A whole-process human resource service management and control system and method

Abstract

The application relates to the field of human resource management and information technology, and discloses a full-process human resource service management and control system and method; through full-process management and control of a link between recruitment and resignation, internal digital behavior traces are acquired by using a behavior sensing acquisition module, and a collaborative knowledge graph is constructed, knowledge flow contribution, cross-system collaboration index and collaboration load risk are quantified, five-dimensional employee dynamic panoramic portraits containing implicit contribution dimensions are formed by fusing business data, human-post matching, resignation early warning and control strategy generation are executed by an intelligent decision engine, integrated evidence of explicit business records and implicit contribution records is stored by a distributed ledger and cross-enterprise credit verification is supported, decision model parameters are continuously corrected by a closed-loop feedback mechanism, so that automatic quantification of implicit contribution and full-process closed-loop management and control are realized, and automatic quantification of implicit contribution and fusion of the same with full-process human resource management and control decision are realized.

Description

Technical Field

[0001] This invention relates to the field of human resource management and information technology, specifically to a full-process human resource service management and control system and method. Background Technology

[0002] In modern organizational management, the core objective of human resource management has shifted from basic transactional management to accurately identifying, motivating, and retaining core talent capable of creating long-term value for the organization. To achieve this goal, the industry has developed human resource management systems covering recruitment, onboarding, in-service management, compensation and benefits, training and development, scheduling, and departure management. Some advanced solutions have also introduced distributed ledger technology for storing and tracing employee professional credit data, leveraging the immutability of blockchain to ensure data authenticity and the credibility of cross-enterprise verification.

[0003] However, existing human resource management systems and credit record systems primarily focus on managing employees' "explicit value"—business results that can be directly recorded and easily quantified, such as sales revenue, code submissions, attendance hours, performance ratings, and reward / punishment records. Existing technologies are almost entirely unable to automatically capture and quantify the "implicit contributions" employees make during their work. These contributions have a profound impact on an organization's long-term competitiveness, yet they have long remained outside the quantitative decision-making system of human resource management due to a lack of effective technological means to perceive them.

[0004] Talent assessment relies excessively on the subjective judgment of superiors and periodic survey feedback, resulting in assessment results that are significantly outdated and subject to cognitive biases. Employees who act as key knowledge hubs within collaborative networks, undertaking substantial implicit guidance work, are often systematically underestimated in traditional performance evaluations, leading to organizational injustice where "the capable do more but receive less," and consequently, the implicit loss of core talent. Managers also lack objective technical means to identify collaboration bottlenecks and knowledge silos within teams, failing to promptly detect single points of failure risks arising from over-reliance on individual expert employees.

[0005] In exploring existing technological approaches, some solutions attempt to conduct behavioral analysis by collecting employee computer operation logs or communication metadata. However, their analysis is limited to surface-level activity indicators such as work duration and application switching frequency, failing to extract deep collaborative semantics and knowledge transfer directions with management and decision-making value. Other solutions in the field of organizational social network analysis have proposed expert discovery algorithms or social network centrality calculation methods. However, these methods mostly remain at the level of static statistical description of relationship strength, unable to dynamically track the actual flow path of knowledge among employees, unable to quantify the specific contribution of knowledge senders to the improvement of receivers' capabilities, and lacking technical mechanisms to integrate such analytical results with core human resource management and decision-making processes such as compensation incentives, promotion evaluations, and turnover warnings.

[0006] The current application of distributed ledger technology in human resource management is also limited to the level of evidence storage. Current solutions write explicit data such as employees' educational background, reward and punishment records, and performance results into the blockchain to ensure their authenticity and traceability. However, for implicit contributions such as mentoring abilities, knowledge sharing contributions, and cross-team collaboration value accumulated by employees throughout their careers, existing solutions lack both the technical means to collect and quantify them, and the corresponding on-chain evidence storage and trusted cross-enterprise transfer mechanisms. This results in the absence of this crucial value dimension—implicit contributions—in the complete professional credit profile of employees when they move between different employers.

[0007] In summary, existing technologies have not yet provided a systematic technical solution that can automatically, continuously, and objectively perceive and quantify employees' implicit contributions from daily digital behavior traces within an organization, integrate implicit contributions and explicit business data into a traceable professional credit system, and deeply integrate the quantification results into the entire process of human resource management and decision-making, including recruitment screening, job matching, compensation incentives, scheduling optimization, and turnover warning. Therefore, a full-process human resource service management and control system and method are proposed. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a full-process human resources service management system and method to solve the problems described in the background.

[0009] To achieve the above objectives, the present invention provides the following technical solution: Firstly, a full-process human resources service management system, comprising: The end-to-end management platform integrates recruitment, onboarding, current employment, payroll, training, scheduling, and offboarding management service modules to achieve business data exchange and process flow. The distributed ledger module is used to acquire business data generated by each service module, as well as key event data on knowledge transfer provided by the implicit contribution quantification module and the collaborative knowledge graph construction module. It generates a data summary from the key fields of the business data and key event data on knowledge transfer, writes the data summary and timestamp into the distributed ledger, generates a unique professional credit account for each employee, and links them to form a traceable and tamper-proof career credit data chain containing explicit business records and implicit contribution records. The behavior perception and acquisition module is used to continuously collect internal digital behavior trace data of multiple employees within the organization, including at least email interaction records, instant messaging records, internal community posting and reply records, online document collaborative editing and annotation records, and extract structured behavior datasets with interaction characteristics between binary entities. The collaborative knowledge graph construction module is used to dynamically construct and update a collaborative knowledge graph with employees as nodes based on the structured behavior dataset. The graph includes "guidance-being guided" edges representing business guidance relationships, "sharing-receiving" edges representing knowledge transfer relationships, and "co-creation" edges representing innovative collaborative relationships. The module also calculates the interaction intensity weight and the quantitative value of the knowledge transfer direction for each edge. The implicit contribution quantification module is used to calculate the knowledge flow contribution, cross-system collaboration index, and collaboration load risk level of any target employee based on the collaborative knowledge graph. The profile building module is used to collect multi-source heterogeneous data from various service modules of the full-process management and control platform, and combine the knowledge flow contribution degree, cross-system collaboration index and collaboration load risk degree output by the implicit contribution quantification module to build a five-dimensional dynamic panoramic profile of employees, including capability dimension, behavior dimension, credit dimension, development dimension and implicit contribution dimension. The intelligent decision engine is used to perform matching degree assessment between candidates and job requirements, job suitability assessment of current employees, and prediction of employee turnover risk based on the five-dimensional dynamic panoramic profile of employees, and to generate management and control strategies. The prediction of employee turnover risk takes the collaboration load risk degree, behavioral dimension and credit dimension features in the five-dimensional dynamic panoramic profile of employees as input features. When the collaboration load risk degree exceeds a preset threshold, it triggers a key talent retention warning and knowledge transfer suggestions. The intelligent decision engine is also used to automatically backtrack the employee's assessment data during the recruitment stage, behavior log data and performance data during employment, as well as the evolution history of its collaborative knowledge graph after an employee resignation event is triggered. It extracts key characteristic variables and variable combinations that affect employee resignation and feeds the analysis results back to the recruitment management service module to optimize talent screening rules and evaluation standards.

[0010] Preferably, the calculation of the knowledge circulation contribution specifically includes: identifying at least one edge in the collaborative knowledge graph where the target employee acts as a knowledge sending node, and obtaining the business performance data of the receiving node connected by the edge; when the business performance data of the receiving node experiences a positive jump within a preset time window and is determined to be related to the knowledge transfer behavior represented by the edge, calculating the performance improvement amount; and attributing the performance improvement amount to the target employee's knowledge circulation contribution amount according to the correlation weight.

[0011] Preferably, the collaborative knowledge graph construction module is further used to: identify question-answer pairs by recognizing question messages containing technical terms or project keywords in emails or instant messaging records, and reply messages containing solution characteristics sent by another employee within a preset response time; determine the relationship between the questioner and the responder in the question-answer pair as a "mentor-mentee" relationship, and create or enhance the corresponding edge; determine the interaction relationship as a "sharer-receiver" relationship by detecting that other employees have read or given positive feedback to technical articles, project reviews, or experience sharing posts published in the internal community for more than a preset time, and create or enhance the corresponding edge; and determine the collaborative creation behavior among employees through collaborative editing and annotation records of online documents, and create or enhance the "co-creation" edge.

[0012] Preferably, the distributed ledger module includes a cross-enterprise credit verification submodule, which, under the consortium blockchain architecture, uses smart contracts to preset hierarchical sharing rules and, with the employee's authorization, securely transmits and reliably verifies the career credit data chain accumulated by the employee in the previous employer, which includes de-identified labels of implicit contribution dimensions, to the human resources system of the next employer in the form of encrypted and verifiable credentials.

[0013] Preferably, the generation of the encrypted verifiable credential specifically includes: performing a hash operation on the employee's career credit data to generate a data fingerprint, and generating a verifiable credential by using the data fingerprint and authorization proof information through a zero-knowledge proof protocol. The receiving employer can verify the authenticity and integrity of the credit data by verifying the verifiable credential without having to access the original data content, thereby protecting the employee's privacy.

[0014] Preferably, the management and control strategies generated by the intelligent decision engine include: based on the implicit contribution dimension in the five-dimensional dynamic panoramic portrait of employees, generating personalized incentive schemes that include suggestions for awarding digital expert badges or expert allowances for employees whose contribution to knowledge circulation exceeds a first preset threshold; and generating key talent retention intervention schemes that include the initiation of knowledge transfer plans and backup succession arrangements for employees whose collaboration load risk exceeds a second preset threshold.

[0015] Preferably, the intelligent decision engine is further used to: identify knowledge silos and hidden key coordinators within the organization based on the cross-system collaboration index and knowledge flow contribution of each employee in the five-dimensional dynamic panoramic portrait of employees, and generate team collaboration structure optimization suggestions that include adding or replacing specific roles in project teams and establishing regular knowledge sharing mechanisms between departments with sparse collaboration but close business connections.

[0016] Preferably, the behavior perception acquisition module and the implicit contribution quantification module use differential privacy technology to add noise during the data acquisition and calculation process, and the construction of the five-dimensional employee dynamic panoramic profile and the output of the control strategy are based on aggregated statistical results or anonymization processing, without exposing the original behavioral data details of individual non-target employees.

[0017] Preferably, the full-process management and control platform further includes a closed-loop feedback control submodule, which is used to collect business result data generated by each service module after executing the management and control strategy generated by the intelligent decision engine, compare and analyze the business result data with the decision suggestions, calculate the decision accuracy index and the deviation index, and adjust the model parameters of the intelligent decision engine based on the deviation index. The adjustment further includes correcting the evaluation weight or quantification model parameters of the intelligent decision engine used to process the implicit contribution dimension data in the five-dimensional employee dynamic panoramic profile.

[0018] Secondly, a full-process human resource service management and control method, applied to the system described in the first aspect, includes: Through the full-process management platform, data from all business processes, including recruitment, onboarding, employment, payroll, training, scheduling, and departure, are uniformly aggregated to achieve data integration and process collaboration. When key events involving business data and knowledge transfer are generated, the relevant summaries and timestamps are written to the distributed ledger through the distributed ledger module, generating a career credit account for each employee and linking it to a career credit data chain containing explicit business records and implicit contribution records. Continuously collect internal digital behavior data of employees within the organization, construct a collaborative knowledge graph, and calculate each employee's knowledge flow contribution, cross-system collaboration index, and collaborative load risk. Construct a five-dimensional dynamic panoramic profile of employees, including capability, behavior, credit, development, and implicit contribution dimensions. Based on the five-dimensional dynamic panoramic profile of employees, the system performs person-job matching assessment, turnover risk prediction, and management strategy generation. The turnover risk prediction integrates the collaborative load risk level, behavioral dimension, and credit dimension features in the five-dimensional dynamic panoramic profile of employees. When the collaborative load risk level exceeds the threshold, the system triggers a key talent retention warning and knowledge transfer suggestions. After an employee resignation event is triggered, the system automatically traces back the evolution history of the employee's recruitment assessment data, on-the-job behavior logs, performance data, and collaborative knowledge graph. It extracts key characteristic variables that influence resignation and feeds them back to the recruitment management module to optimize screening rules, thereby achieving closed-loop optimization of the entire process.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a collaborative knowledge graph and extracts three quantitative indicators from it: knowledge flow contribution, cross-system collaboration index, and collaboration load risk. For the first time, it achieves automated, continuous, and objective measurement of employees' implicit contributions, filling the technological gap in existing technologies that cannot perceive and quantify implicit organizational value such as informal guidance, knowledge sharing, and cross-team coordination from daily digital behavior traces.

[0020] 2. This invention employs a technical means of writing implicit contribution quantification data and explicit business data together into a distributed ledger, so that the employee's career credit data chain simultaneously includes explicit performance records and implicit contribution records, realizing credible evidence of the employee's complete career value, and providing a higher credit dimension for talent evaluation in cross-enterprise mobility scenarios.

[0021] 3. By using the collaborative workload risk level as a key input feature for predicting turnover risk, this invention can identify structural turnover risks caused by individual employees taking on excessive knowledge hub responsibilities. Compared with existing prediction schemes that rely solely on sentiment analysis or behavioral anomalies, the targetedness and interveneability of the early warning are significantly improved.

[0022] 4. This invention deeply integrates the quantification results of implicit contributions into the entire process of management and decision-making, such as job matching, compensation incentives, scheduling and team structure optimization, forming a complete intelligent closed loop from value perception and quantitative assessment to precise intervention. This enables human resource management decisions to shift from relying on subjective experience to objective decisions based on implicit value data.

[0023] 5. This invention uses a closed-loop feedback control submodule to reverse the decision result deviation index and use it to correct the evaluation weight and quantification model parameters of the implicit contribution dimension, thereby realizing the continuous self-optimization of the system's implicit contribution measurement accuracy and ensuring the effectiveness of decision-making under long-term operation. Attached Figure Description

[0024] Figure 1 This is the overall architecture diagram of the full-process human resources service management and control system of the present invention; Figure 2 This is a flowchart of the whole-process human resources service management method of the present invention. Detailed Implementation

[0025] Please see Figure 1 and Figure 2This invention discloses a full-process human resources service management system and method. The system is deployed on a distributed server cluster within an enterprise, integrating various functional modules into a unified management platform through a microservice architecture. This embodiment uses a medium-sized technology company with approximately 800 employees as an example. The company has four main departments: R&D, product, marketing, and operations. Daily collaboration relies on the enterprise email system, instant messaging tools, internal technical communities, and online collaborative document platforms. The specific steps are as follows: Step 1: Business data aggregation and process integration in the full-process management platform The end-to-end management platform serves as the core framework of the system, integrating recruitment management, onboarding management, on-the-job management, payroll and benefits, training and development, intelligent scheduling, and departure management modules into a unified data platform. These modules interact via standardized RESTful service interfaces, with all business data converged on this unified data platform.

[0026] Specifically, after the recruitment management service module completes an interview assessment for a candidate, the system automatically writes the candidate's evaluation data, interviewer evaluation data, and job requirement matching data into the data base. Upon the candidate's onboarding, the onboarding management service module reads the data accumulated during the recruitment phase from the data base and automatically populates the employee's basic information, avoiding duplicate data entry. Performance evaluation data, attendance records, training completion status, salary adjustment records, and other data generated during the employee's employment are all written into the data base in real time by the corresponding service modules, forming a complete business data chain with the employee's unique identifier as the primary key.

[0027] The workflow between service modules is driven by a workflow engine. For example, when an employee's performance rating falls below a preset threshold for two consecutive assessment cycles, the workflow engine automatically triggers the training and development service module to generate a targeted training plan and feeds back the training completion status to the payroll and benefits service module as a reference factor for salary adjustments in the next cycle. This data sharing and process collaboration between modules ensures seamless integration across all stages of the human resources lifecycle.

[0028] Step 2: Behavior perception data collection and generation of structured behavior datasets The behavior awareness and data collection module continuously collects internal digital behavior data from all employees within the organization by connecting to the API interfaces of various digital platforms within the enterprise. The collected data types specifically include four categories: email interaction records, covering sender address, recipient address, CC address, email subject, email body text content, sending timestamp, and email thread identifier; instant messaging records, covering message sender identifier, message recipient identifier, message group identifier, message text content, message sending timestamp, and message type identifier, where the message type identifier is used to distinguish between one-on-one private messages and group messages; internal community posting and reply records, covering post author identifier, post title, post body text content, posting timestamp, as well as replyer identifier, reply text content, reply timestamp, and the identifier of the post to which the reply is directed; and online document collaborative editing and annotation records, covering document identifier, editor identifier, timestamp sequence of editing operations, annotator identifier, annotation text content, and the document paragraph position identifier to which the annotation points.

[0029] The collected raw data is in semi-structured and unstructured form. The behavior-aware acquisition module performs structured preprocessing on the raw data. For email data, the sender and recipient of each email are extracted to form binary interaction pairs. For emails with multiple recipients, the sender and each recipient are formed into independent binary interaction pairs. For instant messaging data, the sender and recipient of one-to-one messages are extracted to form binary interaction pairs. For group messages, the sender and each other member of the group are extracted to form binary interaction pairs. For internal community data, the poster and each replyer are extracted to form binary interaction pairs, and the citation relationship between replyers is also extracted to form binary interaction pairs. For online document data, any two editors who edited the same document are extracted to form binary interaction pairs, and the annotator and the editor of the paragraph to which the annotated content belongs are extracted to form binary interaction pairs.

[0030] After preprocessing, the behavior-aware acquisition module outputs a structured behavior dataset, with each record in the following format: ,in Indicates the identifier of the initiator of the behavior. Indicates the identifier of the recipient of the action. Indicates the timestamp of the action. Indicates behavior type encoding, These correspond to four types: email interaction, instant messaging, community interaction, and document collaboration. Text data representing the interactive content is used for subsequent semantic analysis.

[0031] Step 3: Dynamic Construction and Update of Collaborative Knowledge Graph The collaborative knowledge graph construction module receives a structured behavior dataset output by the behavior perception and collection module, and uses this as input to dynamically construct and continuously update a collaborative knowledge graph with enterprise employees as nodes. ,in express Employee node set This represents the set of directed edges.

[0032] The graph contains three types of edges, each representing a different type of collaborative relationship. The first type is the "mentor-mentee" edge, used to represent the business mentoring relationship between employees. Its construction process involves traversing all instant messaging and email records belonging to the same session thread, and using natural language processing technology to identify question messages containing technical terms or project keywords. The identification of technical terms and project keywords relies on a custom domain dictionary maintained by the company's technical committee, containing approximately 20,000 technical terms and project codes. When a message is detected ending with a question mark or containing question trigger words such as "excuse me," "help," or "who knows," and the message text contains at least one term from the domain dictionary, the message is marked as a candidate question message. Subsequently, a reply message is searched within a preset response time window after the sending time of the candidate question message in the same session thread. The preset response time window can be configured according to the company's actual communication rhythm; in this embodiment, it is set to four hours. Candidate response messages are identified using solution feature recognition to determine whether they contain step-by-step descriptions, code snippets, configuration parameters, or problem-final statements. Step-by-step descriptions are identified by identifying ordered list markers or "first-then-last" conjunctions in the message text; code snippets are identified by identifying programming language keywords or paired curly braces and indented code blocks; configuration parameters are identified by identifying key-value pair formatted text; and problem-final statements are identified by identifying final phrases such as "solved" or "done." Meeting any one of these four criteria indicates the message contains solution features. If the criteria are met, the sender of the question and the sender of the response are identified as a question-and-answer pair, and the questioner node is... With the responder node The relationship between them is defined as a "guide-guided" relationship, and a path is created in the collaborative knowledge graph from... point to The directed edges are labeled as "guided-guided".

[0033] The second type is the "share-receive" edge, used to represent knowledge transfer relationships. Its construction process involves content identification of technical articles, project review reports, or experience-sharing posts published by each employee in the internal community. When meaningful reading behavior or positive feedback interaction is detected from other employees regarding the article or post, the knowledge transfer relationship is established. Meaningful reading behavior is defined as: a dwell time exceeding a preset proportion of the average reading time of the article. In this embodiment, the preset proportion is set to more than one standard deviation; that is, when an employee's reading time exceeds the average reading time of all readers of the article plus one standard deviation, the employee is considered to have engaged in in-depth reading. Positive feedback interaction includes liking, saving, forwarding, and posting comments containing affirmative statements. When the above conditions are met, the article publisher node... With reader or interactor nodes The relationship is defined as a "share-receive" relationship, and a path is created in the collaborative knowledge graph from... point to The directed edges are labeled as "share-receive".

[0034] The third type is the "co-creation" edge, used to represent collaborative innovation relationships. Its construction process involves analyzing the collaborative editing and annotation records of online documents. When the editing time interval between two employees on the same document is less than a preset collaborative time window, it is considered that they have synchronous or quasi-synchronous collaborative creation behavior. In this embodiment, the preset collaborative time window is set to thirty minutes. When the condition is met, a bidirectional or unidirectional "co-creation" edge is created between the nodes of the two employees. The direction depends on the order of editing and the pointing relationship of the annotations, and the edge type is labeled "co-creation".

[0035] For each edge in the graph The collaborative knowledge graph construction module calculates two key attribute values: interaction strength weight. Reflects from the node To the node The frequency and depth of interaction are calculated using the following formula: ,in , , , They represent employees respectively. To employees The normalized frequency of initiating email interactions, instant messaging, community interactions, and document collaborations is calculated by dividing the frequency by [the normalization factor]. The total frequency of all similar interactions initiated. , , , The weight coefficients for each behavior type satisfy the following conditions: In this embodiment, the weighting coefficients are set according to the actual collaboration mode of the enterprise. Instant messaging has the highest weight because daily technical guidance occurs more frequently in instant messaging.

[0036] Quantitative value of knowledge transfer direction Reflects the knowledge from Towards The degree of clarity in the message is calculated using the following formula: ,in express I answered as a responder. The number of question-answer pairs. express The published articles or shares were The number of times of in-depth reading or positive feedback, Indicates from arrive The total number of interactions of all types It is a very small positive number to avoid the denominator being zero. In this embodiment, it is taken as... . The value range is [0, 1]. The larger the value, the more significant the knowledge transfer feature carried by the edge.

[0037] The collaborative knowledge graph is updated using an incremental update mechanism. When the behavior perception and acquisition module outputs new structured behavior data, the collaborative knowledge graph construction module determines whether the nodes and edges involved in the new data already exist in the current graph. If a node does not exist, a new node is created; if an edge does not exist, a new edge is created and its weight and direction quantization values ​​are initialized; if an edge already exists, the interaction strength weight and knowledge transfer direction quantization value of the edge are recalculated using a sliding time window. In this embodiment, the span of the sliding window is set to ninety days to ensure that the graph reflects the recent collaborative status of employees rather than outdated historical relationships.

[0038] Step 4: Multi-dimensional Quantitative Calculation of Implicit Contributions The implicit contribution quantification module calculates three core implicit contribution indicators for each target employee based on the collaborative knowledge graph constructed in step three: knowledge flow contribution, cross-system collaboration index, and collaboration load risk.

[0039] Contribution to Knowledge Circulation Measuring employees As a knowledge provider, this refers to the quantifiable contribution made to improving the capabilities of other employees within the organization. The calculation process involves three steps.

[0040] The first step is to identify all outgoing edges of the target employee as a knowledge-generating node in the collaborative knowledge graph. In the collaborative knowledge graph, from the node... The set consists of all directed edges originating from other nodes. .

[0041] The second step, for Each edge in Obtain the receiving node connected to this edge. Business performance data is derived from quantifiable indicators recorded in the on-the-job management service module and performance management module of the full-process control platform. For R&D employees, business performance data specifically includes the defect rate per thousand lines of code submission, on-time delivery rate of assigned tasks, and customer satisfaction scores for projects participated in. For marketing employees, business performance data includes lead conversion rate and adoption rate of event planning proposals. This embodiment takes the R&D position as an example and integrates the business performance data into a standardized performance score. This score is generated monthly by the enterprise performance management system.

[0042] The third step is to determine the receiving node. Does the performance score experience a positive jump within a preset time window, and is this jump related to the edge? The knowledge transfer behavior it represents is related to the preset time window, which is set to the edge. The timestamp of the most recent confirmed guidance Q&A or knowledge-sharing event. The window's endpoint is set to On the thirtieth day thereafter, the window spans thirty days. Within this window, calculations are performed. The magnitude of the performance score increase: ,in The window start time The performance score, The end time of the window The performance score. When Exceeding the preset positive jump threshold At this point, a positive performance leap event is considered to have occurred. In this embodiment... Set to 0.5 times the standard deviation of the performance score to ensure that meaningful improvements are captured rather than random fluctuations.

[0043] When determining the correlation between performance leaps and knowledge transfer behavior, a multi-factor attribution analysis method is used. The system extracts data within a time window related to... All relevant events that may affect its effectiveness, including: formal training courses attended, changes in the difficulty of assigned projects, changes in team leaders, and related events. Knowledge transfer events between [various entities]. A gradient boosting decision tree algorithm is used to train the attribution model. The input features are the occurrence markers and intensities of the aforementioned events, and the output is the contribution weight of each event to the performance jump. The training samples of the attribution model consist of receiver nodes that experienced performance jumps within a historical time window. The feature vector of each sample includes the occurrence frequency of each type of event within that window, the normalized value of the event intensity, the time interval between the event occurrence time and the performance jump time, and the label is the magnitude of the performance jump. After the model is trained, the feature importance scores output for new samples are normalized to obtain the contribution weight of each event. The contribution weight of the knowledge transfer event is denoted as […]. The value range is [0, 1].

[0044] The attribution calculation for the contribution of knowledge flow is as follows: ,in For the edge The quantification value of the direction of knowledge transfer. The interaction strength weights of the edges. The economic meaning of this formula is: employees The contribution of knowledge circulation is equal to the weighted sum of the efficiency improvement of all knowledge recipients after they have interacted with it. The weights are determined by the clarity of the direction of knowledge transmission and the intensity of interaction.

[0045] Cross-system collaboration index Measuring employees The quantifiable value of facilitating cross-departmental information flow and resource integration. The calculation first determines the organizational structure of the enterprise; in this example, departments are used as the unit. Assume the enterprise has K departments and a number of employees... The department to which it belongs is recorded as Statistics from The sum of the interaction strength weights of all outgoing edges pointing to employees in other departments: ,in This is an indicator function; it takes a value of 1 when the condition within the square brackets is true, and 0 otherwise. Furthermore, the closeness of inter-departmental business relationships is introduced as a moderating factor for the cross-system collaboration index. An inter-departmental business relationship matrix is ​​defined. , of which elements Indicates department With Department The degree of interdependence between the two departments in terms of business processes is calculated based on the ratio of the number of projects jointly participated in by employees of both departments to the total number of projects, as recorded in the company's project collaboration records. Therefore, the revised cross-system collaboration index is: This revision makes the cross-departmental collaboration index reflect not only the frequency of cross-departmental interactions, but also the importance of business relationships between the departments involved.

[0046] Collaborative load risk Assessment of over-reliance on employees The knowledge bottleneck and turnover risk arising from serving as a knowledge hub are comprehensively considered in its calculation, taking into account two dimensions: load intensity and load concentration.

[0047] Load intensity measurement of employees The total interactive pressure borne by the knowledge-issuing node: Load concentration measurement Whether the distribution of guidance and sharing recipients within the organization is too concentrated can be measured using a variant of the Herfindahl-Hirschman index: ,in, The range of values ​​is The closer the value is to 1, the more concentrated the load is on a few receivers; the closer the value is to... This indicates a more uniform load distribution.

[0048] The risk level of collaborative load is derived from a combination of load intensity and load concentration: ,in and The mean and standard deviation of the load intensity are generated for all employees in the company, which are used to standardize the load intensity. and For the combined weight coefficients, satisfying In this embodiment, they are respectively set as follows: , Assign a slightly higher weight to the load intensity.

[0049] when When the preset second threshold is exceeded, the employee Those deemed to be in a high-risk state will, upon leaving the company, experience a significant disruption of tacit knowledge and mentorship relationships, severely impacting team capabilities. In this embodiment, the second threshold is set as the 90th percentile of the risk distribution of collaborative workload across all company employees.

[0050] Step 5: Constructing a 5D Dynamic Panoramic Profile of Employees The profile building module collects multi-source heterogeneous data from various service modules of the end-to-end management platform and integrates it with the three implicit contribution indicators output by the implicit contribution quantification module to construct a five-dimensional dynamic panoramic profile for each employee. The five dimensions, their data sources, and construction methods are as follows.

[0051] Capability dimension feature vector The data originates from candidate assessments recorded in the recruitment management service module, training assessment results recorded in the training and development service module, and competency scores from performance evaluations recorded in the on-the-job management service module. Principal component analysis was used to reduce the dimensionality of the high-dimensional data, extracting the initial... In this embodiment, each principal component serves as a capability dimension feature. More than 85% of the total variance of the original data is retained.

[0052] Behavioral dimension feature vector The data is derived from attendance data, scheduling execution data, and task completion records in the task management system. Specifically, it includes: average monthly attendance rate, scheduling compliance rate, on-time task delivery rate, frequency of voluntary overtime, and internal community activity. The behavioral dimension feature vector is formed by concatenating the above standardized indicators.

[0053] Credit dimension feature vector The credit score originates from the career credit data chain associated with the employee's professional credit account in the distributed ledger module. Specifically, it includes: cumulative scores for rewards and punishments, frequency of compliance records, contract fulfillment completeness scores, and credit evaluation data from past employers. The credit dimension feature uses the weighted sum of these indicators as the comprehensive credit score.

[0054] Developmental dimensional eigenvectors The data originates from learning trajectory and career development path data recorded in the training and development service module. Specifically, it includes: the weighted sum of the number and difficulty level of training courses completed in the past year, the number of skill certificates obtained, promotion speed assessment value, and the richness index of cross-job rotation experience. These developmental features are used to characterize employees' growth potential and career development aspirations.

[0055] Latent contribution dimension eigenvector The vector composed of the three indicators output by the implicit contribution quantification module in step four is used directly: ; The five-dimensional dynamic panoramic portrait of an employee is composed of feature vectors from the above five dimensions, represented as follows: ; The dynamic updating of the profile is handled by the dynamic update submodule within the profile building module. When any service module in the end-to-end management platform generates new business data, or when the behavior awareness and collection module triggers a new data collection cycle, the dynamic update submodule performs incremental update operations on the affected dimensions. For the capability, behavior, credit, and development dimensions, incremental updates are achieved through recalculation of data within a sliding window. For the implicit contribution dimension, since the collaborative knowledge graph itself is dynamically updated, the implicit contribution quantification module recalculates the three indicators after each graph update and pushes them to the profile building module, which then updates the feature vector of the implicit contribution dimension.

[0056] Step Six: Multi-task Execution of the Intelligent Decision Engine The intelligent decision engine is based on a five-dimensional dynamic panoramic profile of employees and performs three core decision-making tasks: assessment of person-job fit, prediction of employee turnover risk, and generation of management and control strategies.

[0057] The job-person fit assessment task addresses two scenarios: assessing the fit between external candidates and job requirements, and assessing the suitability of current employees for their current or target positions. The assessment process involves first extracting the skill requirement vector from the job requirement data. Experience requires vector And quality requirement vector The three vectors are weighted and concatenated to form a comprehensive feature vector of job requirements. Obtain a five-dimensional panoramic portrait of the candidate or employee from the profile building module. Extract the feature subset corresponding to the job requirements from it, and calculate the matching score using cosine similarity: ,in This refers to the sub-vector in the profile that aligns with the feature space of job requirements. For external candidates lacking internal company data in terms of behavioral and credit dimensions, the weights of these two dimensions are appropriately reduced during matching calculations, and compensation is made through weighted cosine similarity. Simultaneously, for candidates with cross-company credit data, the system obtains de-identified labels for implicit contribution dimensions contained in their career credit data chain from their previous employer through the cross-company credit verification submodule, using these as bonus points or reference information in the matching evaluation.

[0058] The task of predicting employee turnover risk integrates multiple dimensions of features from the employee profile. This includes behavioral features from the five-dimensional panoramic profile. Credit dimension characteristics Collaborative load risk in implicit contribution dimension As input features, the fusion of the three is achieved by concatenating feature vectors and then inputting them into a multi-layer fully connected network for non-linear combination. vector, scalar and Scalar features are concatenated into a one-dimensional feature vector, which is then processed by a fully connected network consisting of two hidden layers to output a fused comprehensive feature representation, which is input into a pre-trained employee turnover risk prediction model. This model is constructed using an extreme gradient boosting algorithm and trained with positive and negative samples of historical employee departures. The model's output is the probability of an employee leaving within a predetermined future timeframe. In this embodiment, the preset time period is ninety days.

[0059] Collaborative workload risk plays a crucial role in this predictive model. When employees'... When the threshold is exceeded, regardless of other characteristics, the system automatically marks the employee as a high-risk individual and triggers a key talent retention alert. The alert signal includes the following information: employee identifier, collaboration workload risk level, the top five recipients connected to the employee as a knowledge originator and their dependency ranking, and a recommended knowledge transfer plan.

[0060] After an employee's departure is actually triggered, the intelligent decision engine initiates a correlation analysis process for the reasons for departure. The system automatically traces back the departing employee's assessment data retained during the recruitment phase, complete behavioral log data and performance data during their employment, as well as the complete evolution history of the employee's nodes and their associated edges in the collaborative knowledge graph. Using the Shapley value additive interpretation method in multi-factor correlation analysis, the marginal contribution of each candidate feature variable to the departure outcome is calculated, and the top k feature variables with the largest absolute values ​​of marginal contribution are extracted as key feature variables influencing departure; in this embodiment, k=10. Simultaneously, an association rule mining algorithm is used to identify statistically significant combinations of feature variables. The extracted key feature variables and variable combinations are structured and encapsulated, then fed back to the recruitment management service module to automatically update the risk factor weights in the talent screening rules, or generate new screening condition suggestions for human resource managers to review and adopt.

[0061] The management strategy generation task generates three types of differentiated strategies based on the profile data. The first type is a personalized incentive plan: based on an employee's contribution to knowledge circulation... When the threshold is exceeded, the intelligent decision engine generates incentive suggestions, including automatically generating an expert honor display badge for the employee in the internal community, automatically generating a visual acknowledgment card of mentorship achievements based on the growth data of the recipients mentored by the employee, and suggesting awarding a digital expert badge or expert allowance that matches the level of implicit contribution. In this embodiment, the first threshold is set as the 85th percentile of the knowledge circulation contribution distribution across the entire company.

[0062] The initial setting of the first and second thresholds is based on the statistical analysis of the company's historical incentive adoption rate and early warning hit rate, and the quantile values ​​that make the two indicators optimal are selected. During the operation of the system, the closed-loop feedback control submodule continuously monitors the incentive adoption rate and early warning false alarm rate. When the performance indicators deviate from the preset tolerance range, the thresholds are automatically adjusted by a preset step size until the performance indicators return to the acceptable range.

[0063] The second category is intervention programs for retaining key personnel: when an employee's collaborative workload risk level... When the second threshold is exceeded, the intelligent decision engine generates intervention suggestions, including suggesting that a knowledge transfer program be launched for the employee, transferring some of the mentoring responsibilities to other employees with potential, and suggesting that a backup successor be designated for the employee, with the backup candidate being selected from recipients who have a close "sharing-receiving" relationship with the employee.

[0064] The third category is suggestions for optimizing team collaboration structure: the intelligent decision engine is based on the cross-system collaboration index of all employees across the company. and contribution to knowledge circulation This involves constructing an organizational collaboration network heatmap to identify knowledge silos and hidden key coordinators within the network. Knowledge silos are defined as teams or departments whose contribution to knowledge flow with other departments and their cross-departmental collaboration index are both below the departmental average. Hidden key coordinators are defined as employees with low formal ranks but whose cross-departmental collaboration index and knowledge flow contribution rank in the top 10% of the entire company. For identified knowledge silos, recommendations are generated to establish regular knowledge-sharing mechanisms between the silo team and other teams closely related to its business. For identified hidden key coordinators, recommendations are generated to promote them to higher-level coordination positions or grant them formal cross-departmental coordination authority.

[0065] Step 7: Data Storage and Cross-Enterprise Credit Verification in Distributed Ledgers When business data is generated by various service modules in the end-to-end management platform, and key event data for knowledge transfer is generated by the collaborative knowledge graph construction module and the implicit contribution quantification module, the distributed ledger module performs data notarization. The specific notarization process is as follows: key fields are extracted from the business data or key event data for knowledge transfer. These key fields include employee identifier, event type code, event timestamp, event summary description, and associated quantitative indicator value. A hash operation is performed on the string concatenated with the key fields; in this embodiment, the SHA-256 hash algorithm is used to generate a fixed-length data digest. The data digest and timestamp information are packaged into a transaction record and written into the distributed ledger of the consortium blockchain through a consensus mechanism. Each employee automatically generates a unique professional credit account address upon joining the company. This address is a public key address on the blockchain, and a mapping relationship is established between this address and the employee's real identity within the system. This mapping relationship is encrypted and stored in the system's secure area.

[0066] Key knowledge transfer events written into the distributed ledger include: when a guidance question-and-answer pair identified by the collaborative knowledge graph construction module is confirmed as a valid guidance event, the mentor identifier, the mentor identifier, the timestamp of the guidance event, and a keyword summary of the guidance content are written into the ledger; when technical articles or experience sharing published by employees in the internal community are read in depth or positively interacted with by other employees, the sharer identifier, the shared content identifier, and the timestamp confirming the knowledge transfer are written into the ledger.

[0067] Through continuous data storage, each employee's professional credit account is linked to form a complete career credit data chain. This data chain contains two types of records: explicit business records, including performance ratings, reward and punishment records, contract performance status, training certificates, etc.; and implicit contribution records, including the phased cumulative value of knowledge circulation contributions, the stored summary of guidance Q&A pairs, and the stored summary of experience sharing adoption events, etc. The data chain is organized chronologically, with each block containing a hash pointer to the previous block, ensuring the traceability and immutability of the entire data chain. The two types of records are organized into a unified data chain structure with the employee's professional credit account address as the primary key and the event timestamp as the sorting basis, rather than being stored as two separate chains.

[0068] When employees move between enterprises, the cross-enterprise credit verification submodule executes the secure cross-enterprise transfer of credit data within a consortium blockchain architecture. The consortium blockchain comprises multiple participating enterprises, industry regulatory bodies, and third-party notary offices, with each institution operating one or more verification nodes. The cross-enterprise credit verification submodule uses smart contracts to pre-set tiered sharing rules, defining three visibility levels for credit data: Level 1 is public, including anonymized summaries of basic professional resumes; Level 2 is authorized, including explicit performance evaluation ratings and anonymized labels for implicit contribution dimensions; Level 3 is confidential, including detailed performance scores and raw values ​​for knowledge transfer contributions. When a recipient initiates a query request, the smart contract first verifies the validity of the employee authorization token carried in the request. If the verification is successful, it automatically matches the corresponding visibility level based on the requester's role identifier and query scenario code and returns the appropriate data. Query requests exceeding the authorized level are automatically rejected and recorded in the audit log.

[0069] With authorization granted by the employee to the new employer, the system generates an encrypted verifiable credential. The generation process involves hashing the employee's career credit data accumulated at the previous employer, including anonymized tags for implicit contributions, to generate a data fingerprint. This data fingerprint and authorization proof information are then used to generate a verifiable credential via a zero-knowledge proof protocol. The proving party uses the original credit data, a hash commitment, and a random number as input to generate a proof document. This proof document allows the verifying party to be certain, without accessing the original credit data, that the proving party possesses a copy of the original data with a hash value matching the hash commitment and that the anonymized tags for implicit contributions meet preset threshold conditions. The verifying party can complete the verification using a publicly available verification key and hash commitment on the consortium blockchain. Upon receiving the verifiable credential, the receiving employer verifies the authenticity and integrity of the credit data within the credential using a verification algorithm. The verification process requires only the credential itself and the publicly available verification key on the consortium blockchain; it does not require access to the employee's original credit data or the previous employer's internal database, thus effectively protecting employee privacy while enabling trusted cross-enterprise transfer of credit data.

[0070] Step 8: Optimizing the Implicit Contribution Perception of Intelligent Scheduling When executing scheduling tasks, the intelligent scheduling service module obtains a five-dimensional dynamic panoramic profile of each employee from the profile building module, extracting capability-level features and implicit contribution-level features. Simultaneously, it receives shift demand data from the business side, including a list of job skill requirements for each shift, minimum and maximum staffing requirements, and business fluctuation prediction information derived from a business volume prediction model. The business volume prediction model employs a seasonally differentiated autoregressive moving average model, predicting the business volume distribution for future scheduling cycles based on historical business volume time series.

[0071] The scheduling plan is generated using a multi-objective optimization algorithm. Hard constraints include skill matching, balanced employee working hours, and labor law compliance; soft constraints include employee preference satisfaction. A multi-objective optimization model is constructed based on these constraints. Hard constraints must be strictly satisfied during the optimization process, while soft constraints are optimized as much as possible while still satisfying the hard constraints.

[0072] The skill matching constraint requires that at least a specified percentage of the personnel assigned to each position in each shift possess the core skills required for that position. Employees' core skills are extracted from competency features. When an employee's score for a particular skill in a competency feature exceeds a preset skill qualification threshold, the employee is considered to possess that core skill.

[0073] Under the premise of satisfying hard constraints, the algorithm introduces implicit contribution dimension features during the soft constraint optimization stage. This is relevant to the contribution of knowledge flow. Senior employees will be prioritized for scheduling in the same shifts as the junior employees they mentor, to facilitate face-to-face mentoring during their work hours. Regarding cross-system collaboration indices... For high-performing employees, priority will be given to scheduling shifts or project periods involving multi-departmental collaboration to leverage their cross-departmental coordination abilities. After scheduling is completed, the scheduling execution data will be sent back to the profile building module to update the scheduling execution records in the employee behavior dimension.

[0074] Step Nine: Closed-Loop Feedback Control and Model Self-Optimization The closed-loop feedback control submodule of the end-to-end management platform is responsible for collecting business result data generated by each service module after executing the management and control strategies generated by the intelligent decision engine. It compares and analyzes the business result data with the decision suggestions, calculates the decision accuracy index and deviation index, and adjusts the model parameters of the intelligent decision engine based on the deviation index.

[0075] Specifically, for the task of predicting employee turnover risk, the closed-loop feedback control submodule continuously tracks whether employees marked as high-risk actually leave within ninety days of the warning being issued, calculating prediction accuracy, recall, and F1 score as decision accuracy indicators. For employees who actually leave, the deviation between the predicted probability and the actual result is quantified as a logarithmic loss value as a deviation indicator. For incentive programs and retention intervention programs in the management strategy, changes in employee performance, collaborative workload risk, and turnover intention are tracked after the program is implemented, and a strategy effectiveness score is calculated as a decision accuracy indicator. The difference between the expected value and the actual value of the effectiveness score is used as a deviation indicator.

[0076] The deviation index is further used to correct the evaluation weights or quantify model parameters of the implicit contribution dimension data in the five-dimensional dynamic panoramic portrait of employees by the intelligent decision engine. When the deviation index indicates that the turnover prediction model assigns too high or too low weights to the collaborative load risk feature, the closed-loop feedback control submodule triggers automatic adjustment of model parameters, updating the splitting weights of collaborative load risk-related features in the model through incremental learning to improve extreme gradients. When the deviation index indicates the correlation weights in the attribution calculation of knowledge flow contribution... When systematic biases exist in the estimation, the closed-loop feedback control submodule triggers the retraining of the attribution model, using accumulated business result data as new training samples to update the parameters of the gradient boosting decision tree attribution model, thereby improving the measurement accuracy of the implicit contribution quantification module. Through this continuous self-optimization mechanism, the system continuously improves the effectiveness of decision-making and the accuracy of quantitative evaluation in long-term operation.

[0077] The above correction process consists of three levels: the first level adjusts the gain calculation weights of the collaborative load risk feature in the extreme gradient boosting model when nodes split; the second level retrains the gradient boosting decision tree attribution model using new samples to update the relevance weights. The third level involves adjusting the load intensity weight in the collaborative load risk calculation formula based on decision deviation feedback. and load concentration weight The value of .

[0078] Step 10: Technical Implementation of Privacy Protection Throughout the implementation of the technical solution, the behavior-aware data collection module and the implicit contribution quantification module employ differential privacy technology to protect employee privacy during data collection and computation. Specifically, when the behavior-aware data collection module extracts the structured behavior dataset, Laplace noise is added to the interaction frequency statistics. The noise distribution parameters are determined by the privacy budget. Control, as set in this embodiment The corresponding Laplace distribution scale parameter is Adding noise to the statistics preserves the overall statistical characteristics while ensuring that the original behavioral data of any individual employee cannot be inferred from the statistical results.

[0079] When the implicit contribution quantification module outputs the knowledge flow contribution, cross-system collaboration index, and collaboration load risk, it adds independent and identically distributed Laplace noise to each indicator value before writing it into the profile construction module and the distributed ledger module. For the implicit contribution dimension desensitization labels transmitted in cross-enterprise credit verification scenarios, the original continuous values ​​are mapped to discrete level labels. The process of determining the mapping boundary adopts an exponential mechanism to add noise, ensuring that the generation of desensitization labels meets the differential privacy protection.

[0080] The construction of the five-dimensional dynamic panoramic profile of employees and the output of management strategies are both based on aggregated statistical results or anonymized processing. For team collaboration structure optimization suggestions for managers, the system only presents aggregated feature descriptions of knowledge silos and hidden key coordinators, without exposing the original behavioral data details of individual non-target employees. For cross-enterprise credit verification scenarios, as mentioned earlier, a zero-knowledge proof protocol is adopted to ensure that the verifying party can only know the authenticity and integrity of the credit data, but cannot obtain the original data content.

Claims

1. A full-process human resource service management and control system, characterized in that, include: The end-to-end management platform integrates recruitment, onboarding, current employment, payroll, training, scheduling, and offboarding management service modules to achieve business data exchange and process flow. The distributed ledger module is used to acquire business data generated by each service module, as well as key event data on knowledge transfer provided by the implicit contribution quantification module and the collaborative knowledge graph construction module. It generates a data summary from the key fields of the business data and key event data on knowledge transfer, writes the data summary and timestamp into the distributed ledger, generates a unique professional credit account for each employee, and links them to form a traceable and tamper-proof career credit data chain containing explicit business records and implicit contribution records. The behavior perception and acquisition module is used to continuously collect internal digital behavior trace data of multiple employees within the organization, including at least email interaction records, instant messaging records, internal community posting and reply records, online document collaborative editing and annotation records, and extract structured behavior datasets with interaction characteristics between binary entities. The collaborative knowledge graph construction module is used to dynamically construct and update a collaborative knowledge graph with employees as nodes based on the structured behavior dataset. The graph includes "guidance-being guided" edges representing business guidance relationships, "sharing-receiving" edges representing knowledge transfer relationships, and "co-creation" edges representing innovative collaborative relationships. The module also calculates the interaction intensity weight and the quantitative value of the knowledge transfer direction for each edge. The implicit contribution quantification module is used to calculate the knowledge flow contribution, cross-system collaboration index, and collaboration load risk level of any target employee based on the collaborative knowledge graph. The profile building module is used to collect multi-source heterogeneous data from various service modules of the full-process management and control platform, and combine the knowledge flow contribution degree, cross-system collaboration index and collaboration load risk degree output by the implicit contribution quantification module to build a five-dimensional dynamic panoramic profile of employees, including capability dimension, behavior dimension, credit dimension, development dimension and implicit contribution dimension. The intelligent decision engine is used to perform matching degree assessment between candidates and job requirements, job suitability assessment of current employees, and prediction of employee turnover risk based on the five-dimensional dynamic panoramic profile of employees, and to generate management and control strategies. The prediction of employee turnover risk takes the collaboration load risk degree, behavioral dimension and credit dimension features in the five-dimensional dynamic panoramic profile of employees as input features. When the collaboration load risk degree exceeds a preset threshold, it triggers a key talent retention warning and knowledge transfer suggestions. The intelligent decision engine is also used to automatically backtrack the employee's assessment data during the recruitment stage, behavior log data and performance data during employment, as well as the evolution history of its collaborative knowledge graph after an employee resignation event is triggered. It extracts key characteristic variables and variable combinations that affect employee resignation and feeds the analysis results back to the recruitment management service module to optimize talent screening rules and evaluation standards.

2. The full-process human resource service management and control system according to claim 1, characterized in that, The calculation of the knowledge circulation contribution specifically includes: identifying at least one edge in the collaborative knowledge graph where the target employee acts as a knowledge sending node, and obtaining the business performance data of the receiving node connected by the edge; when the business performance data of the receiving node experiences a positive jump within a preset time window and is determined to be related to the knowledge transfer behavior represented by the edge, calculating the performance improvement amount; and attributing the performance improvement amount to the target employee's knowledge circulation contribution amount according to the correlation weight.

3. The full-process human resource service management and control system according to claim 1, characterized in that, The collaborative knowledge graph construction module is further used to: identify question-answer pairs by recognizing question messages containing technical terms or project keywords in emails or instant messaging records, and reply messages containing solution characteristics sent by another employee within a preset response time; define the relationship between the questioner and the responder in the question-answer pair as a "mentor-mentee" relationship, and create or enhance the corresponding edge; identify the interaction relationship as a "sharer-receiver" relationship by detecting other employees' reading behavior or positive feedback interaction behavior on technical articles, project reviews, or experience sharing posts published in the internal community for more than a preset time, and create or enhance the corresponding edge; and identify the collaborative creation behavior among employees through collaborative editing and annotation records of online documents, and create or enhance the "co-creation" edge.

4. The full-process human resource service management and control system according to claim 1, characterized in that, The distributed ledger module includes a cross-enterprise credit verification submodule, which, under the consortium blockchain architecture, uses smart contracts to preset hierarchical sharing rules and, with employee authorization, securely transmits and reliably verifies the career credit data chain accumulated by employees in their previous employers, which includes de-identified labels of implicit contribution dimensions, to the human resources system of the next employer in the form of encrypted and verifiable credentials.

5. The full-process human resource service management and control system according to claim 4, characterized in that, The generation of the encrypted verifiable credential specifically includes: performing a hash operation on the employee's career credit data to generate a data fingerprint, and generating a verifiable credential by using the data fingerprint and authorization proof information through a zero-knowledge proof protocol. The receiving employer can verify the authenticity and integrity of the credit data by verifying the verifiable credential without having access to the original data content, thus protecting the employee's privacy.

6. The full-process human resource service management and control system according to claim 1, characterized in that, The management and control strategies generated by the intelligent decision engine include: based on the implicit contribution dimension in the five-dimensional dynamic panoramic portrait of employees, generating personalized incentive schemes that include suggestions for awarding digital expert badges or expert allowances for employees whose contribution to knowledge circulation exceeds a first preset threshold; and generating key talent retention intervention schemes that include the initiation of knowledge transfer plans and backup succession arrangements for employees whose collaboration load risk exceeds a second preset threshold.

7. The full-process human resource service management and control system according to claim 1, characterized in that, The intelligent decision engine is further used to: identify knowledge silos and hidden key coordinators within the organization based on the cross-system collaboration index and knowledge flow contribution of each employee in the five-dimensional dynamic panoramic portrait of employees, and generate team collaboration structure optimization suggestions that include adding or replacing specific roles in project teams and establishing regular knowledge sharing mechanisms between departments with sparse collaboration but close business connections.

8. The full-process human resource service management and control system according to claim 1, characterized in that, The behavior perception acquisition module and the implicit contribution quantification module use differential privacy technology to add noise during the data acquisition and calculation process. Furthermore, the construction of the five-dimensional employee dynamic panoramic profile and the output of the control strategy are based on aggregated statistical results or anonymization processing, without exposing the original behavioral data details of individual non-target employees.

9. The full-process human resource service management and control system according to claim 1, characterized in that, The full-process management and control platform also includes a closed-loop feedback control submodule, which is used to collect business result data generated by each service module after executing the management and control strategy generated by the intelligent decision engine, compare and analyze the business result data with the decision suggestions, calculate the decision accuracy index and deviation index, and adjust the model parameters of the intelligent decision engine based on the deviation index. The adjustment further includes correcting the evaluation weight or quantification model parameters of the intelligent decision engine used to process the implicit contribution dimension data in the five-dimensional employee dynamic panoramic profile.

10. A method for managing and controlling the entire human resources service process, characterized in that, The system applied to any one of claims 1 to 9 is characterized in that it comprises: Through the full-process management platform, data from all business processes, including recruitment, onboarding, employment, payroll, training, scheduling, and departure, are uniformly aggregated to achieve data integration and process collaboration. When key events involving business data and knowledge transfer are generated, the relevant summaries and timestamps are written to the distributed ledger through the distributed ledger module, generating a career credit account for each employee and linking it to a career credit data chain containing explicit business records and implicit contribution records. Continuously collect internal digital behavior data of employees within the organization, construct a collaborative knowledge graph, and calculate each employee's knowledge flow contribution, cross-system collaboration index, and collaborative load risk. Construct a five-dimensional dynamic panoramic profile of employees, including capability, behavior, credit, development, and implicit contribution dimensions. Based on the five-dimensional dynamic panoramic profile of employees, the system performs person-job matching assessment, turnover risk prediction, and management strategy generation. The turnover risk prediction integrates the collaborative load risk level, behavioral dimension, and credit dimension features in the five-dimensional dynamic panoramic profile of employees. When the collaborative load risk level exceeds the threshold, the system triggers a key talent retention warning and knowledge transfer suggestions. After an employee resignation event is triggered, the system automatically traces back the evolution history of the employee's recruitment assessment data, on-the-job behavior logs, performance data, and collaborative knowledge graph. It extracts key characteristic variables that influence resignation and feeds them back to the recruitment management module to optimize screening rules, thereby achieving closed-loop optimization of the entire process.

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