Cross-regional human resource allocation and collaborative management method based on big data

Abstract

This invention discloses a cross-regional human resource allocation and collaborative management method based on big data, belonging to the field of human resource management technology. It aims to address the pain points of existing technologies, such as prominent information barriers, low matching accuracy, and lack of early warning and prevention. This method integrates human resource data from multiple regions and ensures data quality by building a data support system; constructs an early warning and risk prevention system to predict human resource shortages and manage risks; achieves three-dimensional matching of "person, job, and location" through a skill tagging system and dynamic algorithms; forms a "training-allocation" closed loop to adapt to the needs of industrial upgrading; optimizes cost and efficiency control; improves allocation flexibility and emergency response capabilities; and improves the collaborative guarantee mechanism to ensure safety and compliance. It continuously optimizes until optimal allocation is achieved. This invention realizes precise, efficient, collaborative, and safe management of cross-regional human resources, reduces allocation costs, solves the imbalance between human resource supply and demand, and has broad application value.

Description

Technical Field

[0001] This invention relates to the field of human resource management technology, and in particular to a method for cross-regional human resource allocation and collaborative management based on big data. Background Technology

[0002] With the continuous advancement of economic globalization and regional industrial collaborative development, cross-regional human resource allocation has become a core support for solving regional imbalances in human resource supply and demand, optimizing human resource allocation efficiency, and promoting industrial collaborative upgrading. It is widely used in various fields such as industrial manufacturing, services, and public utilities. Currently, significant differences in industrial structure exist between regions, and human resource mobility is becoming increasingly frequent. Enterprises and regional management departments have an increasingly urgent need for precise matching, dynamic regulation, cost control, and emergency response of human resources. However, existing cross-regional human resource allocation and collaborative management technologies still have many shortcomings, making it difficult to meet the demands for high efficiency, precision, and collaboration in practical applications. Specifically: First, the data support system is incomplete, with prominent regional information barriers. Human resource databases in different regions are independent and scattered, lacking a unified collaborative platform and data synchronization mechanism. Data quality is poor, and there is insufficient sharing of human resource information across regions and enterprises, making it impossible to provide reliable data support for allocation. Second, there is a lack of a sound early warning and risk prevention mechanism. Data on human resource supply, mobility rate, and industry changes lack real-time monitoring and early warning, resulting in delayed allocation response. In addition, the allocation model lacks flexibility and emergency response capabilities, failing to adapt to changes in demand and personnel status in a timely manner. When dealing with sudden human resource needs, the efficiency of temporary human resource matching and allocation is low. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing human resource allocation and management methods, and to provide a method that enables cross-regional allocation of human resources and collaborative management of human resources.

[0004] In a first aspect, this invention provides a method for cross-regional human resource allocation and collaborative management based on big data, specifically including the following process: Step 1: Establish a data support system, build a cross-regional human resources collaborative management cloud platform, a cross-regional enterprise human resources collaborative alliance platform, and a cross-regional human resources big data analysis and visualization platform, integrate multi-regional human resources databases, and achieve real-time data synchronization and hierarchical access control; adopt cross-regional human resources data quality control methods, based on big data cleaning and verification technology, to ensure the accuracy, completeness, and timeliness of human resources data; Step Two: Construct a dual system of early warning and risk prevention and control. Employ big data fusion technology to achieve cross-regional human resource gap early warning, monitor regional human resource supply, mobility rate and industry change data in real time, build an early warning database and set a unified gap early warning threshold, predict gaps in advance and output corresponding level early warning signals; through a big data-driven mobility risk assessment model, calculate the risk level and formulate targeted prevention and control strategies, and push the early warning signals to the relevant regional collaborative management terminals simultaneously. Step 3: Construct a human resources skills tagging system and mine hidden skills of personnel through big data; combine big data-driven cross-regional human resources dynamic matching algorithm to achieve optimal three-dimensional matching of "people, positions and places"; Step 4: Construct a closed-loop management system for "training-allocation". Through a cross-regional human resources collaborative training system, based on big data analysis of personnel skill gaps and regional job requirements, training content is accurately pushed; a big data-driven skills upgrade prediction model is used to predict changes in skills requirements brought about by regional industrial upgrading and plan the direction of personnel skills upgrades in advance. Step 5: Implement two-way cost and efficiency control. Through a big data-driven cross-regional human resource cost control model, calculate and dynamically optimize various costs such as transportation, accommodation, salary, and training during the allocation process in real time. Simultaneously, build a big data-integrated allocation efficiency evaluation system, establish multi-dimensional evaluation indicators, and achieve quantitative evaluation of allocation effectiveness. Step Six: Conduct emergency deployment based on the warning thresholds and warning signals from Step Two; establish a dynamic adjustment mechanism for cross-regional human resource deployment based on real-time big data to automatically adapt to changes in regional needs and personnel status; and adopt temporary deployment methods to quickly respond to sudden human resource needs such as emergency rescue and project breakthroughs. Step Seven: Improve the end-to-end collaborative guarantee mechanism, and realize personnel information encryption, identity verification and access control based on big data and blockchain technology; link the early warning system of Step Two and the emergency deployment mechanism of Step Six to automatically trigger cross-regional linkage instructions to alleviate manpower shortages; formulate and dynamically update personalized career plans through big data; and use big data traceability technology to ensure management compliance. Step 8: Repeat the above steps. Through the cross-regional human resources big data analysis and visualization platform, visualize the data such as human resource flow, supply and demand status, and allocation efficiency to assist managers in making scientific decisions until the optimal allocation and collaborative management of cross-regional human resources is achieved, at which point the process terminates.

[0005] Preferably, in step one, the cross-regional human resources collaborative management cloud platform and the cross-regional enterprise human resources collaborative alliance platform operate in conjunction. The former is responsible for the integration and synchronous management of human resources data in multiple regions, while the latter is responsible for the sharing of human resources among multiple enterprises and the complementarity of positions. The specific process of the data quality control method is as follows: S1. Utilize big data cleaning technology to remove duplicate, abnormal, and missing data from human resources data; S2. Through big data verification technology, the accuracy, completeness, and timeliness of the cleaned data are verified; S3. Establish a real-time data update mechanism to ensure that human resources data is synchronized with the actual status of personnel and regional needs.

[0006] Preferably, in step two, the specific process of the human resource gap early warning method is as follows: S1. Collect real-time data on labor supply, labor mobility rate, and industrial changes in various regions to build an early warning database; S2. Analyze the above data using big data fusion algorithms and set a gap warning threshold; S3. Compare the real-time analysis results with the early warning threshold. When the early warning conditions are met, automatically output the corresponding level of early warning signal and push it to the relevant regional collaborative management terminal. The identification process for the liquidity risk assessment model is as follows: S1. Screen risk indicators related to staff turnover, cost overruns, and compliance, and establish a multi-dimensional risk assessment system; S2. Calculate the risk level by weighting and analyzing various risk indicators using big data algorithms; S3. Automatically generate targeted prevention and control strategies based on risk levels for managers to refer to and implement.

[0007] Preferably, in step three, the process of constructing the human resource skill tagging system is as follows: S1. Review the personnel's explicit skills, including education, major, professional qualification certificates, etc., and establish basic skill labels; S2. Through big data mining technology, analyze personnel's work history, practical records, performance data, etc., to uncover potential abilities, practical experience and other implicit skills, and establish implicit skill tags. S3. Classify and grade all skill tags to form a standardized human resource skill tag system; The specific process of the optimal three-dimensional matching of "person, place, and location" is as follows: S1. Collect data on job requirements, personnel skills, personnel location preferences, and salary expectations; S2. Through dynamic matching algorithms, precise matching is achieved between job requirements and personnel skills, personnel location preferences and job areas, and salary expectations and job salary standards. S3. Based on the matching results from the three dimensions, select the optimal matching combination and output the matching solution.

[0008] Preferably, in step four, the specific execution process of the "training-deployment" closed loop is as follows: S1. By analyzing big data, identify the differences between the existing skills of personnel and the regional job requirements to pinpoint the personnel's skill gaps; S2. Develop personalized training content based on skill gaps and organize personnel to conduct training. S3. After the training, the skills of the personnel will be assessed. Those who pass the assessment will be included in the human resources allocation pool through a dynamic matching algorithm for job matching and allocation. Those who fail the assessment will be pushed targeted training content again until they pass the assessment. The prediction process of the skill upgrade prediction model is as follows: S1. Collect data on regional industrial development, industrial upgrading plans, and existing job skill requirements; S2. Analyze the impact of industrial upgrading on job skills through big data algorithms to predict future trends in skill demand. S3. Based on the forecast results, formulate a personnel skills upgrade plan and clarify the direction of skills upgrade and training focus.

[0009] Preferably, in step five, the specific process of dynamic cost optimization is as follows: S1. Use a cost control model to collect real-time data on transportation, accommodation, salary, and training costs during the allocation process; S2. Classify, calculate, and summarize all costs to identify weak links in cost control; S3. Based on the analysis results, automatically generate cost optimization plans, adjust various cost expenditures, and achieve dynamic cost optimization.

[0010] Preferably, in step six, the specific process of dynamically adjusting the allocation plan is as follows: S1. Real-time monitoring of changes in regional job demand, as well as changes in personnel skills, location preferences, and salary expectations; S2. When changes in data are detected, analyze the impact of the changes on the existing allocation plan using big data algorithms; S3. Automatically adjust the allocation plan and update the matching combination to ensure that the allocation plan is adapted to the actual needs and personnel status. The specific process of the temporary human resource allocation method is as follows: S1. Receive signals of sudden manpower needs and clarify the required positions, quantities, and time limits. S2. Quickly search the human resources database using big data to match temporary staff who meet the needs; S3. Quickly generate temporary deployment plans and coordinate personnel who meet the relevant requirements to be on duty.

[0011] Preferably, in step seven, the specific process of encrypting and storing personnel information and quickly verifying their identity is as follows: S1. Construct a personnel information encryption storage system based on blockchain technology to encrypt and process personnel information, including personal information, skills, and allocation information. S2. Build an identity verification platform based on big data technology, integrate personnel identity authentication information, and realize rapid verification of personnel identity; S3. Establish a hierarchical access control mechanism to allocate data access and operation permissions according to the level of the administrator to prevent data leakage; The specific process of the big data tracing method is as follows: S1. Collect operational and result data for the entire process of personnel allocation, training, and assessment; S2. Establish a full-process data traceability link through big data traceability technology to realize the queryability and traceability of data at each stage; S3. When management compliance issues arise, relevant data can be queried using the traceability link.

[0012] Preferably, in step seven, the specific execution process of the big data early warning linkage mechanism is as follows: S1. Monitor real-time data on human resource flow and shortage in various regions, and use the unified early warning threshold set in step two as the judgment standard. S2. When an abnormal flow of manpower occurs in a certain area and the shortage is too large, reaching the warning threshold, the system automatically triggers a linkage command and pushes it to the relevant collaborative areas. S3. The relevant collaborative areas shall allocate human resources that meet the needs in accordance with the temporary human resource allocation method described in step six to alleviate the human resource shortage in the area. The process of developing the personalized career plan is as follows: S1. Collect data on personnel skills, career aspirations, and regional industrial development. S2. By combining big data analysis with regional industrial development trends and personnel career aspirations, we can develop personalized career plans and clarify career development directions and skill enhancement paths. S3. Regularly update career development plans based on changes in personnel skills and industry development to improve personnel stability.

[0013] Preferably, in step eight, the content presented by the cross-regional human resources big data analysis and visualization platform includes a human resources flow heat map, a human resources supply and demand comparison chart for each region, a multi-dimensional analysis chart of configuration efficiency, and a warning signal display bar, so as to realize the intuitive presentation of human resources-related data. The specific logic for the cyclical execution of the process is as follows: S1. Real-time collection and updating of data on various human resources, regional needs, and industry changes; S2. Regularly review and optimize processes such as early warning, matching, cost control, and skills training; S3. When the cross-regional human resource allocation and collaborative management reaches the preset optimization goal, the process terminates. If the goal is not reached, the above steps continue to be executed in a loop.

[0014] This invention provides a method for cross-regional human resource allocation and collaborative management based on big data, which has the following beneficial effects: 1. In this invention, by building a cross-regional human resources collaborative management cloud platform, an enterprise alliance platform, and a big data visualization platform, multi-regional human resources databases are integrated to achieve real-time data synchronization and hierarchical access control. At the same time, through big data cleaning, verification technology, and real-time update mechanism, the accuracy, completeness, and timeliness of the data are ensured, providing reliable data support for all subsequent allocation and management processes, breaking down information barriers between regions and enterprises, and improving the efficiency of human resources circulation.

[0015] 2. In this invention, big data fusion technology is used to monitor regional human resource supply, mobility rate and industry change data in real time, set a unified early warning threshold, predict human resource shortages in advance and output early warning signals, realize the transformation of allocation work from "passive response" to "proactive prediction". At the same time, through a big data-driven mobility risk assessment model, risks such as personnel loss, cost overruns and compliance are identified and targeted prevention and control strategies are generated, effectively reducing allocation risks and avoiding problems such as cost overruns and compliance violations.

[0016] 3. In this invention, by constructing a standardized skill tagging system and combining it with big data mining technology to uncover the implicit skills of personnel, the limitations of traditional explicit skill matching are overcome. At the same time, through a dynamic matching algorithm, the optimal matching combination is selected by integrating personnel skills, location preferences, salary expectations and job requirements, which greatly improves job suitability, reduces the turnover rate of personnel after they are hired, and optimizes the efficiency of human resource allocation.

[0017] 4. This invention achieves long-term optimization of human resources by forming an integrated closed-loop management system of "training-allocation," solving the technical problems of existing technologies where skills training is disconnected from job requirements and lacks closed-loop management. Through a cross-regional collaborative training system, this invention uses big data analysis to identify personnel skill gaps and regional job requirements differences, accurately delivering training content. At the same time, through a skills upgrade prediction model, it anticipates changes in skills requirements brought about by regional industrial upgrading, plans skills upgrade directions in advance, and realizes a closed loop of "training-assessment-allocation-skills upgrade," ensuring that personnel skills continuously adapt to the needs of regional industrial development.

[0018] 5. This invention establishes an emergency deployment mechanism based on early warning signals. Through a real-time big data dynamic adjustment mechanism, it adapts to changes in regional job requirements and personnel status in a timely manner. For sudden manpower needs such as emergency rescue and project breakthroughs, it quickly retrieves and matches temporary manpower and generates deployment plans, which greatly improves emergency response efficiency and timely fills sudden manpower gaps. Attached Figure Description

[0019] The following accompanying drawings will provide a better understanding of the invention by those skilled in the art, and will more clearly demonstrate the advantages of the invention. The drawings described herein are for illustrative purposes only, representing selected embodiments and not all possible implementations, and are not intended to limit the scope of the invention.

[0020] In the attached diagram: Figure 1 A flowchart illustrating the operation of a big data-based cross-regional human resource allocation and collaborative management method according to an embodiment of the present invention is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: Please refer to Figure 1 As shown: This invention provides a method for cross-regional human resource allocation and collaborative management based on big data, including the following process: Step 1: Establish a data support system, build a cross-regional human resources collaborative management cloud platform, a cross-regional enterprise human resources collaborative alliance platform, and a cross-regional human resources big data analysis and visualization platform, integrate multi-regional human resources databases, and achieve real-time data synchronization and hierarchical access control; adopt cross-regional human resources data quality control methods, based on big data cleaning and verification technology, to ensure the accuracy, completeness, and timeliness of human resources data; In step one, the cross-regional human resources collaborative management cloud platform and the cross-regional enterprise human resources collaborative alliance platform operate in tandem. The former is responsible for the integration and synchronous management of human resources data in multiple regions, while the latter is responsible for human resource sharing and job complementarity among multiple enterprises. In step one, the specific process of the data quality control method is as follows: S1. Utilize big data cleaning technology to remove duplicate, abnormal, and missing data from human resources data; S2. Through big data verification technology, the accuracy, completeness, and timeliness of the cleaned data are verified; S3. Establish a real-time data update mechanism to ensure that human resources data is synchronized with the actual status of personnel and regional needs.

[0023] Step Two: Construct a dual system of early warning and risk prevention and control. Employ big data fusion technology to achieve cross-regional human resource gap early warning, monitor regional human resource supply, mobility rate and industry change data in real time, build an early warning database and set a unified gap early warning threshold, predict gaps in advance and output corresponding level early warning signals; through a big data-driven mobility risk assessment model, calculate the risk level and formulate targeted prevention and control strategies, and push the early warning signals to the relevant regional collaborative management terminals simultaneously. In step two, the specific process of the human resource gap early warning method is as follows: S1. Collect real-time data on labor supply, labor mobility rate, and industrial changes in various regions to build an early warning database; S2. Analyze the above data using big data fusion algorithms and set a gap warning threshold; S3. Compare the real-time analysis results with the early warning threshold. When the early warning conditions are met, automatically output the corresponding level of early warning signal and push it to the relevant regional collaborative management terminal. In step two, the identification process for the liquidity risk assessment model is as follows: S1. Screen risk indicators related to staff turnover, cost overruns, and compliance, and establish a multi-dimensional risk assessment system; S2. Calculate the risk level by weighting and analyzing various risk indicators using big data algorithms; S3. Automatically generate targeted prevention and control strategies based on risk levels for managers to refer to and implement.

[0024] Step 3: Construct a human resources skills tagging system and mine hidden skills of personnel through big data; combine big data-driven cross-regional human resources dynamic matching algorithm to achieve optimal three-dimensional matching of "people, positions and places"; In step three, the process of constructing the human resources skills tagging system is as follows: S1. Review the personnel's explicit skills, including education, major, professional qualification certificates, etc., and establish basic skill labels; S2. Through big data mining technology, analyze personnel's work history, practical records, performance data, etc., to uncover potential abilities, practical experience and other implicit skills, and establish implicit skill tags. S3. Classify and grade all skill tags to form a standardized human resource skill tag system; In step three, the specific process of optimal matching of the "person, place, and location" three dimensions is as follows: S1. Collect data on job requirements, personnel skills, personnel location preferences, and salary expectations; S2. Through dynamic matching algorithms, precise matching is achieved between job requirements and personnel skills, personnel location preferences and job areas, and salary expectations and job salary standards. S3. Based on the matching results from the three dimensions, select the optimal matching combination and output the matching solution.

[0025] Step 4: Construct a closed-loop management system for "training-allocation". Through a cross-regional human resources collaborative training system, based on big data analysis of personnel skill gaps and regional job requirements, training content is accurately pushed; a big data-driven skills upgrade prediction model is used to predict changes in skills requirements brought about by regional industrial upgrading and plan the direction of personnel skills upgrades in advance. In step four, the specific execution process of the "training-deployment" closed loop is as follows: S1. By analyzing big data, identify the differences between the existing skills of personnel and the regional job requirements to pinpoint the personnel's skill gaps; S2. Develop personalized training content based on skill gaps and organize personnel to conduct training. S3. After the training, the skills of the personnel will be assessed. Those who pass the assessment will be included in the human resources allocation pool through a dynamic matching algorithm for job matching and allocation. Those who fail the assessment will be pushed targeted training content again until they pass the assessment. In step four, the prediction process of the skill upgrade prediction model is as follows: S1. Collect data on regional industrial development, industrial upgrading plans, and existing job skill requirements; S2. Analyze the impact of industrial upgrading on job skills through big data algorithms to predict future trends in skill demand. S3. Based on the forecast results, formulate a personnel skills upgrade plan and clarify the direction of skills upgrade and training focus.

[0026] Step 5: Implement two-way cost and efficiency control. Through a big data-driven cross-regional human resource cost control model, calculate and dynamically optimize various costs such as transportation, accommodation, salary, and training during the allocation process in real time. Simultaneously, build a big data-integrated allocation efficiency evaluation system, establish multi-dimensional evaluation indicators, and achieve quantitative evaluation of allocation effectiveness. In step five, the specific process of dynamic cost optimization is as follows: S1. Use a cost control model to collect real-time data on transportation, accommodation, salary, and training costs during the allocation process; S2. Classify, calculate, and summarize all costs to identify weak links in cost control; S3. Based on the analysis results, automatically generate cost optimization plans, adjust various cost expenditures, and achieve dynamic cost optimization.

[0027] Step Six: Conduct emergency deployment based on the warning thresholds and warning signals from Step Two; establish a dynamic adjustment mechanism for cross-regional human resource deployment based on real-time big data to automatically adapt to changes in regional needs and personnel status; and adopt temporary deployment methods to quickly respond to sudden human resource needs such as emergency rescue and project breakthroughs. In step six, the specific process of dynamically adjusting the allocation plan is as follows: S1. Real-time monitoring of changes in regional job demand, as well as changes in personnel skills, location preferences, and salary expectations; S2. When changes in data are detected, analyze the impact of the changes on the existing allocation plan using big data algorithms; S3. Automatically adjust the allocation plan and update the matching combination to ensure that the allocation plan is adapted to the actual needs and personnel status. Step six involves the following specific steps for the temporary human resource allocation method: S1. Receive signals of sudden manpower needs and clarify the required positions, quantities, and time limits. S2. Quickly search the human resources database using big data to match temporary staff who meet the needs; S3. Quickly generate temporary deployment plans and coordinate personnel who meet the relevant requirements to be on duty.

[0028] Step Seven: Improve the end-to-end collaborative guarantee mechanism, and realize personnel information encryption, identity verification and access control based on big data and blockchain technology; link the early warning system of Step Two and the emergency deployment mechanism of Step Six to automatically trigger cross-regional linkage instructions to alleviate manpower shortages; formulate and dynamically update personalized career plans through big data; and use big data traceability technology to ensure management compliance. In step seven, the specific process of encrypting and storing personnel information and quickly verifying their identity is as follows: S1. Construct a personnel information encryption storage system based on blockchain technology to encrypt and process personnel information, including personal information, skills, and allocation information. S2. Build an identity verification platform based on big data technology, integrate personnel identity authentication information, and realize rapid verification of personnel identity; S3. Establish a hierarchical access control mechanism to allocate data access and operation permissions according to the level of the administrator to prevent data leakage; In step seven, the specific process of the big data tracing method is as follows: S1. Collect operational and result data for the entire process of personnel allocation, training, and assessment; S2. Establish a full-process data traceability link through big data traceability technology to realize the queryability and traceability of data at each stage; S3. When management compliance issues arise, relevant data can be queried using the traceability link; In step seven, the specific execution process of the big data early warning and linkage mechanism is as follows: S1. Monitor real-time data on human resource flow and shortage in various regions, and use the unified early warning threshold set in step two as the judgment standard. S2. When an abnormal flow of manpower occurs in a certain area and the shortage is too large, reaching the warning threshold, the system will automatically trigger a linkage command and push it to the relevant collaborative areas. S3. In accordance with the temporary human resource allocation method in step six, relevant collaborative regions shall allocate human resources that meet the needs to alleviate the human resource shortage in the region. Step seven involves the development of a personalized career plan as follows: S1. Collect data on personnel skills, career aspirations, and regional industrial development. S2. By combining big data analysis with regional industrial development trends and personnel career aspirations, we can develop personalized career plans and clarify career development directions and skill enhancement paths. S3. Regularly update career development plans based on changes in personnel skills and industry development to improve personnel stability.

[0029] Step 8: Repeat the above steps. Through the cross-regional human resources big data analysis and visualization platform, visualize the data such as human resource flow, supply and demand status, and allocation efficiency to assist managers in making scientific decisions until the optimal allocation and collaborative management of cross-regional human resources is achieved, at which point the process terminates.

[0030] In step eight, the content presented by the cross-regional human resources big data analysis and visualization platform includes a human resources flow heat map, a human resources supply and demand comparison chart for each region, a multi-dimensional analysis chart of configuration efficiency, and an early warning signal display bar, so as to realize the intuitive presentation of human resources-related data. In step eight, the specific logic for the cyclical execution of the process is as follows: S1. Real-time collection and updating of data on various human resources, regional needs, and industry changes; S2. Regularly review and optimize processes such as early warning, matching, cost control, and skills training; S3. When the cross-regional human resource allocation and collaborative management reaches the preset optimization goal, the process terminates. If the goal is not reached, the above steps continue to be executed in a loop.

[0031] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for cross-regional human resource allocation and collaborative management based on big data, characterized in that, The process includes the following steps: Step 1: Establish a data support system, build a cross-regional human resources collaborative management cloud platform, a cross-regional enterprise human resources collaborative alliance platform, and a cross-regional human resources big data analysis and visualization platform, integrate multi-regional human resources databases, and achieve real-time data synchronization and hierarchical access control; adopt cross-regional human resources data quality control methods, based on big data cleaning and verification technology, to ensure the accuracy, completeness, and timeliness of human resources data; Step Two: Construct a dual system of early warning and risk prevention and control. Employ big data fusion technology to achieve cross-regional human resource gap early warning, monitor regional human resource supply, mobility rate and industry change data in real time, build an early warning database and set a unified gap early warning threshold, predict gaps in advance and output corresponding level early warning signals; through a big data-driven mobility risk assessment model, calculate the risk level and formulate targeted prevention and control strategies, and push the early warning signals to the relevant regional collaborative management terminals simultaneously. Step 3: Construct a human resources skills tagging system and mine hidden skills of personnel through big data; combine big data-driven cross-regional human resources dynamic matching algorithms to achieve optimal three-dimensional matching of "people, positions, and locations"; Step 4: Construct a closed-loop management system for "training-allocation". Through a cross-regional human resources collaborative training system, based on big data analysis of personnel skill gaps and regional job requirements, training content is accurately pushed; a big data-driven skills upgrade prediction model is used to predict changes in skills requirements brought about by regional industrial upgrading and to plan the direction of personnel skills upgrades in advance. Step 5: Implement two-way cost and efficiency control. Through a big data-driven cross-regional human resource cost control model, calculate and dynamically optimize various costs such as transportation, accommodation, salary, and training during the allocation process in real time. Simultaneously, build a big data-integrated allocation efficiency evaluation system, establish multi-dimensional evaluation indicators, and achieve quantitative evaluation of allocation effectiveness. Step Six: Conduct emergency deployment based on the warning thresholds and warning signals from Step Two; establish a dynamic adjustment mechanism for cross-regional human resource deployment based on real-time big data to automatically adapt to changes in regional needs and personnel status; and adopt temporary deployment methods to quickly respond to sudden human resource needs such as emergency rescue and project breakthroughs. Step Seven: Improve the end-to-end collaborative guarantee mechanism, and realize personnel information encryption, identity verification and access control based on big data and blockchain technology; link the early warning system of Step Two and the emergency deployment mechanism of Step Six to automatically trigger cross-regional linkage instructions to alleviate manpower shortages; formulate and dynamically update personalized career plans through big data; and use big data traceability technology to ensure management compliance. Step 8: Repeat the above steps. Through the cross-regional human resources big data analysis and visualization platform, visualize the data such as human resource flow, supply and demand status, and allocation efficiency to assist managers in making scientific decisions until the optimal allocation and collaborative management of cross-regional human resources is achieved, at which point the process terminates.

2. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step one, the cross-regional human resources collaborative management cloud platform and the cross-regional enterprise human resources collaborative alliance platform operate in conjunction. The former is responsible for the integration and synchronous management of human resources data in multiple regions, while the latter is responsible for the sharing of human resources and job complementarity among multiple enterprises. The specific process of the data quality control method is as follows: S1. Utilize big data cleaning technology to remove duplicate, abnormal, and missing data from human resources data; S2. Through big data verification technology, the accuracy, completeness, and timeliness of the cleaned data are verified; S3. Establish a real-time data update mechanism to ensure that human resources data is synchronized with the actual status of personnel and regional needs.

3. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step two, the specific process of the human resource gap early warning method is as follows: S1. Collect real-time data on labor supply, labor mobility rate, and industrial changes in various regions to build an early warning database; S2. Analyze the above data using big data fusion algorithms and set a gap warning threshold; S3. Compare the real-time analysis results with the early warning threshold. When the early warning conditions are met, automatically output the corresponding level of early warning signal and push it to the relevant regional collaborative management terminal. The identification process for the liquidity risk assessment model is as follows: S1. Screen risk indicators related to staff turnover, cost overruns, and compliance, and establish a multi-dimensional risk assessment system; S2. Calculate the risk level by weighting and analyzing various risk indicators using big data algorithms; S3. Automatically generate targeted prevention and control strategies based on risk levels for managers to refer to and implement.

4. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step three, the construction process of the human resources skill tagging system is as follows: S1. Review the personnel's explicit skills, including education, major, professional qualification certificates, etc., and establish basic skill labels; S2. Through big data mining technology, analyze personnel's work history, practical records, performance data, etc., to uncover potential abilities, practical experience and other implicit skills, and establish implicit skill tags. S3. Classify and grade all skill tags to form a standardized human resource skill tag system; The specific process of the optimal three-dimensional matching of "person, place, and location" is as follows: S1. Collect data on job requirements, personnel skills, personnel location preferences, and salary expectations; S2. Through dynamic matching algorithms, precise matching is achieved between job requirements and personnel skills, personnel location preferences and job areas, and salary expectations and job salary standards. S3. Based on the matching results from the three dimensions, select the optimal matching combination and output the matching solution.

5. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step four, the specific execution process of the "training-deployment" closed loop is as follows: S1. By analyzing big data, identify the differences between the existing skills of personnel and the regional job requirements to pinpoint the personnel's skill gaps; S2. Develop personalized training content based on skill gaps and organize personnel to conduct training. S3. After the training, the skills of the personnel will be assessed. Those who pass the assessment will be included in the human resources allocation pool through a dynamic matching algorithm for job matching and allocation; those who fail the assessment will be pushed targeted training content again until they pass the assessment. The prediction process of the skill upgrade prediction model is as follows: S1. Collect data on regional industrial development, industrial upgrading plans, and existing job skill requirements; S2. Analyze the impact of industrial upgrading on job skills through big data algorithms to predict future trends in skill demand. S3. Based on the forecast results, formulate a personnel skills upgrade plan and clarify the direction of skills upgrade and training focus.

6. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step five, the specific process of dynamic cost optimization is as follows: S1. Use a cost control model to collect real-time data on transportation, accommodation, salary, and training costs during the allocation process; S2. Classify, calculate, and summarize all costs to identify weak links in cost control; S3. Based on the analysis results, automatically generate cost optimization plans, adjust various cost expenditures, and achieve dynamic cost optimization.

7. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step six, the specific process of dynamically adjusting the allocation plan is as follows: S1. Real-time monitoring of changes in regional job demand, as well as changes in personnel skills, location preferences, and salary expectations; S2. When changes in data are detected, analyze the impact of the changes on the existing allocation plan using big data algorithms; S3. Automatically adjust the allocation plan and update the matching combination to ensure that the allocation plan is adapted to the actual needs and personnel status. The specific process of the temporary human resource allocation method is as follows: S1. Receive signals of sudden manpower needs and clarify the required positions, quantities, and time limits. S2. Quickly search the human resources database using big data to match temporary staff who meet the needs; S3. Quickly generate temporary deployment plans and coordinate personnel who meet the relevant requirements to be on duty.

8. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step seven, the specific process of encrypting and storing personnel information and quickly verifying their identity is as follows: S1. Construct a personnel information encryption storage system based on blockchain technology to encrypt and process personnel information, including personal information, skills, and allocation information. S2. Build an identity verification platform based on big data technology, integrate personnel identity authentication information, and realize rapid verification of personnel identity; S3. Establish a hierarchical access control mechanism to allocate data access and operation permissions according to the level of the administrator to prevent data leakage; The specific process of the big data tracing method is as follows: S1. Collect operational and result data for the entire process of personnel allocation, training, and assessment; S2. Establish a full-process data traceability link through big data traceability technology to realize the queryability and traceability of data at each stage; S3. When management compliance issues arise, relevant data can be queried using the traceability link.

9. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step seven, the specific execution process of the big data early warning and linkage mechanism is as follows: S1. Monitor real-time data on human resource flow and shortage in various regions, and use the unified early warning threshold set in step two as the judgment standard. S2. When an abnormal flow of manpower occurs in a certain area and the shortage is too large, reaching the warning threshold, the system automatically triggers a linkage command and pushes it to the relevant collaborative areas. S3. The relevant collaborative areas shall allocate human resources that meet the needs in accordance with the temporary human resource allocation method described in step six to alleviate the human resource shortage in the area. The process of developing the personalized career plan is as follows: S1. Collect data on personnel skills, career aspirations, and regional industrial development. S2. By combining big data analysis with regional industrial development trends and personnel career aspirations, we can develop personalized career plans and clarify career development directions and skill enhancement paths. S3. Regularly update career development plans based on changes in personnel skills and industry development to improve personnel stability.

10. The method for cross-regional human resource allocation and collaborative management based on big data as described in claim 1, characterized in that: In step eight, the content presented by the cross-regional human resources big data analysis and visualization platform includes a human resources flow heat map, a human resources supply and demand comparison chart for each region, a multi-dimensional analysis chart of configuration efficiency, and an early warning signal display bar, so as to realize the intuitive presentation of human resources-related data. The specific logic for the cyclical execution of the process is as follows: S1. Real-time collection and updating of data on various human resources, regional needs, and industry changes; S2. Regularly review and optimize processes such as early warning, matching, cost control, and skills training; S3. When the cross-regional human resource allocation and collaborative management reaches the preset optimization goal, the process terminates. If the goal is not reached, the above steps continue to be executed in a loop.

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