390 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 person and post matching method, system and equipment based on Transform and a medium, belongs to the technical field of cross application of natural language processing and human resource management, and aims to solve the technical problem of how to reduce subjective errors in a person and post matching process, improve matching accuracy, fairness and objectivity and improve manual matching efficiency at the same time. According to the technical scheme, the method comprises the following steps: data collection: collecting post information data and job seeker resume data from a plurality of data sources, and cleaning and sorting the collected data to obtain a post description text and a resume text; text preprocessing: preprocessing the acquired and sorted post description text and resume text to obtain the preprocessed post description text and resume text; the method comprises the following steps of: fusing a Transform model and a BERT model; constructing an encoder-decoder structure based on the Transform model; optimizing the model; multi-label text classification: constructing a multi-label classification system; and outputting and evaluating a matching result.

A method to optimize human resource management within emergency medical services (EMS) according to one approach may have the steps of: receiving inputs from at least one or more the data sources such as traffic conditions, weather, incident location of emergency or non emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, and the like and combinations thereof; providing a scheduling module for building one or more predictive assessment values; the scheduling module outputting automatically a suggested scheduling template; employing one or more machine learning models to generate the one or more predictive assessment values that relate to the comparison of the inputs of the evolution of data over a time interval using machine learning; employing a global positioning systems (GPS) device to provide geo-location information of EMS equipment.

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 discloses an intelligent information screening method and system for a human resource system, and relates to the technical field of human resource systems. The problem that a traditional matching method is difficult to deeply mine semantic association and cannot accurately measure the matching degree of candidates and posts is solved. According to the method, through multi-step intelligent data processing and optimization, the candidate screening efficiency and accuracy are remarkably improved, a semantic analysis technology is adopted to construct a multi-layer topic tree and an emotion analysis model, and deep semantic matching of candidate resumes and position requirements is achieved; meanwhile, candidates are intelligently screened by using a bidirectional semantic matching network, the screening effect is continuously optimized by mining potential high-matching candidates and historical data feedback, bidirectional recommendation is realized through a block chain communication link, the security and efficiency of data transmission are guaranteed, complex enterprise recruitment requirements can be effectively adapted, the recruitment period is shortened, and the recruitment efficiency is improved. The man-post matching accuracy is improved, and the human resource management cost is greatly reduced.

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 relates to the technical field of human resource management and information, and discloses an employment post recommendation method based on a human resource database, and the method comprises the following steps: S1, data collection; s2, data processing; s3, keyword extraction; s4, performing similarity calculation; s5, post filtering and sorting; s6, post recommendation; and S7, effect evaluation: through automatic data processing and similarity calculation, resumes most matched with post demands can be quickly screened out, the workload of recruiters is reduced, accurate matching degree evaluation is beneficial to realizing optimal configuration of talents and posts, employee loss and recruitment cost waste caused by mismatching are reduced, and the efficiency of recruitment is improved. The recruitment will and the matching degree of the job seekers can be improved through accurate recommendation, so that the recruitment success rate and efficiency are improved, more high-quality job seekers are attracted through efficient post recommendation and accurate matching, and the talent reserve and competitiveness of enterprises are enhanced.

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 relates to the technical field of information data processing, in particular to a building engineering project data processing system, which comprises a resource demand analysis module, an engineering project-based human resource management and material demand plan, man-hour record and material inventory data collection module, timetable and resource use record arrangement module, resource utilization rate and gap calculation module and a data processing module, a resource status report is generated, and a resource supply status and potential shortage are determined based on the resource status report. According to the invention, through comprehensive data collection and dynamic analysis, the resource management efficiency of the construction engineering project is improved. Updating resource utilization rate and gap information in real time allows for fast and accurate response to resource changes, reducing resource waste and project delay risks. The resource allocation efficiency and the cost simulation ensure that the resource use reaches the optimal economic benefit, and meanwhile, the capability of the project for adapting to uncertain factors is improved. Time conflicts in resource allocation are reduced through risk assessment, and the continuity of project execution is optimized.

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 relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

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 embodiment of the invention discloses a technical aspect test question generation method and device based on an AI Agent and a medium. The method comprises the following steps: inputting post description and candidate resumes, and processing by using NLP and AI Agent to generate original technical tags; inferring a project technical architecture by using an AI Agent; performing technical label generation and expansion based on the original technical label and the project technical architecture to obtain a full-quantity technical label; and generating technical face test questions and interview feedback. The method has the following advantages: the technical label highly matched with the post is generated based on the NLP and the AI Agent, and the technical label is expanded, so that the accuracy and efficiency of the recruitment process can be remarkably improved; face test questions are generated according to the technical labels, and correlation and pertinence of the face test questions are ensured; the recruitment process can be dynamically fed back and optimized according to an interview result; the human resource management cost is remarkably reduced, and meanwhile the recruitment quality is improved.

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 an interviewer evaluation prejudice correction method and system based on real-time feedback, particularly relates to the technical field of human resource management, and is used for solving the problems that evaluation standards and industry characteristics are disjointed due to standardized semantic analysis in an existing recruitment system, and implicit evaluation prejudice of an interviewer is difficult to dynamically correct. Fuzzy terms in post requirements are dynamically analyzed through the industry knowledge base, industry weights and term thresholds are generated, interview interaction texts are collected in real time, and relevance between candidates and the term thresholds is analyzed; in combination with interviewer question context recognition and deviation calculation, term deviation signals and repeated question intention deviation are marked, and finally, an industry correction rule is matched to dynamically adjust a score weight; on the basis of a semantic analysis and real-time feedback linkage mechanism adaptive to industry characteristics, alignment of evaluation standards and real post requirements is realized, evaluation prejudice caused by term understanding dislocation is effectively eliminated, and objectivity and accuracy of talent selection are improved.

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 human resource interaction system, method and device and a storage medium, and relates to the technical field of human resource information processing, and the system comprises an intention recognition module which is used for splitting input information into a plurality of pieces of sub-information, recognizing the intention of a user, and determining the type of an input problem if the input of the user is the first-round input; the knowledge text determination module is used for collecting attendance checking knowledge documents if the type is an attendance checking system query type, converting the attendance checking knowledge documents into vector representations, storing the vector representations in a vector database, and determining knowledge texts similar to question semantics based on the vector database; the first question reply generation module is used for inputting the knowledge text and the question into a large language model and generating and outputting a first question reply; and the second question reply generation module is used for extracting a key value of a key field corresponding to the question, generating and executing a data analysis code and generating and outputting a second question reply if the type is a personnel information query type. And the efficiency of human resource management is improved.

The invention discloses a human resource intelligent planning method and system based on big data analysis, and relates to the technical field of human resource management, and the method comprises the steps: collecting the work performance data and eye movement tracking data of employees in a simulation training environment; skill labels of the employees are extracted by using a natural language processing technology, a personal skill map is constructed, and advantages and shortages of the employees are analyzed; evaluating the ability level of the employee according to the personal skill map and the work performance data; according to the skill label of the employee, combining the ability evaluation result of the employee, intelligently matching a proper post, and planning the occupational path of the employee; a personalized training plan is intelligently generated according to the skill weakness and post requirements of the employees; the comprehensive human resource intelligent planning method is constructed by integrating big data analysis and natural language processing technologies, and many problems in the prior art can be effectively solved.

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 human resource management system based on a digital intelligent technology, and aims to optimize talent recruitment and management through technologies such as big data, artificial intelligence and cloud computing, and the system comprises the following steps: the system realizes personalized recruitment recommendation by constructing a fine-grained portrait of a job seeker, and improves the recruitment recommendation efficiency; deep learning and integrated learning technologies are adopted to improve the precision of post matching, the system combines big data analysis, predicts a talent flow trend, optimizes a recruitment process and generates employee occupational development suggestions, the system also adopts a national secret encryption technology to guarantee data security, and a user identity is authenticated through a two-factor verification technology, so that the user experience is improved. And the safety and the compliance of the system are ensured. According to the invention, automation and intelligentization of human resource management can be realized, and scientificity and accuracy of talent management are effectively improved.