Data sharing processing method and system applied to human resources

Abstract

The invention discloses a data sharing processing method and system applied to human resources, and relates to the technical field of data processing.The method comprises the steps that after multi-source data are collected and preprocessed, the data are classified, shared and stored, and a public storage area and an authorized access area are included; performing role and access authority management on the stored data, and executing data sharing compliance auditing; the access permission is dynamically adjusted based on the access environment, permission change records are stored through a Fabric alliance chain, anomaly detection is carried out according to the real-time access data, and a security policy is executed; and performing two-way matching of the job seeker and the enterprise based on the stored data. According to the method, the sharing efficiency and security of the human resource data are effectively improved, the expandability of data storage and the intelligence of authority management are enhanced, the method is suitable for application scenes such as enterprise human resource management, recruitment platform and government data sharing, and the value and application potential of the human resource data are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a data sharing and processing method and system applied to human resources. Background Art

[0002] With the acceleration of enterprise digital transformation, the demand for data-driven in the field of human resource management (HRM) is increasing day by day. Modern human resource management systems (HRMS) are widely used in multiple aspects such as recruitment, performance evaluation, salary management, and employee development planning, and utilize technologies such as big data, artificial intelligence (AI), and blockchain to optimize data management and decision-making processes. Among them, the research on human resource data sharing technology mainly focuses on aspects such as data storage, secure sharing, and intelligent matching, making human resource management develop in a more accurate, efficient, and secure direction.

[0003] However, despite the application of a variety of advanced technologies in human resource data management, existing systems still face many challenges. For example, the heterogeneity of data sources leads to inconsistent data formats, affecting data analysis and sharing efficiency; privacy protection and compliance requirements are becoming increasingly strict, especially under the constraints of data protection acts such as GDPR and CCPA, and enterprises need to ensure the security and compliance of data during the sharing process. In addition, the lack of dynamic permission management based on the access environment makes it difficult to adjust permissions in real time according to the user's network environment, device characteristics, and access behavior, increasing the risk of data leakage. Summary of the Invention

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

[0005] Therefore, the present invention provides a data sharing and processing method and system applied to human resources, which solves the problem that enterprises need to ensure the security and compliance of data during the sharing process. In addition, the lack of dynamic permission management based on the access environment makes it difficult to adjust permissions in real time according to the user's network environment, device characteristics, and access behavior, increasing the risk of data leakage.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides a data sharing and processing method applied to human resources, which includes

[0008] After collecting multi-source data and performing preprocessing, the data is classified and shared for storage, including a public storage area and an authorized access area;

[0009] The multi-source data includes recruitment data, employee data, salary and welfare data, and performance data;

[0010] Manage the roles and access permissions of the stored data, and perform compliance audits on data sharing;

[0011] Dynamically adjust access permissions based on the access environment, store permission change records through the Fabric consortium blockchain, detect anomalies based on real-time access data, and enforce security policies;

[0012] Perform two-way matching of job seekers and enterprises based on the stored data, generate a matching ranking list for real-time push, and store the data in the database.

[0013] As a preferred solution of the data sharing processing method applied to human resources described in the present invention, wherein: the dynamically adjusting access permissions based on the access environment includes:

[0014] When a visitor logs in to the system, automatically obtain device hardware information;

[0015] Based on the device hardware information, obtain high-trust devices, medium-trust devices, and low-trust devices, high-trust IPs, medium-trust IPs, and low-trust IPs, as well as high-trust networks, medium-trust networks, and low-trust networks;

[0016] Integrate the collected device, IP, and network information into the access environment database;

[0017] Set corresponding trust scores based on the trust level markings of device, IP, and network information in the environment database, and use a weighted method to calculate the final security score of the user's access environment based on the trust scores;

[0018] Obtain the user identity information, assign default access permissions to the user according to the user's role type using the RBAC rule, and map different permissions to numerical values;

[0019] Collect the user's access behavior data, calculate the access time score, location score, and access frequency score, and perform weighted calculation on the calculated scores after normalization to obtain the environmental adaptability factor;

[0020] Perform weighted calculation on the user access permission score A based on the basic permissions, security score, and environmental adaptability factor calculated by RBRC, compare the score with the preset threshold, and obtain high-level permissions, intermediate-level permissions, and low-level permissions;

[0021] Define all user sets U, role category sets R, and permission sets P within the enterprise;

[0022] Establish the mapping of roles and permissions based on the set using the role permission mapping matrix;

[0023] Calculate the matching degree S of role r on permission p, calculate the coverage Z of role r on the permission set P, and measure the management cost c brought by role adjustment;

[0024] Statistically record the role permission change log, use the non-dominated sorting genetic algorithm to optimize the trade-off between role coverage and role change cost, and obtain the Pareto optimal solution set;

[0025] Traverse the Pareto optimal solution set, obtain role r and its associated user u and permission p, count the frequency of user access to p, compare the frequency with the preset threshold I. If the frequency is greater than or equal to the threshold I, it is a high-frequency access user, and permissions are preferentially assigned; otherwise, it is a low-frequency access user, and access is restricted;

[0026] Calculate the Q-learning access reward value, measure the optimization effect of different role-permission allocation schemes, define the Q-learning update formula and the reward function E;

[0027] Store the calculated Q-learning update value in the reinforcement learning optimization database. Based on the Q-learning calculation result, select the intersection point with the highest Q value as the target point for the crossover operation of the genetic algorithm;

[0028] Starting from the intersection point calculated by Q-learning, forward search for the best access permission allocation scheme. Starting from the end of the role permission allocation scheme, search for the role allocation scheme with the highest matching degree. Combine the results of forward search and backward search to generate the final permission allocation scheme.

[0029] As a preferred solution of the data sharing processing method for human resources described in the present invention, wherein: the two-way matching of job seekers and enterprises based on the stored data and generating a matching ranking list for real-time push includes:

[0030] Use TF-IDF to extract the skill keywords in the job seeker's resume and the skill keywords in the enterprise job description, and use cosine similarity to calculate the matching degree between the job seeker's skills and the job requirements;

[0031] Extract the required years of experience for the position and the actual years of experience of the job seeker for rule matching to obtain the experience matching degree;

[0032] Based on the expected salary of the job seeker and the salary offered by the enterprise for rule matching to obtain the salary matching degree;

[0033] Based on Bayesian classification, calculate the distribution probability of the job seeker in the past positions, and calculate the job matching degree according to the category of the current position;

[0034] Based on the decision tree, calculate the current job vacancy time of the enterprise and the industry average recruitment time to obtain the enterprise recruitment priority;

[0035] The applicant suitability is obtained by performing weighted calculations on the skill matching degree, experience matching degree, and salary expectation matching degree of job applicants, and the enterprise suitability is obtained by performing weighted calculations on the job requirement matching degree and enterprise recruitment priority;

[0036] The average value of the applicant suitability and the enterprise suitability is calculated to obtain the final matching score, and the job applicants are sorted in descending order according to the matching score to generate a matching list and push it to the enterprise HR.

[0037] As a preferred solution of the data sharing and processing method applied to human resources according to the present invention, wherein: the classifying and sharing storage of data includes:

[0038] An index tree is used for hierarchical management of data. Each piece of data generates an index according to the data category, and Kubernetes+Hadoop HDFS is used for distributed storage and hot and cold data hierarchical storage.

[0039] As a preferred solution of the data sharing and processing method applied to human resources according to the present invention, wherein: the role and access permission management of the stored data and the execution of data sharing compliance review include:

[0040] According to the role to which the user belongs, basic access permissions are granted. The data sharing requests submitted by the user are analyzed to identify the data category. When sharing data, zero-knowledge proof is used, and data screening is performed according to the sharing party;

[0041] All data sharing requests are recorded. The Merkle Tree technology is used to calculate the hash for the data sharing records, and the Fabric blockchain is used to store the hash values of the shared data.

[0042] As a preferred solution of the data sharing and processing method applied to human resources according to the present invention, wherein: the anomaly detection based on real-time access data and the execution of security policies include using log analysis to collect user access behavior data, using a time series database to store access logs, using time series analysis to establish the historical access time series of the user, calculating the average value of the user's historical access and the standard deviation of the access frequency, and calculating the access behavior deviation M;

[0043] Anomaly thresholds N and N1 are set, and N < N1. If M is less than the threshold N, it is a low risk, and an anomaly log is recorded and the user can continue to access. If M is less than the threshold N1 and greater than the threshold N, it is a medium risk, and manual review is performed. If M is greater than or equal to the threshold N1, it is a high risk, the access permission is restricted, the account is frozen, and the administrator is notified.

[0044] As a preferred solution of the data sharing processing method applied to human resources according to the present invention, wherein: storing the data in the database means storing the collected real-time data, permission adjustment data, and talent recommendation data in the database. The database stores a timestamp for the stored data and uploads the stored data to the cloud for backup.

[0045] In a second aspect, the present invention provides a data sharing processing system applied to human resources, including

[0046] a data collection module, configured to collect multi-source data and perform data cleaning and standardization processing;

[0047] a permission management module, configured to assign initial user permissions and perform data compliance management using zero-knowledge proof and Fabric deposit;

[0048] a permission adjustment module, configured to automatically adjust access permissions according to the user's access environment and detect abnormal behaviors;

[0049] a matching module, configured to be responsible for the two-way matching between job seekers and enterprises and generate a matching ranking list;

[0050] a data storage module, configured to store matching data, permission adjustment data, sharing logs, and support cloud backup.

[0051] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and wherein: when the computer program is executed by the processor, any step of the data sharing processing method applied to human resources as described in the first aspect of the present invention is implemented.

[0052] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein: when the computer program is executed by the processor, any step of the data sharing processing method applied to human resources as described in the first aspect of the present invention is implemented.

[0053] The beneficial effects of the present invention are as follows: Privacy protection for data sharing is ensured through zero-knowledge proof and Merkle Tree hash deposit. Fine-grained permission management is achieved by combining role-based and attribute-based access control. All data access behaviors are recorded based on the Fabric consortium blockchain deposit to ensure the security and compliance of shared data. A multi-factor authentication mechanism that introduces SHA-256 device identifier calculation, GeoIP positioning, and Wi-Fi BSSID analysis is combined with dynamic permission adjustment, time series analysis, and Q-learning reinforcement learning to achieve dynamic permission adjustment based on the access environment, improve the accuracy and response speed of abnormal access detection, and avoid the risk of data leakage caused by static access control methods. In terms of intelligent talent matching, algorithms such as TF-IDF skill keyword extraction, cosine similarity calculation, Bayesian classification for job suitability, and decision tree calculation for recruitment priority are combined to accurately match job seekers with enterprise job requirements, generate a matching ranking table and push it in real time to improve recruitment efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 It is a flowchart of the data sharing processing method applied to human resources in Embodiment 1.

[0056] Figure 2 It is a structural diagram of the data sharing processing system applied to human resources in Embodiment 1.

[0057] Figure 3 It is a flowchart of permission division in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0059] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0060] Second, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures, or characteristics that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0061] Embodiment 1, referring to Figures 1 to 3 , is the first embodiment of the present invention. This embodiment provides a data sharing and processing method applied to human resources, including the following steps:

[0062] S1. After collecting multi-source data and preprocessing it, classify and share the storage of the data, including a public storage area and an authorized access area;

[0063] Specifically, the multi-source data includes recruitment data (job requirements, resume matching data, interview evaluations), employee data (basic information of employees and corresponding position information), salary and welfare data (including basic salary, post salary, performance salary, salary history, etc.), performance data (performance assessment indicators, assessment cycles, assessment results, rewards and punishments records, performance improvement plans, etc.), and training data (employee training records, training courses, career development plans, etc.);

[0064] After cleaning and standardizing the data, use an Index Tree for hierarchical management of the data to improve query efficiency:

[0065] Each piece of data generates an index according to the data category (recruitment / employee / salary and welfare / performance / training);

[0066] Use Kubernetes + Hadoop HDFS for distributed storage to improve storage reliability;

[0067] Use hierarchical storage of hot and cold data: Frequently accessed data (such as job requirements, resume matching data) → cache layer (Redis) to improve query efficiency, and infrequently accessed data (such as career development records, salary history) → blockchain storage (Fabric) to ensure data security and traceability.

[0068] Kubernetes (K8s) is a container orchestration system used for automating the deployment, scaling, and management of applications. In the present invention, Kubernetes is used for distributed storage management. Combined with Hadoop HDFS, it can efficiently manage the load balancing of data storage nodes, enhancing the scalability and high availability of data storage. HDFS is a distributed file system in the Hadoop ecosystem, which is good at handling large-scale data sets. It adopts a master-slave architecture and performs data sharding through a data block storage mechanism to improve storage reliability. In the present invention, the use of HDFS can effectively store and manage massive recruitment, enterprise, and industry data, ensuring the stability of data storage and enhancing the disaster tolerance ability in combination with Kubernetes. The data sharing and processing method for human resources proposed in the present invention significantly improves the reliability, query efficiency, and security of human resources data storage and management through key technologies such as data cleaning, hierarchical management of index trees, Kubernetes+HDFS distributed storage, hierarchical storage of hot and cold data, Fabric blockchain certification, and Redis cache optimization.

[0069] S2. Perform role and access permission management on the stored data and conduct compliance audits for data sharing;

[0070] Specifically, performing role and access permission management on the stored data and conducting compliance audits for data sharing includes:

[0071] Grant basic access permissions according to the role of the user, including: HR manager → can access complete performance data and salary, department head → can only access the training and attendance data of direct subordinates, recruitment agency → can only access job matching data and cannot view candidate details;

[0072] Analyze the data sharing requests submitted by users and identify data categories: recruitment data: resume matching degree, interview evaluation, employee data: basic identity information;

[0073] When sharing data, use zero-knowledge proofs to ensure that the data meets the desensitization standards, and screen the data according to the sharing party (HR, recruitment agency, government regulatory agency): recruitment agency: can only view the job matching degree of job seekers and cannot access information such as name and contact information. External enterprise: can only view anonymized career development data and cannot access personal information. Government agency: can access all data, but it needs to be certified and strictly approved;

[0074] Conduct certification for all data sharing requests, including: accessing user ID, shared data category, shared timestamp, shared access IP;

[0075] The Merkle Tree technology is adopted to calculate the hash for data sharing records, and the Fabric blockchain is used to store the hash values of the shared data. Fabric is a consortium blockchain suitable for enterprise-level applications. It adopts a modular architecture and supports smart contracts (Chaincode) and private data channels. The present invention uses the Fabric blockchain to store the hash values of data sharing records, ensuring the immutability of data and realizing the traceability of data access. Compared with public blockchains, Fabric has a more powerful permission control mechanism, supporting the compliance requirements for data sharing among enterprises.

[0076] The present invention adopts a role-based access control method to ensure that different users can only access data related to their responsibilities. Users only need to access a limited data set, reducing the system's computational burden and improving the data query speed. Users with different roles can only access necessary data, reducing the risk of data abuse. Zero-knowledge proofs are used to ensure that data sharing complies with the desensitization standard, avoiding the leakage of sensitive information. All shared data is stored on the Fabric blockchain to ensure the traceability of the data source and prevent data tampering. The Fabric blockchain combines with the Merkle Tree to store data hash values, ensuring data integrity. Any data sharing behavior is traceable, improving the compliance of data access. The data sharing records are verifiable, enhancing the trust between enterprises and regulatory agencies.

[0077] S3. Dynamically adjust access permissions based on the access environment, store permission change records through the Fabric consortium chain, detect anomalies according to real-time access data, and execute security policies;

[0078] Specifically, dynamically adjusting access permissions based on the access environment includes:

[0079] When a visitor logs in to the system, automatically obtain the device hardware information, including device type, operating system, browser type, and whether it is a device registered by the enterprise;

[0080] Based on the device hardware information, use the SHA-256 hash algorithm to calculate the device unique identifier, and compare it with the enterprise registered device database. If the unique identifier matches, mark the device as a high-trust device. If the unique identifier does not match but the IP is the same, it is a medium-trust device, triggering secondary authentication. If the unique identifier does not match and there are multiple abnormal logins, it is a low-trust device, restricting some permissions;

[0081] Obtain the access IP through the HTTP request header, use the GeoIP API to parse the country, city, and operator information corresponding to the IP. If the IP is an enterprise internal network IP, it is a high-trust level. If the IP is an enterprise VPN, it is a medium-trust level. If the IP is an unknown IP, it is a low-trust level and requires additional verification;

[0082] Obtain the BSSID of the current Wi-Fi through Windows, analyze the BSSID (Basic Service Set Identifier) to obtain the Wi-Fi connection method. If the BSSID belongs to the enterprise registration scope, it is a high-trust network. If the BSSID belongs to the home network provided by the ISP, it is a medium-trust network. If the BSSID belongs to a public hotspot, such as a coffee shop or an airport, it is a low-trust network;

[0083] Integrate the collected device, IP, and network information into the access environment database;

[0084] Set corresponding trust scores based on the trust level markings of device, IP, and network information in the environment database. Based on the trust scores, use a weighted method to calculate the final security score of the user's access environment;

[0085] Obtain the user identity information, and use the RBAC rules to assign default access permissions to users according to their role types, including: HR manager → access to sensitive data such as salary, performance, and attendance; department head → access to direct subordinate employees' data, such as performance and attendance; ordinary employee → can only access personal data, such as attendance records and training data; and map different permissions to numerical values;

[0086] Collect the user's access behavior data, including access time (Is it within normal working hours?), access location (Is it within the company's scope?), and access frequency (Does it frequently access sensitive data within a short period of time?). Use the time window function to calculate the access time score, determine the location score based on the user's coordinates (1 if located in the company, otherwise 0), calculate the access frequency score through the exponentially weighted moving average method, and perform weighted calculation after normalizing the calculated scores to obtain the environmental adaptability factor;

[0087] Perform a weighted calculation of the user access permission score A based on the basic permissions, security scores, and environmental adaptability factors calculated by RBRC. Compare the score with the preset threshold to obtain high-level permissions, medium-level permissions, and low-level permissions. Specifically: Set the thresholds q and W through statistical analysis of historical data, and q > W. If the score A is greater than or equal to q, it is a high-level permission. If A is less than q and greater than or equal to W, it is a medium-level permission. If A is less than W, it is a low-level permission;

[0088] Define the set U of all users within the enterprise, including user ID, user attributes, and user behaviors;

[0089] The set R of role categories, including but not limited to HR manager, department head, ordinary employee, IT administrator, etc.;

[0090] The set P of permissions, including but not limited to user permissions to access salary and welfare data, performance data, training data, etc.;

[0091] Establish the mapping between roles and permissions based on the role - permission mapping matrix using sets;

[0092] Calculate the matching degree S of role r on permission p:

[0093]

[0094] In the formula, F u is the number of times user u accesses permission p, and F' u is the total number of times user u accesses all permissions;

[0095] Calculate the coverage Z of role r on the permission set P:

[0096]

[0097] In the formula, S r is the matching degree score of role r on the permission set P, and P' is the total number of the permission set;

[0098] Measure the management cost c brought by role adjustment:

[0099]

[0100] In the formula, m is the total number of roles, and C o is the number of permission adjustments required for role change;

[0101] Statistical role - permission change logs, use the non - dominated sorting genetic algorithm to optimize the trade - off between role coverage and role change cost, and obtain the Pareto optimal solution set, including:

[0102] Define the optimization goal as maximizing role coverage and minimizing role change cost. Construct an optimization goal matrix based on the calculated role coverage and the management cost of role change. The rows of the matrix represent roles, and the columns represent optimization goals;

[0103] Read role information as the initial population. The individuals in the population represent a role - permission configuration plan. Sort the population. Based on the concept of non - dominated solutions, if solution A is better than solution B in all goals, then solution A dominates solution B. If solution A and solution B do not dominate each other, they belong to the same layer of the Pareto front. Calculate the Pareto levels, including: the first layer (F1): solutions that are not dominated by any other solutions, the second layer (F2): solutions that are only dominated by solutions in the F1 layer, and so on, to construct multiple Pareto levels;

[0104] Calculate the crowding distance U of solution i based on the Pareto levels:

[0105]

[0106] In the formula, fj (i + 1) is the optimized value of the next solution adjacent to i, i + 1, on objective j in the Pareto hierarchy, f j (i - 1) is the optimized value of the previous solution adjacent to i, i - 1, on objective j in the Pareto hierarchy, and represent the maximum and minimum values in the current population;

[0107] Select the solution set with the lowest Pareto hierarchy (e.g., F1). Within the same hierarchy, select the solution with the largest crowding distance as the Pareto optimal solution;

[0108] Traverse the Pareto optimal solution set, obtain the role r and its associated user u and permission p, count the frequency of user access to p, and compare the frequency with the preset threshold I. If the frequency is greater than or equal to the threshold I, the user has a high access frequency and permissions are preferentially assigned. Otherwise, the user has a low access frequency and access is restricted; the preset threshold I is set through a Z-Score statistical method;

[0109] Calculate the Q-learning access reward value to measure the optimization effect of different role-permission allocation schemes, and define the Q-learning update formula:

[0110] Q(s,a) ← Q(s,a) + α[R + γmaxQ(s',a') - Q(s,a)]

[0111] In the formula, Q(s,a) is the Q value of taking action a in the current state s, α is the weight controlling the new and old Q values, set through experiments, R is the immediate reward value after executing a in the current state s, γ is the factor controlling the influence of future rewards, the larger it is, the more it indicates attention to long-term rewards, and maxQ(s',a') is the maximum Q value of all possible actions a' in the new state s', representing the future reward estimate under the optimal strategy;

[0112] And define the reward function E:

[0113] E = w1 × X + w2 × L

[0114] In the formula, w1 and w2 are weight parameters used to balance the coverage and change cost, determined through an adaptive method, X is the role coverage improved by the new role-permission allocation scheme, and L is the permission change cost reduced after role optimization;

[0115] Store the calculated Q-learning update value in the reinforcement learning optimization database. Based on the Q-learning calculation results, select the crossover point with the highest Q value as the target point for the genetic algorithm crossover operation;

[0116] Starting from the intersection point calculated by Q-learning, perform a forward search for the optimal access right allocation scheme. Starting from the end of the role permission allocation scheme, search for the role allocation scheme with the highest matching degree. Combine the results of the forward search and the backward search to generate the final permission allocation scheme.

[0117] Authentication Based on Device Information to Enhance Access Security The present invention calculates the unique device identifier using the SHA-256 hash algorithm and compares it with the enterprise-registered device database. This mechanism can effectively prevent device forgery attacks and ensure that only trusted devices can access the system. In addition, the present invention supports dynamic trust level adjustment based on access history. If the device identifier does not match but the IP address is the same, secondary authentication is triggered to further enhance security. Dynamic Permission Management Based on IP and Network Environment to Optimize Access Control Policies The present invention combines the GeoIP API to parse IP address information, determines whether the visitor belongs to the enterprise internal network, and analyzes the Wi-Fi connection type based on the BSSID (Basic Service Set Identifier). This mechanism can implement hierarchical access permission management based on the network environment. For example, in the enterprise internal network environment, users can obtain full permissions, while in public Wi-Fi, the system will restrict access to sensitive data. This dynamic adjustment strategy effectively reduces the risk of data leakage caused by external network access. Build an Environment Database to Provide Precise Security Scores The present invention constructs an access environment database through information such as devices, IPs, and network environments, and uses a weighted calculation method to calculate the security score of user access. Different security scores correspond to different access permission levels (high security, medium security, low security). This method overcomes the limitations of traditional static permission management, makes permission configuration more flexible, and adapts to different environmental changes. Achieve Fine-Grained Permission Allocation Based on RBAC and Access Behavior Analysis The present invention adopts RBAC rules to allocate basic permissions according to user roles and dynamically adjusts permissions in combination with access behavior data (such as access time, access frequency, access location). To improve the analysis accuracy, the present invention introduces a time window function and an exponentially weighted moving average method to calculate the access frequency score and the environmental adaptability factor, enabling the system to accurately judge the user's access pattern and adjust permissions accordingly, avoiding misjudging normal users or allowing potential risks. Optimize Role Permission Allocation to Improve the Intelligence of Permission Management In terms of role permission management, the present invention uses the non-dominated sorting genetic algorithm (NSGA-II) to optimize the trade-off between role coverage and role change cost. Specifically, the system first calculates the role matching degree S and the role coverage Z, and uses the Pareto front to optimize and select the optimal role configuration plan. This method avoids the singularity and inefficiency of permission allocation, improves the flexibility of access management while ensuring the security of the system. Reinforcement Learning Optimizes Permission Management to Improve Security Protection Capabilities The present invention further introduces Q-learning reinforcement learning to optimize and evaluate different role permission schemes. Q-learning finds the optimal solution among multiple role permission allocation schemes by calculating the access reward value Q(s,a). This mechanism can continuously adjust access permissions as user behavior changes, improve the adaptive ability of the system, and actively adjust permission policies when detecting abnormal access patterns to prevent malicious attacks or data leakage.

[0118] Further, anomaly detection based on real-time access data and enforcement of security policies include collecting user access behavior data using log analysis, storing access logs in a time series database, establishing a historical access time series of users using time series analysis, calculating the average of the user's historical accesses and the standard deviation of the access frequency, and obtaining an access behavior deviation M by subtracting the historical access average from the current access frequency and then dividing by the standard deviation of the access frequency;

[0119] Based on the Top-K selection method, anomaly thresholds N and N1 are set, and N < N1. If M is less than threshold N, it is a low risk, an anomaly log is recorded, and the user can continue to access. If M is less than threshold N1 and greater than threshold N, it is a medium risk, and manual review is performed. If M is greater than or equal to threshold N1, it is a high risk, the access permission is restricted, the account is frozen, and the administrator is notified.

[0120] Based on multiple technologies such as log analysis, time series database, time series analysis, and access behavior deviation calculation, an efficient real-time anomaly detection and security policy enforcement mechanism is implemented. Compared with traditional static access control methods, this method can dynamically analyze the user access pattern, improve the accuracy of anomaly detection, and optimize access security through hierarchical security policies. Using a time series database to store access logs ensures the efficiency and scalability of data storage, and combined with time series analysis to construct a user access history model, enhancing the adaptability and intelligence level of the system. Finally, by calculating the access behavior deviation M, the system can effectively quantify the degree of abnormality of user access behavior, and based on the set thresholds N and T, take different levels of security response measures to reduce false blockades, improve the accuracy and security of access management, thereby enhancing the overall security and stability of the human resource data sharing system.

[0121] S4. Perform two-way matching between job seekers and enterprises based on the stored data, generate a matching ranking list for real-time push, and store the data in the database;

[0122] Specifically, performing two-way matching between job seekers and enterprises based on the stored data and generating a matching ranking list for real-time push includes:

[0123] Using TF-IDF to extract the skill keywords in the job seeker's resume and the skill keywords in the enterprise job description, and using cosine similarity to calculate the matching degree between the job seeker's skills and the job requirements;

[0124] Extract the required years of experience for the position and the actual years of experience of the job seeker for rule matching to obtain the experience matching degree;

[0125] Based on the expected salary of the job seeker and the salary offered by the enterprise for rule matching to obtain the salary matching degree;

[0126] Calculate the distribution probability of job seekers in past positions based on Bayesian classification, and calculate the job matching degree according to the category of the current position;

[0127] Calculate the enterprise recruitment priority based on the decision tree by obtaining the current job vacancy time of the enterprise and the industry average recruitment time. If the job vacancy time exceeds the industry average time, it indicates that the urgency of this position is relatively high;

[0128] Obtain the job seeker suitability by weighted calculation of the skill matching degree, experience matching degree and salary expectation matching degree of job seekers, and obtain the enterprise suitability by weighted calculation of the job demand matching degree and the enterprise recruitment priority;

[0129] Calculate the mean value of the job seeker suitability and the enterprise suitability to obtain the final matching score, sort the job seekers in descending order according to the matching score, generate a matching list and push it to the enterprise HR.

[0130] Through multi-level matching methods such as calculating skill matching degree by TF-IDF + cosine similarity, calculating experience and salary suitability by rule matching, predicting job suitability by Bayesian classification, and calculating recruitment priority by decision tree, the precise two-way matching between job seekers and enterprises is realized, and an intelligent sorting and pushing mechanism is adopted to optimize the recruitment process. Compared with the traditional keyword matching-based method, the matching strategy of the present invention is more precise and intelligent, which can effectively improve the recruitment conversion rate, reduce the enterprise recruitment cost, and at the same time improve the job hunting efficiency of job seekers, and build a more scientific and reasonable talent recruitment ecosystem. The present invention selects skill matching degree, experience matching degree, salary matching degree, job suitability and enterprise recruitment priority as the core matching factors, mainly based on the key decision points in the actual recruitment process to ensure the precise matching of talents and jobs, and improve the recruitment success rate and matching efficiency. The skill matching degree is the core standard to measure whether a job seeker has the professional abilities required for the job. Using TF-IDF keyword extraction + cosine similarity calculation can accurately evaluate the skill fit and avoid the limitations of traditional keyword matching; the experience matching degree ensures that the candidate has the work experience to meet the job requirements and reduces the screening mistakes caused by insufficient experience in the recruitment process; the salary matching degree is the key factor affecting the recruitment success rate. By matching the expected salary of the job seeker with the salary offered by the enterprise, the practical feasibility of the matching is improved, and the offer rejection rate caused by salary expectation differences is reduced; the job suitability is based on Bayesian classification to analyze the professional background of the job seeker, predict his adaptability in the target job, optimize the career development path, and improve the long-term stability; the enterprise recruitment priority is based on decision tree to calculate the job vacancy time and the industry average recruitment time, ensuring that the jobs with high recruitment urgency can be preferentially matched with candidates with high suitability, thereby optimizing the enterprise's talent acquisition efficiency. Generally speaking, these matching degree indicators comprehensively cover the core elements of the matching between job seekers and jobs, can build a more scientific and reasonable talent recruitment model, and improve the accuracy, stability and overall efficiency of recruitment.

[0131] Further, storing the data in the database means storing the collected real-time data, permission adjustment data and talent recommendation data in the database. The database marks the stored data with a timestamp and uploads the stored data to the cloud for backup.

[0132] This embodiment also provides a data sharing and processing system applied to human resources, including:

[0133] A data collection module, which is used to collect multi-source data and perform data cleaning and standardization processing;

[0134] A permission management module, which is used to allocate initial user permissions and perform data compliance management using zero-knowledge proof and Fabric deposit;

[0135] A permission adjustment module, which is used to automatically adjust access permissions according to the user's access environment and detect abnormal behaviors;

[0136] A matching module, responsible for the two-way matching of job seekers and enterprises, and generating a matching ranking list;

[0137] A data storage module, used to store matching data, permission adjustment data, and sharing logs, and support cloud backup.

[0138] This embodiment also provides a computer device, applicable to the situation of a data sharing processing method for human resources, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the data sharing processing method for human resources proposed in the above embodiment.

[0139] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0140] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the data sharing processing method for human resources proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, abbreviated as SRAM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), a programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), a read-only memory (Read-Only Memory, abbreviated as ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disc.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A data sharing processing method applied to human resources, characterized in that: including After collecting and preprocessing multi-source data, classify and share the data for storage, including a public storage area and an authorized access area; The multi-source data includes recruitment data, employee data, salary and welfare data, and performance data; Manage roles and access permissions for the stored data, and perform compliance audits for data sharing; Dynamically adjust access permissions based on the access environment, store permission change records through the Fabric consortium blockchain, detect anomalies based on real-time access data, and enforce security policies; Perform two-way matching between job seekers and enterprises based on the stored data, generate a matching ranking list for real-time push, and store the data in a database.

2. The data sharing processing method applied to human resources according to claim 1, wherein: The dynamically adjusting access permissions based on the access environment includes: When a visitor logs in to the system, automatically obtain device hardware information; Based on the device hardware information, obtain high-trust devices, medium-trust devices, and low-trust devices, high-trust IPs, medium-trust IPs, and low-trust IPs, and high-trust networks, medium-trust networks, and low-trust networks; Integrate the collected device, IP, and network information into the access environment database; Set corresponding trust scores based on the trust level markings of device, IP, and network information in the environment database. Based on the trust scores, use a weighted method to calculate the final security score of the user's access environment; Obtain the user identity information, use the RBAC rule to assign default access permissions to the user according to the user's role type, and map different permissions to numerical values; Collect the user's access behavior data, calculate the access time score, location score, and access frequency score, and perform weighted calculation after normalizing the calculated scores to obtain the environmental adaptability factor; Perform weighted calculation on the basic permissions, security scores, and environmental adaptability factors calculated based on RBRC to obtain the user access permission score A, compare the score with a preset threshold, and obtain high-level permissions, medium-level permissions, and low-level permissions; Define all user sets U, role category sets R, and permission sets P within the enterprise; Establish a mapping between roles and permissions based on the set using a role permission mapping matrix; Calculate the matching degree S of role r on permission p, calculate the coverage Z of role r on the permission set P, and measure the management cost c brought by role adjustment; Statistically record the role permission change logs, use the non-dominated sorting genetic algorithm to optimize the trade-off between role coverage and role change cost, and obtain the Pareto optimal solution set; Traverse the Pareto optimal solution set, obtain role r and its associated users u and permissions p, count the frequency of user access to p, compare the frequency with the preset threshold I. If the frequency is greater than or equal to the threshold I, it is a high-frequency access user, and permissions are preferentially assigned. Otherwise, it is a low-frequency access user, and access is restricted; Calculate the Q-learning access reward value, measure the optimization effect of different role-permission allocation schemes, define the Q-learning update formula and define the reward function E; Store the calculated Q-learning update value in the reinforcement learning optimization database. Based on the Q-learning calculation result, select the intersection point with the highest Q value as the target point for the genetic algorithm crossover operation; Starting from the intersection calculated by Q-learning, perform a forward search for the optimal access permission allocation scheme. Starting from the end of the role permission allocation scheme, search for the role allocation scheme with the highest matching degree. Combine the results of the forward search and the backward search to generate the final permission allocation scheme.

3. The data sharing processing method applied to human resources according to claim 2, wherein: The two-way matching of job seekers and enterprises based on the stored data and generating a matching ranking table for real-time push includes: Use TF-IDF to extract the skill keywords in the job seeker's resume and the skill keywords in the enterprise job description, and use cosine similarity to calculate the matching degree between the job seeker's skills and the job requirements; Extract the required years of experience for the position and the actual years of experience of the job seeker for rule matching to obtain the experience matching degree; Perform rule matching based on the expected salary of the job seeker and the salary offered by the enterprise to obtain the salary matching degree; Calculate the distribution probability of the job seeker in the past positions based on Bayesian classification, and calculate the job matching degree according to the category of the current position; Calculate the enterprise recruitment priority based on the decision tree for the current job vacancy time of the enterprise and the industry average recruitment time; Obtain the job seeker suitability by performing weighted calculations on the skill matching degree, experience matching degree, and salary expectation matching degree of the job seeker, and obtain the enterprise suitability by performing weighted calculations on the job demand matching degree and the enterprise recruitment priority; Calculate the mean value of the job seeker suitability and the enterprise suitability to obtain the final matching score. Sort the job seekers in descending order according to the matching score, generate a matching list and push it to the enterprise HR.

4. The data sharing processing method applied to human resources according to claim 3, characterized in that: The classification and shared storage of the data includes: Adopt an index tree for hierarchical management of data. Each piece of data generates an index according to the data category, and use Kubernetes + Hadoop HDFS for distributed storage and adopt hot and cold data hierarchical storage.

5. The data sharing processing method applied to human resources as described in claim 4, characterized in that: The management of roles and access permissions for the stored data and the execution of data sharing compliance audits include: Grant basic access permissions according to the role to which the user belongs, analyze the data sharing requests submitted by the user, identify the data category, and use zero-knowledge proof when sharing data and screen the data according to the sharing party; Keep evidence of all data sharing requests. Use the Merkle Tree technology to calculate the hash for the data sharing records, and use the Fabric blockchain to store the hash values of the shared data.

6. The data sharing processing method applied to human resources as described in claim 5, characterized in that: The anomaly detection based on real-time access data and the execution of security policies include collecting user access behavior data using log analysis, storing access logs using a time series database, establishing the historical access time series of the user using time series analysis, calculating the average value of the user's historical access and the standard deviation of the access frequency, and calculating the access behavior deviation M; Set anomaly thresholds N and N1, and N < N1. If M is less than the threshold N, it is a low risk, record the anomaly log, and the user can continue to access. If M is less than the threshold N1 and greater than the threshold N, it is a medium risk, and manual review is required. If M is greater than or equal to the threshold N1, it is a high risk, restrict the access permission, freeze the account, and notify the administrator.

7. The data sharing processing method applied to human resources according to claim 6, wherein: The storing the data in the database means storing the collected real-time data, permission adjustment data, and talent recommendation data in the database. The database marks a timestamp for the stored data and uploads the stored data to the cloud for backup.

8. A data sharing processing system applied to human resources, based on the data sharing processing method applied to human resources according to any one of claims 1 to 7, characterized in that: Including, a data acquisition module, configured to acquire multi-source data and perform data cleaning and standardization processing; a permission management module, configured to assign initial permissions to users and perform data compliance management using zero-knowledge proof and Fabric evidence storage; a permission adjustment module, configured to automatically adjust access permissions according to the user's access environment and detect abnormal behaviors; a matching module, configured to be responsible for the two-way matching between job seekers and enterprises and generate a matching ranking list; a data storage module, configured to store matching data, permission adjustment data, and sharing logs and support cloud backup.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the data sharing processing method for human resources according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the data sharing processing method for human resources according to any one of claims 1 to 7 are implemented.

Images