Intelligent personnel distribution system and method based on grid management

Abstract

The invention relates to the technical field of artificial intelligence and computers, in particular to an intelligent personnel distribution system and method based on grid management, and the method comprises the steps: collecting and arranging the basic information of idle labor force of each grid point, and forming a grid database; analyzing and preprocessing the data in the gridding database, extracting key information, and providing a data basis for intelligent distribution; the enterprise issues specific employment demand information on the platform; the system uses an AI algorithm and a big data algorithm to quickly match the employment demand with the labor force information in the gridding database, and performs nearby pushing based on the geographic position; and delivering the successfully matched employment information to the mobile phone of each grid administrator. The problem of employment of rural labor is solved, and income increase of farmers and rural economic development are promoted. Meanwhile, a stable and reliable labor source is provided for enterprises, and smooth production is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and computer technology, and in particular to a personnel intelligent allocation system and method based on grid management. Background Art

[0002] The traditional method of personnel allocation is first guided by job requirements. Companies will clarify the number, skills and qualities of the required personnel based on their own development strategy, business scale and specific requirements of the position. After clarifying the job requirements, companies will publish recruitment information through various channels to attract potential job seekers. Companies will evaluate whether job seekers' professional skills, work experience, and comprehensive qualities meet the job requirements.

[0003] However, in rural areas, especially remote areas, the collection and release of employment information is difficult to effectively cover all farmers, and the popularity of new media is still relatively low, and farmers' channels for obtaining information are still limited.

[0004] Due to the information asymmetry between rural migrant workers and enterprises, it is difficult for rural migrant workers to find jobs and for enterprises to hire workers. Summary of the invention

[0005] The purpose of the present invention is to provide a personnel intelligent allocation system and method based on grid management, which solves the problem that it is difficult for rural workers to find jobs and for enterprises to hire workers due to information asymmetry between rural workers and enterprises.

[0006] To achieve the above object, the present invention provides a personnel intelligent allocation method based on grid management, comprising the following steps:

[0007] Collect and organize basic information on the labor force in each village group to form a grid database;

[0008] Analyze and preprocess the data in the grid database to extract key information and prepare for intelligent allocation;

[0009] Enterprises publish specific employment demand information on the platform;

[0010] AI algorithms and big data algorithms are used to quickly match labor demand with labor information in the grid database, and push jobs nearby based on geographic location;

[0011] The successfully matched employment information will be sent to the mobile phones of the village administrators.

[0012] Among them, the basic information of the living labor force of each village group is collected and sorted to form a grid database, and the steps also include:

[0013] Organize investigators to go deep into each village group to collect basic information on the labor force through face-to-face interviews and questionnaires, and use the village group's existing archives, census data, and social security system to collect information through multiple channels. Compare and verify the data collected through multiple channels, and provide information officers with a port for entering idle labor;

[0014] Clean and deduplicate the collected data to remove invalid, erroneous and duplicate data;

[0015] Import the cleaned data into the grid database and create indexes for the key fields in the grid database.

[0016] The data in the grid database is analyzed and preprocessed to extract key information in preparation for intelligent allocation. The steps also include:

[0017] Conduct preliminary quality assessment of data in the grid database, check the completeness, accuracy, consistency and timeliness of the data, and identify and handle errors, missing values ​​and outliers in the data;

[0018] Integrate data from different channels or at different time points to form a unified data set, and clean the data;

[0019] Standardize the data;

[0020] Extract key information on the age, skills and location of the workforce;

[0021] Use statistical analysis and data mining techniques to conduct in-depth analysis of data in grid databases to identify patterns, trends and correlations in the data.

[0022] The enterprise publishes specific employment demand information on the platform, and the steps also include:

[0023] Enterprises submit employment demand information, including detailed information on job type, skill requirements, work location, working hours, and salary and benefits;

[0024] The submitted employment demand information will be verified to check its completeness, rationality and legality. Once approved, the employment demand information will be included in the database.

[0025] Among them, AI algorithms and big data algorithms are used to quickly match employment needs with labor information in the grid database, and push them nearby based on geographic location. The steps also include:

[0026] Collect comprehensive labor information from the grid database, and collect and update the employment demand information published by enterprises on the platform in real time;

[0027] Using AI algorithms, we can screen out suitable candidates by analyzing the matching degree between key elements of employment demand and labor information, and use big data analysis technology to deeply mine historical data of employment demand and labor information to discover potential trends and patterns;

[0028] Use GIS technology to accurately locate labor demand and the geographic location of the workforce;

[0029] According to the geographical location of employment demand and the distribution of labor, spatial analysis algorithms are used to perform nearby matching;

[0030] The matching results will be sorted by distance, and candidates who are closer to the employment location will be given priority.

[0031] The successfully matched employment information is delivered to the mobile phones of the village group administrators. The steps also include:

[0032] The matching results will be promptly notified to the administrators of each village group through SMS, email, and APP push;

[0033] Each village group administrator checks the list of pushed personnel and calls to confirm whether the pushed personnel can go to work and confirm the method of going;

[0034] Organize and push personnel to the designated center to gather and go to the employment location.

[0035] A personnel intelligent allocation system based on grid management, comprising a worker end, an enterprise end and an allocation end, wherein the worker end is connected to the allocation end, and the enterprise end is connected to the allocation end;

[0036] The migrant worker terminal is used to collect and organize the basic information of the living labor force in each village group to form a grid database;

[0037] The enterprise side is used for enterprises to publish specific employment demand information on the platform;

[0038] The distribution end is used to analyze and preprocess the data in the grid database, extract key information, and use AI algorithms and big data algorithms to quickly match employment needs with the labor information in the grid database, and push the matching employment information nearby based on the geographic location, and deliver it to the mobile phones of the administrators of each village group.

[0039] The present invention discloses a personnel intelligent allocation system and method based on grid management, which collects basic information of the living labor force from each village group, including but not limited to age, gender, skills, work experience, place of residence, etc., and then organizes and constructs this information into a grid database, ensuring the comprehensiveness and systematicness of the data, and providing a solid foundation for subsequent analysis and matching. The data in the grid database is deeply analyzed and preprocessed to extract key information that is crucial to intelligent allocation, such as professional skills, work experience, available working time, etc., to improve the accuracy and effectiveness of the data, so as to more accurately match the employment needs. Enterprises publish specific employment demand information on the platform, including job type, skill requirements, work location, working hours, etc. The openness and transparency of this information enables both the supply and demand sides of the labor force to connect more effectively. Using advanced AI algorithms and big data algorithms, the employment needs are quickly matched with the labor information in the grid database. In the matching process, not only hard conditions such as skills and experience are considered, but also nearby push is performed based on geographical location to reduce commuting time and cost, thereby achieving the optimal allocation of labor resources and improving employment efficiency. The successfully matched employment information will be delivered to the mobile phones of the village administrators in a timely manner. The administrators will be responsible for notifying the corresponding labor force. At the same time, feedback information will be collected to evaluate and optimize the matching effect to form a closed-loop management. Rapid matching through AI algorithms and big data algorithms greatly improves the matching efficiency of both labor supply and demand, reduces information asymmetry and search costs. The push strategy based on geographical location enables a more reasonable allocation of labor resources, reduces commuting time and costs, and improves work efficiency. The openness and transparency of employment demand information enables both labor supply and demand to understand each other's needs and conditions more clearly, and enhances the symmetry and credibility of information. Through grid management, labor information is included in a unified management platform, which facilitates the government and enterprises to effectively manage and supervise labor resources, and improves the scientificity and standardization of management. This method helps to solve the employment problem of rural labor, promote farmers' income growth and rural economic development. At the same time, it also provides enterprises with a stable and reliable source of labor and ensures the smooth progress of production. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0041] Figure 1 It is a flowchart of the steps of the intelligent personnel allocation method based on grid management according to the first embodiment of the present invention.

[0042] Figure 2 It is a flowchart of the steps of collecting and organizing the basic information of the living labor force of each village group to form a grid database in the first embodiment of the present invention.

[0043] Figure 3 It is a flowchart of the steps of the first embodiment of the present invention, which uses AI algorithms and big data algorithms to quickly match employment needs with labor information in a grid database, and pushes them nearby based on geographic location.

[0044] Figure 4 It is a principle block diagram of a personnel intelligent allocation system based on grid management according to the second embodiment of the present invention.

[0045] In the figure: 201 – worker end, 202 – enterprise end, 203 – distribution end. DETAILED DESCRIPTION

[0046] Embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0047] The first embodiment of the present application is:

[0048] See also Figures 1 to 3 ,in, Figure 1 It is a flowchart of the steps of the intelligent personnel allocation method based on grid management according to the first embodiment of the present invention. Figure 2 It is a flowchart of the steps of collecting and organizing the basic information of the living labor force of each village group to form a grid database in the first embodiment of the present invention. Figure 3 It is a flowchart of the steps of the first embodiment of the present invention, which uses AI algorithms and big data algorithms to quickly match employment needs with labor information in a grid database, and pushes them nearby based on geographic location.

[0049] The present invention provides a personnel intelligent allocation method based on grid management, comprising the following steps:

[0050] S101: Collect and organize basic information on the labor force of each village group to form a grid database;

[0051] S1011: Organize investigators to go deep into villages and groups, collect basic information on the labor force through face-to-face interviews and questionnaires, and use the existing archives, census data, and social security system to collect information through multiple channels. Compare and verify the data collected through multiple channels, and provide a portal for information officers to enter idle labor;

[0052] S1012: Clean and deduplicate the collected data to remove invalid data, erroneous data, and duplicate data;

[0053] S1013: Import the cleaned data into the grid database, and create indexes for key fields in the grid database.

[0054] Specifically, organize investigators to go deep into each village and group, and collect basic information on the labor force through face-to-face interviews, filling out questionnaires, etc. In addition to field surveys, you can also use the village and group's existing archival materials, census data, social security systems and other channels to collect information, compare and verify the data collected from multiple channels to ensure the accuracy and completeness of the data, enter the collected original data into a spreadsheet or database, clean and deduplicate the entered data, eliminate invalid data, erroneous data and duplicate data, supplement or mark missing data for consideration in subsequent processing, design a reasonable database structure based on the content and needs of data collection, determine data tables, fields, data types, etc., ensure that the database can efficiently store and query data, import the cleaned data into a grid database, classify, encode and mark the data to achieve grid management, establish indexes for key fields in the database to improve query efficiency, establish associations between data tables as needed, and achieve cross-query and analysis of data.

[0055] S102: Analyze and preprocess the data in the grid database to extract key information and prepare for intelligent allocation;

[0056] Specifically, detect and handle missing values ​​in the data by deleting or interpolating, such as using the mean, median, or other methods. Identify and handle outliers, choose to delete outliers or replace them with reasonable values, or use statistical methods and visualization tools, such as box plots, to identify them. Detect and delete duplicate records in the data to ensure the uniqueness of the data. Scale numerical features to ensure that they are on the same scale to prevent certain features from having too much influence on the model. Convert categorical variables to binary vectors for use in the model. Converting continuous data to discrete data can sometimes improve the performance of the model. According to the analysis objectives, select features related to the target variable to reduce computational complexity and improve the interpretability of the model. Create new features by combining, transforming, or extracting original features to improve model performance. Integrate data from different data sources to form a consistent data set. Pay attention to handling problems such as homonymy, synonymy, and data redundancy. Calculate statistics such as the mean, variance, and standard deviation of the data to understand the distribution of the data. Use association rule mining algorithms to analyze the association between different features in the data. For time series data, trend analysis is performed to predict future trends and patterns. Data is divided into several clusters with similarities to better understand data distribution and characteristics. Key areas or high-risk areas in the gridded database are identified through visualization methods such as cluster analysis or heat maps. Based on the analysis results, resource requirements such as manpower and material resources are evaluated for each area or grid. Methods such as time series analysis are used to predict resource requirements or risk trends over a period of time in the future.

[0057] S103: Enterprises publish specific employment demand information on the platform;

[0058] Specifically, enterprises submit employment demand information through the platform, including detailed information such as job type, skill requirements, work location, working hours, salary and benefits. The platform verifies the submitted employment demand information, checks its completeness, rationality and legality, and after the review is passed, the employment demand information will be included in the database of the intelligent allocation system.

[0059] S104: Use AI algorithms and big data algorithms to quickly match labor demand with labor information in the grid database, and push it to nearby locations based on geographic location;

[0060] S1041: Collect comprehensive labor information from the grid database, and collect and update the employment demand information published by enterprises on the platform in real time;

[0061] S1042: Use AI algorithms to screen suitable candidates by analyzing the matching degree between key elements of employment demand and labor information, and use big data analysis technology to conduct in-depth mining of historical data on employment demand and labor information to discover potential trends and patterns;

[0062] S1043: Use GIS technology to accurately locate labor demand and the geographic location of the workforce;

[0063] S1044: Based on the geographical location of the employment demand and the distribution of the labor force, spatial analysis algorithms are used to perform nearby matching;

[0064] S1045: Sort the matching results by distance, and give priority to pushing candidates who are closer to the employment location.

[0065] Specifically, AI algorithms, such as machine learning and deep learning models, are used to quickly match employment needs with labor information. By analyzing the key elements of employment needs, such as job types, skill requirements, salary and benefits, and the matching degree with labor information, suitable candidates can be screened out. Big data analysis technology is used to deeply mine the historical data of employment needs and labor information to discover potential trends and patterns. These patterns are used to optimize matching algorithms and improve the accuracy and efficiency of matching. GIS (geographic information system) technology is used to accurately locate the geographical location of employment needs and labor, including building a spatial database to store and manage data related to geographical location. According to the geographical location of employment needs and the distribution of labor, spatial analysis algorithms, such as K-means clustering and Voronoi diagrams, are used to perform nearby matching, sort the matching results by distance, and give priority to pushing candidates who are closer to the employment location. A real-time push system is established. When new employment needs are released or labor information is updated, the matching and push process is immediately triggered, and the matching results are promptly notified to enterprises and candidates through SMS, email, APP push, etc. Track and evaluate push results and collect feedback from companies and candidates. Analyze push results, including matching success rate, response time, satisfaction and other indicators. Based on the evaluation results, continuously optimize and adjust AI algorithms and big data algorithms.

[0066] S105: Send the successfully matched employment information to the mobile phones of the village group administrators.

[0067] Specifically, the matching results will be promptly notified to the village administrators through SMS, email, and APP push. The village administrators will check the list of pushed personnel, confirm by phone whether the pushed personnel can go to work and confirm the way to go, and organize the pushed personnel to gather at the designated center to go to the employment location.

[0068] A personnel intelligent allocation method based on grid management of this embodiment is used to quickly match through AI algorithm and big data algorithm, which greatly improves the matching efficiency of both labor supply and demand sides and reduces information asymmetry and search costs. The push strategy based on geographical location enables a more reasonable allocation of labor resources, reduces commuting time and cost, and improves work efficiency. The openness and transparency of labor demand information enables both labor supply and demand sides to understand each other's needs and conditions more clearly, and enhances the symmetry and credibility of information. Through grid management, labor information is included in a unified management platform, which facilitates the government and enterprises to effectively manage and supervise labor resources, and improves the scientificity and standardization of management. This method helps to solve the employment problem of rural labor, promote farmers' income and rural economic development. At the same time, it also provides enterprises with a stable and reliable source of labor, ensuring the smooth progress of production.

[0069] The second embodiment of the present application is:

[0070] Based on the first embodiment, please refer to Figure 4 ,in, Figure 4 The second embodiment of the present invention is a schematic diagram of a personnel intelligent allocation system based on grid management. The second embodiment of the present invention is a personnel intelligent allocation system based on grid management, including a worker terminal 201, an enterprise terminal 202 and an allocation terminal 203.

[0071] According to this specific implementation, the worker end 201 is connected to the distribution end 203, and the enterprise end 202 is connected to the distribution end 203;

[0072] The migrant worker terminal 201 is used to collect and organize the basic information of the living labor force in each village group to form a grid database;

[0073] The enterprise end 202 is used for the enterprise to publish specific employment demand information on the platform;

[0074] The distribution terminal 203 is used to analyze and preprocess the data in the grid database, extract key information, and use AI algorithms and big data algorithms to quickly match employment needs with the labor information in the grid database, and push the matching employment information to the mobile phones of the village group administrators based on the geographic location.

[0075] Using a personnel intelligent allocation system based on grid management of this embodiment, each village group administrator uploads the basic information of the living labor force of each village group on the worker end 201 to form a grid database, and the enterprise publishes specific labor demand information on the enterprise end 202. The allocation end 203 collects comprehensive labor information from the grid database, and collects and updates the labor demand information published by the enterprise on the platform in real time. By using AI algorithms, the matching degree between the key elements of labor demand and labor information is analyzed to screen out suitable candidates, and the big data analysis technology is used to deeply mine the historical data of labor demand and labor information to discover potential trends and laws. The GIS technology is used to accurately locate the geographical location of labor demand and labor. According to the geographical location of labor demand and the distribution of labor, the spatial analysis algorithm is used to match nearby, and the matching results are sorted according to the distance. Candidates closer to the employment location are pushed first, and the successfully matched employment information is sent to the mobile phones of the village group administrators. Rapid matching through AI algorithms and big data algorithms greatly improves the matching efficiency of both the supply and demand sides of labor, and reduces information asymmetry and search costs. The push strategy based on geographic location enables a more reasonable allocation of labor resources, reduces commuting time and costs, and improves work efficiency. The openness and transparency of labor demand information enables both labor supply and demand parties to understand each other's needs and conditions more clearly, enhancing the symmetry and credibility of information. By incorporating labor information into a unified management platform through grid management, it is convenient for governments and enterprises to effectively manage and supervise labor resources, and improves the scientificity and standardization of management. This method helps solve the employment problem of rural labor, promote farmers' income growth and rural economic development. At the same time, it also provides enterprises with a stable and reliable source of labor, ensuring the smooth progress of production.

[0076] What is disclosed above is only one or more preferred embodiments of the present application, and cannot be used to limit the scope of rights of the present application. Ordinary technicians in this field can understand that all or part of the processes of implementing the above embodiments and equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A personnel intelligent allocation method based on grid management, characterized in that: The following steps are involved: Collect and organize basic information on the labor force in each village group to form a grid database; Analyze and preprocess the data in the grid database to extract key information and prepare for intelligent allocation; Enterprises publish specific employment demand information on the platform; AI algorithms and big data algorithms are used to quickly match labor demand with labor information in the grid database, and push jobs nearby based on geographic location; The successfully matched employment information will be sent to the mobile phones of the village administrators.

2. The intelligent personnel allocation method based on grid management as claimed in claim 1, characterized in that: Collect and organize the basic information of the labor force in each village group to form a grid database. The steps also include: Organize investigators to go deep into each village group to collect basic information on the living labor force through face-to-face interviews and filling out questionnaires, and use the village group's existing archives, census data, and social security system to collect information through multiple channels. Compare and verify the data collected through multiple channels, and provide information officers with a port for entering idle labor; Clean and deduplicate the collected data to remove invalid, erroneous and duplicate data; Import the cleaned data into the grid database and create indexes for the key fields in the grid database.

3. The intelligent personnel allocation method based on grid management as claimed in claim 2, characterized in that: Analyze and preprocess the data in the grid database to extract key information and prepare for intelligent allocation. The steps also include: Conduct preliminary quality assessment of data in the grid database, check the completeness, accuracy, consistency and timeliness of the data, and identify and handle errors, missing values ​​and outliers in the data; Integrate data from different channels or at different time points to form a unified data set, and clean the data; Standardize the data; Extract key information on the age, skills and location of the workforce; Use statistical analysis and data mining techniques to conduct in-depth analysis of data in grid databases to identify patterns, trends and correlations in the data.

4. The intelligent personnel allocation method based on grid management as claimed in claim 3 is characterized in that: The enterprise publishes specific employment demand information on the platform, and the steps also include: Enterprises submit employment demand information, including detailed information on job type, skill requirements, work location, working hours, and salary and benefits; The submitted employment demand information will be verified to check its completeness, rationality and legality. Once approved, the employment demand information will be included in the database.

5. The intelligent personnel allocation method based on grid management as claimed in claim 4, characterized in that: AI algorithms and big data algorithms are used to quickly match labor demand with labor information in the grid database, and push it nearby based on geographic location. The steps also include: Collect comprehensive labor information from the grid database, and collect and update the employment demand information published by enterprises on the platform in real time; Using AI algorithms, we can screen out suitable candidates by analyzing the matching degree between key elements of employment demand and labor information, and use big data analysis technology to deeply mine historical data of employment demand and labor information to discover potential trends and patterns; Use GIS technology to accurately locate labor demand and the geographic location of the workforce; According to the geographical location of employment demand and the distribution of labor, spatial analysis algorithms are used to perform nearby matching; The matching results will be sorted by distance, and candidates who are closer to the employment location will be given priority.

6. The intelligent personnel allocation method based on grid management as claimed in claim 5, characterized in that: The successfully matched employment information is sent to the mobile phones of the village group administrators. The steps also include: The matching results will be promptly notified to the administrators of each village group through SMS, email, and APP push; Each village group administrator checks the list of pushed personnel and calls to confirm whether the pushed personnel can go to work and confirm the method of going; Organize and push personnel to the designated center to gather and go to the employment location.

7. A personnel intelligent allocation system based on grid management, applicable to the personnel intelligent allocation method based on grid management as claimed in claim 1, characterized in that: It includes a worker end, an enterprise end and a distribution end, wherein the worker end is connected to the distribution end, and the enterprise end is connected to the distribution end; The migrant worker terminal is used to collect and organize the basic information of the living labor force in each village group to form a grid database; The enterprise side is used for enterprises to publish specific employment demand information on the platform; The distribution end is used to analyze and preprocess the data in the grid database, extract key information, and use AI algorithms and big data algorithms to quickly match employment needs with the labor information in the grid database, and push the matching employment information nearby based on the geographic location, and deliver it to the mobile phones of the administrators of each village group.

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