system are often incapacitated, and one-stop accurate analysis tools such as electric power personnel configuration, perfect analysis and prediction of talent demands and the like are lacked, so that a great amount of time and energy of human managers are wasted, and the cost is increased.
For example, chinese patent CN105787706a discloses a human resource management system based on a network platform, which 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 a wage management module. The invention provides a human resource management system based on a network platform, which can play a role of reducing manpower and the like when carrying out conventional human resource management of small and medium-sized enterprises, but can not realize the work of personnel configuration, annual personnel demand, post circulation of internal electric power professionals, post optimization and the like of each system requiring electric power of an electric power system, and can not solve the technical problems of manpower analysis, post matching and the like of a huge electric power system.
The power enterprise system is huge, the professionals are numerous, the manpower management methods and systems used among different branch organizations are different, the manpower resource information data of the whole system are inconsistent, and the prior art lacks a solution scheme for setting the manpower resource data to be uniform under the condition of a complicated organization of the power grid system, so that the talents of the whole system are seriously wasted, the personnel post matching degree is not high, and the cost is wasted. In view of this, the overall configuration manpower of the power system often depends only on experience, rule, or post-adjustment and conventional human resource management system. Accurate analysis of electric power and human resources, optimized configuration of electric power professionals and perfect analysis and prediction of talent demands are difficult to realize, short sight is easy to cause, and reasonable configuration of posts and personnel is difficult to carry out from the global perspective of strategic development of an electric power company.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a power grid manpower configuration application system and a power grid manpower configuration application method, after configuration setting, manual interference is not needed, and the post matching of power system personnel and the flow of power system manpower resources are independently and accurately analyzed and predicted; and (4) carrying out accurate management on manpower resources, training arrangement and the like. And the knowledge sample base is continuously updated and perfected in the matching process, so that the labor cost is saved, and the rationality and the accuracy of the power grid human resource management are improved.
In order to achieve the above object, the present invention provides a system for manually configuring a power grid, which comprises the following modules,
(1) A data source module: the acquired personnel basic information data comprise a plurality of data information systems used by electric power systems such as an ERP (enterprise resource planning) human resource centralized deployment database, a national grid recruitment platform, a national grid member system, a human resource management and control system and the like.
(2) The data integration module: and unifying the formats of the plurality of system databases to a manpower configuration analysis and management database of the power system by adopting extraction, conversion and loading methods. The method comprises the steps of extracting data to a data analysis platform by using an ETL tool monthly, and monitoring data quality and indexes such as system resource utilization rate, memory utilization rate, database space utilization rate, system average response time and the like.
(3) A knowledge base module: the post flow data is obtained through a human resource management system, a post and professional knowledge base is established by combining basic data of people, the personnel characteristics and the post description in the data are timely updated in the knowledge base, and the post configuration rule of the power grid personnel is determined according to the category and the historical data in the knowledge base.
(4) A data processing module: and selecting a proper data range to process the data according to the distribution condition of the data, wherein the data is standardized and characterized after processing. And determining the support degree and the confidence degree of each item in each personnel flow data through an Apriori algorithm. Each piece of data flowing among the personnel posts is taken as a sample, and the learning history, the age, the gender, the professional technical qualification and the skill level are taken as the characteristics of the personnel, so that the original data are converted into post-related sample data.
(5) The data center analysis module: the data center analysis module is a core module of the power grid human configuration application system, is used for being connected with other modules, and realizes the optimal configuration of human resources of the power system on the basis of meeting the power grid stability management by acquiring the personnel basic information, the personnel characteristics and the flow data in real time. And completing personnel configuration analysis, talent demand prediction analysis, personnel training prediction and analysis and the like. And optimizing, adjusting and controlling the demand degree of the post personnel at different time scales, and realizing the global balance of electric power and human resources and the lean management of the whole power grid.
The module starts from the requirements of professional personnel of the power grid, the shortage of people and the actual employment, combines basic information of organization units and employees and the like, constructs a model according to dimension and other information of unit, age, post classification and the like, and carries out deep analysis and prediction on the requirements of the professional personnel (personnel flow), personnel configuration condition, retirement condition, employment rationality and the like by analyzing the flow condition of the professional personnel.
(6) A data display module: and displaying the query result and the visual display analysis result, displaying the requirements and the variation trend of each professional in the future by taking the professional of the staff as an analysis angle, performing comparative analysis on the age structure, the academic structure and the post level mechanism of each unit staff, visually displaying the configuration condition of each professional according to the unit category, the staff category, the age structure, the academic structure, the professional technical qualification, the skill level and the job level, predicting the requirement degree of the staff in the future, and providing auxiliary support for a company decision layer.
(7) An instruction issuing module: for issuing instructions.
The data source module, the data integration module, the knowledge base module, the data processing module and the data display module are all connected with the data center analysis module.
Preferably, the data source module: the collected basic information data of the personnel comprises the age, the gender, the specialty, the highest scholarship, the post category, the post grade, the skill grade, the job title, the time of job entry, the working age, the affiliated units, the department and the like.
The invention provides a power grid manpower configuration application method, which 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 a power grid manpower configuration system are utilized, wherein the basic information data of the personnel collected by the data source module comprise the age, the sex, the specialty, the highest academic history, the post category, the post grade, the skill grade, the title, the time of employment, the working age, the affiliated unit and the department;
step two: extracting data to a power system manpower configuration analysis and management database by using an ETL tool according to months, 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: and selecting a proper data range to process the data according to the distribution condition of the data, wherein the data is standardized and characterized after processing. And determining the support degree and the confidence degree of each item in each personnel flow data 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 unit categories, personnel categories, age structures, academic structures, professional technical qualifications, skill levels and job level information, and predicting personnel configuration trends according to job levels;
step nine: inputting basic information of personnel;
step ten: and issuing the notice according to the rule.
Preferably, the data center analysis module of the power grid human power configuration application system is used for mining potential information in the personnel flow data among the posts, a matching model is built for post characteristics and personnel characteristics by adopting an artificial neural network algorithm, post matching is reasonably carried out on the personnel, and the post number is accurately predicted. According to the power grid manpower configuration application system and method, the general rules and special cases of talent requirements of all posts are mastered by analyzing and summarizing historical personnel flow information and historical data of all posts, analyzing post characteristics, and statistically analyzing characteristic data closely linked with personnel basic information. The method is characterized in that the occurrence frequency of the staff flow under different academic calendars, different professions and different skill levels is detected through methods such as variance analysis, cross validation and the like, output results are the distribution characteristics of the staff number in different dimensions and the post categories or the post categories with obvious differences in professional distribution, and support is provided for grasping the general rule of the staff post distribution characteristics on the whole. And establishing the standard of normal circulation between the posts according to the index fluctuation. In the post with normal flowing, each index can fluctuate in a reasonable interval, and the post without personnel change in a certain time period is analyzed to establish the standard of the flowing state of normal personnel in the post. Firstly, index data with normal flowing state is input, and a centralized distribution interval of each index is researched through a data mining algorithm such as correlation analysis. The output result is a reasonable value range of each index in the normal running state. Before personnel loss occurs, a post can present certain precursor characteristics on the aspect of operation indexes, the relationship between post index characteristics and personnel change occurrence is researched by constructing a relationship model between outflow and inflow types of various personnel and the operation indexes, and personnel circulation precursor characteristics are identified.
Preferably, the data center analysis module of the power grid human configuration application system predicts and analyzes the flow trend of the power grid personnel, and by mining the potential information in the personnel flow data between the posts and the units and adopting an association analysis algorithm, a prediction model of inflow and outflow conditions is established for different flow characteristics and personnel characteristics in each post and unit, so that the talents between the posts and the units are reasonably configured, and the number of the posts is accurately predicted. The general rules and special cases of talent requirements of each post and unit are mastered by analyzing and summarizing historical staff flow information and flow historical data among posts and units, analyzing flow characteristics, and statistically analyzing characteristic data closely linked with basic staff information. The occurrence frequency of the staff flow under different academic calendars, different professions and different skill levels is detected through methods such as variance analysis, cross validation and the like, the output result is the distribution characteristics of the number of the posts in different dimensions and the post categories or the post categories with obvious differences in professional distribution, and support is provided for the general rule of mastering the post distribution characteristics of the staff as a whole. And establishing a standard of normal circulation between the posts according to the index fluctuation. In the normally flowing position, all indexes fluctuate in a reasonable interval, and the normal personnel flowing state standard is constructed by analyzing the positions and units which do not change personnel in a certain time period. Firstly, index data with normal flowing state is input, and a centralized distribution interval of each index is researched through a data mining algorithm such as correlation analysis. The output result is a reasonable value range of each index in the normal running state. Before personnel loss occurs, a post can present certain precursor characteristics on the aspect of operation indexes, the relationship between post index characteristics and personnel change occurrence is researched by constructing a relationship model between outflow and inflow types of various personnel and the operation indexes, and personnel circulation precursor characteristics are identified.
Preferably, the data center analysis module of the power grid human configuration application system can analyze, match and manage power and human training, the method combines power grid personnel with different characteristics with training content and frequency, the organization and arrangement of personnel advancing training are carried out regularly, interaction is carried out through the human resource management and control system and the power grid personnel training system, the human resource management system can automatically carry out collaborative arrangement on different personnel respectively, accordingly, conventional personnel training organizations are developed into automatic personnel training distribution, and according to the characteristics of personnel posts, the accumulation of working experience and different requirements on personnel capacity, relevance analysis is carried out on the personnel, the post characteristics and the training content, and potential relation between the personnel and the training content is searched. The method overcomes the defect that the existing method only depends on the qualitative distribution of experts or leader experience, fully applies the correlation analysis method in the carding statistics from the factors influencing the training effect of the personnel at different posts, analyzes and calculates a large amount of basic information and training data of the personnel, greatly improves the theoretical support of the method, and can provide related data references for different units, different departments, different posts and different personnel.
The invention has the beneficial effects that: the method solves the technical problem of inconsistent human resource data under the setting of complex mechanisms of a power grid system in the prior art, overcomes the defect that the human resource of the power system is qualitatively distributed and guided to work only by experts or leadership experience in the prior art, analyzes a large amount of personnel flow data from the factors influencing the exertion of the personnel capacity of the posts of the power system, can provide an accurate one-stop analysis application system and method for different units, different departments, different posts and different personnel of the power system, and performs visual display. Not only provides accurate convenient application tool for power system manpower management, greatly practices thrift power system human cost simultaneously.
Drawings
Fig. 1 is a structural framework diagram of a power grid manpower configuration application system provided by the present invention.
FIG. 2 is a flow chart of a method for configuring a power and human power station.
FIG. 3 is a flow chart of a method for predicting the flow trend of electric power and manpower
FIG. 4 is a flow chart of a method for training and analyzing a power and human power station.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, the present invention provides a power grid manpower configuration application system, which includes:
(1) A data source module: the acquired personnel basic information data comprise a plurality of data information systems used by electric power systems such as an ERP (enterprise resource planning) human resource centralized deployment database, a national grid recruitment platform, a national grid member system, a human resource management and control system and the like. The collected basic information data of the personnel comprises the age, the gender, the specialty, the highest scholarship, the post category, the post grade, the skill grade, the job title, the time of job entry, the working age, the affiliated units, the department and the like.
(2) The data integration module: and unifying the formats of the plurality of system databases to a manpower configuration analysis and management database of the power system by adopting extraction, conversion and loading methods. Preferably, the ETL tool is used for extracting data to the data analysis platform monthly, and data quality and system resource utilization rate indexes are monitored.
Furthermore, the module mainly performs calculation statistics and data integration on historical service data such as reservation information, personal data, education information, organization information, post information, staff statistics, professional information and the like in a data source, and realizes extraction and collection of structured data (relational database records), semi-structured data (logs, mails and the like), unstructured data (files, videos, audios, network data streams and the like) from systems such as an ERP (enterprise resource planning) person resource centralized deployment system, a national network recruitment platform, a national network reservation system, a personnel management information system and the like through tools such as ETL (extract transform and load) and data replication, and realizes real-time and non-real-time collection of data and the like to access a data warehouse (MPP). And meanwhile, the system is responsible for storing mass data, is characterized by mass scale storage and rapid query reading aiming at the full data type and various calculation requirements, stores various data extracted and collected in the data integration stage, and supports the advanced application of the data processing layer.
Generally, unstructured data are stored in a distributed file system, semi-structured data are stored by using a column database or a key value database, structured data are stored by using a line storage database, and data with high real-time performance and high calculation performance requirements are stored in an in-memory database or a real-time database. The method provides the calculation functions of stream calculation, batch calculation, memory calculation, query calculation and the like for diversified big data, and allows the query and calculation of data files or memory data stored in a distributed mode. Based on the batch computing component, the data is periodically preprocessed to form a wide table to support subsequent data mining analysis. And unifying the formats of the multiple system databases to a manpower configuration analysis and management database of the power system by adopting extraction, conversion and loading methods.
(3) A knowledge base module: the post flow data is obtained through a human resource management system, a post and professional knowledge base is established by combining basic data of people, the personnel characteristics and the post description in the data are timely updated in the knowledge base, and the post configuration rule of the power grid personnel is determined according to the category and the historical data in the knowledge base.
(4) A data processing module: and selecting a proper data range to process the data according to the distribution condition of the data, wherein the data is standardized and characterized after processing. And determining the support degree and the confidence degree of each item in each personnel flow data through an Apriori algorithm. Each piece of data flowing among the personnel posts is taken as a sample, and the learning history, the age, the gender, the professional technical qualification and the skill level are taken as the characteristics of the personnel, so that the original data are converted into post-related sample data.
(5) A data center analysis module: the data center analysis module is a core module of the power grid human configuration application system, is used for being connected with other modules, and realizes the optimal configuration of human resources of the power system on the basis of meeting the power grid stability management by acquiring the personnel basic information, the personnel characteristics and the flow data in real time. And completing personnel configuration analysis, talent demand prediction analysis, personnel training prediction and analysis and the like. And optimizing, adjusting and controlling the demand degree of the post personnel at different time scales, and realizing the global balance of electric power and human resources and the lean management of the whole power grid.
(6) A data display module: and displaying the query result and the visual display analysis result, displaying the requirements and the variation trend of each professional in the future by taking the professional of the staff as an analysis angle, performing comparative analysis on the age structure, the academic structure and the post level mechanism of each unit staff, visually displaying the configuration condition of each professional according to the unit category, the staff category, the age structure, the academic structure, the professional technical qualification, the skill level and the job level, predicting the requirement degree of the staff in the future, and providing auxiliary support for a company decision layer.
(7) An instruction issuing module: for issuing instructions.
Example 2
As shown in FIG. 2, the invention provides a power grid manpower configuration method, which is used for mining potential information in staff flow data among posts, establishing a matching model for post characteristics and staff characteristics by adopting an artificial neural network algorithm, reasonably matching the posts of the staff and accurately predicting the number of the posts. The general rule and special case of talent requirements of each post are mastered by analyzing and summarizing historical staff flow information and historical data of each post, analyzing post characteristics, and carrying out statistical analysis on characteristic data closely linked with basic staff information. The occurrence frequency of the staff flow under different academic calendars, different professions and different skill levels is detected through methods such as variance analysis, cross validation and the like, the output result is the distribution characteristics of the number of the posts in different dimensions and the post categories or the post categories with obvious differences in professional distribution, and support is provided for the general rule of mastering the post distribution characteristics of the staff as a whole. And establishing a standard of normal circulation between the posts according to the index fluctuation. In the post with normal flowing, each index can fluctuate in a reasonable interval, and the post without personnel change in a certain time period is analyzed to establish the standard of the flowing state of normal personnel in the post. Firstly, index data with normal flowing state is input, and a centralized distribution interval of each index is researched through a data mining algorithm such as correlation analysis. The output result is a reasonable value range of each index in the normal running state. Before personnel loss occurs, a post can present certain precursor characteristics on the aspect of operation indexes, the relationship between post index characteristics and personnel change occurrence is researched by constructing a relationship model between outflow and inflow types of various personnel and the operation indexes, and personnel circulation precursor characteristics are identified.
The method specifically comprises the following steps:
the method comprises the following steps: the data source module collects basic information data of personnel, wherein the basic information data of the personnel comprises the age, the sex, the specialty, the highest academic history, the post category, the post grade, the skill grade, the title, the time of employment, the working age, the affiliated unit, the department and the like of the personnel.
Step two: the extraction, conversion and loading method unifies the formats of the plurality of system databases into the manpower configuration analysis and management database of the power system. Preferably, the ETL tool is used for extracting data to the data analysis platform monthly, and the indexes of data quality, system resource utilization rate, memory utilization rate, database space utilization rate, system average response time and the like are monitored.
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: and selecting a proper data range to process the data according to the distribution condition of the data, wherein the data is standardized and characterized after processing. And 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 flowing among the personnel posts as a sample, taking the academic history, the age, the sex, the professional technical qualification and the skill level as the characteristics of the personnel, and converting the original data into post-related sample data;
step five: setting a confidence threshold, and regularly extracting characteristics such as professional, skill level, academic calendar, professional technical qualification and the like of a person and rules for configuring large, medium and small posts according to the rules of sample data;
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 unit category, personnel category, age structure, academic structure, professional technical qualification, skill level and job level information, and predicting the personnel configuration trend according to the job level;
step nine: inputting basic information of personnel;
step ten: and issuing the notice according to the rule.
Example 3
As shown in FIG. 3, the invention provides a method for predicting the flow trend of electric power and manpower, which is used for mining potential information in personnel flow data among posts and units, 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. The general rules and special cases of talent requirements of each post and unit are mastered by analyzing and summarizing historical staff flow information and flow historical data among posts and units, analyzing flow characteristics, and statistically analyzing characteristic data closely linked with basic staff information. The occurrence frequency of the staff flow under different academic calendars, different professions and different skill levels is detected through methods such as variance analysis, cross validation and the like, the output result is the distribution characteristics of the number of the posts in different dimensions and the post categories or the post categories with obvious differences in professional distribution, and support is provided for the general rule of mastering the post distribution characteristics of the staff as a whole. And establishing a standard of normal circulation between the posts according to the index fluctuation. In the normally flowing position, each index can fluctuate in a reasonable interval, and the normal personnel flowing state standard is constructed by analyzing the position and the unit which are not changed by personnel in a certain time period. Firstly, index data with normal flowing state is input, and a centralized distribution interval of each index is researched through a data mining algorithm such as correlation analysis. The output result is a reasonable value range of each index in the normal running state. Before personnel loss occurs, a post can present certain precursor characteristics on the aspect of operation indexes, the relationship between post index characteristics and personnel change occurrence is researched by constructing a relationship model between outflow and inflow types of various personnel and the operation indexes, and personnel circulation precursor characteristics are identified.
Example 4
As shown in FIG. 4, the invention provides an electric power and manpower training analysis method, which combines power grid personnel with different characteristics with training content and frequency, regularly organizes and arranges personnel advancing training, interacts with a power grid personnel training system through a manpower resource management and control system, and the manpower resource management system can automatically and respectively arrange different personnel in a coordinated manner, so that a conventional personnel training organization is developed into automatic personnel training distribution, and performs relevance analysis on personnel and post characteristics and training content according to the characteristics of personnel posts, the accumulation of working experience and different requirements on personnel capacity to find potential contact between the personnel and the training content. The method overcomes the defect that the existing method only depends on the qualitative distribution of experts or leader experience, fully applies the correlation analysis method in the carding statistics from the factors influencing the training effect of the personnel at different posts, analyzes and calculates a large amount of basic information and training data of the personnel, greatly improves the theoretical support of the method, and can provide related data references for different units, different departments, different posts and different personnel. The invention comprises the following functions:
● The situation of power grid per-person training in the past year is measured, and when a human resource management department is helped to make a reasonable training class, the training effect is better exerted without influencing the daily work of personnel;
● Arranging training courses which are consistent with the personnel according to different characteristics of the personnel to maximize the similar training effect, and making the training courses which are consistent with the characteristics of the personnel;
● And analyzing the association relationship between the training courses and the performance of the personnel, and helping the manager to judge the significance of the effect of the training on the actual work.
● Showing the influence of previous quarter or year training on the enterprise benefit of the next quarter or year;
the power grid personnel training organization based on the correlation analysis meets the constraint conditions of personnel training and assessment methods and the like of each unit and post on the basis of a training plan, and aims at maximizing the training effect, and the method specifically comprises the following steps:
the method comprises the following steps: acquiring basic information of personnel, including professionalism, 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: and selecting a proper data range to process the data according to the distribution condition of the data, wherein the data is standardized and characterized after processing. And 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.
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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