Enterprise talent post dynamic adaptation and performance optimization management method and system

Abstract

The application discloses an enterprise talent post dynamic adaptation and performance optimization management method and system, belongs to the technical field of big data processing, and aims to solve the technical problems that how to overcome the defects of the prior art, such as that talent evaluation weights are separated from industry attributes, post standards are statically solidified, matching and performance system data are split, and there is no automatic grading optimization disposal scheme. The method comprises the following steps: when the similarity of a work order text and keywords in a process change keyword library reaches a preset threshold value during post side image modeling, an automatic up-regulation process of a corresponding post index threshold value is triggered, and the updated post index threshold value is written into a post image database; during employee side image modeling, a quantitative score of an employee under each post index is calculated based on standardized human resource data to form an employee image vector; an industry correction coefficient alpha is introduced to correct post index basic information entropy, and the comprehensive adaptation degree score of the employee and the target post is calculated based on the weighted summation of objective weights of each post index.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, specifically to a method and system for dynamic talent and performance optimization management in enterprises. Background Technology

[0002] Large-scale coal chemical enterprises cover dozens of differentiated positions, including gasification, synthesis, urea, thermal power, equipment maintenance, and process technology management. Production facilities undergo continuous technological upgrades and frequent capacity adjustments, leading to dynamic changes in safety and operational skill assessment standards for each position. Traditional human resource information management systems and existing patented technologies suffer from multiple technical deficiencies.

[0003] (i) Limited data dimensions and low matching accuracy in talent evaluation: Existing talent matching systems only collect static qualification data such as academic qualifications and professional certificates, and cannot access dynamic business data such as production performance, team collaboration, and skills training. For example, Chinese patent CN121279653A, a method for matching people to jobs, proposes to collect dynamic behavioral data and ability evaluation data with timestamps, but its technical solution uses a time decay function and a deep learning model, focusing on resume-job matching in recruitment scenarios, without involving the continuous dynamic evaluation and performance linkage of on-the-job employees. Conventional standard entropy weight evaluation algorithms rely solely on the dispersion of indicator data to calculate weights, without setting correction parameters in conjunction with the special safety control requirements of high-risk chemical production positions. The allocation of indicator weights is divorced from the actual on-site production, resulting in distorted person-job fit evaluation results.

[0004] (ii) Static and fixed job competency standards, unable to be automatically updated: In the existing technology, the Chinese patent with publication number CN121235531A discloses an assessment question and job competency analysis system based on grey relational analysis. Although it realizes the correlation analysis between competency and job, its job model is also statically constructed and cannot automatically iterate with enterprise technological transformation and process changes. The job indicator thresholds of traditional HR systems require HR specialists to manually enter and modify them one by one. When enterprises add new production equipment or update process flow, the job competency requirements change synchronously, resulting in a large workload for manual maintenance and a long-term lag between the person-job matching standards and business changes.

[0005] (III) Data isolation between job matching and performance appraisal modules, lack of automated closed-loop linkage: In existing commercial human resources software and published patents, the talent matching module and performance calculation module are independent of each other and their data is not interconnected. For example, Chinese patent CN117132172B discloses a method for employee job matching and performance appraisal management. Although job matching and performance appraisal are included in the same patent framework, its technical solution is essentially: automatically classifying employee positions based on resume information, conducting attendance and performance appraisals for employees in different positions, and evaluating promotion targets, punishment targets, and bonus distribution standards based on the evaluation results—that is, a serial mode of "matching first, then independent evaluation." The matching result is only used as the basis for job classification and does not realize the closed-loop linkage mechanism of "matching degree driving dynamic adjustment of performance weight." The parallel linkage scheme of "adaptation and grading results as the driving source of performance weight adjustment" proposed in this invention has not been disclosed in the above-mentioned prior art. The existing scheme only outputs a single adaptation score, without grading and handling logic, and cannot automatically generate training, job transfer, and personnel diversion optimization schemes. The business process does not form a data closed loop.

[0006] (iv) High cost and insufficient fairness in evaluation of purely manual management: Relying on human resources specialists to manually screen for talent idleness, job vacancies and mismatch between people and positions consumes a lot of working hours every month. The subjective bias of manual empowerment and scoring makes it difficult to guarantee the fairness of performance appraisal and talent allocation.

[0007] (v) Existing technical solutions do not cover the specific needs of high-risk scenarios in coal chemical industry: Chinese patent with publication number CN122198508A discloses an employee skill iteration and job adaptation management method based on AI Agent. Although it proposes skill vector matching and closed-loop optimization, its technical solution focuses on the skill training and matching of general positions. It does not set specific safety control parameters for high-risk chemical positions, nor does it establish an automatic linkage logic between adaptation level and performance weight. Chinese patent with publication number CN116993222A discloses the competency assessment of chemical safety management personnel and uses order relationship analysis + entropy weight method to determine weight. However, its technical solution is a static assessment for safety management positions. The job model is fixed and does not involve a mechanism for automatic iteration of job standards with technical transformation work orders, nor does it involve the linkage between matching results and performance accounting.

[0008] Among the existing publicly available patent documents, there are two main types of representative related technical solutions: one type of solution only uses the standard entropy weight method or grey relational analysis to complete the static ability scoring of employees, without building a job threshold model that can automatically iterate with the technical transformation work order, without performance accounting linkage adjustment logic, and without setting entropy value correction parameters for high-risk chemical jobs; the other type of solution only realizes the static talent profile and job matching, the matching results are only used for personnel screening, cannot drive the dynamic adjustment of performance weights in reverse, the job standards are fixed and cannot be automatically updated, and lacks a five-dimensional dynamic data collection architecture for chemical production.

[0009] The two existing technologies mentioned above cannot be integrated, and neither has constructed an integrated closed-loop technical architecture of "multi-source five-dimensional data acquisition—improved entropy weight adaptive scoring—three-level adaptation and grading—automatic adjustment of performance weights—human resource risk early warning," thus failing to solve the core pain point of dynamic and refined human resource management in large coal chemical groups. Furthermore, existing technologies generally suffer from technical bias: industry R&D personnel typically separate job matching and performance evaluation into two independent business modules, or simply perform a serial connection, without proposing an integrated parallel linkage solution that uses job matching and grading results as the driving factor for automatic adjustment of performance weights.

[0010] Overcoming the shortcomings of existing technical talent evaluation methods, such as weighting detached from industry attributes, static and rigid job standards, fragmented data matching and performance systems, and lack of automated hierarchical optimization and handling solutions, are technical problems that need to be solved. Summary of the Invention

[0011] The technical objective of this invention is to address the above-mentioned shortcomings by providing a method and system for dynamic adaptation and performance optimization management of enterprise talent positions. This aims to overcome the technical problems of existing technical talent evaluation weights being detached from industry attributes, job standards being statically fixed, matching and performance system data being fragmented, and the lack of automated hierarchical optimization solutions.

[0012] In a first aspect, the present invention provides a method for dynamic talent job matching and performance optimization management in enterprises, comprising the following steps:

[0013] Data processing: Real-time synchronous collection of raw, multi-dimensional human resources data from multiple business systems and data preprocessing; Through data preprocessing, anomaly cleaning, missing value imputation, and dimensionless standardization operations are performed to obtain standardized human resources data;

[0014] Two-way dynamic profile modeling: Construct two-way profile modeling on the job side and the employee side. When modeling the job side profile, obtain the work order text including process change content based on the technical modification work order and capacity adjustment document. Perform fuzzy matching between the work order text and the process change keyword library. When the similarity between the work order text and the keywords in the process change keyword library reaches a preset threshold, trigger the automatic upward adjustment process of the corresponding job indicator threshold. Write the updated job indicator threshold into the job profile database and record the job indicator threshold update log synchronously. When modeling the employee side profile, calculate the quantitative score of the employee under each job indicator based on standardized human resources data to form the employee profile vector and write the employee profile vector into the employee profile database.

[0015] Employee-Job Comprehensive Suitability Calculation: A standardized indicator matrix is ​​constructed based on employee and job indicators. The basic information entropy of job indicators is calculated based on the proportion of each employee indicator under the job indicators. An industry correction coefficient α is introduced to correct the basic information entropy of job indicators. Based on the corrected basic information entropy of job indicators, the difference coefficient of the corrected indicators is calculated and normalized to obtain the objective weight of each job indicator. The comprehensive suitability score between the employee and the target job is calculated by weighted summation based on the objective weight of each job indicator. Among them, the industry correction coefficient α is a quantitative coefficient based on the three dimensions of job safety risk level, operational complexity and severity of accident consequences.

[0016] Adaptability Level Classification: Based on the comprehensive adaptability score between employees and positions, employees are classified into adaptability levels according to the predetermined job adaptability threshold, and differentiated talent handling plans are generated for different employee adaptability levels.

[0017] Adaptability Level Linked to Dynamic Performance Weight Adjustment: Adjust performance calculation weights based on employee adaptability level, simultaneously identify three types of abnormal human resource allocation: understaffing, talent idleness, and job redundancy, and push early warning information and monthly human resource optimization reports;

[0018] Visualized terminal interaction and encrypted data storage: View the comprehensive suitability score of employees and positions, the details of performance accounting weight adjustment, and the list of human resource risk warnings through a visual interface. Adjust the threshold of position indicators and performance accounting weights. The entire process of fuzzy matching records, performance adjustment records, and warning documents are encrypted and stored in the backend database. It supports exporting to Excel at any time and archiving paper ledgers for future reference.

[0019] As a preferred approach, during data collection, standardized interfaces are used to connect to the enterprise's OA office system, production performance reporting system, employee training management system, and project construction and technical transformation management system to collect raw human resource data from five dimensions in real time, including employee static qualification data, job performance and production data, employee work behavior data, skills training and growth data, and cross-team collaboration data.

[0020] Among them, the static qualification data of employees includes their education, chemical professional title, special operation certificate, and years of front-line work experience;

[0021] Job performance production data includes unit production task completion rate, process operation accuracy rate, monthly production target achievement rate, and effective project performance duration.

[0022] Employee work behavior data includes monthly attendance compliance rate, average equipment failure response time, and annual process operation error rate;

[0023] Skills training growth data includes safety training assessment scores, pass rates for new process-specific training, and records of skills improvement at each stage;

[0024] Cross-shift team collaboration data includes cross-shift collaboration mutual evaluation scores and joint production unit collaborative task contribution scores.

[0025] Correspondingly, data preprocessing includes the following operations:

[0026] Abnormal cleaning: bilateral The criteria identify extreme data that exceeds the normal fluctuation range and simultaneously delete duplicate, blank, or invalid data.

[0027] Missing value imputation: For compliance missing indicators, the average value of the indicators of all employees in the same workshop and the same job level is used to imput them;

[0028] Dimensionless standardization: Positive and negative indicators are calculated separately, and all indicator values ​​are uniformly mapped to the 0-1 range, eliminating calculation biases caused by different dimensions such as years, duration, and scores. The standardization formula for positive indicators is:

[0029] ,

[0030] The standardized formula for the negative index is:

[0031] ,

[0032] In the formula, This represents the original index value. Indicates the first Maximum value of the indicator Indicates the first Minimum value of the indicator This represents the standardized index value.

[0033] As a preferred approach, job profile modeling includes the following operations:

[0034] Work order text extraction and cleaning: Real-time collection of enterprise technical transformation work orders and capacity adjustment documents; extraction of text content of three fields: title, description, and change content from the work orders and capacity adjustment documents; removal of spaces and punctuation marks to obtain work order text including process change content;

[0035] Fuzzy matching: Based on the edit distance algorithm, the similarity between the work order text and each keyword in the process change keyword library is calculated, and the synonym expansion matching is supplemented. The words in the synonym mapping table are regarded as equivalent keywords to participate in the similarity calculation. When the similarity value between a keyword and the work order text is greater than or equal to the preset threshold, it is judged as a successful match, and the automatic upward adjustment process of the corresponding job indicator threshold is triggered.

[0036] Threshold adjustment range calculation: The impact coefficient of process change is calculated by weighting the change range coefficient and the safety sensitivity coefficient. Based on the baseline threshold range and the impact coefficient of process changes Calculate the threshold adjustment amount;

[0037] Threshold Update and Compliance Verification: The threshold increase is verified for compliance based on the dual constraints of the single increase limit and the cumulative increase limit. After the verification is passed, the updated job indicator threshold is written to the job profile database and the job indicator threshold update log is recorded synchronously. The update log includes the update reason, trigger work order number, threshold increase amount and operation timestamp.

[0038] As a preferred method, fuzzy matching includes the following operations:

[0039] Synonym Enhancement Candidate Set Construction: Traverse each keyword in the process change keyword library, and for the current keyword... Search the synonym mapping table, if There is a synonym mapping, which will Expanding itself and all its synonyms into a flat list forms keywords. Enhanced candidate set The enhanced keyword candidate sets of all keywords are merged to form a complete enhanced keyword library, and the enhanced keyword library is deduplicated. ;

[0040] Work order text window segmentation: The work order text is segmented using a sliding window to generate several text segments to be matched. During segmentation, punctuation marks including periods, semicolons, and commas are used as sentence boundaries to divide the work order text into short sentences. For long sentences exceeding a threshold, a length of [missing information] is used. The sliding window, with a step size Overlapping segmentation is performed to generate multiple text windows, ensuring that keywords are captured even when embedded in long sentences. The sliding window length... and step length All support minor adjustments based on the company's work order writing habits;

[0041] Edit distance similarity matrix calculation: An enhanced keyword and a selected work order text window are combined for edit distance calculation using the Levenshtein Distance algorithm. This calculates the difference between the enhanced keyword E and the selected work order text window. Minimum number of insert, delete, and replace character operations required As edit distance, convert edit distance to The similarity score within the interval is used for each work order text window. Records and work order text window The enhanced keywords with the highest similarity and their similarity scores are denoted as follows: The edit distance conversion formula is as follows:

[0042] ,

[0043] In the formula, and The character lengths of the enhanced keywords and the text window are respectively used. When the two are exactly the same, the similarity is... When the edit distance equals the maximum length, the similarity is... ;

[0044] Deduplication and score merging within synonym groups: To prevent multiple synonyms of the same core keyword from matching successfully simultaneously and triggering duplicate events, deduplication and merging logic is executed. In Belongs to a certain core keyword Enhanced candidate set Then merge the matching record into Under the same core keyword Matching multiple text windows in the same work order, and retrieving all windows belonging to the same category. The highest similarity score is used as The final matching score is calculated using the following formula:

[0045] ,

[0046] Threshold determination and triggering: Traverse all core keywords Final match score ,like Then determine the core keywords. If a match is successful, output the matching result, trigger the corresponding job threshold adjustment process, and perform the associated job and indicator identification operation. If the final matching score of all core keywords is lower than [the specified threshold], [the process will continue]. If no keyword is matched in this work order, the process will be terminated, the work order will be marked as not requiring threshold update, and written to the log for future reference.

[0047] Job and indicator identification operation: Based on the successfully matched keyword categories and the equipment / section information involved in the work order, retrieve and lock the list of jobs affected by the technical change from the job management database. The keyword categories include operation and safety control. Match and optimize indicators according to the keyword categories. Operation keywords are associated with two indicators: professional operation skills and production work experience. Safety control keywords are associated with two indicators: safety professional ethics and fault emergency stress resistance.

[0048] As a preferred method, the calculation of the threshold adjustment magnitude includes the following operations:

[0049] Change range coefficient Determined: Based on the description of the scope of change in the work order text, the scope of change coefficient is determined when modifying partial equipment. Values Change range coefficient during the transformation of the entire production line Values Change range coefficient during plant-wide process upgrade Values ;

[0050] Security Sensitivity Determined: Automatic matching based on the safety risk level of the affected positions, with the safety sensitivity coefficient for general operational positions... Values Safety sensitivity coefficient of high-risk operation positions Values Safety sensitivity coefficient of hazardous operation positions Values ;

[0051] Calculation of the impact coefficient of process change: based on the change scope coefficient and security sensitivity coefficient Weighted summation yields the impact coefficient of process change. , ;

[0052] Final upward adjustment calculation: Set baseline upward adjustment range Based on the benchmark upward adjustment Impact coefficient of process change The final upward adjustment is calculated using the following formula: ;

[0053] Correspondingly, threshold updates and compliance checks include the following operations:

[0054] Single-time increase magnitude verification: Verify whether the threshold increase magnitude is less than or equal to the upper limit of the single-time increase magnitude. If it exceeds this limit, truncate the threshold increase magnitude to [a lower limit]. And record logs;

[0055] Cumulative upward adjustment verification: Calculate the cumulative upward percentage of the target position indicator since its initial setting, and verify whether the cumulative upward percentage is less than or equal to the upper limit of the cumulative upward adjustment. If the upper limit of the cumulative upward adjustment is reached, a prompt will be pushed with the message: "The position standard has reached the upper limit. Please evaluate whether the position grade needs to be adjusted."

[0056] Threshold update submission: After compliance verification is passed, the more detailed job indicator thresholds will be written into the job profile database and the logs will be updated synchronously.

[0057] As a preferred method, employee profiling modeling includes the following operations:

[0058] Dimension mapping rule loading and field matching: Obtain the pre-configured dimension-original field mapping rule table, and classify the standardized human resources data fields into six job indicators, namely professional operation skills, generated work experience, safety professional ethics, fault emergency stress resistance, ability, team collaboration ability and process innovation ability;

[0059] Sub-indicator aggregation calculation: For each employee, all sub-indicator values ​​in their standardized dataset are iterated and assigned to the corresponding job indicator according to the mapping rule table. The comprehensive score of each job indicator is calculated using a weighted average method. Negative indicators are converted to positive values ​​according to the negative indicator formula. Negative indicators include the annual process operation error rate and equipment failure response time. Quantitative Scores under Dimensional Job Indicators The calculation formula is:

[0060] ,

[0061] In the formula, Indicates the th in the current dimension Standardized values ​​of individual sub-indicators As weight;

[0062] Employee profile vector generation and compliance verification: The quantitative scores of the six aggregated job indicators are assembled into an employee profile vector in a fixed order. If there is no sub-indicator data to aggregate under a certain job indicator, the mean value of that dimension for employees of the same level in the same workshop under that job indicator is used temporarily, and the following is marked in the profile notes: "Dimension data is missing, filled with the mean value". The notes are then pushed to the HR specialist for review to verify whether the quantitative scores of all job indicators are within the specified range. If the range is exceeded, it is truncated to the boundary value and an exception is logged.

[0063] Persistent storage of employee profile data: After successful verification of the employee profile vector, the employee profile vector, employee ID, current position ID, data version number and timestamp of each employee are bound and written to the employee profile database, overwriting the old version of the employee profile vector. The old version of the employee profile vector is archived to the historical profile table, and it supports tracing the historical profile change trajectory.

[0064] As a preferred method, the calculation of employee-job fit includes the following steps:

[0065] Constructing the original matrix of standardized indicators: Using the number of employees in the same position as rows and the position indicators as columns, construct the original matrix of standardized indicators. , Indicates the number of employees in the sample. Indicates the number of job openings. This represents the standardized index value;

[0066] Calculate the percentage of employee metrics: Calculate the percentage of each employee metric under each job metric dimension. The first item under the job quota Percentage of individual employee indicators for:

[0067] ;

[0068] Calculate the basic information entropy of the indicators: Calculate the basic information entropy of the indicators for each dimension of the job indicators, the first... Basic information entropy of job indicators The calculation formula is:

[0069] ,

[0070] Among them, if ,Pick ;

[0071] Introducing an industry correction coefficient to correct the basic information entropy: The industry correction coefficient is calculated based on a two-layer mechanism of initial interpolation mapping and post-run grid search optimization. During the initial interpolation mapping, a quantitative score is calculated based on three dimensions: job safety risk level, operational complexity, and severity of accident consequences. Basic information entropy after adjustment of job indicators The calculation formula is:

[0072] ;

[0073] Calculate the coefficient of variation of the corrected index: for the first Job indicators, and the coefficient of variation of the adjusted indicators. The calculation formula is:

[0074] ;

[0075] Normalization solution for objective weights of job indicators: For the first Job indicators, normalized objective weights The calculation formula is:

[0076] ;

[0077] Overall fit calculation: The overall fit score between the employee and the target position is calculated by weighting and summing the objective weights of each position's indicators. The formula for calculating the overall fit between the employee and the target position is as follows:

[0078] ;

[0079] The industry correction coefficient is calculated based on a two-layer mechanism: initial interpolation mapping and post-run grid search optimization. This includes the following operations:

[0080] Initial interpolation mapping: For the position to be evaluated, a quantitative score is calculated based on three dimensions: safety risk level, operational complexity, and severity of accident consequences, and a comprehensive corrected score is then calculated. Based on the comprehensive revised score The initial linear interpolation mapping in the given interval determines the initial... value;

[0081] Post-run grid search optimization: After accumulating a predetermined number of historical employee job matching data, use the initial... Centered on a target, a candidate sequence of parameters is constructed with a preset radius and step size. A local grid search is performed with the objective function of maximizing the accuracy of employee job matching in historical data. The optimal value is then output and replaced with the initial value. The value includes historical data on employee job matching, which are adaptation records that have been manually reviewed and confirmed.

[0082] The local grid search, which uses maximizing the accuracy of employee job matching in historical data as the objective function, includes the following operations:

[0083] Prepare the training dataset: Select historical data of employee job matching that has been manually reviewed and confirmed as labeled samples to construct the training dataset. The training dataset includes employee profile vectors, job profiles and manually confirmed fit levels.

[0084] Construct a parameter search grid: Centered on the initial value of the current position, generate a discrete parameter candidate sequence according to the preset search radius and search step size. When the minimum value in the parameter candidate sequence is less than the lower threshold or the maximum value in the parameter candidate sequence is greater than the upper threshold, the search boundary is truncated to a predetermined interval, and a prompt is pushed that the search boundary has reached the preset threshold.

[0085] Perform a parameter-by-parameter person-job matching assessment: Iterate through each parameter candidate sequence For each sample in the training data, the value is... Substitute the values ​​into the employee-job comprehensive fit calculation, calculate the comprehensive fit score for each employee, and classify the employee fit level according to the job fit threshold of the fit level division, output the fit level, compare the output fit level with the manually confirmed fit level in the training sample, and calculate the matching accuracy.

[0086] Determine the optimal value: Among all candidate values, select the one with the highest matching accuracy. The value serves as the optimized industry correction coefficient for the current job position;

[0087] Results Validation and Persistence: The industry correction factor is compared with the initial... The values ​​are compared; if the difference is less than or equal to the threshold, the initial values ​​are maintained. If the value remains unchanged, but the difference exceeds the threshold, the industry correction coefficient will be written into the job profile database, replacing the original value. Values ​​are recorded and optimization logs are logged, including the search scope and various parameters. The values ​​correspond to accuracy, optimal value, and timestamp. Once optimization is complete, a notification will be sent indicating that the industry correction coefficient has been optimized and updated, which can be manually reviewed and confirmed.

[0088] As a preferred approach, when classifying the suitability levels, two fixed score thresholds are preset to divide the overall suitability into three levels. Differentiated talent handling plans are then generated based on the suitability level. The suitability level classification and corresponding manual handling plans are as follows:

[0089] If the fit score is greater than or equal to 85, the fit level is job fit, and the talent disposal plan is to include the employee in the core talent reserve pool.

[0090] If the fit score is greater than or equal to 60 points and less than 85 points, the fit level is mildly unsuitable. The talent disposal plan is to output a monthly special skills training program to specifically strengthen the weak indicators.

[0091] If the fit score is less than 60, the fit level is severely unsuitable, and the talent disposal plan is to optimize the situation by transferring to another workshop or by reassigning personnel.

[0092] As a preferred approach, dynamic performance weighting is implemented based on the recalculation cost-saving mechanism and the performance snapshot effective rules, with the performance weighting adjusted according to the adaptation level.

[0093] When adjusting performance evaluation weights based on employee suitability levels, the specific rules for dynamic weight adjustment are as follows:

[0094] For employees whose positions are well-matched to their jobs: the basic performance evaluation weight remains unchanged, but an additional 5% job suitability bonus coefficient is added.

[0095] For employees with mild mismatch: reduce the weight of fixed production performance by 10%, and simultaneously increase the weight of training and development assessment by 10%;

[0096] Severely mismatched employees: Directly lock annual performance evaluation authority and push human resource rectification warning to the management terminal;

[0097] When adjusting performance evaluation weights based on employee suitability levels, the recalculation and cost-saving mechanism and the performance snapshot effective rules shall be followed. The recalculation and cost-saving mechanism and the performance snapshot effective rules include the following:

[0098] Full recalculation trigger frequency limit: Only one full employee compatibility recalculation is allowed per day. Job threshold changes triggered by a single work order will be included in the batch recalculation task at midnight the next day. Real-time full recalculation will not be triggered for non-urgent manpower warnings.

[0099] Effective time of performance weight adjustment: The performance weight adjustment result is based on the snapshot at the time of recalculation completion and will officially take effect at 0:00 the next day. Performance records generated before the adjustment takes effect will not be retroactively applied. Human resources managers can manually trigger the immediate recalculation of individual positions.

[0100] Secondly, the present invention provides a dynamic adaptation and performance optimization management system for enterprise talent positions, which realizes dynamic adaptation and performance optimization management of enterprise talent positions based on a dynamic adaptation and performance optimization management method for enterprise talent positions as described in any of the first aspects. The system includes a data processing module, a two-way dynamic profile modeling module, an employee-position comprehensive fit calculation module, a fit level classification module, a fit level-linked performance dynamic weighting module, and a visual terminal interaction and data encryption storage module.

[0101] The data processing module is used to perform the following: real-time synchronous collection of raw multi-dimensional human resources data from multiple source business systems and data preprocessing; through data preprocessing, anomaly cleaning, missing value filling and dimensionless standardization operations are performed to obtain standardized human resources data;

[0102] The two-way dynamic profile modeling module is used to perform the following: construct two-way profile modeling on the job side and the employee side. When modeling the job side profile, the work order text including process change content is obtained based on the technical modification work order and capacity adjustment document. The work order text is fuzzy matched with the process change keyword library. When the similarity between the work order text and the keywords in the process change keyword library reaches the preset threshold, the automatic upward adjustment process of the corresponding job indicator threshold is triggered. The updated job indicator threshold is written into the job profile database, and the job indicator threshold update log is recorded synchronously. When modeling the employee side profile, the quantitative score of the employee under each job indicator is calculated based on standardized human resources data to form the employee profile vector, and the employee profile vector is written into the employee profile database.

[0103] The employee-job comprehensive suitability calculation module is used to perform the following: construct a standardized indicator original matrix based on employee and job indicators; calculate the basic information entropy of job indicators based on the proportion of each employee indicator under the job indicators; introduce an industry correction coefficient α to correct the basic information entropy of job indicators; calculate the correction indicator differentiation coefficient based on the corrected basic information entropy of job indicators and normalize it to obtain the objective weight of each job indicator; and calculate the comprehensive suitability score between the employee and the target job based on the weighted sum of the objective weights of each job indicator. Among them, the industry correction coefficient α is a quantitative coefficient based on the comprehensive evaluation of three dimensions: job safety risk level, operational complexity, and severity of accident consequences.

[0104] The adaptation level classification module is used to perform the following: based on the comprehensive adaptation score between employees and positions, the module classifies employees into adaptation levels according to the predetermined job adaptation threshold, and generates differentiated talent handling plans for different employee adaptation levels.

[0105] The Adaptation Level Linked Performance Dynamic Adjustment Module is used to perform the following: adjust the performance calculation weight according to the employee's adaptation level, simultaneously identify three types of abnormal human resource allocation: understaffing, idle talent, and redundant positions, and push early warning information and monthly human resource optimization reports.

[0106] The visual terminal interaction and data encryption storage module is used to perform the following: View the comprehensive suitability score of employees and positions, the details of performance accounting weight adjustment, and the human resource risk warning list through the visual interface; adjust the position indicator threshold and performance accounting weight; encrypt and store the entire process of fuzzy matching records, performance adjustment records, and warning documents to the backend database; support exporting to Excel at any time; and archive paper ledgers for future reference.

[0107] The enterprise talent dynamic job matching and performance optimization management method and system of the present invention have the following advantages:

[0108] 1. When calculating the overall suitability of employees and positions, an industry correction coefficient is introduced to correct the basic information entropy of the position indicators. This solves the problem that the standard entropy weighting method relies solely on data distribution for weighting and ignores the operation and safety priorities of high-risk chemical positions. The weighting of indicators is more in line with the actual production situation in coal chemical plants, and the accuracy of matching people with positions is greatly improved.

[0109] 2. When modeling job profiles, the system obtains work order texts containing process changes based on technical upgrade work orders and capacity adjustment documents. The work order texts are then fuzzily matched with a process change keyword library. When the similarity between the work order text and the keywords in the process change keyword library reaches a preset threshold, the system triggers an automatic upward adjustment process for the corresponding job indicator thresholds. This enables automatic iterative updates of job capability thresholds. After a company adds new production units or adjusts its processes, the system automatically updates indicator standards by capturing technical upgrade work orders. This eliminates the need for manual maintenance of job parameters, significantly reducing the time required for job standard maintenance and adapting to the continuous technical upgrade business characteristics of chemical companies.

[0110] 3. Adjust performance accounting weights based on employee suitability levels, and simultaneously identify three types of abnormal human resource allocation: understaffing, talent idleness, and job redundancy. This achieves a closed-loop linkage between job matching and performance accounting. The suitability level directly drives the automatic adjustment of performance weights through a trigger mechanism, connecting the entire business chain of talent assessment, performance appraisal, and human resource optimization, and achieving true integrated parallel closed-loop linkage.

[0111] 4. Based on the comprehensive fit score between employees and positions, employees are classified into fit levels according to the predetermined job fit threshold. Differentiated talent handling plans are generated for different employee fit levels. By setting up an automatic handling mechanism for fit level classification, targeted training plans, cross-workshop job transfer suggestions, and early warning of abnormal human resource allocation are output simultaneously, which greatly reduces the workload of human resource specialists in manual screening and reduces the performance unfairness caused by subjective scoring.

[0112] 5. The industry correction coefficient is calculated based on a two-layer mechanism of initial interpolation mapping and post-run grid search optimization. The selected industry correction coefficient values ​​are not arbitrarily set conventional parameters, but optimal technical parameters verified by systematic grid search. The technical effect is unpredictable. Attached Figure Description

[0113] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0114] The invention will be further described below with reference to the accompanying drawings.

[0115] Figure 1 Example 1 is a flowchart of a method for dynamic adaptation and performance optimization management of enterprise talent positions. Detailed Implementation

[0116] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0117] This invention provides a method and system for dynamic talent job matching and performance optimization management in enterprises, which addresses the technical problems of how to overcome the shortcomings of existing technical talent evaluation weights being detached from industry attributes, job standards being statically fixed, matching and performance system data being fragmented, and the lack of automated hierarchical optimization and handling schemes.

[0118] Example 1:

[0119] This invention provides a method for dynamic talent-job matching and performance optimization management in enterprises, including data processing, two-way dynamic profile modeling, calculation of employee-job comprehensive fit, matching level classification, dynamic adjustment of performance based on matching level, and visual terminal interaction and encrypted data storage.

[0120] Step S100 Data Processing: Collect raw, multi-dimensional human resource data from multiple source business systems in real time and perform data preprocessing. Through data preprocessing, perform anomaly cleaning, missing value filling, and dimensionless standardization operations to obtain standardized human resource data.

[0121] As a specific implementation of data processing, during data collection, standardized interfaces are used to connect with the enterprise's OA office system, production performance reporting system, employee training management system, and project construction and technical transformation management system to collect raw human resource data in five dimensions in real time, including employee static qualification data, job performance and production data, employee work behavior data, skills training and growth data, and cross-team collaboration data.

[0122] The data includes: static employee qualification data (education, chemical engineering professional title, special operation certificate, and years of front-line work experience); job performance production data (completion rate of unit production tasks, accuracy rate of process operation, monthly production target achievement rate, and effective project performance duration); employee work behavior data (monthly attendance compliance rate, average response time for equipment failure, and annual process operation error rate); skills training and development data (safety training assessment score, pass rate of new process-specific training, and records of phased skills improvement); and cross-shift team collaboration data (cross-shift collaboration mutual evaluation score and contribution score for collaborative tasks of joint production units).

[0123] As a specific implementation of data preprocessing, it includes the following operations:

[0124] (1) Abnormal cleaning: bilateral The criteria identify extreme data that exceeds the normal fluctuation range and simultaneously delete duplicate, blank, or invalid data.

[0125] (2) Filling missing values: For compliance missing indicators, fill them with the average value of the indicators of all employees in the same workshop and at the same job level;

[0126] (3) Dimensionless Standardization: Positive and negative indicators are calculated separately, and all indicator values ​​are uniformly mapped to the 0~1 range to eliminate calculation biases caused by different dimensions such as years, duration, and scores. The standardization formula for positive indicators is:

[0127] ,

[0128] The standardized formula for the negative index is:

[0129] ,

[0130] In the formula, This represents the original index value. Indicates the first Maximum value of the indicator Indicates the first Minimum value of the indicator This represents the standardized index value.

[0131] In this embodiment, during data processing, standardized interfaces are used to connect to the enterprise OA office system, production performance reporting system, employee training management system, and project construction and technical transformation management system. Five dimensions of raw human resources data are collected in real time: employee static qualification data, job performance production data, employee work behavior data, skills training and growth data, and cross-team collaboration data. Abnormal cleanup, missing data filling, and dimensionless standardization are automatically completed.

[0132] Step S200 Two-way Dynamic Profile Modeling: Construct two-way profile modeling for both job positions and employees. When modeling job positions, obtain work order texts containing process changes based on technical modification work orders and capacity adjustment documents. Perform fuzzy matching between the work order text and the process change keyword library. When the similarity between the work order text and the keywords in the process change keyword library reaches a preset threshold, trigger the automatic upward adjustment process of the corresponding job indicator threshold. Write the updated job indicator threshold into the job profile database and record the job indicator threshold update log synchronously. When modeling employee profiles, calculate the quantitative score of employees under each job indicator based on standardized human resource data to form an employee profile vector and write the employee profile vector into the employee profile database.

[0133] As a specific implementation of job-related profile modeling, it includes the following operations:

[0134] (1) Work order text extraction and cleaning: Real-time collection of enterprise technical transformation work orders and capacity adjustment documents, extract the text content of the title, description and change content fields from the work order and capacity adjustment documents, remove spaces and punctuation marks, and obtain work order text including process change content;

[0135] (2) Fuzzy matching: Based on the edit distance algorithm, the similarity between the work order text and each keyword in the process change keyword library is calculated, and the synonym expansion matching is supplemented. The words in the synonym mapping table are regarded as equivalent keywords to participate in the similarity calculation. When the similarity value between a keyword and the work order text is greater than or equal to the preset threshold, it is judged as a successful match, and the automatic upward adjustment process of the corresponding job indicator threshold is triggered.

[0136] (3) Calculation of threshold adjustment range: The impact coefficient of process change is calculated by weighting the change range coefficient and the safety sensitivity coefficient. Based on the baseline threshold range and the impact coefficient of process changes Calculate the threshold adjustment amount;

[0137] (4) Threshold update and compliance verification: The threshold increase is verified for compliance based on the dual constraints of the single increase limit and the cumulative increase limit. After the verification is passed, the updated job indicator threshold is written into the job profile database and the job indicator threshold update log is recorded synchronously. The update log includes the update reason, trigger work order number, threshold increase amount and operation timestamp.

[0138] As a specific implementation of fuzzy matching, it includes the following operations:

[0139] (1) Construction of the candidate set for synonym enhancement: Traverse each keyword in the process change keyword library, and for the current keyword Search the synonym mapping table, if There is a synonym mapping, which will Expanding itself and all its synonyms into a flat list forms keywords. Enhanced candidate set The enhanced keyword candidate sets of all keywords are merged to form a complete enhanced keyword library, and the enhanced keyword library is deduplicated. ;

[0140] (2) Work order text window segmentation: The work order text is segmented using a sliding window to generate several text segments to be matched. During segmentation, punctuation marks including periods, semicolons, and commas are used as sentence boundaries to divide the work order text into short sentences. For long sentences exceeding the threshold, a length of [missing information] is used. The sliding window, with a step size Overlapping segmentation is performed to generate multiple text windows, ensuring that keywords are captured even when embedded in long sentences. The sliding window length... and step length All support minor adjustments based on the company's work order writing habits;

[0141] (3) Calculation of edit distance similarity matrix: Select an enhanced keyword and the selected work order text window as a combination, and calculate the edit distance using the Levenshtein Distance algorithm to convert the enhanced keyword E into a text window. Minimum number of insert, delete, and replace character operations required As edit distance, convert edit distance to The similarity score within the interval is used for each work order text window. Records and work order text window The enhanced keywords with the highest similarity and their similarity scores are denoted as follows: The edit distance conversion formula is as follows:

[0142] ,

[0143] In the formula, and The character lengths of the enhanced keywords and the text window are respectively used. When the two are exactly the same, the similarity is... When the edit distance equals the maximum length, the similarity is... ;

[0144] (4) Deduplication and score merging within synonym groups: To prevent multiple synonyms of the same core keyword from matching successfully at the same time, resulting in duplicate triggers, deduplication and merging logic is executed. In Belongs to a certain core keyword Enhanced candidate set Then merge the matching record into Under the same core keyword Matching multiple text windows in the same work order, and retrieving all windows belonging to the same category. The highest similarity score is used as The final matching score is calculated using the following formula:

[0145] ,

[0146] (5) Threshold determination and triggering: Traverse all core keywords Final match score ,like Then determine the core keywords. If a match is successful, output the matching result, trigger the corresponding job threshold adjustment process, and perform the associated job and indicator identification operation. If the final matching score of all core keywords is lower than [the specified threshold], [the process will continue]. If no keyword is matched in this work order, the process will be terminated, the work order will be marked as not requiring threshold update, and written to the log for future reference.

[0147] (6) Identification of related positions and indicators: Based on the matched keyword categories and the equipment / section information involved in the work order, retrieve and lock the list of positions affected by the technical change from the position management database. The keyword categories include operation and safety control. Match and optimize indicators according to the keyword categories. Operation keywords are associated with two indicators: professional operation skills and production work experience. Safety control keywords are associated with two indicators: safety professional qualities and fault emergency stress resistance.

[0148] The specific implementation of the threshold adjustment calculation includes the following operations:

[0149] (1) Change range coefficient Determined: Based on the description of the scope of change in the work order text, the scope of change coefficient is determined when modifying partial equipment. Values Change range coefficient during the transformation of the entire production line Values Change range coefficient during plant-wide process upgrade Values ;

[0150] (2) Safety sensitivity coefficient Determined: Automatic matching based on the safety risk level of the affected positions, with the safety sensitivity coefficient for general operational positions... Values Safety sensitivity coefficient of high-risk operation positions Values Safety sensitivity coefficient of hazardous operation positions Values ;

[0151] (3) Calculation of the impact coefficient of process change: based on the change range coefficient and security sensitivity coefficient Weighted summation yields the impact coefficient of process change. , ;

[0152] (4) Calculation of final upward adjustment: Set the baseline upward adjustment. Based on the benchmark upward adjustment Impact coefficient of process change The final upward adjustment is calculated using the following formula: .

[0153] Correspondingly, threshold updates and compliance checks include the following operations:

[0154] (1) Single-time increase range verification: Verify whether the threshold increase range is less than or equal to the upper limit of the single-time increase range. If it exceeds, truncate the threshold increase range to [the limit value]. And record logs;

[0155] (2) Cumulative upward adjustment verification: Calculate the cumulative upward percentage of the target position indicator since its initial setting, and verify whether the cumulative upward percentage is less than or equal to the upper limit of the cumulative upward adjustment. If the upper limit of the cumulative upward adjustment is reached, a prompt will be pushed with the message: The position standard has reached the upper limit. Please evaluate whether the position grade needs to be adjusted.

[0156] (3) Threshold update submission: After compliance verification is passed, the more detailed job indicator thresholds are written into the job profile database and the log is updated synchronously.

[0157] The job-side modeling in this embodiment is implemented based on a job standard automatic iteration mechanism driven by technical modification work orders. This involves constructing a keyword library for process changes, keyword fuzzy matching trigger rules, rules for determining the threshold adjustment range, and upper and lower limit constraints for threshold updates.

[0158] A process change keyword library is constructed. The pre-set process change keyword library is divided into two categories according to the impact dimension: operation keywords and safety control keywords, as shown in Table 1.

[0159] Table 1. Keyword Database for Process Changes

[0160]

[0161] The keyword library allows human resource managers to incrementally expand it based on the company's actual production needs.

[0162] Keyword fuzzy matching trigger rules: Text scanning is performed on the three fields of the technical upgrade work order title, work order description, and change content. The similarity between the work order text and the keyword database is calculated using the Levenshtein Distance algorithm, supplemented by synonym expansion (e.g., "new device" matches "new equipment" or "capacity expansion"). When the similarity is greater than or equal to a preset threshold (default 0.75), the automatic upward adjustment process of the corresponding job indicator threshold is triggered. The "corresponding job" is determined as follows: based on the device / section name involved in the work order, the list of jobs bound to that device / section in the enterprise job management database is matched; if the work order does not specify device information, the job under the department that initiated the work order is used as the target job. The above job-device binding relationship is configured by human resources managers during system initialization and supports dynamic adjustment according to the enterprise organizational structure. The above matching threshold can be manually adjusted by managers in the system configuration interface.

[0163] For the automatic adjustment process that triggers the corresponding job indicator thresholds, taking the example of "the system for entering the technical renovation work order for the newly added No. 5 synthetic ammonia unit in the plant area automatically adjusting the thresholds for the two indicators 'professional operation skills' and 'safety professional ethics' by 15%", the complete "trigger → adjustment process" is as follows:

[0164] (1) Trigger: The word "new device" in the work order description has a similarity of 0.95 > 0.75 with the keyword database, and the start button is pressed;

[0165] (2) Job posting and standard setting: Focus on "synthetic ammonia operation position" + "professional operation skills and safety professional qualities";

[0166] (3) Calculation of the range: If the entire production line is to be renovated (R=1.5) or the position is to be classified as high-risk (S=1.2), the calculated range is Δ=10%×(0.5×1.5+ 0.5×1.2) =13.5%, which is rounded to the nearest whole number or calculated as 15% based on the actual value.

[0167] (4) Verification: 15% < 20% and cumulative percentage not reaching 150%, pass;

[0168] (5) Submission: The job threshold in the database has been officially increased by 15%.

[0169] The automatic adjustment process triggered in this embodiment starts a complete automated closed-loop procedure consisting of "precise positioning → weighted calculation → dual verification → data entry linkage", rather than simply and crudely increasing the value.

[0170] The rules for determining the threshold adjustment range are as follows: A baseline adjustment range is established. The final adjustment range is calculated based on the product of the baseline adjustment range and the process change coefficient. The process change impact coefficient is obtained by weighted summation of the change scope coefficient and the safety sensitivity coefficient. For the change scope coefficient, 1.0 is used for local equipment modification, 1.5 for the entire production line modification, and 2.0 for the entire plant process upgrade. For the safety sensitivity coefficient, 1.0 is used for general operation positions, 1.2 for high-risk operation positions, and 1.5 for extremely dangerous operation positions. The range of the process change impact coefficient is limited to [0.8, 2.0]. When the process change impact coefficient exceeds this range, the system automatically truncates to the boundary value and pushes a "threshold adjustment range abnormal" prompt, which is then manually reviewed and confirmed by human resources management personnel.

[0171] Threshold update upper and lower limits constraints: To prevent excessive threshold deviation from causing standard distortion, the upper limit of a single increase is 20%, and the upper limit of the cumulative increase is 150% of the initial threshold; when the cumulative increase reaches the upper limit, the system will automatically push a prompt "The job standard has reached the upper limit. Please evaluate whether the job grade needs to be adjusted".

[0172] When the process change coefficient If a value exceeding the range [0.8, 2.0] is truncated, and the calculated upward adjustment after truncation conflicts with the upper limit of the cumulative upward adjustment, parallel adjudication will be performed according to the following priority rule: the upper limit of the cumulative upward adjustment takes precedence over the upper limit of a single upward adjustment. The specific adjudication process is as follows: first, calculate the remaining available space between the current cumulative upward adjustment of the indicator and the 150% upper limit. Then take the proposed upward adjustment amount. and and The minimum of the three will be used as the final upward adjustment amount, i.e. This ensures that the cumulative magnitude after the final update strictly does not exceed the initial threshold. .like Instead of implementing any upward adjustment, a notification will be sent stating "The cumulative limit has been reached, and no adjustment will be made in this technical upgrade".

[0173] when When a value exceeds the range [0.8, 2.0] and triggers a manual review prompt, the system automatically suspends the threshold update process corresponding to that work order. The job indicator threshold remains unchanged until the human resources manager completes the review and confirmation on the visual terminal. If the review passes, the system performs a threshold update based on the truncation value; if the review fails, the system terminates the update and records a "manual rejection" log. During the suspension period, the suitability calculation for that job still uses the old threshold and will be recalculated after the update is completed.

[0174] The six dimensions of the employee's comprehensive competence profile are scored by standardized human resources data, categorized and aggregated according to the system's pre-defined "dimensional-original field mapping rule table." Specific mapping rules and calculation methods for each dimension are detailed below.

[0175] As a specific implementation of employee profile modeling, it includes the following operations:

[0176] (1) Dimension mapping rule loading and field matching: Obtain the pre-configured dimension-original field mapping rule table, and classify the standardized human resources data fields into six job indicators, namely professional operation skills, generated work experience, safety professional qualities, fault emergency stress resistance, ability, team collaboration ability and process innovation ability;

[0177] (2) Aggregation calculation of sub-indicators of each dimension: For each employee, all sub-indicator values ​​in their standardized dataset are iterated and assigned to the corresponding job indicators according to the mapping rule table. The comprehensive score of each job indicator is calculated using the weighted average method. Among them, negative indicators are converted to positive indicators according to the negative indicator formula. Negative indicators include annual process operation error rate and equipment failure response time. Quantitative Scores under Dimensional Job Indicators The calculation formula is:

[0178] ,

[0179] In the formula, Indicates the th in the current dimension Standardized values ​​of individual sub-indicators As weight;

[0180] (3) Profile Vector Generation and Compliance Verification: The quantitative scores of the six aggregated job indicators are assembled into employee profile vectors in a fixed order. If there is no sub-indicator data to aggregate under a certain job indicator, the mean of that dimension for employees of the same level in the same workshop under that job indicator is used as a substitute, and the following is marked in the profile notes: Dimension data is missing, filled with the mean. The notes are then pushed to the HR specialist for review to verify whether the quantitative scores of all job indicators are within the specified range. If the range is exceeded, it is truncated to the boundary value and an exception is logged.

[0181] (4) Persistent storage of profile data: After the employee profile vector is successfully verified, the employee profile vector, employee ID, current position ID, data version number and timestamp of each employee are bound and written into the employee profile database, overwriting the old version of the employee profile vector. The old version of the employee profile vector is archived to the historical profile table and supports the tracing of historical profile change trajectory.

[0182] In this embodiment, during the bidirectional dynamic profile modeling, enterprise technical transformation work orders and capacity adjustment documents are captured in real time, and the threshold requirements of the six core indicators for each position are dynamically updated to construct a dynamic job competency profile model; based on the standardized dataset, the corresponding quantitative scores of employees are extracted to generate a comprehensive personal quality profile of employees.

[0183] The six core job indicators are: professional operational skills, production work experience, safety professional ethics, emergency response and stress resistance, teamwork ability, and process innovation ability.

[0184] Step S300: Calculation of Employee-Job Comprehensive Suitability: Construct a standardized original matrix of indicators based on employee and job indicators. Calculate the basic information entropy of job indicators based on the proportion of each employee indicator under the job indicators. Introduce an industry correction coefficient α to correct the basic information entropy of job indicators. Calculate the correction coefficient of indicator differentiation based on the corrected basic information entropy of job indicators and normalize it to obtain the objective weight of each job indicator. Calculate the comprehensive suitability score between the employee and the target job based on the weighted sum of the objective weights of each job indicator. The industry correction coefficient α is a quantitative coefficient comprehensively evaluated based on three dimensions: job safety risk level, operational complexity, and the severity of accident consequences.

[0185] As a specific implementation of the employee-job fit calculation, this step includes the following operations:

[0186] (1) Constructing the original matrix of standardized indicators: Construct the original matrix of standardized indicators with the number of employees in the same position as the rows and the position indicators as the columns. , Indicates the number of employees in the sample. Indicates the number of job openings. This represents the standardized index value;

[0187] (2) Calculate the employee indicator percentage: Calculate the percentage of each employee indicator under each dimension of job indicators, the first... The first item under the job quota Percentage of individual employee indicators for:

[0188] ;

[0189] (3) Calculate the basic information entropy of the indicators: Calculate the basic information entropy of the indicators for each dimension of the job indicators, the first... Basic information entropy of job indicators The calculation formula is:

[0190] ,

[0191] Among them, if ,Pick ;

[0192] (4) Introduce industry correction coefficient to correct basic information entropy: calculate industry correction coefficient based on a two-layer mechanism of initial interpolation mapping and post-run grid search optimization. During the initial interpolation mapping, a quantitative score is calculated based on three dimensions: job safety risk level, operational complexity, and severity of accident consequences. Basic information entropy after adjustment of job indicators The calculation formula is:

[0193] ;

[0194] (5) Calculate the coefficient of variation of the corrected index: For the first Job indicators, and the coefficient of variation of the adjusted indicators. The calculation formula is:

[0195] ;

[0196] (6) Normalization solution for objective weights of job indicators: For the first Job indicators, normalized objective weights The calculation formula is:

[0197] ;

[0198] (7) Overall Fit Calculation: The overall fit score between the employee and the target position is calculated by weighting and summing the objective weights of each position indicator. The formula for calculating the overall fit between the employee and the target position is as follows:

[0199] .

[0200] The industry correction coefficient is calculated based on a two-layer mechanism: initial interpolation mapping and post-run grid search optimization. This includes the following operations:

[0201] (1) Initial interpolation mapping: For the position to be evaluated, a comprehensive corrected score is calculated based on three dimensions: safety risk level, operational complexity, and severity of accident consequences. Based on the comprehensive revised score The initial linear interpolation mapping in the given interval determines the initial... value;

[0202] (2) Post-run grid search optimization: After accumulating a predetermined number of historical employee job matching data, the initial grid search is then optimized. Centered on a target, a candidate sequence of parameters is constructed with a preset radius and step size. A local grid search is performed with the objective function of maximizing the accuracy of employee job matching in historical data. The optimal value is then output and replaced with the initial value. The value includes historical data on employee job matching, which are adaptation records that have been manually verified and confirmed.

[0203] The local grid search, which uses maximizing the accuracy of employee job matching in historical data as the objective function, includes the following operations:

[0204] (1) Prepare training dataset: Select the historical data of employee job matching confirmed by manual review as the labeled sample to construct the training dataset. The training dataset includes employee profile vector, job profile and manual confirmation of fit level;

[0205] (2) Construct a parameter search grid: With the initial value of the current position as the center, generate a discrete parameter candidate sequence according to the preset search radius and search step size. When the minimum value in the parameter candidate sequence is less than the lower threshold or the maximum value in the parameter candidate sequence is greater than the upper threshold, cut off the search boundary to the predetermined interval and push a prompt that the search boundary has touched the preset threshold.

[0206] (3) Perform person-job matching assessment parameter by parameter: traverse each parameter candidate sequence For each sample in the training data, the value is... Substitute the values ​​into the employee-job comprehensive fit calculation, calculate the comprehensive fit score for each employee, and classify the employee fit level according to the job fit threshold of the fit level division, output the fit level, compare the output fit level with the manually confirmed fit level in the training sample, and calculate the matching accuracy.

[0207] (4) Determine the optimal value: Among all candidate values, select the one with the highest matching accuracy. The value serves as the optimized industry correction coefficient for the current job position;

[0208] (5) Result verification and persistence: The industry correction coefficient is compared with the initial... The values ​​are compared; if the difference is less than or equal to the threshold, the initial values ​​are maintained. If the value remains unchanged, but the difference exceeds the threshold, the industry correction coefficient will be written into the job profile database, replacing the original value. Values ​​are recorded and optimization logs are logged, including the search scope and various parameters. The values ​​correspond to accuracy, optimal value, and timestamp. Once optimization is complete, a notification will be sent indicating that the industry correction coefficient has been optimized and updated, which can be manually reviewed and confirmed.

[0209] This embodiment introduces a job correction coefficient in the chemical industry to correct the basic information entropy, thereby achieving an objective weight allocation that fits the production scenario.

[0210] Industry adjustment coefficient It is not an arbitrarily set constant, but a quantitative coefficient based on a comprehensive assessment of three dimensions: job safety risk level, operational complexity, and the severity of accident consequences. The specific determination method involves three-dimensional measurement scoring, comprehensive scoring, and... Mapping, coal chemical industry scenarios A two-layer mechanism for value determination and grid search optimization.

[0211] Three-dimensional scoring: The positions to be evaluated are scored sequentially across three dimensions: safety risk level, operational complexity, and severity of accident consequences. See Table 2 for details.

[0212] Table 2, Three-Dimensional Assessment

[0213]

[0214] Overall score and Mapping: Let the three-dimensional scores be respectively , , Overall Corrected Score for the Position ( The range of values ​​is ). The values ​​are generated according to the mapping in Table 3.

[0215] Table 3. Mapping Information

[0216]

[0217] Linear interpolation formula: ;

[0218] In the above formula, the parameters have the following meanings:

[0219] Meaning: The actual comprehensive revised score of the position to be evaluated. The numerical source / determination method is: derived by adding the scores of the three dimensions. ( Safety risk level Operational complexity, The severity of the accident consequences ranges from 3 to 36.

[0220] The meaning is: This position belongs to The lower limit of the value range, the source / determination method of the value is: based on The mapping table for the value's location is determined. For example... If it falls within the interval [18, 24), then .

[0221] The meaning is: This position belongs to The upper limit of the value range, the source / determination method of the value is: based on The value's location is determined by looking up the mapping table. For example... If it falls within the interval [18, 24), then .

[0222] This means: the lower limit of α values ​​corresponding to the T value interval, and the source / determination method of the value is: determined by looking up a mapping table. For example, the interval [18, 24) corresponds to... If it is between 0.95 and 1.10, then .

[0223] Meaning: The interval corresponding to this T value The upper limit of the range is determined by looking up a mapping table. For example, the interval [18, 24) corresponds to... If it is between 0.95 and 1.10, then .

[0224] Meaning: The industry correction coefficient output after linear interpolation calculation. The source / determination method of the value is: ultimately used to correct the entropy value.

[0225] Coal chemical industry scenario The two-layer mechanism for value determination adopts a two-layer structure of "initial interpolation mapping + post-run grid search optimization".

[0226] First layer (initial) (Confirmed): For newly created positions or initial evaluation scenarios, the system directly uses the linear interpolation result based on the T-value as the initial value. This value ensures that any position can obtain a reasonable industry correction factor even without historical data.

[0227] Second layer (after running) Optimization): After the system runs and accumulates sufficient historical data on person-job matching (the default sample threshold is 200 employees with 6 months of matching records), the grid search optimization process is automatically started (the search range is based on the initial...). Centered on a radius of ±0.4 and with a step size of 0.05, the objective function is to maximize the historical accuracy of job matching. Perform local optimization around the value and output the optimal value for that position. Value, and automatically replace the initial value. The value is used as a correction coefficient for subsequent calculations. The output of the second-level optimization... The value weight is higher than the initial interpolation result of the first layer.

[0228] Through the above two mechanisms, the optimal value for final application in the coal chemical industry scenario is determined: front-line production operation positions (T value approximately 24-30). Technical management position (T-value approximately 6-12) The above-mentioned optimal values ​​are based on data from a 6-month controlled trial involving 200 employees, which validated the optimality (see the section on beneficial effects for details). The matching accuracy drops significantly when deviating from the preferred value. When extending to the metallurgical and thermal power industries, a custom three-dimensional scoring rule can be applied based on the industry's safety control level. Values.

[0229] The specific software implementation process of the second layer, "post-run grid search optimization," uses historical data on person-job matching as a sample, in the initial... A local exhaustive search is performed in the vicinity of the value to maximize matching accuracy, and the optimal industry correction coefficient for the job position is automatically determined. This value enables continuous calibration and optimization of the initial interpolation results. Details are as follows.

[0230] Step G1: Prepare the training dataset. Extract historical adaptation records of at least 200 employees over a period of more than 6 months from the historical judgment database. Select the records that have been manually reviewed and confirmed by human resources management personnel through the S7 visualization terminal as labeled samples to construct the training dataset. The elements in the dataset include employee profiles, job profiles, and manually confirmed adaptation levels.

[0231] Step G2: Construct the parameter search grid. Starting with the initial parameters for the current position... Value as the center Generate discrete candidate parameter sequences according to the preset search radius (default ±0.4) and search step size (default 0.05): .when or When the search boundary is reached, it will automatically truncate the search boundary to the range of [0.4, 1.8] and push a "Search boundary has reached the preset threshold" prompt.

[0232] Step G3: Perform person-job matching assessment parameter by parameter. The system iterates through each α value in the candidate parameter sequence, and for each sample in the training dataset D, performs a parameter-based assessment. Substituting the values ​​into the complete entropy weight algorithm, calculate the comprehensive fit score for each employee and output the fit level according to the three-level threshold rule. Compare the output fit level with the manually confirmed fit levels in the training sample to calculate the matching accuracy.

[0233] ;

[0234] Step G4: Determine the optimal Value. The system considers all candidates Among the values, select the one that maximizes the matching accuracy. The value is used as the optimization correction coefficient for this position.

[0235] That is, for each of the candidate parameter sequences The value is substituted into the training dataset to calculate the corresponding matching accuracy. Accuracy Output the α value corresponding to the maximum value. If there are multiple If the values ​​are tied for the highest (accuracy difference ≤ 0.5%), the system takes the median as the optimal value and records a "multiple peak values ​​tied" message in the log.

[0236] Step G5: Result Verification and Persistence. The system will perform optimized... With the initial Compare: If (i.e., the change does not exceed one step size), then maintain the initial state. Keep it unchanged to avoid overfitting; if the change exceeds 0.05, then... Write to the job profile database and replace the original one. Values, and record optimization logs (including search scope, etc.). (Values ​​correspond to accuracy, optimal value, and timestamp). After optimization, the system pushes a "Job Correction Coefficient has been optimized and updated" notification to the visualization terminal for human resources managers to review and confirm.

[0237] Step S400 Adaptation Level Classification: Based on the comprehensive adaptability score between employees and positions, employees are classified into adaptation levels according to the predetermined job adaptability threshold, and differentiated talent handling plans are generated for different employee adaptation levels.

[0238] As a specific implementation of the suitability level classification, two fixed score thresholds are preset to divide the overall suitability into three levels. Differentiated talent handling plans are generated based on the suitability level. The suitability level classification and corresponding manual handling plans are as follows:

[0239] (1) If the fit score is greater than or equal to 85 points, the fit level is job fit, and the talent disposal plan is to include the employee in the core talent reserve pool.

[0240] (2) If the fit score is greater than or equal to 60 points and less than 85 points, the fit level is slightly unsuitable. The talent disposal plan is to output a monthly special skills training plan and make targeted improvements to the weak indicators.

[0241] (3) If the fit score is less than 60 points, the fit level is severely unsuitable, and the talent disposal plan is to transfer to another workshop and optimize the personnel diversion.

[0242] Step S500 Adaptation Level Linked to Dynamic Performance Weight Adjustment: Adjust the performance calculation weight according to the employee's adaptation level, simultaneously identify three types of abnormal human resource allocation: understaffing, talent idleness, and job redundancy, and push early warning information and monthly human resource optimization reports.

[0243] This embodiment is based on the recalculation cost-saving mechanism and the performance snapshot effective rules to implement adaptive level linkage performance dynamic adjustment.

[0244] When adjusting performance evaluation weights based on employee suitability levels, the specific rules for dynamic weight adjustment are as follows:

[0245] (1) Job-matched employees: The basic performance calculation weight remains unchanged, and an additional 5% job-matching bonus coefficient is added;

[0246] (2) Mildly mismatched employees: Reduce the weight of fixed production performance by 10%, and simultaneously increase the weight of training and growth assessment by 10%;

[0247] (3) Severely mismatched employees: Directly lock the annual performance evaluation authority and push the human resources rectification warning to the management terminal.

[0248] When adjusting performance evaluation weights based on employee suitability levels, the recalculation and cost-saving mechanism and the performance snapshot effective rules shall be followed. The recalculation and cost-saving mechanism and the performance snapshot effective rules include the following:

[0249] (1) Full recalculation trigger frequency limit: Only one full employee fit recalculation is allowed per day. Job threshold changes triggered by a single work order are uniformly included in the batch recalculation task at midnight the next day. Real-time full recalculation is not triggered for non-emergency manpower warnings.

[0250] (2) Effective time of performance weight adjustment: The performance weight adjustment result shall be based on the snapshot of the time of recalculation completion and shall be officially effective at 0:00 the next day. Performance records generated before the adjustment takes effect shall not be retroactively applied. Human resources management personnel can manually trigger the immediate recalculation of a single position.

[0251] To avoid frequent changes in job thresholds leading to a full recalculation of fit and high-frequency fluctuations in performance weights, a built-in cost-saving mechanism and performance snapshot activation rules are implemented:

[0252] (i) Full recalculation trigger frequency limit: Only one full employee fit recalculation is allowed per day (default setting is 2:00 AM daily). Non-emergency manpower warnings (such as the proportion of severely unfit personnel exceeding 15% of the total number of employees in the factory area) will not trigger a real-time full recalculation. Job threshold changes triggered by a single work order will be uniformly included in the batch recalculation task at midnight the following day to avoid database read / write lock conflicts and wasted computing power.

[0253] (II) Effective Time of Performance Weight Adjustment: The performance weight adjustment result shall be based on the system snapshot at the time of recalculation completion and shall officially take effect at 0:00 on the following day (T+1 day). Performance records generated before the adjustment takes effect shall not be retrospectively applied to ensure the predictability and historical traceability of performance accounting. In extreme scenarios such as emergency staff shortages, human resource managers can manually trigger real-time recalculation of single positions on the visual terminal without being subject to the above frequency restrictions.

[0254] Step S600 Visual Terminal Interaction and Data Encryption Storage: View the comprehensive suitability score of employees and positions, performance accounting weight adjustment details, and human resource risk warning list through the visual interface. Adjust the position indicator threshold and performance accounting weight. The entire process of fuzzy matching records, performance adjustment records, and warning documents are encrypted and stored in the backend database. It supports exporting to Excel at any time and archiving paper ledgers for future reference.

[0255] In this embodiment (Frontline operational staff) and The preferred value for the (Technical Management) position was determined through grid search optimization. The search range was... With a step size of 0.05, the objective function is to maximize matching accuracy. (Different...) The accuracy rates for each value are shown in Table 4.

[0256] Table 4. Different Accuracy under the given value

[0257]

[0258] Comparative test data show that: when When the value is 1.0 (i.e., no industry correction, equivalent to the standard entropy weight method), the person-job matching accuracy rate is 78.0%; when When α is optimally set to 1.2, the accuracy rate increases to 92.3%; however, when α continues to increase to 1.5, the accuracy rate decreases to 85.0%. This inverted U-shaped relationship demonstrates the effectiveness of the method selected in this invention. The values ​​are not arbitrarily set conventional parameters, but rather optimal technical parameters verified through systematic grid search; their technical effects are unpredictable. (Technical Management Position) It also exhibits a similar inverted U-shaped characteristic (peak accuracy 88.6%).

[0259] A six-month simulation-controlled experiment was conducted using 200 frontline operators and technical management personnel from Jineng Holdings' coal-to-ammonia synthesis plant. The experiment used standardized assessment indicators, the same plant area samples, and the same frequency of skill changes. Traditional manual labor combined with a conventional HR entropy-weighted matching system resulted in a 61.7% accuracy rate for person-job matching; HR specialists spent 42 man-hours per month checking for mismatched personnel; and the skill attainment rate for mildly mismatched employees was 38% after six months. After implementing this method: the accuracy rate for person-job matching increased to 92.3%; manual checking time decreased to 13.5 man-hours, and the overall cost of human resource management decreased by 67.9%; and the skill attainment rate for mildly mismatched employees increased to 91% after six months of targeted training.

[0260] Simulation data can intuitively demonstrate that the method in this embodiment has significant technical advantages in terms of matching accuracy, human resource management costs, and talent training efficiency.

[0261] Example 2:

[0262] This invention relates to a dynamic talent-job matching and performance optimization management system for enterprises, comprising a data processing module, a two-way dynamic profile modeling module, an employee-job comprehensive fit calculation module, a fit level classification module, a fit level-linked performance dynamic adjustment module, and a visual terminal interaction and data encryption storage module.

[0263] The data processing module is used to perform the following: real-time synchronous collection of raw multi-dimensional human resources data from multiple source business systems and data preprocessing; through data preprocessing, anomaly cleaning, missing value filling and dimensionless standardization operations are performed to obtain standardized human resources data.

[0264] The two-way dynamic profile modeling module is used to perform the following: Constructing two-way profiles for both job positions and employees. During job position profile modeling, it obtains work order texts containing process changes based on technical modification work orders and capacity adjustment documents. It then performs fuzzy matching between the work order text and a process change keyword library. When the similarity between the work order text and keywords in the process change keyword library reaches a preset threshold, it triggers an automatic upward adjustment process for the corresponding job indicator threshold. The updated job indicator threshold is written to the job profile database, and a job indicator threshold update log is recorded synchronously. During employee profile modeling, it calculates the quantitative score of each employee under each job indicator based on standardized human resource data, forming an employee profile vector, and writes the employee profile vector to the employee profile database.

[0265] The employee-job comprehensive suitability calculation module is used to perform the following: Construct a standardized original matrix of indicators based on employee and job indicators; calculate the basic information entropy of job indicators based on the proportion of each employee indicator under the job indicators; introduce an industry correction coefficient α to correct the basic information entropy of job indicators; calculate the correction coefficient of indicator differentiation based on the corrected basic information entropy of job indicators and normalize it to obtain the objective weight of each job indicator; and calculate the comprehensive suitability score between the employee and the target job based on the weighted sum of the objective weights of each job indicator. The industry correction coefficient α is a quantitative coefficient comprehensively evaluated based on three dimensions: job safety risk level, operational complexity, and the severity of accident consequences.

[0266] The adaptation level classification module is used to perform the following: based on the comprehensive adaptation score between employees and positions, employees are classified into adaptation levels according to the predetermined job adaptation threshold, and differentiated talent handling plans are generated for different employee adaptation levels.

[0267] The Adaptation Level Linked Performance Dynamic Weight Adjustment Module is used to perform the following: adjust the performance calculation weight according to the employee's adaptation level, simultaneously identify three types of abnormal human resource allocation: understaffing, idle talent, and redundant positions, and push early warning information and monthly human resource optimization reports.

[0268] The visual terminal interaction and data encryption storage module is used to perform the following: View the comprehensive suitability score of employees and positions, the details of performance accounting weight adjustment, and the human resource risk warning list through the visual interface; adjust the position indicator threshold and performance accounting weight; encrypt and store the entire process of fuzzy matching records, performance adjustment records, and warning documents to the backend database; support exporting to Excel at any time; and archive paper ledgers for future reference.

[0269] The system in this embodiment can execute the method disclosed in Embodiment 1 to realize dynamic adaptation of enterprise talent positions and performance optimization management.

[0270] The above provides a detailed description of the enterprise talent job dynamic adaptation and performance optimization management method and system provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for dynamic talent-job matching and performance optimization management in enterprises, characterized in that, Includes the following steps: Data processing: Real-time synchronous collection of raw, multi-dimensional human resources data from multiple business systems and data preprocessing; Through data preprocessing, anomaly cleaning, missing value imputation, and dimensionless standardization operations are performed to obtain standardized human resources data; Two-way dynamic profile modeling: Construct two-way profile modeling on the job side and the employee side. When modeling the job side profile, obtain the work order text including process change content based on the technical modification work order and capacity adjustment document. Perform fuzzy matching between the work order text and the process change keyword library. When the similarity between the work order text and the keywords in the process change keyword library reaches a preset threshold, trigger the automatic upward adjustment process of the corresponding job indicator threshold. Write the updated job indicator threshold into the job profile database and record the job indicator threshold update log synchronously. When modeling the employee side profile, calculate the quantitative score of the employee under each job indicator based on standardized human resources data to form the employee profile vector and write the employee profile vector into the employee profile database. Employee-Job Comprehensive Suitability Calculation: A standardized indicator matrix is ​​constructed based on employee and job indicators. The basic information entropy of job indicators is calculated based on the proportion of each employee indicator under the job indicators. An industry correction coefficient α is introduced to correct the basic information entropy of job indicators. Based on the corrected basic information entropy of job indicators, the difference coefficient of the corrected indicators is calculated and normalized to obtain the objective weight of each job indicator. The comprehensive suitability score between the employee and the target job is calculated by weighted summation based on the objective weight of each job indicator. Among them, the industry correction coefficient α is a quantitative coefficient based on the three dimensions of job safety risk level, operational complexity and severity of accident consequences. Adaptability Level Classification: Based on the comprehensive adaptability score between employees and positions, employees are classified into adaptability levels according to the predetermined job adaptability threshold, and differentiated talent handling plans are generated for different employee adaptability levels. Adaptability Level Linked to Dynamic Performance Weight Adjustment: Adjust performance calculation weights based on employee adaptability level, simultaneously identify three types of abnormal human resource allocation: understaffing, talent idleness, and job redundancy, and push early warning information and monthly human resource optimization reports; Visualized terminal interaction and encrypted data storage: View the comprehensive suitability score of employees and positions, the details of performance accounting weight adjustment, and the list of human resource risk warnings through a visual interface. Adjust the threshold of position indicators and performance accounting weights. The entire process of fuzzy matching records, performance adjustment records, and warning documents are encrypted and stored in the backend database. It supports exporting to Excel at any time and archiving paper ledgers for future reference.

2. The enterprise talent dynamic job matching and performance optimization management method according to claim 1, characterized in that, During data collection, standardized interfaces are used to connect to the enterprise OA office system, production performance reporting system, employee training management system and project construction and technical transformation management system to collect raw human resources data in five dimensions in real time, including employee static qualification data, job performance and production data, employee work behavior data, skills training and growth data and cross-team collaboration data. Among them, the static qualification data of employees includes their education, chemical professional title, special operation certificate, and years of front-line work experience; Job performance production data includes unit production task completion rate, process operation accuracy rate, monthly production target achievement rate, and effective project performance duration. Employee work behavior data includes monthly attendance compliance rate, average equipment failure response time, and annual process operation error rate; Skills training growth data includes safety training assessment scores, pass rates for new process-specific training, and records of skills improvement at each stage; Cross-shift team collaboration data includes cross-shift collaboration mutual evaluation scores and joint production unit collaborative task contribution scores. Correspondingly, data preprocessing includes the following operations: Abnormal cleaning: bilateral The criteria identify extreme data that exceeds the normal fluctuation range and simultaneously delete duplicate, blank, or invalid data. Missing value imputation: For compliance missing indicators, the average value of the indicators of all employees in the same workshop and the same job level is used to imput them; Dimensionless standardization: Positive and negative indicators are calculated separately, and all indicator values ​​are uniformly mapped to the 0-1 range, eliminating calculation biases caused by different dimensions such as years, duration, and scores. The standardization formula for positive indicators is: ， The standardized formula for the negative index is: ， In the formula, This represents the original index value. Indicates the first Maximum value of the indicator Indicates the first Minimum value of the indicator This represents the standardized index value.

3. The enterprise talent dynamic job matching and performance optimization management method according to claim 1, characterized in that, Job profile modeling includes the following operations: Work order text extraction and cleaning: Real-time collection of enterprise technical transformation work orders and capacity adjustment documents; extraction of text content of three fields: title, description, and change content from the work orders and capacity adjustment documents; removal of spaces and punctuation marks to obtain work order text including process change content; Fuzzy matching: Based on the edit distance algorithm, the similarity between the work order text and each keyword in the process change keyword library is calculated, and the synonym expansion matching is supplemented. The words in the synonym mapping table are regarded as equivalent keywords to participate in the similarity calculation. When the similarity value between a keyword and the work order text is greater than or equal to the preset threshold, it is judged as a successful match, and the automatic upward adjustment process of the corresponding job indicator threshold is triggered. Threshold adjustment range calculation: The impact coefficient of process change is calculated by weighting the change range coefficient and the safety sensitivity coefficient. Based on the baseline threshold range and the impact coefficient of process changes Calculate the threshold adjustment amount; Threshold Update and Compliance Verification: The threshold increase is verified for compliance based on the dual constraints of the single increase limit and the cumulative increase limit. After the verification is passed, the updated job indicator threshold is written to the job profile database and the job indicator threshold update log is recorded synchronously. The update log includes the update reason, trigger work order number, threshold increase amount and operation timestamp.

4. The enterprise talent dynamic job matching and performance optimization management method according to claim 3, characterized in that, Fuzzy matching includes the following operations: Synonym Enhancement Candidate Set Construction: Traverse each keyword in the process change keyword library, and for the current keyword... Search the synonym mapping table, if There is a synonym mapping, which will Expanding itself and all its synonyms into a flat list forms keywords. Enhanced candidate set The enhanced keyword candidate sets of all keywords are merged to form a complete enhanced keyword library, and the enhanced keyword library is deduplicated. ; Work order text window segmentation: The work order text is segmented using a sliding window to generate several text segments to be matched. During segmentation, punctuation marks including periods, semicolons, and commas are used as sentence boundaries to divide the work order text into short sentences. For long sentences exceeding a threshold, a length of [missing information] is used. The sliding window, with a step size Overlapping segmentation is performed to generate multiple text windows, ensuring that keywords are captured even when embedded in long sentences. The sliding window length... and step length All support minor adjustments based on the company's work order writing habits; Edit distance similarity matrix calculation: An enhanced keyword and a selected work order text window are combined for edit distance calculation using the Levenshtein Distance algorithm. This calculates the difference between the enhanced keyword E and the selected work order text window. Minimum number of insert, delete, and replace character operations required As edit distance, convert edit distance to The similarity score within the interval is used for each work order text window. Records and work order text window The enhanced keywords with the highest similarity and their similarity scores are denoted as follows: The edit distance conversion formula is as follows: ， In the formula, and The character lengths of the enhanced keywords and the text window are respectively used. When the two are exactly the same, the similarity is... When the edit distance equals the maximum length, the similarity is... ; Deduplication and score merging within synonym groups: To prevent multiple synonyms of the same core keyword from matching successfully simultaneously and triggering duplicate events, deduplication and merging logic is executed. In Belongs to a certain core keyword Enhanced candidate set Then merge the matching record into Under the same core keyword Matching multiple text windows in the same work order, and retrieving all windows belonging to the same category. The highest similarity score is used as The final matching score is calculated using the following formula: ， Threshold determination and triggering: Traverse all core keywords Final match score ,like Then determine the core keywords. If a match is successful, output the matching result, trigger the corresponding job threshold adjustment process, and perform the associated job and indicator identification operation. If the final matching score of all core keywords is lower than [the specified threshold], [the process will continue]. If no keyword is matched in this work order, the process will be terminated, the work order will be marked as not requiring threshold update, and written to the log for future reference. Job and indicator identification operation: Based on the successfully matched keyword categories and the equipment / section information involved in the work order, retrieve and lock the list of jobs affected by the technical change from the job management database. The keyword categories include operation and safety control. Match and optimize indicators according to the keyword categories. Operation keywords are associated with two indicators: professional operation skills and production work experience. Safety control keywords are associated with two indicators: safety professional ethics and fault emergency stress resistance.

5. The enterprise talent dynamic job matching and performance optimization management method according to claim 3, characterized in that, The calculation of the threshold adjustment amount includes the following operations: Change range coefficient Determined: Based on the description of the scope of change in the work order text, the scope of change coefficient is determined when modifying partial equipment. Values Change range coefficient during the transformation of the entire production line Values Change range coefficient during plant-wide process upgrade Values ; Security Sensitivity Determined: Automatic matching based on the safety risk level of the affected positions, with the safety sensitivity coefficient for general operational positions... Values Safety sensitivity coefficient of high-risk operation positions Values Safety sensitivity coefficient of hazardous operation positions Values ; Calculation of the impact coefficient of process change: based on the change scope coefficient and security sensitivity coefficient Weighted summation yields the impact coefficient of process change. , ; Final upward adjustment calculation: Set baseline upward adjustment range Based on the benchmark upward adjustment Impact coefficient of process change The final upward adjustment is calculated using the following formula: ; Correspondingly, threshold updates and compliance checks include the following operations: Single-time increase magnitude verification: Verify whether the threshold increase magnitude is less than or equal to the upper limit of the single-time increase magnitude. If it exceeds this limit, truncate the threshold increase magnitude to [a lower limit]. And record logs; Cumulative upward adjustment verification: Calculate the cumulative upward percentage of the target position indicator since its initial setting, and verify whether the cumulative upward percentage is less than or equal to the upper limit of the cumulative upward adjustment. If the upper limit of the cumulative upward adjustment is reached, a prompt will be pushed with the message: "The position standard has reached the upper limit. Please evaluate whether the position grade needs to be adjusted." Threshold update submission: After compliance verification is passed, the more detailed job indicator thresholds will be written into the job profile database and the logs will be updated synchronously.

6. The enterprise talent dynamic job matching and performance optimization management method according to claim 3, characterized in that, Employee profiling modeling includes the following operations: Dimension mapping rule loading and field matching: Obtain the pre-configured dimension-original field mapping rule table, and classify the standardized human resources data fields into six job indicators, namely professional operation skills, generated work experience, safety professional ethics, fault emergency stress resistance, ability, team collaboration ability and process innovation ability; Sub-indicator aggregation calculation: For each employee, all sub-indicator values ​​in their standardized dataset are iterated and assigned to the corresponding job indicator according to the mapping rule table. The comprehensive score of each job indicator is calculated using a weighted average method. Negative indicators are converted to positive values ​​according to the negative indicator formula. Negative indicators include the annual process operation error rate and equipment failure response time. Quantitative Scores under Dimensional Job Indicators The calculation formula is: ， In the formula, Indicates the th in the current dimension Standardized values ​​of individual sub-indicators As weight; Employee profile vector generation and compliance verification: The quantitative scores of the six aggregated job indicators are assembled into an employee profile vector in a fixed order. If there is no sub-indicator data to aggregate under a certain job indicator, the mean value of that dimension for employees of the same level in the same workshop under that job indicator is used temporarily, and the following is marked in the profile notes: "Dimension data is missing, filled with the mean value". The notes are then pushed to the HR specialist for review to verify whether the quantitative scores of all job indicators are within the specified range. If the range is exceeded, it is truncated to the boundary value and an exception is logged. Persistent storage of employee profile data: After successful verification of the employee profile vector, the employee profile vector, employee ID, current position ID, data version number and timestamp of each employee are bound and written to the employee profile database, overwriting the old version of the employee profile vector. The old version of the employee profile vector is archived to the historical profile table, and it supports tracing the historical profile change trajectory.

7. The enterprise talent dynamic job matching and performance optimization management method according to claim 1, characterized in that, The calculation of employee-job fit includes the following steps: Constructing the original matrix of standardized indicators: Using the number of employees in the same position as rows and the position indicators as columns, construct the original matrix of standardized indicators. , Indicates the number of employees in the sample. Indicates the number of job openings. This represents the standardized index value; Calculate the percentage of employee metrics: Calculate the percentage of each employee metric under each job metric dimension. The first item under the job quota Percentage of individual employee indicators for: ； Calculate the basic information entropy of the indicators: Calculate the basic information entropy of the indicators for each dimension of the job indicators, the first... Basic information entropy of job indicators The calculation formula is: ， Among them, if ,Pick ; Introducing an industry correction coefficient to correct the basic information entropy: The industry correction coefficient is calculated based on a two-layer mechanism of initial interpolation mapping and post-run grid search optimization. During the initial interpolation mapping, a quantitative score is calculated based on three dimensions: job safety risk level, operational complexity, and severity of accident consequences. Basic information entropy after adjustment of job indicators The calculation formula is: ； Calculate the coefficient of variation of the corrected index: for the first Job indicators, and the coefficient of variation of the adjusted indicators. The calculation formula is: ； Normalization solution for objective weights of job indicators: For the first Job indicators, normalized objective weights The calculation formula is: ； Overall fit calculation: The overall fit score between the employee and the target position is calculated by weighting and summing the objective weights of each position's indicators. The formula for calculating the overall fit between the employee and the target position is as follows: ； The industry correction coefficient is calculated based on a two-layer mechanism: initial interpolation mapping and post-run grid search optimization. This includes the following operations: Initial interpolation mapping: For the position to be evaluated, a quantitative score is calculated based on three dimensions: safety risk level, operational complexity, and severity of accident consequences, and a comprehensive corrected score is then calculated. Based on the comprehensive revised score The initial linear interpolation mapping in the given interval determines the initial... value; Post-run grid search optimization: After accumulating a predetermined number of historical employee job matching data, use the initial... Centered on a target, a candidate sequence of parameters is constructed with a preset radius and step size. A local grid search is performed with the objective function of maximizing the accuracy of employee job matching in historical data. The optimal value is then output and replaced with the initial value. The value includes historical data on employee job matching, which are adaptation records that have been manually reviewed and confirmed. The local grid search, which uses maximizing the accuracy of employee job matching in historical data as the objective function, includes the following operations: Prepare the training dataset: Select historical data of employee job matching that has been manually reviewed and confirmed as labeled samples to construct the training dataset. The training dataset includes employee profile vectors, job profiles and manually confirmed fit levels. Construct a parameter search grid: Centered on the initial value of the current position, generate a discrete parameter candidate sequence according to the preset search radius and search step size. When the minimum value in the parameter candidate sequence is less than the lower threshold or the maximum value in the parameter candidate sequence is greater than the upper threshold, the search boundary is truncated to a predetermined interval, and a prompt is pushed that the search boundary has reached the preset threshold. Perform a parameter-by-parameter person-job matching assessment: Iterate through each parameter candidate sequence For each sample in the training data, the value is... Substitute the values ​​into the employee-job comprehensive fit calculation, calculate the comprehensive fit score for each employee, and classify the employee fit level according to the job fit threshold of the fit level division, output the fit level, compare the output fit level with the manually confirmed fit level in the training sample, and calculate the matching accuracy. Determine the optimal value: Among all candidate values, select the one with the highest matching accuracy. The value serves as the optimized industry correction coefficient for the current job position; Results Validation and Persistence: The industry correction factor is compared with the initial... The values ​​are compared; if the difference is less than or equal to the threshold, the initial values ​​are maintained. If the value remains unchanged, but the difference exceeds the threshold, the industry correction coefficient will be written into the job profile database, replacing the original value. Values ​​are recorded and optimization logs are logged, including the search scope and various parameters. The values ​​correspond to accuracy, optimal value, and timestamp. Once optimization is complete, a notification will be sent indicating that the industry correction coefficient has been optimized and updated, which can be manually reviewed and confirmed.

8. The enterprise talent dynamic job matching and performance optimization management method according to claim 1, characterized in that, When classifying the suitability level, two fixed score thresholds are preset to divide the overall suitability into three levels. Differentiated talent handling plans are generated based on the suitability level. The suitability level classification and corresponding manual handling plans are as follows: If the fit score is greater than or equal to 85, the fit level is job fit, and the talent disposal plan is to include the employee in the core talent reserve pool. If the fit score is greater than or equal to 60 points and less than 85 points, the fit level is mildly unsuitable. The talent disposal plan is to output a monthly special skills training program to specifically strengthen the weak indicators. If the fit score is less than 60, the fit level is severely unsuitable, and the talent disposal plan is to optimize the situation by transferring to another workshop or by reassigning personnel.

9. The enterprise talent dynamic job matching and performance optimization management method according to claim 8, characterized in that, Based on the recalculation and cost-saving mechanism and the performance snapshot effective rule, the performance dynamic adjustment is linked to the level of performance; When adjusting performance evaluation weights based on employee suitability levels, the specific rules for dynamic weight adjustment are as follows: For employees whose positions are well-matched to their jobs: the basic performance evaluation weight remains unchanged, but an additional 5% job suitability bonus coefficient is added. For employees with mild mismatch: reduce the weight of fixed production performance by 10%, and simultaneously increase the weight of training and development assessment by 10%; Severely mismatched employees: Directly lock annual performance evaluation authority and push human resource rectification warning to the management terminal; When adjusting performance evaluation weights based on employee suitability levels, the recalculation and cost-saving mechanism and the performance snapshot effective rules shall be followed. The recalculation and cost-saving mechanism and the performance snapshot effective rules include the following: Full recalculation trigger frequency limit: Only one full employee compatibility recalculation is allowed per day. Job threshold changes triggered by a single work order will be included in the batch recalculation task at midnight the next day. Real-time full recalculation will not be triggered for non-urgent manpower warnings. Effective time of performance weight adjustment: The performance weight adjustment result is based on the snapshot at the time of recalculation completion and will officially take effect at 0:00 the next day. Performance records generated before the adjustment takes effect will not be retroactively applied. Human resources managers can manually trigger the immediate recalculation of individual positions.

10. A dynamic talent-job matching and performance optimization management system for enterprises, characterized in that, Based on the enterprise talent job dynamic adaptation and performance optimization management method as described in any one of claims 1-9, the system realizes enterprise talent job dynamic adaptation and performance optimization management, the system includes a data processing module, a two-way dynamic profile modeling module, an employee-job comprehensive fit calculation module, a fit level classification module, a fit level linkage performance dynamic adjustment module, and a visual terminal interaction and data encryption storage module. The data processing module is used to perform the following: real-time synchronous collection of raw multi-dimensional human resources data from multiple source business systems and data preprocessing; through data preprocessing, anomaly cleaning, missing value filling and dimensionless standardization operations are performed to obtain standardized human resources data; The two-way dynamic profile modeling module is used to perform the following: construct two-way profile modeling on the job side and the employee side. When modeling the job side profile, the work order text including process change content is obtained based on the technical modification work order and capacity adjustment document. The work order text is fuzzy matched with the process change keyword library. When the similarity between the work order text and the keywords in the process change keyword library reaches the preset threshold, the automatic upward adjustment process of the corresponding job indicator threshold is triggered. The updated job indicator threshold is written into the job profile database, and the job indicator threshold update log is recorded synchronously. When modeling the employee side profile, the quantitative score of the employee under each job indicator is calculated based on standardized human resources data to form the employee profile vector, and the employee profile vector is written into the employee profile database. The employee-job comprehensive suitability calculation module is used to perform the following: construct a standardized indicator original matrix based on employee and job indicators; calculate the basic information entropy of job indicators based on the proportion of each employee indicator under the job indicators; introduce an industry correction coefficient α to correct the basic information entropy of job indicators; calculate the correction indicator differentiation coefficient based on the corrected basic information entropy of job indicators and normalize it to obtain the objective weight of each job indicator; and calculate the comprehensive suitability score between the employee and the target job based on the weighted sum of the objective weights of each job indicator. Among them, the industry correction coefficient α is a quantitative coefficient based on the comprehensive evaluation of three dimensions: job safety risk level, operational complexity, and severity of accident consequences. The adaptation level classification module is used to perform the following: based on the comprehensive adaptation score between employees and positions, the module classifies employees into adaptation levels according to the predetermined job adaptation threshold, and generates differentiated talent handling plans for different employee adaptation levels. The Adaptation Level Linked Performance Dynamic Adjustment Module is used to perform the following: adjust the performance calculation weight according to the employee's adaptation level, simultaneously identify three types of abnormal human resource allocation: understaffing, idle talent, and redundant positions, and push early warning information and monthly human resource optimization reports. The visual terminal interaction and data encryption storage module is used to perform the following: View the comprehensive suitability score of employees and positions, the details of performance accounting weight adjustment, and the human resource risk warning list through the visual interface; adjust the position indicator threshold and performance accounting weight; encrypt and store the entire process of fuzzy matching records, performance adjustment records, and warning documents to the backend database; support exporting to Excel at any time; and archive paper ledgers for future reference.

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