314 results about "Human resource management" patented technology

The invention discloses a human resource management method and system based on data security. The method comprises the steps that multi-source human resource data are collected through a standardized interface, and sensitive levels are marked in a classified mode; calling a dynamic encryption engine based on the sensitive level and the data type, performing asymmetric encryption on the structured data, and performing hybrid encryption on the unstructured data; a dynamic hierarchical access control strategy is generated in combination with user roles and service scenes, and the permission range is adjusted in real time; laws and regulations such as GDPR and CCPA are analyzed through a compliance rule base, data operation legality is automatically verified, and illegal behaviors are blocked; k-anonymity and differential privacy technologies are adopted to desensitize sensitive data, and privacy protection and data availability balance are ensured; and recording a full-process operation log based on the block chain and generating a non-tampering audit report. The system comprises a data classification acquisition module, a dynamic encryption engine module, an access control engine module and the like. According to the method, the problems of data protection rigidness, compliance response lag and privacy-utility imbalance of a traditional HR system are solved.

The invention discloses a supply and demand data management system based on human resources, and relates to the technical field of human resource management, and the system comprises a human resource management center which is in communication connection with a data collection module, a knowledge graph construction module, a post supply and demand matching module, a dynamic adjustment optimization module and a user interaction module. Wherein the modules are connected through electric signals, and the data collection module is used for collecting and preprocessing human resource supply and demand data from different channels. By constructing the knowledge graph based on recruitment data and employee information, the matching relationship between post demands and job seeker skills can be accurately identified, the efficiency of human resource allocation is remarkably improved, and compared with a traditional manual matching mode, the system can quickly analyze a large amount of data, automatically generate post matching coefficients, and improve the efficiency of human resource allocation. And the matching degree is quantitatively evaluated, so that the key post is ensured to obtain the most appropriate talent configuration, and the recruitment cycle is shortened.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The invention discloses a building industry human resource management system based on a block chain technology, and aims to solve the problems that a current building industry human resource management system is slow in processing speed and difficult to meet flexible employment and rapid recruitment requirements of the building industry, so that transaction processing is delayed, sensitive information of employees may be leaked, a decentralized architecture needs multi-party coordination, and the efficiency is high. The system comprises a block chain module which is used for data storage, security and automatic execution of a consensus mechanism; and the business management module is used for carrying out precise management on complex scenes in the building industry through characteristics of decentralization, non-tampering and the like. According to the method, a decentralized distributed architecture is adopted, the data is stored on a plurality of nodes, the security of the data is ensured, each piece of data is recorded on the block chain and cannot be tampered by any person, the authenticity and integrity of the data are ensured, intermediate links can be reduced, and the management cost and risk are reduced.

The invention discloses an electric power engineering project management talent intelligent evaluation and dynamic training system, and the system comprises a multi-dimensional quality capability model construction module which constructs a four-dimensional capability framework based on the characteristics of an electric power engineering industry, generates a differentiated post capability model through a dynamic weight distribution algorithm, and carries out the dynamic training of the differentiated post capability model; constructing an industry ability dictionary library by utilizing a knowledge graph technology; the intelligent evaluation and matching module integrates a multi-modal evaluation tool to collect dominant and implicit capability data, dynamically adjusts capability index weights through a pre-trained AI model based on project characteristic parameters, and calculates man-post adaptation indexes by adopting a cosine similarity algorithm; and the dynamic training and development module is used for generating a personalized training scheme by quantifying the capability difference, tracking the training effect in a closed-loop manner based on a Korotkoff four-level evaluation model, and dynamically optimizing a training path by adopting a Bayesian network. The technical defects of single evaluation dimension, low man-post matching degree and insufficient cultivation efficiency of traditional power engineering human resource management can be solved.

The invention discloses a human resource risk management and control method and system based on big data, and relates to the technical field of human resource management, and the method comprises the following components: S1, a data collection and integration step, S2, an intelligent risk early warning step, S3, a visual risk monitoring step, and S4, a risk response step. According to the invention, human resource related data is comprehensively collected from a plurality of data sources, deep integration and cleaning are carried out, and a recurrent neural network risk prediction model based on a multilayer attention mechanism and deep reinforcement learning is utilized, so that the system can intelligently identify and early warn potential human resource risks, and the human resource risk prediction efficiency is improved. According to the method, the accuracy of risk identification is improved, the timeliness of risk management and control is ensured through real-time data updating and a dynamic early warning mechanism, and enterprises can quickly respond to risk signals and take effective measures for intervention, so that the possibility and the influence degree of risk occurrence are reduced.

The invention relates to the technical field of human resource management, and provides a one-stop talent screening method and system. The method comprises the steps of performing text analysis on a job seeker resume, identifying structured information in the job seeker resume, performing weight distribution on the analyzed information through a preset post competency model, and generating a resume analysis result containing a candidate and target post matching degree score; consultation information about enterprise information, position treatment and office environment of the job seeker is obtained, the consultation information is analyzed to obtain a question intention of the user, and response content corresponding to the question intention is generated; performing semantic analysis on the communication record corresponding to the job seeker, and identifying the job hunting willingness level of the job seeker; and dynamically generating personalized interview questions through an interview question generation model in combination with the matching degree score and the job hunting willingness level to complete talent screening. The method is suitable for full-process automatic processing of resume screening, intelligent communication and adaptive interview in the enterprise recruitment process.

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.

The invention relates to the technical field of human resource management, and provides an intelligent paper marking system and method for talent selection and recruitment. The method comprises the following steps: cutting test paper according to a preset test paper template by using an image processing technology to obtain a plurality of plates corresponding to the test paper; converting the image content of each section of the cut test paper into a text format which can be edited and processed by adopting a character recognition technology; setting a preliminary scoring standard according to question type information and knowledge point information corresponding to the test paper, and optimizing the preliminary scoring standard by using a large language model; performing semantic understanding and logic analysis on the converted test paper text answers from a plurality of scoring dimensions including semantic accuracy, logic integrity and knowledge point coverage by using two preset scoring large models to give corresponding scoring information, and marking advantage information and defect information in the answers; and obtaining a comprehensive score corresponding to the test paper according to score information printed by each score big model for each question of each test paper.

The invention discloses a human resource intelligent management method and system based on man-post matching, and the method comprises the steps: receiving an unstructured post description text, extracting key information through a natural language processing technology, generating a structured multi-dimensional post portrait, and classifying the structured multi-dimensional post portrait; on the basis of historical recruitment data, predicting the number of future post demands by means of a time sequence analysis model; for the candidate resumes and the target post portraits, keyword correlation scores, depth semantic similarity scores and predictive stability scores are calculated in parallel; according to the post portrait classification application dynamic weight, performing weighted summation on the scores to generate a comprehensive matching score; and sorting the candidates according to the comprehensive matching scores, and outputting a sorted candidate list. According to the method and the system provided by the invention, the man-post matching accuracy and the recruitment efficiency are effectively improved, the core pain points of intelligent recruitment, man-post matching and demand prediction in human resource management are solved, and the method and the system are particularly suitable for the demands of human resource outsourcing and labor dispatching industries for stable service selection.

The invention discloses a multi-dimensional candidate matching evaluation method and system, and relates to the technical field of human resource management. The method comprises the following steps: constructing an evaluation index system containing multiple dimensions according to a target post demand; collecting multi-dimensional data of candidates, wherein the multi-dimensional data comprises personal backgrounds, skill levels, character traits and occupational experiences; based on the evaluation index system, performing standardization processing on the multi-dimensional data of candidates; calculating the score of each dimension in the evaluation index system according to the standardized multi-dimensional data; calculating a matching weight of each dimension through a preset weight distribution model; and according to the score and the matching weight of each dimension in the obtained evaluation index system, calculating the comprehensive matching degree of the candidate and the target post, and outputting a matching degree analysis report. Through multi-dimensional comprehensive evaluation, a weight distribution model and a personalized matching degree analysis report, more objective and scientific candidate matching evaluation is provided, and the accuracy of recruitment decision is improved.

The invention provides a matching recommendation method and system based on large-model digital talent inventory, relates to the technical field of artificial intelligence and human resource management, integrates large-model analysis, employee portrait modeling, intelligent matching recommendation and visual display of comparative analysis, has the characteristics of automation, high precision and interpretability, and is suitable for popularization and application. The problems of difficult man-post matching, inaccurate talent evaluation, opaque selection process and the like are solved, and digital talent selection and intelligent decision making are realized.

The invention provides a human resource management interaction method, system and device and a medium, and relates to the technical field of human resource management.The method comprises the steps that firstly, organizational structure management, electronic contract signing and employee management functions are unified in a single interface through full-process integrated operation, the time cost of multi-module switching is reduced, one-stop processing is achieved, and the management efficiency is improved; the processing efficiency of the human resource business is improved; secondly, realizing dynamic organization architecture management based on a tree diagram structure, allowing real-time adjustment of a department and post hierarchical relationship, generating a version snapshot with a timestamp, ensuring real-time performance and traceability of data, and avoiding errors caused by a traditional static architecture; furthermore, the electronic contract voucher is directly associated with the employee archive, so that the consistency of contract data, the organization structure and the employee state is ensured, and the data security is enhanced; and finally, related configuration is executed by using the current organization structure version snapshot and the electronic contract certificate, and real-time linkage of organization structure change and employee adjustment is realized.

The invention discloses a query condition analysis method and device and a medium, and the method comprises the steps: receiving a natural language query statement input by a user for a human resource management system, and extracting a core semantic unit in the natural language query statement; based on a preset knowledge base, mapping the core semantic unit into a semantic structure corresponding to an Elasticsearch database to generate a corresponding key vector diagram; based on a preset pseudo code normal form, generating a pseudo code according to the key vector diagram; wherein the pseudo code comprises a query node set and a logical relationship between query nodes; and performing code verification on the pseudo-code, and converting the pseudo-code into a DSL query statement in an Elasticsearch database under the condition that the pseudo-code passes the code verification, so as to realize semantic analysis on the natural language query statement through the DSL query statement.

The invention discloses a man-post matching adaptive optimization system and a method thereof, and relates to the technical field of human resource intelligent management. Comprising the following steps: acquiring post demand data containing experience years, post vacancy days and post types, and candidate ability data containing implicit ability scores; candidate growth coefficients are generated based on post demands; outputting an initial matching degree through data comparison; when the matching degree exceeds a preset threshold value, the implicit capability score and the empirical age limit are converted into a communication value and an empirical value respectively, and a soft compensation coefficient is obtained through weighted calculation; generating a risk coefficient according to the number of post vacancy days, and loading a basic weight according to the post type (if the vacancy exceeds the limit, increasing a second weight); and calculating a comprehensive matching degree and outputting a grading decision instruction. Through coupling growth prediction, implicit capability compensation and a dynamic risk parameter adjustment mechanism, the accuracy and adaptability of man-post matching are significantly improved.

The invention discloses an intelligent recruitment matching platform and method driven by multi-modal behavior data, relates to the technical field of human resource management and intelligent information processing, and is used for solving the problem that efficiency and accuracy of a system are difficult to consider at the same time due to change of the number of links in an existing recruitment process. According to the technical means, multi-modal portraits of posts and candidates are constructed, consistency checking and credibility correction are carried out among links, when the number of links exceeds a threshold value, deep comparison is carried out on abnormal links, simplification processing is carried out on other links, and the threshold value and parameters are continuously adjusted in combination with entry performance feedback. Through the above technical means, false or contradictory information is effectively identified while the processing time is ensured, and the matching accuracy and the stability of the recruitment process are improved.

The invention discloses a detection personnel occupational portrait dynamic modeling method based on multi-dimensional data analysis, and relates to the technical field of artificial intelligence and human resource management crossing. The detection personnel occupational portrait dynamic modeling method based on multi-dimensional data analysis specifically comprises the following steps: collecting multi-modal operation data of detection personnel; establishing a detection personnel capability quantitative evaluation model, and determining occupational roles of detection personnel; determining feature vectors and feature factors meeting vocational ability requirements, and obtaining vocational levels of the detection personnel; the detection tasks are classified to obtain demand vectors of different task types, a quantitative relation is established, and a visual occupational portrait is generated; establishing a cosine similarity matching model of the capability vector and the demand vector; compared with a traditional ordering method, the scheme has the advantages that the task matching accuracy is improved, the detection period is shortened, the laboratory human resource configuration efficiency is optimized, and the technical blank of human resource management of artificial intelligence in the detection field is filled.

The invention relates to the technical field of human resource management, in particular to a labor dispatch outsourcing management and intelligent human dispatch outsourcing system, which comprises a process disassembly module, a condition check module, a capability construction module, a connection screening module and a dispatch adaptation module. According to the method, the post task is subjected to nodal sequence processing, digital expression of the post operation process is realized in combination with action labels and operation stage mapping, deviation nodes in the operation process are identified by means of device numbers and sorting verification of operation sections, and the task process precision and the quality control capability are improved; post boundary condition constraint verification is completed by fusing post equipment permission and an operation section period table, the probability of occurrence of task matching conflicts is remarkably reduced, and the coherence consistency of personnel and post actions is improved by comparing behavior tags with a post action sequence, so that accurate matching and efficient coherence deployment of tasks and personnel are realized, and the task matching efficiency is improved. And the intelligence, the matching degree and the performability of labor dispatching are enhanced.

The invention discloses a recruitment full-process automatic cooperation method and system based on a large language model, and relates to the technical field of artificial intelligence and human resource management, and the method specifically comprises the following steps: a recruitment platform receives recruitment position information issued by an enterprise recruiter, and generates a standardized recruitment position information description; performing text semantic analysis on the standardized recruitment post information description, and extracting a job key field of a recruitment post; extracting job seeker resume key fields from the job seeker resume; converting the job key field of the recruitment post into a high-dimensional semantic query vector, constructing a resume semantic vector library based on the resume key field of the job seeker, and performing query matching to generate a candidate resume list set; generating an interview comprehensive evaluation report based on the interview data; returning the interview comprehensive evaluation report to a training library of the large language model, and updating parameters of the large language model; and the enterprise recruiter determines an intention job seeker according to the interview comprehensive evaluation report, and generates an in-job notification and an in-job guide process.

The invention discloses a job-hunting interview question AI generation method based on a large language model, and relates to the technical field of human resource management, and the method comprises the following steps: S1, analyzing post information, and extracting core skill points; s2, constructing candidate feature matching vectors based on the core skill points; s3, performing knowledge graph expansion by using the core skill points and candidate feature matching vectors; s4, performing semantic fusion on the basis of the extended knowledge graph to obtain a knowledge association set; s5, performing question generation and difficulty grading by using the knowledge association set; and S6, performing logic consistency detection and diversification generation by using the question set. By setting knowledge graph extension and semantic fusion based on post core skill points and candidate feature matching vectors, a knowledge association set with a wider coverage and a more reasonable structure can be established before topic generation, and it is ensured that the extended knowledge graph not only embodies post requirements but also is matched with candidate features.

The invention discloses a human resource production and teaching fusion supply and demand matching system based on a dynamic ability portrait, and relates to the field of human resource management. Comprising a capability analysis module, a structure modeling module, an occupational mapping module, a selection monitoring module, a path generation module, a behavior analysis module, a task matching module, a supply and demand evaluation module, a guide iteration module and a recruitment screening module. According to the method, capability portraits do not depend on subjective resumes any more, the accuracy and credibility are remarkably improved, the problem that a static label model cannot reflect a real growth track is solved, the competency and potential rising space of talents for posts can be more accurately judged, the problem that traditional HR cannot process cross-domain task matching is solved, and the method is suitable for popularization and application. And real-time closed-loop alignment of supply and demand of production and education is realized.

The invention relates to the technical field of talent cultivation management systems, in particular to an enterprise talent cultivation and management platform based on big data, which constructs a'data perception-portrait construction-intelligent decision-feedback closed loop 'dynamic ecological system, realizes continuous perception and dynamic evaluation of employee ability, and improves the enterprise talent cultivation and management efficiency. The problem of evaluation lag of a traditional culture management platform is solved; the multi-source data acquisition and multi-source data fusion module is matched with the dynamic ability portrait construction and updating module in real time, and evaluation, cultivation and selection of talents become extremely objective and accurate through data-driven ability portraits; prediction and early warning capabilities are provided, so that human resource management is changed from passive response to active planning and intervention; according to the method, the isolated relationship between enterprise talent management and talent cultivation is broken through, various HR data islands are broken through, the situation that cultivation resources are input to employees with high demission risks is avoided, human resource transactional work is greatly reduced, and management efficiency and decision scientificity are improved.

The invention relates to a human resource information management method and device based on LLM + RPA, and the method comprises the following steps: recognizing the abnormality of employee basic information in a human resource system through RPA, marking and generating abnormal marking information; and generating a correction notification template, and forming a notification delivery record by combining batch filling and distribution of employee contact information. And based on the delivery record, intelligently sorting the overdue uncorrected items according to the abnormal level, the overdue day number and the influence range by utilizing LLM, generating an urging sequence, sequentially triggering secondary reminding by RPA, and obtaining an execution result. And finally, the RPA associates and verifies the secondary reminding result and the corrected information, converts the secondary reminding result and the corrected information into a system compatible format, updates the secondary reminding result and the corrected information to a human resource system, and generates an employee information management table, thereby solving the technical problems that a traditional human resource management system still depends on a large amount of manual intervention in the aspect of dynamic update of employee information, so that the management efficiency is low and errors are easy to occur.

The invention discloses a human resource data classification statistical method and system based on big data. The method comprises the following steps: collecting employee basic information and employee behavior data; performing coding processing on the staff statistical data, establishing a staff multi-modal feature set according to the staff basic information and the staff behavior data, inputting the staff multi-modal feature set into a deep learning model, and predicting staff skill scores; extracting the employee behavior data and the employee skill score, constructing an employee behavior feature matrix and a post skill feature matrix, setting a first potential factor and predicting an employee stable value according to the behavior feature matrix, and setting a second potential factor and predicting a post matching value according to the post skill feature matrix; according to the employee stable value and the post matching degree, post recommendation scores are distributed, and a recommendation score thermodynamic density map is visually output; according to the method, the whole process from data acquisition to decision recommendation is automatic, talent evaluation precision is realized, employee demission risks are effectively predicted, and digital transformation of human resource management is realized.

The invention provides an interpretable employee demission prediction method and system, and relates to the field of human resource management, and the method comprises the steps: carrying out the data cleaning, data standardization and sample construction of historical data, so as to obtain a sample data set; performing model training on a machine learning model based on the sample data set; explaining a prediction result obtained by model training by adopting an SHAP explaining model to obtain an SHAP value of the prediction result corresponding to each feature; and performing demission prediction on samples in the sample data set by adopting the final machine learning model to obtain a prediction result, and explaining the prediction result by adopting an SHAP explanation model to obtain an SHAP value of each feature corresponding to the prediction result and a final prediction result. According to the invention, employee demission prediction and interpretation are carried out based on the machine learning model and the SHAP interpretation model, and the problems of insufficient employee demission tendency prediction accuracy and poor interpretability in enterprise human resource management can be solved.

The invention relates to the technical field of artificial intelligence and human resource management, and discloses an intelligent talent matching method and device based on a dynamic post portrait, equipment and a storage medium, which are used for improving the accuracy of talent matching and the system adaptability. The intelligent talent matching method based on the dynamic post portraits comprises the steps of obtaining post description and candidate skill information, performing semantic enhancement processing on post texts through local and global attention mechanisms, and constructing the dynamic post portraits; performing similarity comparison on the post portraits and candidate skills, and screening out primary candidate subsets; then, precise matching degree prediction is achieved through deep interactive calculation, and a recommendation list is generated; user feedback data and industry trend information are continuously collected, a closed-loop learning mechanism is established, and dynamic adjustment and continuous optimization of post portraits are achieved.

The invention relates to the technical field of scheduling management, and discloses a scheduling management method and system for new energy automobile commutator production, and the method comprises the steps: firstly constructing an employee skill and available database, and integrating the employee basic information and skill archives in a human resource management system and the real-time state data of an attendance system; based on the database, a standardized process-skill mapping table is defined, and skill requirements of each process are clarified. In the face of a task to be distributed, a list of qualified candidate employees meeting a preset condition is screened out according to the mapping table, and consideration factors comprise proficiency level, qualification certificate validity and current employee available state. And finally, analyzing a qualified candidate employee list through a task allocation decision module, and generating an optimal task-employee allocation scheme. Thus, through systematized evaluation and a dynamic matching mechanism, it is ensured that each process can be accurately matched with the most appropriate employee, the problem of human resource allocation is effectively solved, and the production efficiency and flexibility are improved.

The invention relates to the technical field of human resource management, in particular to a human resource management system and method based on a block chain. The method comprises the following steps: firstly, dividing personnel into a plurality of talent unit groups based on functions, professions and levels, performing quantitative evaluation from two dimensions of talent loss risk and post influence degree, and setting evaluation factors such as work satisfaction, occupational development opportunities, work pressure indexes and team relationship scores, so as to obtain evaluation results of the talent loss risk and the post influence degree; calculating a demission tendency index in combination with an analytic hierarchy process; generating an early warning signal based on the evaluation result; and finally, performing credible storage on the whole-process data by utilizing a block chain technology. According to the method, the conversion from passive statistics to active early warning is realized, the evaluation granularity is optimized by introducing the talent unit group, the accuracy of risk identification is improved, and the credibility of data is ensured by using the block chain technology.

The present invention discloses an enterprise human resources comprehensive management platform, which relates to the field of data management technology and includes: a resource management system determination module for determining a resource management system; a management logic chain determination module for determining a management logic chain; a task management strategy determination module for determining a task management strategy; a sensitive management mode setting module for setting a sensitive management mode for sensitive data classes; a sensitive management strategy determination module for determining a sensitive management strategy; and a pre-management task control module for executing and controlling pre-management tasks. The platform solves the technical problems of the existing enterprise human resources management, such as the difficulty in coping with complex and changing enterprise management needs, the lack of integration of multiple enterprise application systems, and the risks of sensitive data management, which lead to the inaccuracy and security of enterprise human resources management. The platform intelligently realizes the comprehensive integration and efficient use of human resources information, achieving the technical effect of improving the accuracy and security of enterprise management.

The invention discloses a human resource management method and system, and relates to the technical field of human resource management, and the method comprises the steps: obtaining task data from a company project end, collecting employee information, recording emotion information and performance information corresponding to the employee information, building a comprehensive scoring mechanism, outputting an employee score, and formulating a reward mechanism and a qualification mechanism. And obtaining reward information and task progress information, establishing task priority distribution, and outputting a task distribution model. By accurately analyzing task requirements, intelligently matching specialties, experiences, interests and skills of employees, comprehensively recording performance and emotions of the employees and realizing comprehensive scoring, the method is beneficial to preferentially selecting high-score employees, improving task completion quality, formulating an award and qualification mechanism, motivating enthusiasm of the employees, dynamically adjusting task priorities and optimizing resource allocation, and the task quality is improved. After the employees grow in the specific field, the new task priority is reasonably reduced, resources are balanced, and the overall working efficiency is improved.