Talent resource information processing method and platform based on big data analysis

Abstract

The invention provides a talent resource information processing method and platform based on big data analysis, and relates to the technical field of data processing, and the method comprises the steps: configuring a resource directory attribute tag based on big data, and configuring a multi-dimensional and multi-granularity increment resource space; performing static resource information matching projection on the to-be-processed talent resource information, and performing dynamic sampling parameter analysis on the dynamic resource information; labeling based on the dynamic sampling instruction, and feeding back to a sampling talent for incremental sampling; and performing evaluation according to the incremental sampling data, projecting the incremental sampling data to a resource space to perform resource information evaluation labeling, and combining with a static resource information matching projection result to construct a talent resource information base. Through the method and the device, the technical problem that talent portraits are rough and distorted due to staticizing of talent resource information, extensive acquisition modes and lack of privacy protection mechanisms in the prior art can be solved, and fine management of talent resource information is realized by constructing a dynamic and static combined resource directory system.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method and platform for processing human resource information based on big data analysis. Background Technology

[0002] Existing talent resource information management methods generally rely on static, fixed basic information databases for data processing, resulting in simplistic and outdated talent profiles that fail to accurately reflect an individual's dynamic skill development and project experience. Traditional centralized data collection methods face compliance challenges due to a lack of effective privacy protection and access control for sensitive data, particularly in the collection of dynamic information. This not only increases the difficulty of data collection but also limits the breadth and depth of information. Current methods lack dynamic incremental processing capabilities, failing to support individuals in independently supplementing and updating their information at different stages. The ineffective inclusion of high-value dynamic data leads to low matching accuracy in the talent pool, ultimately affecting the accuracy of talent evaluation and the efficiency of resource allocation.

[0003] In summary, existing technologies suffer from technical problems such as static talent resource information, crude collection methods, and a lack of privacy protection mechanisms, resulting in rough and distorted talent profiles, which further affect the accuracy of talent matching. Summary of the Invention

[0004] The purpose of this application is to provide a method and platform for processing human resource information based on big data analysis, in order to solve the technical problems in the existing technology that lead to rough and distorted talent profiles due to the static nature of human resource information, the extensive collection methods, and the lack of privacy protection mechanisms, which further affect the accuracy of talent matching.

[0005] To achieve the above objectives, this application provides a method and platform for processing human resource information based on big data analysis.

[0006] Firstly, this application provides a talent resource information processing method based on big data analysis. This method is implemented through a talent resource information processing platform based on big data analysis. The method includes: statically and dynamically parsing a resource catalog based on big data, configuring resource catalog attribute tags, and parsing dynamic resource information by dimension and granularity to configure a multi-dimensional, multi-granularity incremental resource space; performing static resource information matching and projection on the talent resource information to be processed according to the resource catalog; using the incremental parameters of the multi-dimensional, multi-granularity incremental resource space to perform dynamic sampling parameter parsing on the dynamic resource information to generate dynamic sampling instructions; performing multi-dimensional, multi-granularity annotation based on the dynamic sampling instructions and feeding it back to the sampled talent for incremental sampling; performing multi-dimensional, multi-granularity evaluation based on the incremental sampling data, projecting it to the multi-dimensional, multi-granularity incremental resource space for resource information evaluation and annotation, and merging the evaluation and projection results with the static resource information matching results to construct a talent resource information database.

[0007] Optionally, resource catalog samples are collected based on big data. Static and dynamic analysis is performed on the sample data of the resource catalog to determine the structured attributes of the catalog data. Static and dynamic resource information are configured in a structured manner, and resource catalog attribute tags are inserted. The static resource information is stored in the form of a static content annotation table. The dynamic resource information is analyzed for resource profile evolution at the horizontal dimension and vertical granularity to obtain multi-dimensional and multi-granularity analysis features, which are stored in a dynamically expanded space structure. The dynamically expanded space structure has a static content annotation table and an insertion expansion space. The insertion expansion space has dimension and granularity identifiers and a relationship with the static content annotation table, thus establishing the multi-dimensional and multi-granularity incremental resource space.

[0008] Optionally, based on the descriptive dimensions of talent resources using big data, relevant resource analysis and feature expansion are performed on the static basic content of dynamic resource information to identify multi-dimensional analytical features of dynamic resource information. These multi-dimensional analytical features include emotional features, behavioral features, social features, and learning ability features. The multi-dimensional analytical features are then decomposed vertically into multiple granularity intervals based on time series, behavioral frequency, and information refinement, including fine granularity and time granularity. A hierarchical mapping relationship is established between the multi-dimensional analytical features and the multiple granularity intervals, forming a hierarchical structure of multi-dimensional, multi-granularity analytical features, thus obtaining the multi-dimensional, multi-granularity analytical features.

[0009] Optionally, the static content annotation table and the insertion extension space are associated through a resource mapping index table, which records the correspondence between static content field identifiers and dynamic slot positions.

[0010] Optionally, each incremental unit in the insertion expansion space is automatically assigned a granularity hierarchical identifier by the system during insertion; the uncertainty parameter of the corresponding incremental unit is calculated based on information entropy, time difference, or behavior frequency; when the uncertainty parameter exceeds a threshold, a dynamic sampling instruction is triggered to request sampling personnel to supplement or correct the corresponding information.

[0011] Optionally, when performing resource directory parsing, basic fields are extracted from the static content annotation table of the dynamic expansion space structure to form a static tag set; based on the static tags, corresponding multi-dimensional incremental units are loaded from the insertion expansion space, and a configuration matrix of multi-dimensional, multi-granular incremental resource space is generated based on the dimension mapping relationship and granularity hierarchy rules.

[0012] Optionally, the insertion extension space adopts a hierarchical nested structure, including a public layer, a semi-public layer, and a private layer. Permission verification is performed according to the sampling task, and based on the authorization scope of the semi-public layer and the private layer, sensitive labeled content in the dynamic extension slot is parsed or sampled.

[0013] Optionally, the dynamic sampling instruction also includes sampling incentive parameters, which weight the talent resource information according to the incremental value of the sampled data based on resource level and credit reward, for use in talent resource scenario matching applications.

[0014] Optionally, a credibility parameter is determined based on the source and sampling method of the incremental sampling data. The sampling data includes at least one of the following types: system-automated sampling data, user-initiated sampling data, and third-party verification data. Based on the credibility parameter, the incremental sampling data is weighted and evaluated according to resource dimensions and granularity levels to obtain evaluation information of the incremental sampling data and generate evaluation labels for resource information.

[0015] Secondly, this application also provides a talent resource information processing platform based on big data analysis, used to execute the talent resource information processing method based on big data analysis as described in the first aspect. The platform includes: a resource configuration module, used to perform static and dynamic parsing of the resource catalog based on big data, configure resource catalog attribute tags, and perform dimensional and granular parsing of dynamic resource information to configure a multi-dimensional, multi-granular incremental resource space; a parameter parsing module, used to perform static resource information matching and projection on the talent resource information to be processed according to the resource catalog, and to perform dynamic sampling parameter parsing on the dynamic resource information using the incremental parameters of the multi-dimensional, multi-granular incremental resource space to generate dynamic sampling instructions; an incremental sampling module, used to perform multi-dimensional, multi-granular annotation based on the dynamic sampling instructions, and to feed back to the sampled talent for incremental sampling; and an information evaluation and annotation module, used to perform multi-dimensional, multi-granular evaluation based on the incremental sampling data, project it to the multi-dimensional, multi-granular incremental resource space for resource information evaluation and annotation, and merge it with the static resource information matching and projection results to construct a talent resource information database.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: By leveraging big data, the system performs static and dynamic analysis of the resource catalog, configures resource catalog attribute tags, and analyzes dynamic resource information at both the dimensional and granular levels, configuring a multi-dimensional, multi-granular incremental resource space. Static resource information matching and projection are performed on the talent resource information to be processed according to the resource catalog. The incremental parameters of the multi-dimensional, multi-granular incremental resource space are used to analyze dynamic sampling parameters of the dynamic resource information, generating dynamic sampling instructions. Multi-dimensional, multi-granular annotation is performed based on these dynamic sampling instructions, and the results are fed back to the sampled talent for incremental sampling. Multi-dimensional, multi-granular evaluation is performed based on the incremental sampling data, projected onto the multi-dimensional, multi-granular incremental resource space for resource information evaluation and annotation, and then merged with the static resource information matching and projection results to construct a talent resource information database. In other words, by constructing a dynamic and static combined resource catalog system, configuring a multi-dimensional, multi-granular incremental resource space, performing incremental data sampling based on dynamic sampling instructions, and evaluating resource information in a multi-dimensional, multi-granular manner, the collected dynamic data is evaluated and annotated, and then merged with static data to form a complete talent resource database. This achieves refined management of the talent resource information database and improves the accuracy and efficiency of talent matching.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the human resource information processing method based on big data analysis proposed in this application.

[0020] Figure 2 This is a schematic diagram of the structure of the human resource information processing platform based on big data analysis in this application.

[0021] Figure labeling: Resource configuration module 11, parameter parsing module 12, incremental sampling module 13, information evaluation labeling module 14. Detailed Implementation

[0022] This application provides a talent resource information processing method and platform based on big data analysis, solving the technical problems in existing technologies where static talent resource information, crude collection methods, and a lack of privacy protection mechanisms lead to coarse and distorted talent profiles, further affecting the accuracy of talent matching. By constructing a dynamic and static resource catalog system, configuring multi-dimensional and multi-granular incremental resource spaces, and performing incremental data sampling based on dynamic sampling instructions, the application evaluates resource information in a multi-dimensional and multi-granular manner. The collected dynamic data is evaluated and labeled, then merged with static data to form a complete talent resource database. This enables refined management of the talent resource information database, improving the accuracy and efficiency of talent matching.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides a method for processing human resource information based on big data analysis. The method is applied to a human resource information processing platform based on big data analysis, and specifically includes the following steps: Based on big data, the resource catalog is statically and dynamically parsed, resource catalog attribute tags are configured, and dynamic resource information is parsed in terms of dimension and granularity to configure multi-dimensional and multi-granular incremental resource space.

[0025] Furthermore, this application also includes the following steps: collecting resource catalog samples based on big data; performing static and dynamic analysis on the sample data of the resource catalog to determine the structured attributes of the catalog data; configuring the static resource information and dynamic resource information in a structured manner and inserting resource catalog attribute tags; storing the static resource information in the form of a static content annotation table; performing horizontal dimension and vertical granularity resource profile evolution analysis on the dynamic resource information to obtain multi-dimensional and multi-granularity analysis features, and storing them in a dynamically expanded space structure. The dynamically expanded space structure has a static content annotation table and an insertion expansion space. The insertion expansion space has dimension and granularity identifiers and a relationship with the static content annotation table, thus establishing the multi-dimensional and multi-granularity incremental resource space.

[0026] Furthermore, this application also includes the following steps: based on the descriptive dimensions of talent resources using big data, relevant resource analysis and feature expansion are performed on the static basic content of dynamic resource information to identify multi-dimensional analytical features of dynamic resource information, wherein the multi-dimensional analytical features include emotional features, behavioral features, social features, and learning ability features; the multi-dimensional analytical features are decomposed vertically into multiple granularity intervals based on time series, behavioral frequency, and information refinement, including fine granularity and time granularity; a hierarchical mapping relationship is established between the multi-dimensional analytical features and the multiple granularity intervals to form a hierarchical structure of multi-dimensional and multi-granular analytical features, thereby obtaining the multi-dimensional and multi-granular analytical features.

[0027] Furthermore, this application also includes the following steps: the static content annotation table and the insertion extension space are associated through a resource mapping index table, wherein the resource mapping index table records the correspondence between static content field identifiers and dynamic slot positions.

[0028] Specifically, through big data sampling, sample data is selected and collected from existing resource catalogs. This involves gathering a large amount of diverse raw talent information from various big data sources to analyze and define the content that the resource catalog should include. The sample data from the resource catalog undergoes static and dynamic analysis. Static resource information is analyzed separately and configured according to certain rules; dynamic resource information undergoes more complex processing, analyzed through horizontal and vertical dimensions.

[0029] Static resource information refers to basic information that changes very infrequently throughout a talent's lifecycle and has long-term validity, such as name, gender, place of origin, education level, ID number, and fixed professional qualification certificate number. Dynamic resource information is data that is constantly updated with changes in time or behavior, such as work performance, skills improvement, and project experience. Information determined to be static is systematically stored in a fixed static content annotation table, forming a stable foundation for talent data. The static content annotation table is a traditional database table used to store static resource information. The table structure is fixed, with each row representing a person and each column representing a static attribute.

[0030] Based on the descriptive dimensions of talent resources derived from big data, we conduct in-depth mining of the static foundational content within dynamic resource information. Descriptive dimensions are different perspectives or classification frameworks used to characterize and define talent resources, forming the basic skeleton of a talent profile. For example, they comprehensively describe a person from multiple aspects such as skills, behavior, and psychology. Multi-dimensional and multi-granularity feature analysis is organized by dimensions such as skills, behavior, performance, social skills, learning, and project achievements, with granularities such as coarse / medium / fine; and an expandable space organized by time windows of year / quarter / month / week / day. Many dimensions cannot be directly obtained from the information entered, or require changes over time, making data collection difficult. Privacy concerns also prevent direct data collection and observation. Data collection can be conducted through user-authorized feedback.

[0031] Static foundational content comprises the relatively stable core elements within dynamic resource information, serving as the starting point for description. For example, in the dynamic information of project experience, the project name and role played are static foundational content that do not change frequently over time. Using static foundational content as a guide, external big data resources can be linked and mined to derive and calculate deeper, more insightful new features, identifying multi-dimensional analytical features of dynamic resource information, including emotional characteristics, behavioral characteristics, social characteristics, and learning ability characteristics. Emotional characteristics refer to the emotional state tendencies exhibited by talent at work, such as enthusiasm, stability, and stress tolerance; behavioral characteristics refer to the work patterns and habits of talent, such as self-discipline, collaboration, and innovation; social characteristics refer to the communication and influence of talent within and outside the organization, such as leadership, network breadth, and willingness to share knowledge; and learning ability characteristics refer to the efficiency and depth of talent in acquiring and mastering new knowledge and skills.

[0032] Multi-dimensional analytical features are decomposed vertically at a granular level, hierarchically divided according to different time dimensions, behavioral frequencies, and information granularity, into multiple granularity intervals, including fine granularity and time granularity. For example, an employee's emotional stability over the past year can be recorded monthly or quarterly to help analyze changes in their emotional management ability over different time periods; the frequency of employee participation in projects and problem-solving can be captured by regularly recording behavioral data. Time series represents the recording of data changes over time; behavioral frequency is the frequency with which a specific behavior occurs within a certain period; information granularity refers to the level of detail of the information, with finer information providing more background information and facilitating deeper analysis. Granularity intervals decompose information into different levels or levels of detail, typically divided into fine granularity (detailed information) and coarse granularity (general information).

[0033] After completing the multi-dimensional and multi-granular analysis, a hierarchical mapping relationship is established, mapping the multi-dimensional analytical features and multi-granularity intervals according to a hierarchy, forming a hierarchical structure of multi-dimensional and multi-granular analytical features, thus obtaining multi-dimensional and multi-granular analytical features. By establishing a hierarchical structure of multi-dimensional and multi-granular analytical features, more detailed analysis results are provided at different granularities, helping companies to accurately assess and match talent. These analyzed and decomposed features are integrated into a hierarchical structure of multi-dimensional and multi-granular analytical features, forming a complete talent profile. This profile includes not only basic information about each talent but also dynamically updated information, such as emotional changes, behavioral characteristics, and social skills, helping companies to comprehensively understand the potential and strengths of each employee.

[0034] Multi-dimensional and multi-granularity analytical features refine data through multiple dimensions, forming data structures of varying granularities. Each dimension can have multiple granularities, providing a more comprehensive understanding of a talent's characteristics. These multi-dimensional and multi-granularity analytical features are stored in a dynamically expanding space structure. This structure includes a static content annotation table and an insertion expansion space. The static content annotation table is a fixed-structure, normalized database table specifically designed to store long-term, stable, and infrequently changing basic information about talent resources. The insertion expansion space is a flexible, dynamically expandable data storage area separate from the static table, used to store multi-dimensional and multi-granular dynamic information related to talent. Its structure is not fixed; new slots can be created as needed to store new types of dynamic data. The static content annotation table and the insertion expansion space are linked through a resource mapping index table. This resource mapping index table is a core relational table, acting like a navigation map, recording the correspondence between static content fields and dynamic slot locations. In other words, it records the correspondence between each record in the static content annotation table and its specific storage location in the insertion expansion space, achieving a seamless bridge between structured static data and unstructured dynamic data. The static content field identifier is an identifier that records static data; the dynamic slot position is the position of dynamic data in the insertion extension space, ensuring that data can be updated and queried at different points in time.

[0035] The insertion extension space is a part of the dynamic extension space. It has dimension and granularity identifiers and a relationship with the static content annotation table. It is used to store changing data, especially the incremental part of dynamic information such as the performance of employees or projects over time. It is associated with the static content annotation table and can insert relevant data according to different dimensions and granularities.

[0036] By combining static basic information with dynamic extended information, a multi-dimensional, multi-granular incremental resource space is formed. When it is necessary to query or update the dynamic information of a certain talent, the talent is first located in the static table, and then all relevant data entries in the extended space are quickly found through the resource mapping index table, thereby realizing the collaborative management and joint query of static and dynamic data. By establishing the resource mapping index table, static and dynamic data can be seamlessly integrated to form a complete employee profile. Through multi-dimensional analysis at various dimensions and granularities, the characteristics of talents are comprehensively evaluated, helping to make more accurate resource allocation decisions.

[0037] Furthermore, this application also includes the following steps: when performing resource catalog parsing, extracting basic fields from the static content annotation table of the dynamic extended space structure to form a static tag set; loading corresponding multi-dimensional incremental units from the inserted extended space based on the static tags, and generating a configuration matrix of multi-dimensional, multi-granular incremental resource space based on dimension mapping relationship and granularity hierarchy rules.

[0038] Specifically, when performing resource catalog parsing, key basic fields are first extracted from the static content tagging table to form a standardized set of static tags, which serves as the indexing foundation for the entire data system. Based on these static tags, all related multidimensional incremental units are automatically retrieved and loaded from the insertion expansion space. Through predefined dimension mapping relationships, the correspondence rules between each dynamic data dimension and the static tags are determined, and the data is then layered according to granularity hierarchy rules to determine the level of detail. Multidimensional incremental units are subdivided units of dynamic data; each unit represents a data change or increment, such as monthly performance changes for employees.

[0039] Based on static labels and loaded incremental units, a configuration matrix for a multi-dimensional, multi-granularity incremental resource space is generated according to dimensional mapping relationships and granularity hierarchy rules. This matrix defines how data is organized across different dimensions and granularities, clarifying the location, attributes, and access rules of each data unit. The multi-dimensional, multi-granularity incremental resource space is a space for storing multi-dimensional data, capable of being organized not only by different dimensions but also by different granularities. The configuration matrix is ​​a generated structured framework used to define how data is organized and accessed according to dimensions and granularity, determining how each data unit is organized, labeled, and accessed.

[0040] Furthermore, this application also includes the following steps: each incremental unit in the insertion expansion space is automatically assigned a granularity hierarchical identifier by the system during insertion; the uncertainty parameter of the corresponding incremental unit is calculated based on information entropy, time difference, or behavior frequency; when the uncertainty parameter exceeds a threshold, a dynamic sampling instruction is triggered to request the sampling personnel to supplement or correct the corresponding information.

[0041] Furthermore, this application also includes the following steps: the insertion extension space adopts a hierarchical nested structure, including a public layer, a semi-public layer and a private layer, wherein, permission verification is performed according to the sampling task, and based on the authorization scope of the semi-public layer and the private layer, the sensitive labeled content in the dynamic extension slot is parsed or sampled.

[0042] Specifically, the insertion expansion space refers to a dedicated area for storing newly added or changed data. As the data changes, new incremental data is inserted into this space to track dynamic resource information. Each incremental unit of data insertion is accompanied by a granularity hierarchical identifier to distinguish different levels of analytical dimensions.

[0043] Each incremental unit inserted into the expansion space automatically generates a granularity layer identifier each time a new incremental unit is inserted. An incremental unit is the smallest data unit used in a dynamic expansion space for adding or updating data; each incremental unit represents an independent piece of multi-dimensional, multi-granularity dynamic information. The granularity layer identifier is metadata automatically generated by the system to identify the detailed data level to which the incremental unit belongs, clarifying the unit's position in terms of vertical granularity.

[0044] A continuously running monitoring process is initiated to calculate the uncertainty parameters of the corresponding incremental units based on information entropy time difference or behavior frequency. The uncertainty parameter is a quantified confidence index used to measure the degree of uncertainty regarding the reliability, timeliness, or completeness of the information carried by a given incremental unit. A higher uncertainty parameter indicates greater uncertainty. Information entropy is a measure of data uncertainty or complexity, representing the degree of data change or fluctuation. Higher information entropy indicates more unpredictable data changes, requiring greater attention. Time difference refers to assessing data volatility by calculating the amount of change in data between different points in time. Behavior frequency refers to the frequency with which a certain behavior or event occurs; high-frequency behavior usually indicates a greater impact on the data model.

[0045] When the uncertainty parameter of an incremental unit exceeds a set threshold, a dynamic sampling instruction is generated, requesting relevant personnel to supplement or correct the data to ensure its accuracy. For example, consider employee A's Python data analysis skill. Employee A voluntarily uploaded a skill description stating "proficient in Python data analysis," which is stored as an incremental unit in the expansion space, automatically generating a granularity layer identifier of granularity 1, self-declared level. A new round of uncertainty assessment is initiated. Analyzing the text "proficient in Python data analysis," the description is broad, lacking specific libraries, tools, and quantitative indicators, resulting in high information entropy and a calculated score of 0.9, with a threshold <0.7. This data has not been updated for 9 months, a significant time lag, resulting in a calculated score of 0.8, with a threshold <0.6, approximately six months. Linking the code repository and project data, it is found that employee A has not submitted any Python data analysis-related scripts or notes in the past 6 months, resulting in a behavior frequency score of 0.7, with a threshold <0.5. Based on the weighted average of these three scores, the comprehensive uncertainty parameter for this skill unit is calculated to be 0.82, while the preset threshold is 0.65, indicating that this unit has significantly exceeded the limit. Immediately generate a dynamic sampling instruction, the core of which is: request supplementary information to reduce uncertainty. Specific requirements: please list your commonly used Python data analysis libraries and provide a specific work example or project name that you have completed using this skill in the past six months.

[0046] The insertion expansion space adopts a hierarchical nested structure, dividing the storage space into three permission levels: public, semi-public, and private. Each expansion slot storing dynamic information is marked with its corresponding permission level. Data in the public layer is freely accessible and unrestricted, suitable for publicly available, non-sensitive data; data in the semi-public layer has certain access restrictions, requiring specific permissions or authentication, suitable for information that needs control but not complete confidentiality; data in the private layer is extremely sensitive, accessible only to specific users or authorized users, suitable for personal privacy data or trade secrets.

[0047] When performing sampling tasks, an access verification process is initiated to determine the scope of access permissions based on the nature of the sampling task and the identity of the requester. For public layer information, direct access is allowed; for sensitive information in the semi-public and private layers, access is dynamically determined based on preset authorization rules and the current task's permission level, ensuring that sensitive data can only be legally used with appropriate authorization. Even if a user has permission to access data in these layers, data operations will still be performed according to their access control. By setting access verification and authorization scopes, even when dealing with highly sensitive data, operations are performed according to the user's access level, ensuring data privacy while meeting the data sampling needs of specific tasks. By establishing a refined data permission hierarchical management system, an optimal balance between the security and availability of talent dynamic information is achieved, protecting the privacy of sensitive personal data while ensuring that non-sensitive information can fully realize its business value.

[0048] According to the resource catalog, static resource information matching and projection are performed on the talent resource information to be processed. The incremental parameters of the multi-dimensional and multi-granularity incremental resource space are used to perform dynamic sampling parameter parsing on the dynamic resource information to generate dynamic sampling instructions.

[0049] Furthermore, this application also includes the following steps: the dynamic sampling instruction further includes sampling incentive parameters, which weight the talent resource information according to the incremental value of the sampling data based on resource level and credit reward, for use in talent resource scenario matching applications.

[0050] Specifically, the process involves static resource information matching and projection of the talent resource information to be processed according to the resource catalog. This matches the static information of the talent with the resource catalog, and maps and projects the matching results to ensure the identification of talent information matching specific resource categories. A deep analysis of the talent's dynamic data status in a multi-dimensional, multi-granular incremental resource space is then performed. By calculating various incremental parameters, the system intelligently parses out the types and levels of detail of dynamic information that need to be prioritized for supplementation, thereby generating dynamic sampling instructions. Incremental parameters refer to data or information that increases over time, serving as quantitative indicators to measure the degree to which dynamic resource information needs to be supplemented or updated, including dimensions such as data freshness, completeness, and reliability. Dynamic sampling parameter parsing refers to identifying data parameters that need to be sampled and updated through real-time analysis of dynamic resource information. For example, if employee M's mood rating was 6 in January 2023, 7 in February, and 5 in March, these changes are identified and used as incremental parameters to generate instructions to collect the employee's latest mood data.

[0051] Dynamic sampling instructions also include sampling incentive parameters, which are reward coefficients tied to the sampling instructions. These incentive values ​​are set based on the scarcity, importance, and difficulty of collecting the requested data. For talent providing high-value, frequently updated data, their resource level is increased. For example, an employee who regularly provides high-quality data on work behavior, project experience, etc., will have their resource level increased, thus giving them higher priority in the matching process. Dynamic resource information is weighted through credit rewards, increasing the credit score of talent providing high-quality information. For example, employees who consistently update sentiment data, project experience, and other key indicators will receive higher credit scores, which may increase their chances in talent recruitment or job recommendations.

[0052] By combining static matching and dynamic updates, more accurate talent profiles are generated for talent resource matching applications such as recruitment and job recommendations. Through resource level and credit weighting, companies can prioritize candidates with rich dynamic data and high evaluation levels based on their actual needs. For example, suppose an employer is looking for a project manager with high emotional stability; based on the candidate's dynamic resource information and resource level, candidates with this characteristic can be prioritized. The updating and sampling of dynamic resource information ensures the accuracy of talent profiles, thereby helping employers find the most suitable candidates. Through the comprehensive processing of static and dynamic resource information, more accurate talent profiles are generated, ensuring that employers can find the most suitable candidates.

[0053] Based on the dynamic sampling instructions, multi-dimensional and multi-granular annotations are performed and fed back to the sampling personnel for incremental sampling.

[0054] Specifically, after generating the original dynamic sampling instructions, they are first subjected to in-depth multi-dimensional and multi-granular annotation. Horizontally, the classification attributes of the data are clarified from multiple dimensions such as technical capabilities, project experience, and behavioral characteristics. Vertically, different levels of detail are divided according to actual needs, ranging from simple confirmatory answers to detailed case descriptions. After annotation, the technical instructions are transformed into specific tasks that are easy for talents to understand and operate, and fed back to the target talents through a user-friendly interface. After receiving these clearly annotated sampling requests, talents can accurately understand what type and level of detail of information is required, thereby supplementing the data with targeted incremental data, ensuring that the collected data meets the requirements without placing an excessive burden on the talents. Multi-dimensional and multi-granular annotation refers to considering different analytical dimensions, such as time, behavior, and emotion, as well as the granularity level of the data, such as high-precision granularity and coarse-grained granularity, when annotating the data. For example, emotional fluctuations can be divided into monthly, weekly, or daily granularities, while behavioral characteristics can be divided into multiple dimensions such as work efficiency, teamwork, and problem-solving. Incremental sampling refers to updating or supplementing target data based on changes in the original data. Compared to full sampling, it focuses more on capturing the changing parts of the data, and can obtain the latest dynamic information while reducing computational and storage overhead.

[0055] Multi-dimensional and multi-granular evaluations are performed based on incremental sampling data, and the results are projected onto the multi-dimensional and multi-granular incremental resource space for resource information evaluation and labeling. The results are then combined with the static resource information to construct a talent resource information database.

[0056] Furthermore, this application also includes the following steps: determining a credibility parameter based on the source and sampling method of the incremental sampling data, wherein the sampling data includes at least one of the following types: system-automated sampling data, user-initiated sampling data, and third-party verification data; and, based on the credibility parameter, weighting the incremental sampling data according to resource dimensions and granularity levels to obtain evaluation information of the incremental sampling data and generate evaluation labels for resource information.

[0057] Specifically, incremental sampling data refers to data collected based on changes in existing data. Sources of incremental sampling data include automatically collected system data, user-initiated data, and third-party verified data. Automatically collected system data is data automatically collected and updated by the system, such as code commit records, system operation logs, and work platform activity data. User-initiated data is data actively provided by users, such as dynamic information reported self-reported or manually entered. Third-party verified data is data provided by third-party organizations or systems, such as academic credentials, professional qualification certificates, and project acceptance reports.

[0058] Automated sampling data typically has the highest reliability because it is usually acquired by automated systems, ensuring strong objectivity and high credibility. Third-party verified data has the next highest reliability, as it comes from independent and verified sources, such as behavioral data from social media platforms or externally certified monitoring data. User-submitted data has lower reliability because it may be influenced by subjective factors, such as users exaggerating or concealing information. The reliability parameter is determined based on the source and sampling method of incremental sampling data, reflecting the objectivity, authenticity, and verifiability of the information.

[0059] Based on the credibility parameter, the data is weighted and evaluated according to resource dimensions and granularity levels, with higher credibility data receiving higher weight in the evaluation. After the evaluation is completed, the system generates an evaluation label containing quality information for each data point. For example, assuming the system-sampling data has a score of 92 and a credibility parameter of 0.95, the user-sampled data has a score of 88 and a credibility parameter of 0.70, and the third-party verified data has a score of 86 and a credibility parameter of 0.85, after multi-dimensional weighted calculation, the evaluation score is (92*0.95+88*0.7+86*0.85) / (0.95+0.70+0.85)=0.87.

[0060] Based on the weighted evaluation results, corresponding evaluation labels for resource information are generated. These labels are then merged with the static resource information matching and projection results to construct a talent resource information database, forming a complete talent resource profile. In other words, all matching results from incremental sampling data and static resource information are aggregated into a single talent resource information database, containing a large amount of weighted and evaluated static and dynamic data. By assessing the credibility of the data sources and sampling methods, the quality and accuracy of talent information can be effectively guaranteed, avoiding subjective bias. Through weighted evaluation and credibility parameters, not only is data quality improved, but the dynamic updating of talent profiles and the accuracy of resource matching are also optimized, promoting the intelligent and personalized development of talent management.

[0061] In summary, the human resource information processing method based on big data analysis provided in this application has the following technical effects: By leveraging big data, the system performs static and dynamic analysis of the resource catalog, configures resource catalog attribute tags, and analyzes dynamic resource information at both the dimensional and granular levels, configuring a multi-dimensional, multi-granular incremental resource space. Static resource information matching and projection are performed on the talent resource information to be processed according to the resource catalog. The incremental parameters of the multi-dimensional, multi-granular incremental resource space are used to analyze dynamic sampling parameters of the dynamic resource information, generating dynamic sampling instructions. Multi-dimensional, multi-granular annotation is performed based on these dynamic sampling instructions, and the results are fed back to the sampled talent for incremental sampling. Multi-dimensional, multi-granular evaluation is performed based on the incremental sampling data, projected onto the multi-dimensional, multi-granular incremental resource space for resource information evaluation and annotation, and then merged with the static resource information matching and projection results to construct a talent resource information database. In other words, by constructing a dynamic and static combined resource catalog system, configuring a multi-dimensional, multi-granular incremental resource space, performing incremental data sampling based on dynamic sampling instructions, and evaluating resource information in a multi-dimensional, multi-granular manner, the collected dynamic data is evaluated and annotated, and then merged with static data to form a complete talent resource database. This achieves refined management of the talent resource information database and improves the accuracy and efficiency of talent matching.

[0062] Example 2: Based on the same inventive concept as the big data analysis-based human resource information processing method in Example 1, this application also provides a big data analysis-based human resource information processing platform. Please refer to the appendix. Figure 2 The big data analytics-based human resource information processing platform includes: The resource configuration module 11 is used to perform static and dynamic parsing of the resource catalog based on big data, configure resource catalog attribute tags, and perform dimensional and granular parsing of dynamic resource information to configure a multi-dimensional and multi-granular incremental resource space. The parameter parsing module 12 is used to perform static resource information matching and projection on the talent resource information to be processed according to the resource catalog, and to perform dynamic sampling parameter parsing on the dynamic resource information using the incremental parameters of the multi-dimensional and multi-granular incremental resource space to generate dynamic sampling instructions. The incremental sampling module 13 is used to perform multi-dimensional and multi-granular annotation based on the dynamic sampling instructions and feed it back to the sampled talents for incremental sampling. The information evaluation and annotation module 14 is used to perform multi-dimensional and multi-granular evaluation based on the incremental sampling data, project it to the multi-dimensional and multi-granular incremental resource space for resource information evaluation and annotation, and merge it with the static resource information matching and projection results to construct a talent resource information database.

[0063] Furthermore, the resource configuration module 11 in the big data-based talent resource information processing platform is also used for: collecting resource catalog samples based on big data; performing static and dynamic analysis on the sample data of the resource catalog to determine the structured attributes of the catalog data; configuring the static resource information and dynamic resource information in a structured manner and inserting resource catalog attribute tags; storing the static resource information in the form of a static content annotation table; performing horizontal dimension and vertical granularity resource profile evolution analysis on the dynamic resource information to obtain multi-dimensional and multi-granularity analysis features, and storing them in a dynamic expansion space structure. The dynamic expansion space structure has a static content annotation table and an insertion expansion space. The insertion expansion space has dimension and granularity identifiers and a relationship with the static content annotation table, thus establishing the multi-dimensional and multi-granularity incremental resource space.

[0064] Furthermore, the resource allocation module 11 in the big data-based talent resource information processing platform is also used for: based on the descriptive dimensions of talent resources using big data, performing relevant resource analysis and feature expansion on the static basic content of dynamic resource information, identifying multi-dimensional analytical features of dynamic resource information, wherein the multi-dimensional analytical features include emotional features, behavioral features, social features, and learning ability features; performing vertical granular decomposition on the multi-dimensional analytical features, dividing them into multiple granularity intervals based on time series, behavioral frequency, and information refinement, including fine granularity and time granularity; establishing a hierarchical mapping relationship between the multi-dimensional analytical features and the multi-granularity intervals, forming a hierarchical structure of multi-dimensional and multi-granular analytical features, and obtaining the multi-dimensional and multi-granular analytical features.

[0065] Furthermore, the resource configuration module 11 in the big data analysis-based talent resource information processing platform is also used to: associate the static content labeling table and the insertion extension space through a resource mapping index table, wherein the resource mapping index table records the correspondence between static content field identifiers and dynamic slot positions.

[0066] Furthermore, the resource configuration module 11 in the big data analysis-based talent resource information processing platform is also used for: automatically generating a granularity hierarchical identifier for each incremental unit in the insertion expansion space when it is inserted; calculating the uncertainty parameter of the corresponding incremental unit based on information entropy, time difference, or behavior frequency; and triggering a dynamic sampling instruction to request the sampled talent to supplement or correct the corresponding information when the uncertainty parameter exceeds the threshold.

[0067] Furthermore, the resource configuration module 11 in the big data analysis-based talent resource information processing platform is also used to: extract basic fields from the static content labeling table of the dynamic expansion space structure to form a static tag set when performing resource catalog parsing; load corresponding multi-dimensional incremental units from the insertion expansion space based on the static tags; and generate a configuration matrix of multi-dimensional, multi-granular incremental resource space based on dimension mapping relationship and granularity hierarchy rules.

[0068] Furthermore, the resource configuration module 11 in the big data analysis-based talent resource information processing platform is also used for: the insertion expansion space adopts a hierarchical nested structure, including a public layer, a semi-public layer and a private layer, wherein, permission verification is performed according to the sampling task, and based on the authorization scope of the semi-public layer and the private layer, the sensitive labeled content in the dynamic expansion slot is parsed or sampled.

[0069] Furthermore, the parameter parsing module 12 in the big data-based talent resource information processing platform is also used for: the dynamic sampling instruction further includes sampling incentive parameters, which weight the talent resource information according to the incremental value of the sampled data, and use it for talent resource scenario matching applications.

[0070] Furthermore, the information evaluation and labeling module 14 in the big data analysis-based talent resource information processing platform is also used to: determine the credibility parameter based on the source and sampling method of the incremental sampling data, wherein the sampling data includes at least one of the following types: system-automated sampling data, user-initiated sampling data, and third-party verified data; and based on the credibility parameter, perform weighted evaluation on the incremental sampling data according to resource dimensions and granularity levels to obtain the evaluation information of the incremental sampling data and generate evaluation labels for resource information.

[0071] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The big data analysis-based human resource information processing method and specific examples in Example 1 are also applicable to the big data analysis-based human resource information processing platform in this example. Through the foregoing detailed description of the big data analysis-based human resource information processing method, those skilled in the art can clearly understand the big data analysis-based human resource information processing platform in this example. Therefore, for the sake of brevity, it will not be described in detail here.

[0072] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0073] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for processing talent resource information based on big data analysis, characterized in that, Comprise: Based on big data, the resource directory is parsed statically and dynamically, the resource directory attribute label is configured, and the dynamic resource information is parsed in dimension and granularity, and the multi-dimensional and multi-granularity incremental resource space is configured; According to the resource directory, the static resource information matching projection is carried out on the personnel resource information to be processed, the incremental parameters of the multi-dimensional and multi-granularity incremental resource space are used to analyze the dynamic sampling parameters of the dynamic resource information, and the dynamic sampling instruction is generated; Based on the dynamic sampling instruction, multi-dimensional and multi-granularity labeling is carried out, and feedback is given to the sampling talents for incremental sampling; According to the incremental sampling data, multi-dimensional and multi-granularity evaluation is carried out, and the resource information evaluation label is projected to the multi-dimensional and multi-granularity incremental resource space, and the static resource information matching projection result is combined to construct the talent resource information library.

2. The big data analysis based human resource information processing method according to claim 1, characterized in that, The resource directory is parsed statically and dynamically, the resource directory attribute label is configured, and the dynamic resource information is parsed in dimension and granularity, and the multi-dimensional and multi-granularity incremental resource space is configured, comprising: Based on big data, resource directory sample collection is carried out, static and dynamic analysis is carried out according to the sample data of the resource directory, the structured attributes of the directory data are determined, the static resource information and the dynamic resource information are configured respectively, and the resource directory attribute label is inserted; The static resource information is stored in the form of static content labeling table; The dynamic resource information is analyzed in horizontal dimension and vertical granularity, and the multi-dimensional and multi-granularity analysis features are obtained, and the dynamic expansion space structure is stored, the dynamic expansion space structure has a static content labeling table and an inserted expansion space, the inserted expansion space has dimension, granularity identification and association relationship with the static content labeling table, and the multi-dimensional and multi-granularity incremental resource space is established. 3.The big data analysis based human resource information processing method according to claim 2, characterized in that, The dynamic expansion space structure has a static content labeling table and an inserted expansion space, the inserted expansion space has dimension, granularity identification and association relationship with the static content labeling table, comprising: The static content labeling table and the inserted expansion space are associated through a resource mapping index table, which records the corresponding relationship between the static content field identification and the dynamic slot position.

4. The big data analysis based human resource information processing method according to claim 3, characterized in that, Each incremental unit in the inserted expansion space generates a granularity hierarchical identification automatically when inserted by the system; According to information entropy, time difference or behavior frequency, the uncertainty parameter of the corresponding incremental unit is calculated; When the uncertainty parameter exceeds the threshold value, the dynamic sampling instruction is triggered to request the sampling talents to supplement or correct the corresponding information. 5.The big data analysis based human resource information processing method according to claim 3, characterized in that, Further comprising: When the resource directory parsing is executed, the basic fields are extracted from the static content labeling table of the dynamic expansion space structure to form a static label set; Based on the static label, the corresponding multi-dimensional incremental unit is loaded from the inserted expansion space, and the configuration matrix of the multi-dimensional and multi-granularity incremental resource space is generated based on the dimension mapping relationship and the granularity level rule. 6.The big data analysis based human resource information processing method according to claim 3, characterized in that, Further comprising: The insertion expansion space adopts a hierarchical nested structure, including a public layer, a semi-public layer and a private layer, wherein, according to the permission verification of the sampling task, the sensitive annotation content in the dynamically expanded slot is parsed or sampled based on the authorized range of the semi-public layer and the private layer. 7.The big data analysis based human resource information processing method according to claim 2, characterized in that, The dynamic resource information is analyzed in the horizontal dimension and the longitudinal granularity to obtain multi-dimensional and multi-granularity analysis features, including: Based on the description dimension of talent resources in big data, the static basic content of dynamic resource information is analyzed and the features are extended, and the multi-dimensional analysis features of dynamic resource information are identified, wherein the multi-dimensional analysis features include emotional features, behavior features, social features and learning ability features; The multi-dimensional analysis features are decomposed in the longitudinal granularity, and are divided into multiple granularity intervals according to the time sequence, the behavior frequency and the information precision, including fine granularity and time granularity; The hierarchical mapping relationship between the multi-dimensional analysis features and the multi-granularity intervals is established to form a hierarchical structure of multi-dimensional and multi-granularity analysis features, and the multi-dimensional and multi-granularity analysis features are obtained. 8.The big data analysis based human resource information processing method according to claim 1, wherein, According to the incremental sampling data, multi-dimensional and multi-granularity evaluation is performed, including: According to the source and sampling method of the incremental sampling data, a credibility parameter is determined, and the sampling data at least includes one of the following types: system automatic sampling data, user autonomous sampling data and third-party verification data; Based on the credibility parameter, the incremental sampling data is weighted and evaluated according to the resource dimension and the granularity level to obtain the evaluation information of the incremental sampling data, and the evaluation annotation of the resource information is generated. 9.The big data analysis based human resource information processing method according to claim 1, wherein, The dynamic sampling instruction also includes a sampling incentive parameter, which is used for resource level and credit reward weighting of talent resource information according to the incremental value of the sampling data, and is used for talent resource scene matching application.

10. A talent resource information processing platform based on big data analysis, characterized in that, The steps of the talent resource information processing method based on big data analysis in any one of claims 1 to 9 are implemented, and the talent resource information processing platform based on big data analysis includes: A resource configuration module is configured to analyze the resource directory based on big data, configure resource directory attribute tags, and analyze the dimensions and granularity of dynamic resources to configure multi-dimensional and multi-granularity incremental resource space; A parameter analysis module is configured to project the static resource information matching of the talent resource information to be processed according to the resource directory, analyze the dynamic sampling parameters of the dynamic resource information using the incremental parameters of the multi-dimensional and multi-granularity incremental resource space, and generate dynamic sampling instructions; An incremental sampling module is configured to perform multi-dimensional and multi-granularity annotation based on the dynamic sampling instructions and feed back to the sampling talent for incremental sampling; An information evaluation annotation module is configured to perform multi-dimensional and multi-granularity evaluation according to the incremental sampling data, project the resource information evaluation annotation to the multi-dimensional and multi-granularity incremental resource space, and merge the static resource information matching projection result to construct a talent resource information library.

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