A post value evaluation method and system based on multi-source data analysis

Abstract

This invention discloses a method and system for job value assessment based on multi-source data analysis, belonging to the field of data processing technology. The method includes: constructing a job time-series knowledge graph based on a basic job data package, organizing the changes in job relationships over time, and generating a job time-series relationship diagram; extracting job responsibility and workload change characteristics from the job time-series relationship diagram, and performing credibility correction to generate a candidate job value profile; performing real-time value calculation and grade mapping based on the net job value profile, identifying the reasons for value changes, and generating real-time job value results; and generating a job value assessment report based on the real-time job value results through result integration analysis and correlation path tracing. This invention provides a stable and reliable benchmark by separating job characteristics from personnel characteristics in the candidate job value profile and performing counterfactual calibration, thereby improving the objectivity and comparability of the assessment.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for job value assessment based on multi-source data analysis. Background Technology

[0002] With the deep integration of big data analytics and artificial intelligence technologies, the field of organizational management and human resource assessment is undergoing a paradigm shift from static description to dynamic perception, and from experience-based judgment to data-driven approaches. Related technological developments are reflected in the integrated processing of multi-source heterogeneous data, the mining of relationships based on knowledge graphs, and the widespread application of time-series data analysis methods. In particular, cutting-edge theories such as causal inference and counterfactual analysis are being introduced to more accurately remove confounding factors, explore pure causal relationships between variables, and provide new methodological tools for measuring the essential attributes of assessment objects.

[0003] Existing technologies still have significant limitations when applied to job value assessment in dynamic organizational environments. A core challenge lies in the difficulty of effectively distinguishing the value created by the inherent responsibilities and task requirements of a "job" from the value fluctuations resulting from the individual abilities and performance of a specific "personnel member." The high coupling between personnel characteristics and job characteristics means that the assessment results are essentially a comprehensive reflection of the "person-job" hybrid value over a specific period, rather than a pure and stable measure of the job's value itself. This makes the assessment results susceptible to interference from external factors such as personnel changes and fluctuations in individual performance, failing to accurately depict the true baseline of job value and its pure trajectory of evolution over time. Consequently, the reliability and fairness of the assessment results in long-term human resource decisions such as salary design and staffing determination are weakened. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a job value assessment method based on multi-source data analysis to solve the problems of difficulty in separating job characteristics from personnel characteristics and difficulty in accurately describing the value of the job itself in a dynamic organizational environment.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, this invention provides a job value assessment method based on multi-source data analysis, comprising: collecting multi-source raw data related to job positions, and uniformly classifying and time-stamping the data from different sources to form a basic job data package; constructing a job time-series knowledge graph based on the basic job data package, and organizing the changes in job relationships over time to generate a job time-series relationship diagram; extracting job responsibility change and task load change features from the job time-series relationship diagram, and performing credibility correction to generate a candidate job value profile; separating job characteristics from personnel characteristics in the candidate job value profile and performing counterfactual calibration to generate a net job value profile; performing real-time value calculation and level mapping based on the net job value profile and identifying the reasons for value changes to generate a real-time job value result; and generating a job value assessment report based on the real-time job value result through result integration analysis and correlation path tracing.

[0007] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the steps of collecting multi-source raw data related to the job and uniformly classifying and time-stamping the data from different sources to form a basic job data package are as follows: Read the job title, job code, department, job description, job requirements and reporting relationship information, create a job identifier for each job to be evaluated, and collect multi-source raw data around the job identifier; Based on job identification, job attribution processing is performed on multi-source raw data to generate job attribution data. The record time information in the job attribution data is processed in a unified format to generate time job attribution data. The data is then standardized, organized, and aggregated to form a basic job data package.

[0008] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the specific steps for constructing a job time-series knowledge graph based on a job basic data package are as follows: Read the basic job information and job association information from the job basic data package, and extract the set of job relationship elements based on the job identifier; Semantic analysis and classification of the set of job relationship elements are performed to identify job positions, responsibilities, tasks, processes, projects, collaborating objects, system objects, and market objects, and to establish the relationship between the target job and various objects, thus obtaining the set of job relationships; Extract the time stamps corresponding to each relationship in the job relationship set, and construct a job time sequence knowledge graph based on job identifiers and time stamps.

[0009] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the specific steps for generating the job time-series relationship diagram are as follows: Read the relationships and time information corresponding to the target job from the job time sequence knowledge graph, arrange the relationships in chronological order to obtain the job relationship time sequence, and compare the relationships in the job relationship time sequence before and after to form the job relationship change results; Centered on the target position, and based on the changes in position relationships, various position relationships at different times are organized and associated to generate a position time sequence relationship diagram.

[0010] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the specific steps for generating candidate job value profiles are as follows: Read the job relationship and time change information corresponding to the target job in the job time sequence relationship diagram, and extract the job responsibility change characteristics, task load change characteristics, collaborative association change characteristics and responsibility burden change characteristics based on the job relationship and time change information to form a set of job value characteristics; The consistency test and source comparison of the set of job value characteristics are carried out to obtain the credibility of the characteristics. Based on the credibility of the characteristics, the characteristics of changes in job responsibilities, changes in task load, changes in collaborative relationships and changes in responsibility are corrected to obtain the corrected set of job value characteristics. Based on the modified set of job value features, the target job is characterized by feature organization and content representation to generate a candidate profile of job value.

[0011] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the specific steps for separating job characteristics from personnel characteristics and performing counterfactual calibration on the candidate job value profile to generate a net job value profile are as follows: Read the feature information of the candidate profile of job value, divide the candidate profile of job value into a set of job features and a set of personnel features according to the content reflected by the features, and perform separation processing on the candidate profile of job value based on the set of job features and the set of personnel features to obtain the set of job features. Counterfactual calibration is performed on the set of job features, and job features affected by personnel features are adjusted to generate a calibrated set of job features. The calibrated set of job features is then organized and its content is represented to generate a net job value profile.

[0012] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the specific steps for performing real-time value calculation and grade mapping based on the net job value profile, identifying the reasons for value changes, and generating real-time job value results are as follows: Read the various job-specific feature information from the net job value profile, and organize them according to the changes in the job-specific features in the current time period to form real-time job value data; Based on real-time job value data, job value is calculated and graded to form job value levels. The various job-specific features involved in the job value calculation are compared before and after, the changes in features are extracted, and the job value levels are integrated with the changes in features to obtain the real-time job value results.

[0013] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the step of generating a job value assessment report based on real-time job value results through result integration analysis and correlation path tracing includes the following specific steps: Based on the changes in job value level and characteristics in the real-time job value results, the changes in job value level and characteristics are integrated and analyzed to form job value assessment content. Based on the job value assessment content, the change paths of job relationships and characteristics corresponding to the target job are traced to form value path information. The job value assessment content and value path information are then organized and represented to generate a job value assessment report.

[0014] As a preferred embodiment of the job value assessment method based on multi-source data analysis described in this invention, the step of tracing the job relationship change path and feature change path corresponding to the target job refers to extracting the relationship change records and job value feature change records corresponding to the target job based on the job value assessment content.

[0015] Secondly, this invention provides a job value assessment system based on multi-source data analysis, comprising: a data acquisition module, which collects multi-source raw data related to job positions and performs unified attribution and time stamping processing on data from different sources to form a basic job data package; a knowledge graph construction module, which constructs a job time-series knowledge graph based on the basic job data package and organizes the changes in job relationships over time to generate a job time-series relationship diagram; a job value profile initial construction module, which extracts the characteristics of job responsibility changes and task load changes from the job time-series relationship diagram and performs credibility correction to obtain a candidate job value profile; a net job value calibration module, which separates job characteristics from personnel characteristics in the candidate job value profile and performs counterfactual calibration to obtain a net job value profile; a real-time value calculation module, which performs real-time value calculation and level mapping based on the net job value profile and identifies the reasons for value changes to obtain a real-time job value result; and an intelligent reporting module, which generates a job value assessment report based on the real-time job value result through result integration analysis and correlation path tracing.

[0016] The beneficial effects of this invention are as follows: by performing job value candidate profile separation of job characteristics and personnel characteristics and conducting counterfactual calibration, a net job value profile is generated. This effectively extracts pure value signals that belong only to the job itself. By introducing a counterfactual calibration mechanism, a set of characteristic data that eliminates personnel confusion effects and more purely reflects the connotation of job responsibilities, task load, and expected organizational contribution is obtained. This provides a stable and reliable benchmark, ensuring that the evaluation results point to the job itself rather than a specific incumbent, thereby improving the objectivity and comparability of the evaluation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0018] Figure 1 This is a flowchart of a job value assessment method based on multi-source data analysis.

[0019] Figure 2 This is a schematic diagram of a job value assessment system based on multi-source data analysis.

[0020] Figure 3 A flowchart for generating candidate profiles of job value.

[0021] Figure 4 A flowchart for obtaining real-time job value results.

[0022] Figure 5 Error comparison chart between different schemes and the true value of the simulation of the job.

[0023] Figure 6 A line graph showing how real-time job value changes over time. Detailed Implementation

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0027] Reference Figures 1-6 This is one embodiment of the present invention, which provides a job value assessment method based on multi-source data analysis, including the following steps: S1. Collect multi-source raw data related to job positions, and perform unified attribution and time stamping processing on data from different sources to form a basic data package for job positions.

[0028] S1.1. Read the job title, job code, department, job description, job requirements and reporting relationship information, establish a job identifier for each job to be evaluated, and collect multi-source raw data around the job identifier.

[0029] Specifically, the process begins by collecting information on job titles, job codes, departments, job descriptions, qualifications, and reporting relationships. The correspondence between job titles, job codes, and departments is then verified. Job descriptions, qualifications, and reporting relationships are then mapped to each job to be evaluated, creating a unique job identifier for each position. Using this job identifier as the basis for association, multi-source raw data reflecting job activities, responsibilities, and relationships are extracted from records that have business connections with the job to be evaluated.

[0030] It should be noted that multi-source raw data includes at least one or more of the following: organizational structure records, job description records, task allocation records, process flow records, project participation records, collaborative interaction records, and system configuration records. When processing job attribution for multi-source raw data, matching is prioritized based on job codes. When job codes are missing, joint matching is performed by combining job name, department level, reporting relationship, historical job mapping table, and similarity of responsibility text.

[0031] S1.2. Based on job identification, perform job attribution processing on multi-source raw data to generate job attribution data. Process the record time information in the job attribution data into a unified format to generate time job attribution data. Then, standardize, organize, and aggregate the data to form a basic job data package.

[0032] Specifically, based on job identifiers, the job title, department, job description, qualifications, and reporting relationship information in the multi-source raw data are compared with the corresponding information of each job to be evaluated. Content with the same name is directly assigned, while content with different names but the same job description and reporting relationship is assigned to the corresponding job identifier, generating job attribution data. Then, the record time information is extracted from the job attribution data, and different time formats are converted to a unified time format to generate time-based job attribution data. Finally, the field names, record order, and content format in the time-based job attribution data are uniformly organized and aggregated according to job identifiers to form a basic job data package.

[0033] S2. Construct a job time-series knowledge graph based on the job basic data package, and organize the changes in job relationships over time to generate a job time-series relationship graph.

[0034] S2.1. Read the basic job information and job association information from the job basic data package, and extract the set of job relationship elements based on the job identifier.

[0035] Specifically, after reading the basic job information and job-related information from the basic job data package, the corresponding job to be evaluated is located according to the job identifier. Then, the job name, department, job description, job requirements, and reporting relationship information are extracted from the basic job information, and the related content corresponding to the job identifier is extracted from the job-related information. The basic job information and job-related information are then organized according to the job identifier, and the records that can represent the job-related content are merged into the same job identifier to form a set of job relationship elements.

[0036] S2.2. Perform semantic analysis and classification on the set of job relationship elements to identify job positions, responsibilities, tasks, processes, projects, collaborating objects, system objects, and market objects, establish the relationship between the target job position and various objects, and obtain the set of job relationships.

[0037] Specifically, when performing semantic parsing on the set of job relationship elements, the textual content and corresponding record content in the set of job relationship elements are read one by one. Content that can represent job identity is classified as job, content that can represent the matters undertaken by the job is classified as responsibilities and tasks, content that can represent the business processing process is classified as process, content that can represent the matters participated in by the job is classified as project, content that can represent the interaction object is classified as collaboration object, content that can represent the basis of constraint is classified as system object, and content that can represent external reference content is classified as market object. Then, the various types of content after classification and organization are matched with the same job identifier, and the corresponding content between the target job and responsibilities, tasks, processes, projects, collaboration objects, system objects, and market objects (referring to the objects of external value reference relationships of the target job, including one or more of industry job salary reference items, external job supply and demand reference items, and industry common job requirement reference items) are connected item by item to obtain the set of job relationships.

[0038] S2.3. Extract the time stamps corresponding to each relationship in the job relationship set, and construct a job time sequence knowledge graph based on the job identifier and time stamps.

[0039] Specifically, each relationship is read from the job relationship set one by one. For each relationship, the corresponding job identifier and time stamp are found in the job relationship set. The time stamp can be derived from the occurrence time, change time, effective time, and record time already existing in the job basic information and job relationship information. Using the job identifier as the basis for organization, relationships with the same job identifier in the job relationship set are merged into the same job relationship sequence. The relationships in the same job relationship sequence are then arranged according to the time stamp, with relationships with earlier time stamps listed first and relationships with later time stamps listed last, thus obtaining the relationships corresponding to the job identifier. The relationship is ordered by time; the job identifier, each relationship, and the corresponding time mark are associated to ensure that each relationship in the job relationship set has a clear job affiliation and a clear time position; the relationships under the same job identifier that have been arranged with time marks are organized continuously according to time sequence to form a job time sequence knowledge graph that can represent the process of job relationship changes over time. By constructing the job time sequence knowledge graph, the various relationships formed around the target job are organized in a unified manner according to job identifier and time mark, so that each relationship has a clear job affiliation and time position, and can continuously represent the process of job relationship changes over time.

[0040] S2.4. Read the relationships and time information corresponding to the target job in the job time sequence knowledge graph, arrange the relationships in chronological order to obtain the job relationship time sequence, and compare the relationships in the job relationship time sequence before and after to form the job relationship change results.

[0041] Specifically, after reading the relationships and time information corresponding to the target position in the job time sequence knowledge graph, all relationships formed by the target position at different times are filtered out according to the job identifier. Then, the time information in each relationship is converted into a unified time sequence and arranged one by one in chronological order, so that the relationships of responsibility, task, process, project, collaboration object, system object, and market object form a continuously unfolding job relationship time sequence. Using the adjacent relationships in the job relationship time sequence as comparison objects, the consistency of the associated objects is checked item by item, and the addition, reduction, or change of the associated content is checked item by item. The changes obtained from each comparison are recorded in the corresponding time position. The changes corresponding to each time position are organized in chronological order to form the job relationship change result. The job relationship change result refers to the result formed after comparing the relationships of the target position item by item in the job relationship time sequence, which is used to represent the increase, decrease, or change of the relationship of responsibility, task, process, project, collaboration object, system object, and market object at each time position.

[0042] S2.5. Centered on the target position, based on the results of changes in position relationships, organize and associate various position relationships at different times to generate a position time sequence relationship diagram.

[0043] Specifically, taking the target position as the center, the changes in the position relationship changes are read from the corresponding time positions, and the responsibilities, tasks, processes, projects, collaborations, systems, and markets are assigned to their respective time positions. At each time position, the target position is connected to each of the various position relationships that have changed or continued, and the position relationships at each time position are organized sequentially according to time to form a position time sequence diagram that reflects the changes in various position relationships at different times for the target position.

[0044] It should be noted that by generating a job sequence diagram, the responsibilities, tasks, processes, projects, collaborations, systems, and external references of the target job at different time points can be uniformly organized and graphically presented. This gives the changes in job relationships an intuitive time sequence. For example, taking the "Procurement Supervisor" job as the target job, at the first time point, the job is associated with "Supplier Management," "Procurement Approval Process," and "Participation in Procurement Projects." At the second time point, "Cost Analysis Responsibilities" and "Collaboration with the Finance Department" are added. At the third time point, "Participation in the Budget Review Process" is added.

[0045] S3. Extract the characteristics of job responsibility changes and task load changes from the job time sequence relationship diagram, and perform credibility correction to generate job value candidate profiles.

[0046] S3.1. Read the job relationship and time change information corresponding to the target job in the job time sequence relationship diagram, and extract the job responsibility change characteristics, task load change characteristics, collaborative association change characteristics and responsibility bearing change characteristics based on the job relationship and time change information to form a set of job value characteristics.

[0047] Specifically, after reading the job relationship and time change information corresponding to each job in the job time sequence diagram, the responsibilities, tasks, collaborations, and duties of the target job at different time positions are extracted in chronological order. The responsibilities are compared item by item at different time positions to extract changes in the scope of responsibilities, content of responsibilities, and objects of responsibility, forming job responsibility change characteristics that characterize changes in the scope of responsibilities, content of responsibilities, level of responsibilities, or objects of responsibility of the target job at different time positions. Similarly, the tasks are compared item by item at different time positions to extract changes in the number of tasks, content of tasks, and number of task collaborations, forming task load change characteristics that characterize changes in the target job at different time positions. The characteristics of changes in task assignment status are analyzed. A point-by-point comparison of collaborative relationships across different time periods is performed to extract changes in the number, type, frequency, and scope of collaborative objects, forming characteristics of changes in collaborative relationships that characterize changes in the collaborative relationships between the target position and other objects at different time periods. A point-by-point comparison of responsibility relationships across different time periods is also performed to extract changes in the number, level, scope, and degree of responsibility undertaken, forming characteristics of changes in responsibility burden that characterize changes in the level of responsibility undertaken by the target position at different time periods. These characteristics of changes in job responsibilities, task load, collaborative relationships, and responsibility burden are then summarized and organized to form a set of job value characteristics.

[0048] S3.2. Perform consistency checks and source comparisons on the set of job value characteristics to obtain the credibility of the characteristics. Based on the credibility of the characteristics, modify the characteristics of changes in job responsibilities, changes in task load, changes in collaborative relationships, and changes in responsibility burden to obtain the modified set of job value characteristics.

[0049] Specifically, the consistency of each characteristic in the job value feature set—including changes in job responsibilities, workload, collaboration, and responsibility—is checked. The job relationship and time-related change information corresponding to each characteristic are retrieved item by item, and the feature's credibility is calculated. The continuity of changes in the same characteristic across different time periods is verified, as well as the consistency between the job relationship changes and time-related change information. The source records for each feature are compared, and the number of job relationship and time-related change information records reflecting the same characteristic is counted. When the same characteristic is reflected by multiple job relationship and time-related change information records with consistent changes, the feature's credibility is increased; when the same characteristic is reflected by only a single job relationship and time-related change information record or the changes are inconsistent, the feature's credibility is decreased. Based on the obtained feature credibility, the corresponding features are retained, weakened, or eliminated to obtain a revised job value feature set.

[0050] It should be noted that the expression for calculating the confidence level of a feature is: ; in, Indicates the first The reliability of a feature. Indicates the first Consistency results of features (test results that characterize the consistency of the recorded content of each feature item). Indicates the first The source comparison results of the features, This indicates the weight of the consistency result in the feature credibility calculation. This indicates the weight of the source comparison result in the calculation of feature credibility. Indicates the first The serial number of the feature item, S3.3. Based on the modified set of job value features, the target job is characterized by feature organization and content representation to generate a candidate profile of job value.

[0051] Specifically, based on the revised set of job value features, the characteristics of job responsibility changes, task load changes, collaborative relationship changes, and responsibility burden changes are read item by item around the target job. These features are then organized according to the time sequence and feature categories corresponding to the target job, ensuring a continuous correspondence among these characteristics under the same target job. The revised set of job value features then performs content representation on these characteristics, writing the content of responsibility changes corresponding to the job responsibility changes and the content of task changes corresponding to the task load changes into the feature description of the target job. This ensures that the job value change status of the target job can be fully expressed by the revised set of job value features. Finally, the revised set of job responsibility changes, task load changes, and feature descriptions are merged under the target job name to generate a candidate job value profile.

[0052] S4. Separate job value candidate profiles from job characteristics and personnel characteristics and perform counterfactual calibration to generate net job value profiles.

[0053] S4.1. Read the feature information of each candidate profile of job value, divide the candidate profile of job value into a set of job features and a set of personnel features according to the content reflected by the features, and perform separation processing on the candidate profile of job value based on the set of job features and the set of personnel features to obtain the set of job features.

[0054] Specifically, each characteristic information in the candidate profile of job value is read item by item, and the corresponding recorded content is compared. During the reading process, the focus is on identifying the content reflected by each characteristic information. Characteristic information reflecting the scope of job responsibilities, job tasks, job accountability, job collaboration scope, and job requirements is classified into the job characteristic set, while characteristic information reflecting the incumbent's ability level, experience, performance, and behavior is classified into the personnel characteristic set. The characteristic information that has been classified in the job characteristic set and the personnel characteristic set is then organized to ensure that each characteristic information in the candidate profile of job value corresponds to a set in either the job characteristic set or the personnel characteristic set. Based on the job characteristic set and the personnel characteristic set, the candidate profile of job value is separated. Characteristic information belonging to the job characteristic set is extracted from the candidate profile of job value, while characteristic information belonging to the personnel characteristic set is separated from the candidate profile of job value, thus separating the characteristic information reflecting the attributes of the job itself from the characteristic information reflecting the individual attributes of the incumbent. The characteristic information in the separated job characteristic set is then summarized and organized to form the job-specific characteristic set.

[0055] S4.2. Perform counterfactual calibration on the job ontology feature set, adjust the job ontology features affected by personnel features, generate a calibrated job feature set, perform feature organization and content representation on the calibrated job feature set, and generate a net job value profile.

[0056] Specifically, the process involves reading the set of job-specific features from the candidate job value profile and the set of personnel features corresponding to the target job. The personnel feature set includes the incumbent's ability level, experience, performance, and behavior. Each job-specific feature in the set is compared with its corresponding personnel feature in the set to determine if any deviation exists due to differences in the incumbent's ability level, experience, performance, or behavior. When a job-specific feature shows a deviation caused by personnel factors, the direction and degree of deviation are extracted, and the feature is adjusted by deduction or weakening based on these factors, resulting in job-specific features stripped of personnel influence. These stripped-off features are then aggregated to generate a calibrated job feature set. Finally, the calibrated job feature set is organized by category and its content is represented to generate a net job value profile.

[0057] Furthermore, Figure 5 This graph compares the errors between different schemes and the true values ​​of the simulated job, illustrating the deviations between the job value results output by different schemes and the true values ​​of the simulated job. This verifies the impact of different processing methods on the accuracy of job value assessment. The graph compares schemes A, B, and C using two indicators: mean absolute error (MAE) and root mean square error (RMSE). MAE reflects the average deviation of the assessment results from the true values ​​of the simulated job, while RMSE reflects the overall level of deviation and the amplification of larger deviations. The graph visually demonstrates the degree of closeness between the job value results and the true value of the job under different conditions, including whether job characteristics and personnel characteristics are separated and whether counterfactual calibration is performed. This verifies the technical effectiveness of this invention in reducing personnel confusion effects and improving the objectivity and comparability of assessments.

[0058] Among them, Scheme A is a comparison scheme that does not separate job characteristics from personnel characteristics. It directly outputs job value results based on the candidate profile of job value, without distinguishing between the job ontology information and personnel-related information contained in the candidate profile of job value, and without adjusting the job ontology characteristics affected by personnel characteristics. Since job performance, behavioral differences and individual influences directly participate in the formation process of job value results, the job value results obtained by this scheme are easily affected by specific personnel factors, resulting in a large deviation between the output results and the true value of job ontology simulation. Scheme B is a comparative scheme that separates job characteristics from personnel characteristics but does not perform counterfactual calibration. In this scheme, the candidate profiles for job value are first divided into job characteristic sets and personnel characteristic sets, resulting in a job ontology characteristic set. The job value result is then output based on this set. Since this scheme has initially separated job ontology information and personnel information, it can reduce the interference of personnel factors on the job value assessment results to some extent. However, it has not further corrected for parts of the job ontology characteristics that still change synchronously with personnel characteristics and are driven by personnel factors. Therefore, its output results still have a certain deviation from the true value of the job ontology simulation. Solution C is the solution of this invention, which involves separating job characteristics from personnel characteristics and performing counterfactual calibration. In this solution, not only are job value candidate profiles separated from personnel characteristics, but counterfactual calibration is further performed on the job ontology feature set. This involves matching job ontology features with personnel characteristics item by item, identifying and deducting or weakening changes caused by performance, behavioral differences, and individual influence, thereby generating a calibrated job ontology feature set and a net job value profile, and outputting the job value result based on the net job value profile. Because this solution can more fully extract the pure value signal belonging solely to the job itself, its result is closer to the true value of the job ontology simulation, and better reflects the true job value corresponding to the scope of job responsibilities, changes in workload, degree of collaboration, and responsibility bearing.

[0059] S5. Calculate and map the value in real time based on the net job value profile, identify the reasons for value changes, and generate real-time job value results. S5.1. Read the various job-specific feature information from the net job value profile, and organize them according to the changes in the job-specific features in the current time period to form real-time job value data.

[0060] Specifically, after reading the various job-specific characteristic information from the net job value profile, the job responsibilities, workload changes, collaborative relationships, and responsibility bearing are extracted from the net job value profile for the target job in the current time period. Each job-specific characteristic information for the current time period is then separated from the net job value profile. Each job-specific characteristic information is then compared with the job-specific characteristic information for the previous or adjacent time periods in the net job value profile, and the changes, directions, and degrees of change of each job-specific characteristic information in the current time period are summarized. Finally, the summarized job-specific characteristic information is consolidated according to the target job in the current time period, so that the job responsibilities, workload changes, collaborative relationships, and responsibility bearing form a unified correspondence in the current time period, forming real-time job value data. This real-time job value data represents the current status of the target job in terms of job responsibilities, workload, collaborative relationships, and responsibility bearing in the current time period, and its changes relative to the previous or adjacent time periods.

[0061] S5.2. Based on real-time job value data, calculate job value and perform hierarchical processing to form job value levels. Compare the various job-related features involved in the job value calculation before and after, extract the changes in features, and integrate the job value levels with the changes in features to obtain real-time job value results.

[0062] Specifically, based on real-time job value data, the scope of job responsibilities, changes in workload, degree of collaboration, and responsibility are read item by item for each target job. These factors are then centrally compiled and used to calculate job value. The results are then categorized to create job value levels. Furthermore, the various job-related characteristics involved in the value calculation are compared before and after the previous period. The job-related characteristics corresponding to the current period are compared item by item with those corresponding to the previous period to extract changes in aspects such as scope of job responsibilities, workload, degree of collaboration, and responsibility. The content of feature changes refers to the set of specific changes obtained by comparing each feature of the target position in the current time period with the feature of the target position in the previous or adjacent time periods. This includes changes in the scope of job responsibilities, changes in workload, changes in the degree of collaboration, and changes in the level of responsibility. The real-time job value result is obtained by integrating the job value level and feature changes of the target position. The real-time job value result is formed by integrating the job value level and feature changes of the target position and is used to characterize the job value status of the target position in the current time period.

[0063] Furthermore, Figure 6The graph shows the changes in real-time job value over time, with the horizontal axis representing the time period and the vertical axis representing the real-time job value. In the graph, Option A ("no separation of job characteristics and personnel characteristics") consistently shows the highest curve with significant fluctuations, indicating that job value is easily influenced by the performance, behavior, and individual factors of the incumbent. Option B ("separation of job characteristics and personnel characteristics") shows a curve lower than Option A, indicating that the personnel confusion effect has been weakened after separation, and the job value begins to reflect the job itself more accurately. Option C ("separation of job characteristics and personnel characteristics with counterfactual calibration") shows the lowest curve and is more stable overall, indicating that further counterfactual calibration of the job's intrinsic characteristics can more effectively eliminate biases caused by specific incumbents, allowing the real-time job value to more centrally reflect the job's intrinsic value corresponding to the scope of responsibilities, changes in workload, degree of collaboration, and responsibility.

[0064] S6. Based on real-time job value results, generate a job value assessment report through result integration analysis and correlation path tracing.

[0065] S6.1. Based on the changes in job value level and characteristics in the real-time job value results, integrate and analyze the changes in job value level and characteristics to form job value assessment content.

[0066] Specifically, based on the job value level and characteristic changes in the real-time job value results, the level results corresponding to the job value level are read according to the target job, and the changes in job responsibility scope, task load, degree of collaboration, and responsibility are extracted simultaneously from the real-time job value results. The job value level is then systematically matched with the changes in job responsibility scope, task load, degree of collaboration, and responsibility to clarify the correspondence between each characteristic change and the job value level. The job value level and characteristic changes are integrated and analyzed, and the content that reflects the change in job value is grouped under the same target job to form job value assessment content. Job value assessment content refers to the content formed by integrating the job value level and characteristic changes corresponding to the target job, which is used to characterize the change in the value of the target job.

[0067] S6.2. Based on the job value assessment content, trace the change path and characteristic change path of the job relationship corresponding to the target job to form value path information, organize and represent the job value assessment content and value path information, and generate a job value assessment report.

[0068] Specifically, based on the job value assessment content, the job value level, changes in job responsibilities, changes in workload, changes in collaboration, and changes in responsibility are read item by item for each target job. These changes are then mapped to the job relationship changes and job value characteristic sets mentioned earlier. The positions of job relationship changes and job characteristic changes corresponding to each change are traced back chronologically to form job relationship change paths and characteristic change paths. These paths are then organized into value path information. The job value assessment content and value path information are organized by target job, and the job value level, characteristic changes, and value path information are written into the same representation result to generate a job value assessment report.

[0069] It should be noted that tracing the change path of job relationship and the change path of characteristics corresponding to the target job refers to extracting the change records of the relationship and the change records of job value characteristics corresponding to the target job based on the job value assessment content.

[0070] This embodiment also provides a job value assessment system based on multi-source data analysis, including: a data acquisition module, which collects multi-source raw data related to jobs and performs unified attribution and time stamping processing on data from different sources to form a basic job data package; a knowledge graph construction module, which constructs a job time-series knowledge graph based on the basic job data package and organizes the changes in job relationships over time to generate a job time-series relationship diagram; a job value profile initial construction module, which extracts job responsibility change and task load change features from the job time-series relationship diagram and performs credibility correction to obtain a candidate job value profile; a net job value calibration module, which separates job characteristics from personnel characteristics in the candidate job value profile and performs counterfactual calibration to obtain a net job value profile; a real-time value calculation module, which performs real-time value calculation and level mapping based on the net job value profile and identifies the reasons for value changes to obtain a real-time job value result; and an intelligent reporting module, which generates a job value assessment report based on the real-time job value result through result integration analysis and correlation path tracing.

[0071] In summary, this invention, by performing a counterfactual calibration on the candidate job value profile to separate job characteristics from personnel characteristics and generating a net job value profile, can effectively extract pure value signals that belong only to the job itself. By introducing a counterfactual calibration mechanism, a set of characteristic data that eliminates personnel confusion effects and more purely reflects the connotation of job responsibilities, task load, and expected organizational contribution is obtained, providing a stable and reliable benchmark. This ensures that the evaluation results point to the job itself rather than a specific incumbent, thus improving the objectivity and comparability of the evaluation.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A job value assessment method based on multi-source data analysis, characterized in that, include: Collect raw data from multiple sources related to job positions, and perform unified attribution and time stamping on data from different sources to form a basic data package for job positions; A job time-series knowledge graph is constructed based on the job basic data package, and the changes in job relationships over time are organized to generate a job time-series relationship graph; The characteristics of job responsibility changes and task load changes are extracted from the job time sequence diagram, and the credibility is corrected to generate a candidate profile of job value. Separate job value candidate profiles from job characteristics and personnel characteristics and perform counterfactual calibration to generate net job value profiles; Real-time value calculation and grade mapping are performed based on the net job value profile, and the reasons for value changes are identified to generate real-time job value results. Based on real-time job value results, a job value assessment report is generated through result integration analysis and correlation path tracing.

2. The job value assessment method based on multi-source data analysis as described in claim 1, characterized in that, The process involves collecting multi-source raw data associated with different job positions, and uniformly assigning and time-stamping the data from different sources to form a basic job data package. The specific steps are as follows: Read the job title, job code, department, job description, job requirements and reporting relationship information, create a job identifier for each job to be evaluated, and collect multi-source raw data around the job identifier; Based on job identification, job attribution processing is performed on multi-source raw data to generate job attribution data. The record time information in the job attribution data is processed in a unified format to generate time job attribution data. The data is then standardized, organized, and aggregated to form a basic job data package.

3. The job value assessment method based on multi-source data analysis as described in claim 1, characterized in that, The specific steps for constructing a job-specific time-series knowledge graph based on the job-specific basic data package are as follows: Read the basic job information and job association information from the job basic data package, and extract the set of job relationship elements based on the job identifier; Semantic analysis and classification of the set of job relationship elements are performed to identify job positions, responsibilities, tasks, processes, projects, collaborating objects, system objects, and market objects, and to establish the relationship between the target job and various objects, thus obtaining the set of job relationships; Extract the time stamps corresponding to each relationship in the job relationship set, and construct a job time sequence knowledge graph based on job identifiers and time stamps.

4. The job value assessment method based on multi-source data analysis as described in claim 1, characterized in that, The specific steps for generating the job sequence diagram are as follows: Read the relationships and time information corresponding to the target job from the job time sequence knowledge graph, arrange the relationships in chronological order to obtain the job relationship time sequence, and compare the relationships in the job relationship time sequence before and after to form the job relationship change results; Centered on the target position, and based on the changes in position relationships, various position relationships at different times are organized and associated to generate a position time sequence relationship diagram.

5. The job value assessment method based on multi-source data analysis as described in claim 1, characterized in that, The specific steps for generating candidate job value profiles are as follows: Read the job relationship and time change information corresponding to the target job in the job time sequence relationship diagram, and extract the job responsibility change characteristics, task load change characteristics, collaborative association change characteristics and responsibility burden change characteristics based on the job relationship and time change information to form a set of job value characteristics; The consistency test and source comparison of the set of job value characteristics are carried out to obtain the credibility of the characteristics. Based on the credibility of the characteristics, the characteristics of changes in job responsibilities, changes in task load, changes in collaborative relationships and changes in responsibility are corrected to obtain the corrected set of job value characteristics. Based on the modified set of job value features, the target job is characterized by feature organization and content representation to generate a candidate profile of job value.

6. The job value assessment method based on multi-source data analysis as described in claim 5, characterized in that, The specific steps for separating job value candidate profiles from job characteristics and performing counterfactual calibration to generate a net job value profile are as follows: Read the feature information of the candidate profile of job value, divide the candidate profile of job value into a set of job features and a set of personnel features according to the content reflected by the features, and perform separation processing on the candidate profile of job value based on the set of job features and the set of personnel features to obtain the set of job features. Counterfactual calibration is performed on the set of job features, and job features affected by personnel features are adjusted to generate a calibrated set of job features. The calibrated set of job features is then organized and its content is represented to generate a net job value profile.

7. The job value assessment method based on multi-source data analysis as described in claim 6, characterized in that, The specific steps for calculating and mapping real-time value based on the net job value profile, identifying the reasons for value changes, and generating real-time job value results are as follows: Read the various job-specific characteristics from the net job value profile, and organize them according to the changes in the job-specific characteristics in the current time period to form real-time job value data; Based on real-time job value data, job value is calculated and graded to form job value levels. The various job-specific features involved in the job value calculation are compared before and after, the changes in features are extracted, and the job value levels are integrated with the changes in features to obtain the real-time job value results.

8. The job value assessment method based on multi-source data analysis as described in claim 7, characterized in that, The process of generating a job value assessment report based on real-time job value results, through result integration analysis and correlation path tracing, involves the following steps: Based on the changes in job value level and characteristics in the real-time job value results, the changes in job value level and characteristics are integrated and analyzed to form job value assessment content. Based on the job value assessment content, the change paths of job relationships and characteristics corresponding to the target job are traced to form value path information. The job value assessment content and value path information are then organized and represented to generate a job value assessment report.

9. The job value assessment method based on multi-source data analysis as described in claim 8, characterized in that, The aforementioned tracing of the job relationship change path and characteristic change path corresponding to the target job refers to extracting the records of changes in the relationship and the records of changes in the job value characteristics corresponding to the target job based on the job value assessment content.

10. A job value assessment system based on multi-source data analysis, based on the job value assessment method based on multi-source data analysis as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module collects raw data from multiple sources related to job positions, and performs unified attribution and time stamping on data from different sources to form a basic data package for job positions. The knowledge graph construction module builds a job time-series knowledge graph based on the job basic data package, and organizes the changes in job relationships over time to generate a job time-series relationship graph; The initial module for constructing a job value profile extracts the characteristics of job responsibility changes and task load changes from the job time sequence relationship diagram, and performs credibility correction to obtain candidate job value profiles. The Net Job Value Calibration Module separates job characteristics from personnel characteristics in the candidate job value profile and performs counterfactual calibration to obtain the net job value profile. The real-time value calculation module performs real-time value calculation and grade mapping based on the net job value profile and identifies the reasons for value changes to obtain real-time job value results. The intelligent reporting module generates job value assessment reports based on real-time job value results through result integration analysis and correlation path tracing.

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