Industrial talent post matching method and system based on multi-source data fusion

Abstract

The invention relates to the technical field of post matching, in particular to an industrial talent post matching method and system based on multi-source data fusion, and the method comprises the steps: obtaining a post demand condition and a post scene condition, and obtaining a corresponding post candidate condition in a candidate library; the matching degree of candidates and post demand future development is evaluated through corresponding post candidate conditions and post demand conditions, and the matching degree of candidates and scenes is evaluated through corresponding post candidate conditions and post scene conditions. The method comprises the following steps: performing man-post matching degree analysis according to the matching degree between candidates and post demand future development and the matching degree between the candidates and a scene, and selecting proper interview personnel to a management department based on the analysis result of the man-post matching degree. And more accurate and more prospective talent post matching is realized.

Description

Technical Field

[0001] This invention relates to the field of job matching technology, and in particular to a method and system for matching industry talent to jobs based on multi-source data fusion. Background Technology

[0002] In existing human resource management and recruitment technologies, talent matching technology focuses on the static matching of current skills and job requirements. The core logic is to extract core skill requirements from job descriptions and compare candidates’ resumes, skills tests or interview performance to determine whether they meet the current job requirements. However, this type of technology has significant shortcomings such as ignoring scenario adaptability, lacking foresight, and having a single matching dimension. Existing technologies do not take into account factors such as job environment, teamwork, and data scale. They usually rely on recruiters to make subjective inferences based on the size of the candidate's previous company and project descriptions, or to conduct oral assessments through situational questions during the interview. This results in vague, subjective, and difficult-to-compare evaluation results, which may lead to candidates leaving the company after joining due to being unable to adapt to the environment or handle the data volume. Existing technologies do not assess the degree of match between candidates and future job development. For example, they do not consider changes in future job skill requirements or candidates' learning abilities, resulting in talent being unable to adapt to the job in the long term. Recruiters post job descriptions that include requirements for skills, experience, and education. Talent pools or recruitment systems conduct preliminary screening of candidate resumes through keyword filtering and calculate a static matching score. This score is usually based on the degree of overlap between the candidate's past experience and the current explicit requirements of the position. Existing technology judges the fit between people and positions solely based on whether the static matching score is met, resulting in an insufficiently comprehensive matching analysis. To address the aforementioned issues, this invention provides a method and system for matching industry talent to job positions based on multi-source data fusion. Summary of the Invention

[0003] To overcome the defects and shortcomings of existing technologies, this invention provides an industry talent job matching method and system based on multi-source data fusion. By combining job development and work scenarios, this invention constructs a dynamic talent-job matching analysis framework, achieving more accurate and forward-looking talent-job matching.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, this invention provides a method for matching industry talent to job positions based on multi-source data fusion, comprising the following specific steps: Step 1: Obtain information on job requirements and scenarios, and simultaneously obtain information on candidates for the corresponding positions from the candidate database; Step 2: Assess the degree of match between the candidate's qualifications and the job requirements for future career development by analyzing the candidate's information and the job's needs. Step 3: Assess the match between the candidate and the scenario based on the candidate's information and the scenario. Step 4: Analyze the person-job fit by assessing the degree of match between the candidate and the future development needs of the position, as well as the degree of match between the candidate and the scenario. Step 5: Based on the analysis results of the person-job fit, recommend suitable interviewees to the management department.

[0005] In one implementation of the present invention, step one includes the following specific contents: Step 11: Obtain job requirement data, which includes the current job skill requirement set and cross-job communication frequency. The current job skill requirement set includes the job requirement professional background, job requirement courses, and job course mastery level. Obtain historical data on collaboration between this job and other departments or jobs. The cross-job communication frequency is obtained by the ratio of the total number of interactions with a single other job within the statistical period to the statistical duration. Step 12: Obtain job scenario data, which includes an environment set, current data processing volume, and current task load. The environment set includes ambient decibels, office location, and office permissions. Step 13: Based on the job title or keywords, initially filter out the relevant candidate set from the candidate database, and obtain the corresponding job candidate data in the candidate database. The corresponding job candidate data includes personal skill set and work experience set. The personal skill set includes professional background, courses taken, and course grades. In this embodiment, the course grades are the degree of course mastery. The work experience set includes work experience duration, peak historical data processing volume, and historical data processing efficiency. The work experience set can be obtained through system logs.

[0006] In one implementation of the present invention, step two includes the following specific steps: Step 21: Obtain the skill learning rate by the ratio of the number of professional qualification certificates obtained to the standardized work experience duration. Obtain the growth potential assessment value by weighting the skill learning rate and the average course grade point average. The standardized work experience duration is obtained by the ratio of the work experience duration to the reference duration, which can be 1 year. Step 22: Obtain the static skill matching assessment value by the ratio of the number of elements in the intersection of the individual skill set and the current job skill requirement set to the number of elements in the current job skill requirement set; Step 23: Obtain the number of future skills to be followed up by multiplying the skill learning rate, the future set duration and the skill conversion efficiency; obtain the number of future skills to be mastered by adding the number of elements at the intersection of the individual skill set and the current job skill requirement set; obtain the dynamic matching score evaluation value by the ratio of the number of future skills mastered to the number of elements in the future job skill requirement set. Step 24: Obtain the future task load by multiplying the total number of future tasks by the average processing time of a single historical task; obtain the performance improvement prediction value by multiplying the growth potential assessment value by the experience improvement coefficient, adding 1, and then multiplying by the historical data processing performance; obtain the load adaptability assessment value by the ratio of the performance improvement prediction value to the future task load. Step 25: Obtain the matching degree assessment value between the candidate and the future development of the job requirements by weighting and summing the static skills matching assessment value, dynamic matching score assessment value and load adaptability assessment value.

[0007] In one implementation of the present invention, step three includes the following specific contents: Step 31: Conduct a questionnaire survey on candidates' environmental decibel levels, office location, and office authority. Quantify these parameters to obtain the noise range. Normalize the environmental decibel levels. Office location is divided into indoor and outdoor, with indoor corresponding to a value of 1 and outdoor to a value of 0. Office authority is divided into basic, intermediate, and advanced levels, with basic authority corresponding to a value of 0.2, intermediate authority to a value of 0.6, and advanced authority to a value of 1. Obtain the environmental compatibility assessment value using Euclidean distance. The formula for calculating Euclidean distance is: ,in, Let j be the quantized value of the j-th environment set element. For the quantified value of the j-th environmental set element, Let j be the weight of the j-th environment set element. This represents the total number of elements in the environment set. The weights of the environment dimensions are set according to the job position. For example, the weight of office access for R&D positions can be set to 0.6, the weight of environmental decibels can be set to 0.3, and the weight of office location can be set to 0.1. The weight of office access for marketing positions can be set to 0.3, the weight of environmental decibels can be set to 0.2, and the weight of office location can be set to 0.5. Step 32: Collect historical data volume records for this position within the past period, and predict future data processing volume using the weighted moving average method. The formula for the weighted moving average method is: ,in, The data weight for the i-th historical period can be determined by... Obtain Let i be the amount of data processed in the i-th historical period. The total number of historical periods is used to obtain the data load adaptation assessment value by the ratio of the peak historical data processing volume to the future data processing volume. Step 33: Obtain the matching degree evaluation value between the candidate and the scenario by weighted summation of the environmental compatibility evaluation value and the data load adaptation evaluation value.

[0008] In one implementation of the present invention, step four includes the following specific steps: The job-person matching score is obtained by weighted summing the matching scores between the candidate and the job requirements for future development and the candidate and the scenario.

[0009] In one implementation of the present invention, step five includes the following specific steps: The candidate with the highest job-person matching score is recommended to the management department by sorting the job-person matching score in reverse order.

[0010] Secondly, the present invention also provides an industry talent job matching system based on multi-source data fusion, including the following specific steps: The data acquisition module is used to acquire information on job requirements and job scenarios, as well as information on candidates for the corresponding job in the candidate database. The job matching analysis module is used to assess the degree of match between candidates and job requirements in terms of their future development and the job requirements. The scenario matching analysis module is used to assess the degree of matching between candidates and scenarios based on the candidate information and the scenario of the job. The person-job fit analysis module is used to analyze the person-job fit by assessing the degree of match between the candidate and the job requirements for future development, as well as the degree of match between the candidate and the scenario. The interviewee recommendation module is used to recommend suitable interviewees to the management department based on the analysis results of person-job matching.

[0011] Thirdly, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an industry talent job matching method based on multi-source data fusion by calling the computer program stored in the memory.

[0012] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an industry talent job matching method based on multi-source data fusion.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention obtains information on job requirements and job scenarios, and simultaneously obtains information on candidates for the corresponding positions from a candidate pool. It assesses the degree of matching between candidates and future job requirements based on the candidate information and job requirements, and assesses the degree of matching between candidates and scenarios based on the candidate information and job scenarios. It then analyzes the person-job fit based on the degree of matching between candidates and future job requirements and the candidate-scenario fit, and recommends suitable interviewees to management based on the analysis results. This invention, by combining job development and work scenarios, constructs a dynamic person-job fit analysis framework, achieving more accurate and forward-looking talent-job matching. Attached Figure Description

[0014] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process structure of an embodiment of the method of the present invention; Figure 2 This is a schematic diagram of the flow structure of step two in an embodiment of the method of the present invention; Figure 3 This is a schematic diagram of the process structure for step three of the method embodiment of the present invention; Figure 4 This is a schematic diagram of the module composition structure of an embodiment of the system of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0016] Please see Figure 1 This invention provides a method for matching industry talent to job positions based on multi-source data fusion, specifically including the following steps: Step 1: Obtain information on job requirements and scenarios, and simultaneously obtain information on candidates for the corresponding positions from the candidate database; In this embodiment, step one includes the following specific contents: Step 11: Obtain job requirement data, which includes the current job skill requirement set and cross-job communication frequency. The current job skill requirement set includes the job requirement professional background, job requirement courses, and job course mastery level. Obtain historical data on collaboration between this job and other departments or jobs. The cross-job communication frequency is obtained by the ratio of the total number of interactions with a single other job within the statistical period to the statistical duration. Step 12: Obtain job scenario data, which includes an environment set, current data processing volume, and current task load. The environment set includes ambient decibels, office location, and office permissions. Step 13: Based on the job title or keywords, initially filter out the relevant candidate set from the candidate database, and obtain the corresponding job candidate data in the candidate database. The corresponding job candidate data includes personal skill set and work experience set. The personal skill set includes professional background, courses taken, and course grades. In this embodiment, the course grades are the degree of course mastery. The work experience set includes work experience duration, peak historical data processing volume, and historical data processing efficiency. The work experience set can be obtained through system logs.

[0017] Step 2: Assess the degree of match between the candidate's qualifications and the job requirements for future career development by analyzing the candidate's information and the job's needs. Please see Figure 2 In this embodiment, step two includes the following specific steps: Step 21: Obtain the skill learning rate by the ratio of the number of professional qualification certificates obtained to the standardized work experience duration. Obtain the growth potential assessment value by weighting the skill learning rate and the average course grade point average. The standardized work experience duration is obtained by the ratio of the work experience duration to the reference duration, which can be 1 year. Step 22: Obtain the static skill matching assessment value by the ratio of the number of elements in the intersection of the individual skill set and the current job skill requirement set to the number of elements in the current job skill requirement set; Step 23: Obtain the number of future skills to be acquired by multiplying the skill learning rate, the future set duration, and the skill conversion efficiency. Obtain the number of future skills to be mastered by adding the number of elements in the intersection of the individual skill set and the current job skill requirement set to the number of future skills acquired. Obtain the dynamic matching score evaluation value by the ratio of the number of future skills mastered to the number of elements in the future job skill requirement set. The skill conversion efficiency is obtained through historical data regression. Analyze the application rate of new skills in actual work. In this embodiment, the future set duration is a future set duration standardized from the reference duration. The future job skill requirement set is obtained by combining the current job skill requirement set, core skill supplementation, and cross-job skill supplementation with industry trends. The report screens and supplements core skills, and then identifies positions that require frequent interaction through organizational structure analysis. For example, data analysts need to communicate with product managers, engineers, and business operations personnel. For each cross-position, its core knowledge area is extracted. For example, the knowledge area of ​​product managers is user needs analysis, the knowledge area of ​​engineers is technical implementation, and the knowledge area of ​​business operations personnel is business process optimization. The cross-position knowledge weight is obtained by the ratio of the cross-position communication frequency to the total communication frequency between that position and all cross-positions. This weight is used to reflect the intensity of cross-position communication frequency. By correlating the cross-position knowledge weight with the cross-position knowledge area, the necessity of including the knowledge of that cross-position in the future job skill requirement set is reflected. Step 24: Based on the business development plan (e.g., launching 3 projects next quarter, each with 100 tasks) or historical data growth trends (e.g., quarterly task volume increasing by 20% year-on-year), estimate the total number of tasks to be processed within the set timeframe in the future. Obtain the future task load by multiplying the total number of future tasks by the average processing time of a single historical task. Obtain the performance improvement prediction value by multiplying the growth potential assessment value by the experience improvement coefficient, adding 1, and then multiplying by the historical data processing efficiency. Obtain the load adaptability assessment value by the ratio of the performance improvement prediction value to the future task load. The average processing time of a single historical task is obtained from historical task records. Statistically calculate the duration of all historical tasks and average the result. Obtain the experience improvement coefficient through historical data regression and analyze the relationship between experience and efficiency. Step 25: Obtain the matching degree assessment value between the candidate and the future development of the job requirements by weighting and summing the static skills matching assessment value, dynamic matching score assessment value and load adaptability assessment value.

[0018] This embodiment can proactively identify high-potential candidates whose current skills do not perfectly match the job requirements, but whose learning abilities and development potential are highly aligned with the future strategic needs of the position, thus providing a basis for strategic talent reserves and training.

[0019] Step 3: Assess the match between the candidate and the scenario based on the candidate's information and the scenario. Please see Figure 3 In this embodiment, step three includes the following specific contents: Step 31: Conduct a questionnaire survey on candidates' environmental decibel levels, office location, and office authority. Quantify these parameters to obtain the noise range. Normalize the environmental decibel levels. Office location is divided into indoor and outdoor, with indoor corresponding to a value of 1 and outdoor to a value of 0. Office authority is divided into basic, intermediate, and advanced levels, with basic authority corresponding to a value of 0.2, intermediate authority to a value of 0.6, and advanced authority to a value of 1. Obtain the environmental compatibility assessment value using Euclidean distance. The formula for calculating Euclidean distance is: ,in, Let j be the quantized value of the j-th environment set element. For the quantified value of the j-th environmental set element, Let j be the weight of the j-th environment set element. This represents the total number of elements in the environment set. The weights of the environment dimensions are set according to the job position. For example, the weight of office access for R&D positions can be set to 0.6, the weight of environmental decibels can be set to 0.3, and the weight of office location can be set to 0.1. The weight of office access for marketing positions can be set to 0.3, the weight of environmental decibels can be set to 0.2, and the weight of office location can be set to 0.5. Step 32: Collect historical data volume records for this position within the past period, and predict future data processing volume using the weighted moving average method. The formula for the weighted moving average method is: ,in, The data weight for the i-th historical period can be determined by... Obtain Let i be the amount of data processed in the i-th historical period. The total number of historical periods is used to obtain the data load adaptation assessment value by the ratio of the peak historical data processing volume to the future data processing volume. Step 33: Obtain the matching degree evaluation value between the candidate and the scenario by weighted summation of the environmental compatibility evaluation value and the data load adaptation evaluation value.

[0020] This embodiment assesses the candidate's adaptability to the real work environment, predicts potential cultural or work pattern conflicts, and reduces the risk of early turnover due to maladaptation to the environment.

[0021] Step 4: Analyze the person-job fit by assessing the degree of match between the candidate and the future development needs of the position, as well as the degree of match between the candidate and the scenario. In this embodiment, step four includes the following specific steps: The job-person matching score is obtained by weighted summing the matching scores between the candidate and the job requirements for future development and the candidate and the scenario.

[0022] In this embodiment, the weights in all weighted summation steps are obtained using the coefficient of variation method. The specific steps are as follows: calculate the mean and standard deviation of each indicator, then divide the standard deviation of each indicator by its mean to obtain the coefficient of variation of that indicator, which represents the degree of dispersion of the indicator data. Then, add the coefficients of variation of each indicator together, and finally, calculate the proportion of each coefficient of variation to the total to obtain the weight coefficient of each indicator.

[0023] This embodiment allows for flexible selection of candidates based on the urgency of business operations or long-term development, thus linking talent recommendations with business strategy.

[0024] Step 5: Based on the analysis results of the person-job fit, recommend suitable interviewees to the management department.

[0025] In this embodiment, step five includes the following specific steps: The candidate with the highest job-person matching score is recommended to the management department by sorting the job-person matching score in reverse order.

[0026] This embodiment introduces a time dimension (development matching) and a spatial dimension (scenario matching) to construct a dynamic talent-job matching analysis framework, which can more comprehensively, proactively, and accurately identify and recommend talent, thereby creating a significant advantage for enterprises in talent competition and business transformation.

[0027] Please see Figure 4 This invention also provides an industry talent job matching system based on multi-source data fusion, including: The data acquisition module is used to acquire information on job requirements and job scenarios, as well as information on candidates for the corresponding job in the candidate database. The job matching analysis module is used to assess the degree of match between candidates and job requirements in terms of their future development and the job requirements. The scenario matching analysis module is used to assess the degree of matching between candidates and scenarios based on the candidate information and the scenario of the job. The person-job fit analysis module is used to analyze the person-job fit by assessing the degree of match between the candidate and the job requirements for future development, as well as the degree of match between the candidate and the scenario. The interviewee recommendation module is used to recommend suitable interviewees to the management department based on the analysis results of person-job matching.

[0028] The parameters and steps for implementing the corresponding functions of each unit module in the industry talent job matching system based on multi-source data fusion of the present invention can be referred to the parameters and steps in the embodiments of the industry talent job matching method based on multi-source data fusion described above, and will not be repeated here.

[0029] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus, and the memory stores the following steps that can be loaded by the processor and executed as provided in the above embodiments for the industry talent job matching method based on multi-source data fusion: Obtain information on job requirements and scenarios, and also obtain information on candidates for the corresponding positions from the candidate database; The degree of match between the candidate and the job requirements is assessed by examining the candidate's qualifications and the job's future development. The degree of match between candidates and the job scenario is assessed by considering the candidates' qualifications and the specific job requirements. The degree of person-job fit is analyzed by the degree of matching between the candidate and the future development of the job requirements, as well as the degree of matching between the candidate and the scenario. Based on the analysis results of the person-job fit, suitable interview candidates are recommended to the management department.

[0030] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the industry talent job matching method based on multi-source data fusion provided in the above embodiments, etc. The data storage area may store data involved in the industry talent job matching method based on multi-source data fusion provided in the above embodiments, etc.

[0031] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data according to the present invention. The processor may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of the present invention do not specifically limit this.

[0032] A communication bus can include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0033] This invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments for the industry talent job matching method based on multi-source data fusion.

[0034] In this embodiment of the invention, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0035] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0036] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to the technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this invention.

Claims

1. An industry talent post matching method based on multi-source data fusion, characterized in that, Comprise the following specific steps: Step one, obtain the post demand situation and post scene situation, and obtain the corresponding post candidate situation in the candidate pool; Step two, evaluate the matching degree of the candidate and the future development of the post demand through the corresponding post candidate situation and the post demand situation; Step three, evaluate the matching degree of the candidate and the scene through the corresponding post candidate situation and the post scene situation; Step four, analyze the matching degree of the candidate and the post through the matching degree of the candidate and the future development of the post demand and the matching degree of the candidate and the scene; Step five, based on the analysis result of the matching degree of the candidate and the post, recommend suitable interview personnel to the management department. 2.The method of claim 1, wherein, The step one comprises the following specific contents: Step 11, obtain post demand data, the post demand data comprises current post skill demand set and cross post communication frequency, the current post skill demand set comprises post demand professional background, post demand course and post course demand mastering degree, the cross post communication frequency is obtained by the ratio of the total number of interactions with a single other post in the statistical period to the statistical time length; Step 12, obtain post scene data, the post scene data comprises environment set, current data processing amount and current task load, the environment set comprises environment decibel, office location and office authority; Step 13, obtain the corresponding post candidate data in the candidate pool, the corresponding post candidate data comprises personal skill set and work experience set, the personal skill set comprises professional background, completed course and course grade point, the work experience set comprises work experience time length, historical data processing amount peak value and historical data processing efficiency. 3.The method of claim 2, wherein, The step two comprises the following specific steps: Step 21, obtain skill learning rate through the ratio of the number of professional qualification certificates to the standard work experience time length, obtain growth potential evaluation value through the weighted sum of skill learning rate and average course grade point; Step 22, obtain static skill matching evaluation value through the ratio of the number of elements in the intersection of personal skill set and current post skill demand set to the number of elements in current post skill demand set; Step 23, obtain future skill follow-up number through the product of skill learning rate, future set time length and skill conversion efficiency, obtain future skill mastering number through the number of elements in the intersection of personal skill set and current post skill demand set plus future skill follow-up number, obtain dynamic matching score evaluation value through the ratio of future skill mastering number to the number of elements in future post skill demand set; Step 24, obtain future task load through the product of future total number of tasks and average processing time length of historical single task, obtain efficiency improvement prediction value through the product of growth potential evaluation value and experience improvement coefficient plus 1 multiplied by historical data processing efficiency, obtain load adaptability evaluation value through the ratio of efficiency improvement prediction value to future task load; Step 25, obtain the matching degree evaluation value of the corresponding post candidate and the future development of the post demand through the weighted sum of static skill matching evaluation value, dynamic matching score evaluation value and load adaptability evaluation value. 4.The method of claim 3, wherein, The step three comprises the following specific steps: Step 31, quantifying environmental decibels, office locations and office permissions, obtaining an environment compatibility evaluation value through the Euclidean distance; Step 32, obtaining a data load adaptation evaluation value by processing the ratio of peak value to future data processing value of historical data; Step 33, obtaining a matching degree evaluation value of the candidate and the scene by weighted summation of the environment compatibility evaluation value and the data load adaptation evaluation value.

5. The multi-source data fusion-based industrial talent and post matching method according to claim 4, characterized in that, The step four includes the following specific contents: Obtaining a person-job matching degree evaluation value by weighted summation of the matching degree evaluation value of the corresponding post candidate and the future development of the post demand and the matching degree evaluation value of the candidate and the scene. 6.The method of matching industrial talents to positions based on multi-source data fusion according to claim 5, characterized in that, The step five includes the following specific steps: The person-job matching degree evaluation value is sorted in descending order, and the candidate corresponding to the highest value of the person-job matching degree evaluation value is recommended to the management department.

7. An industrial talent post matching system based on multi-source data fusion, the industrial talent post matching method based on any one of claims 1-6 is realized, characterized in that, Specifically comprising: A data acquisition module for acquiring post demand conditions and post scene conditions, and acquiring corresponding post candidate conditions in the candidate library; A post matching analysis module for evaluating the matching degree of the candidate and the future development of the post demand based on the corresponding post candidate conditions and the post demand conditions; A scene matching analysis module for evaluating the matching degree of the candidate and the scene based on the corresponding post candidate conditions and the post scene conditions; A person-job matching degree analysis module for analyzing the person-job matching degree based on the matching degree of the candidate and the future development of the post demand and the matching degree of the candidate and the scene; An interview personnel recommendation module for recommending suitable interview personnel to the management department based on the analysis result of the person-job matching degree.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the method of claim 1-6 based on multi-source data fusion by calling the computer program stored in the memory.

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