Human resource intelligent distribution system based on big data

Abstract

The invention discloses a human resource intelligent distribution system based on big data, which comprises a factory division unit, a data acquisition unit, a network transmission unit, a preprocessing unit, a data processing unit and a remote monitoring unit, and belongs to the technical field of human resources. Accurate evaluation of the talent grade, the performance index and the score index of the job hunters is realized through collecting the college entrance examination ranking, the graduation thesis total score, the actual on-duty and off-duty time and the actual age of the job hunters and the income multi-dimensional information data created for the enterprise factory in real time and through preprocessing and deep analysis of a formula algorithm; and through the intelligent distribution unit, the analyzed personnel are distributed to the enterprise factory area which is most matched with the ability, performance and income contribution of the analyzed personnel to work, so that the problem of demission caused by incapability of adapting to post work due to lack of understanding of job hunters on the working posts in the traditional human resource distribution process is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of human resources, and more specifically discloses a human resources intelligent allocation system based on big data. Background Art

[0002] Human resources are personnel. The broadest definition refers to human resource management, which includes six major modules: human resource planning, recruitment, training, performance, compensation and labor relations. It is an important position in a company. The company's recruitment, training, employee assessment, employee compensation and employee transfer are all related to personnel. It also refers to the education, ability, skills, experience, physical strength, etc. that people in an organization possess within a certain period of time that can be used by the enterprise and contribute to value creation. Human resources are also the capabilities that independent business groups of enterprises and institutions need personnel to possess.

[0003] However, in the process of allocating personnel resources, since each job seeker has different job-seeking needs and work abilities, and their expected working hours are different, if the job seekers are only matched based on job-seeking needs during the selection of suitable factories, it is easy to cause job seekers to lack understanding of the job positions, be unable to adapt to the job positions and then leave, resulting in the company having to conduct secondary recruitment in the later stage, which affects the efficiency of the company's human resources utilization. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to solve the problem that in the process of personnel resource allocation, due to the different job-seeking needs and work abilities of each job seeker, and the expected working hours are different, if matching is only carried out based on job-seeking needs during the selection of suitable factories, it is easy to cause job seekers to lack understanding of the job positions, be unable to adapt to the job positions and then resign, resulting in the need for the company to conduct secondary recruitment in the later stage, affecting the efficiency of the company's human resource utilization.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, a human resources intelligent allocation system based on big data includes: a plant division unit, a data acquisition unit, a network transmission unit, a preprocessing unit, a data processing unit and a remote monitoring unit; Plant division unit: used to divide the plant into several areas according to different production objects; Data collection unit: used to collect data generated by the personnel being analyzed during their work in the enterprise factory in real time; Network transmission unit: used for real-time transmission of information data generated by the system; Preprocessing unit: used for preprocessing the information data collected by the data collection unit; Data processing unit: used to perform calculation and analysis on the information data pre-processed by the pre-processing unit, and obtain the calculation and analysis results, and generate a manpower allocation report according to the calculation and analysis results, and forward the obtained calculation and analysis results to the remote monitoring unit; Remote monitoring unit: used to display the information data pre-processed by the pre-processing unit and the analysis results of the data processing unit in real time on the background terminal. At the same time, according to the analysis results of the data processing unit, the analyzed personnel are intelligently allocated to the enterprise factory area.

[0006] Furthermore, the data collection unit includes: a ranking collection module, a score collection module, a time collection module, an age collection module and an income collection module; Ranking collection module: used to collect the college entrance examination ranking information data of the analyzed personnel in real time; Score collection module: used to collect the total score information data of the analyzed person's graduation thesis in real time; Time collection module: used to collect the actual arrival and departure time information data of the analyzed personnel in real time; Age collection module: used to collect the actual age information data of the analyzed person in real time; Revenue collection module: used to collect the revenue information data generated by the analyzed personnel for the enterprise factory in real time.

[0007] Furthermore, the pre-processing unit includes: a data cleaning module, a data conversion module and a data storage module; Data cleaning module: used to remove duplicate records, correct errors and inconsistent data, and remove noise, outliers and duplicate data from the information data collected by the data acquisition unit; Data conversion module: used to convert the information data cleaned by the data cleaning module into a format suitable for analysis and storage; Data storage module: used for real-time storage of information data converted by the data conversion module.

[0008] Furthermore, the data processing unit includes: a data acquisition module, an analysis and judgment module, a report generation module and a result forwarding module; Data acquisition module: used to acquire the information data stored in the data storage module in real time; Analysis and judgment module: used to analyze and judge the information data obtained by the data acquisition module, so as to obtain the results of the analysis and judgment; Report generation module: used to generate a human resource allocation report based on the analysis and judgment results of the analysis and judgment module; Result forwarding module: used to forward the results analyzed and judged by the analysis and judgment module to the remote monitoring unit.

[0009] Furthermore, the remote monitoring unit includes: a data receiving module, a data display module and an intelligent allocation module; Data receiving module: used for receiving the information data stored in the data storage module and the analysis result information of the data processing unit in real time; Data display module: used to display the information data received by the data receiving module on the background terminal display screen; Intelligent allocation module: It is used to intelligently allocate the analyzed personnel to the enterprise factory area according to the analysis and judgment results of the data processing unit, and display the allocation results on the background terminal display screen.

[0010] Furthermore, the analysis and judgment module can obtain the college entrance examination ranking and graduation thesis total score information data of the analyzed person from the data acquisition module, and obtain the talent level through the analysis of the above acquired data: in, For talent level, is the college entrance examination ranking of the person being analyzed, is the total number of college entrance examination candidates in the province where the person being analyzed is located, is the total score of the analyzed person’s graduation thesis, is the conversion factor.

[0011] Furthermore, the analysis and judgment module can obtain the actual arrival and departure time of the analyzed personnel and the actual age information data of the analyzed personnel from the data acquisition module, and obtain the performance index of the analyzed personnel through the analysis of the above acquired data: in, is the performance index of the person being analyzed, is the total time for data analysis. For the The difference between the actual arrival time of the analyzed personnel and the arrival time stipulated by the enterprise, For The latest time for the analyzed personnel to leave their jobs during a normal working day. For The earliest time that the analyzed personnel leave their posts on a normal working day within the time period. is the actual age of the person being analyzed, The statutory retirement age.

[0012] Furthermore, the analysis and judgment module can obtain the revenue data generated by the analyzed personnel for the enterprise plant from the data acquisition module, and obtain a score index through the analysis of the above acquired data: in, For the analyzed persons The score index of each enterprise factory area, is the total time for data analysis. For the analyzed personnel in a single enterprise plant The income created by the enterprise factory every day, For all factories of the enterprise The average revenue generated over time, is the performance index of the person being analyzed, For talent level, is the comprehensive score index of the person being analyzed, Is a mathematical constant.

[0013] The beneficial effects of the human resources intelligent allocation system based on big data of the present invention are: by real-time collection of multi-dimensional information data of job seekers' college entrance examination rankings, total scores of graduation thesis, actual arrival and departure times, actual age, and the income created for the enterprise plant, and through pre-processing and in-depth analysis of formula algorithms, accurate evaluation of the talent level, performance index and score index of job seekers is achieved, and then through the intelligent allocation unit, the analyzed personnel are allocated to the enterprise plant that best matches their ability, performance and income contribution, effectively solving the problem of resignation caused by job seekers' lack of understanding of jobs and inability to adapt to job jobs in the traditional human resources allocation process, significantly improving the utilization efficiency of enterprise human resources, reducing the cost of secondary recruitment, and also providing job seekers with more suitable jobs, promoting their personal career development. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0015] Figure 1 This is a schematic diagram of the system principle. DETAILED DESCRIPTION

[0016] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0017] According to one aspect of the present invention, Figure 1As shown, a human resource intelligent allocation system based on big data is provided. First, the enterprise factory is divided into several areas according to different production objects through the factory division unit, so as to facilitate the analyzed personnel to perform adaptive work in different enterprise factories. Then, the ranking collection module, score collection module, time collection module, age collection module and income collection module are used to collect the college entrance examination ranking, total score of graduation thesis, actual arrival and departure time, actual age and income information data created by the analyzed personnel for the enterprise factory in real time.

[0018] Then, the collected data is transmitted to the preprocessing unit in real time through the network transmission unit. Through the data cleaning module and the data conversion module, the collected information data is subjected to the removal of duplicate records, correction of errors and inconsistent data, removal of noise, abnormal values ​​and duplicate data, and the information data cleaned by the data cleaning module is converted into a format suitable for analysis and storage, including data type conversion, data format standardization, and data structure adjustment. The converted information data is then stored in the data storage module, and then the information data is obtained from the data storage module through the data acquisition module in the data analysis unit. Furthermore, the obtained information data is processed and analyzed through the analysis and judgment module.

[0019] Afterwards, the data receiving module in the remote monitoring unit can receive the information data stored in the data storage module and the analysis results of the data processing unit in real time, and display the received information data on the background terminal display screen through the data display module.

[0020] The analysis and judgment module can obtain the college entrance examination ranking and graduation thesis total score information data of the analyzed personnel from the data acquisition module, and analyze the above acquired data to obtain the talent level: in, For talent level, is the college entrance examination ranking of the person being analyzed, is the total number of college entrance examination candidates in the province where the person being analyzed is located, The total score of the thesis of the person being analyzed. If there is no thesis score, it will be treated as zero. is the conversion coefficient, and its value is 0.01.

[0021] The analysis and judgment module can obtain the actual arrival and departure time of the analyzed personnel and the actual age information data of the analyzed personnel from the data acquisition module, and obtain the performance index of the analyzed personnel through the analysis of the above acquired data: in, is the performance index of the person being analyzed, is the total time for data analysis. For the The difference between the actual arrival time of the analyzed personnel and the arrival time stipulated by the enterprise, For The latest time for the analyzed personnel to leave their jobs during a normal working day. For The earliest time that the person being analyzed leaves work on a normal working day, and the leave does not fall within the scope of a normal working day. is the actual age of the person being analyzed, The current statutory retirement age for employees in my country is 60 for men, 55 for female cadres, and 50 for female workers.

[0022] The analysis and judgment module can obtain the revenue data generated by the analyzed personnel for the enterprise plant from the data acquisition module, and obtain the score index through the analysis of the above acquired data: The intelligent allocation unit is used to intelligently allocate the analyzed personnel to the enterprise factory area. Specifically, The highest score of the analyzed personnel is obtained, and the analyzed personnel are assigned to work in the enterprise plant with the highest score. For the analyzed persons The score index of each enterprise factory area, is the total time for data analysis. For the analyzed personnel in a single enterprise plant The income created by the enterprise factory every day, For all factories of the enterprise The average revenue generated over time, is the performance index of the person being analyzed, For talent level, is the comprehensive score index of the person being analyzed, Is a mathematical constant.

[0023] The above-mentioned embodiment only expresses one implementation mode of the present invention, and its description is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A human resources intelligent allocation system based on big data, characterized in that: include: Plant division unit, data acquisition unit, network transmission unit, pre-processing unit, data processing unit and remote monitoring unit; Plant division unit: used to divide the plant into several areas according to different production objects; Data collection unit: used to collect data generated by the personnel being analyzed during their work in the enterprise factory in real time; Network transmission unit: used for real-time transmission of information data generated by the system; Preprocessing unit: used for preprocessing the information data collected by the data collection unit; Data processing unit: used to calculate and analyze the information data pre-processed by the pre-processing unit, and obtain the calculation and analysis results, and generate a manpower allocation report according to the calculation and analysis results, and forward the obtained calculation and analysis results to the remote monitoring unit; Remote monitoring unit: used to display the information data pre-processed by the pre-processing unit and the analysis results of the data processing unit in real time on the background terminal. At the same time, according to the analysis results of the data processing unit, the analyzed personnel are intelligently allocated to the enterprise factory area.

2. According to the big data-based human resources intelligent allocation system of claim 1, it is characterized by: The data collection unit includes: a ranking collection module, a score collection module, a time collection module, an age collection module and an income collection module; Ranking collection module: used to collect the college entrance examination ranking information data of the analyzed personnel in real time; Score collection module: used to collect the total score information data of the analyzed person's graduation thesis in real time; Time collection module: used to collect the actual arrival and departure time information data of the analyzed personnel in real time; Age collection module: used to collect the actual age information data of the analyzed person in real time; Revenue collection module: used to collect the revenue information data generated by the analyzed personnel for the enterprise's factory in real time.

3. According to the big data-based human resources intelligent allocation system of claim 2, it is characterized by: The pre-processing unit includes: a data cleaning module, a data conversion module and a data storage module; Data cleaning module: used to remove duplicate records, correct errors and inconsistent data, and remove noise, outliers and duplicate data from the information data collected by the data acquisition unit; Data conversion module: used to convert the information data cleaned by the data cleaning module into a format suitable for analysis and storage; Data storage module: used for real-time storage of information data converted by the data conversion module.

4. The human resources intelligent allocation system based on big data according to claim 3 is characterized by: The data processing unit includes: a data acquisition module, an analysis and judgment module, a report generation module and a result forwarding module; Data acquisition module: used to acquire the information data stored in the data storage module in real time; Analysis and judgment module: used to analyze and judge the information data obtained by the data acquisition module, so as to obtain the results of the analysis and judgment; Report generation module: used to generate a human resource allocation report based on the analysis and judgment results of the analysis and judgment module; Result forwarding module: used to forward the results analyzed and judged by the analysis and judgment module to the remote monitoring unit.

5. The human resources intelligent allocation system based on big data according to claim 4 is characterized by: The remote monitoring unit includes: a data receiving module, a data display module and an intelligent distribution module; Data receiving module: used for receiving the information data stored in the data storage module and the analysis result information of the data processing unit in real time; Data display module: used to display the information data received by the data receiving module on the background terminal display screen; Intelligent allocation module: It is used to intelligently allocate the analyzed personnel to the enterprise factory area according to the analysis and judgment results of the data processing unit, and display the allocation results on the background terminal display screen.

6. The human resources intelligent allocation system based on big data according to claim 5 is characterized by: The analysis and judgment module can obtain the college entrance examination ranking and graduation thesis total score information data of the analyzed person from the data acquisition module, and obtain the talent level through the analysis of the above acquired data: in, For talent level, is the college entrance examination ranking of the person being analyzed, is the total number of college entrance examination candidates in the province where the person being analyzed is located, is the total score of the analyzed person’s graduation thesis, is the conversion factor.

7. The human resources intelligent allocation system based on big data according to claim 6 is characterized by: The analysis and judgment module can obtain the actual arrival and departure time of the analyzed personnel and the actual age information data of the analyzed personnel from the data acquisition module, and obtain the performance index of the analyzed personnel through the analysis of the above acquired data: in, is the performance index of the person being analyzed, is the total time for data analysis. For the The difference between the actual arrival time of the analyzed personnel and the arrival time stipulated by the enterprise, For The latest time for the analyzed personnel to leave their jobs during a normal working day. For The earliest time that the analyzed personnel leave their posts on a normal working day within the time period. is the actual age of the person being analyzed, The statutory retirement age.

8. The human resources intelligent allocation system based on big data according to claim 7 is characterized by: The analysis and judgment module can obtain the revenue data generated by the analyzed personnel for the enterprise plant from the data acquisition module, and obtain the score index through the analysis of the above acquired data: in, For the analyzed persons The score index of each enterprise factory area, is the total time for data analysis. For the analyzed personnel in a single enterprise plant The income created by the enterprise factory every day, For all factories of the enterprise The average revenue generated over time, is the performance index of the person being analyzed, For talent level, is the comprehensive score index of the person being analyzed, Is a mathematical constant.

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