325 results about "Skill sets" patented technology

The invention relates to a talent matching and intelligent recruitment method and system based on an AI large model. The method comprises the steps of obtaining multi-modal data of candidates and post demand data of recruiters; performing semantic extraction and feature fusion on the multi-modal data through a multi-modal fusion encoder to generate a comprehensive feature vector containing semantic association features and behavior features; performing field adaptation processing on the comprehensive feature vector based on a dynamically updated industry knowledge graph to generate a skill label set of candidates; performing dynamic matching degree calculation on the skill label set and post demand data according to historical recruitment feedback data by adopting a reinforcement learning model to generate a recommendation list; and outputting the recommendation list to a recruiter terminal, and performing online optimization on the reinforcement learning model based on operation behavior data of a recruiter. According to the invention, intelligent matching of the job seeker and the recruitment demand is realized, and the recruitment efficiency and accuracy are improved.

The invention relates to the technical field of education management, in particular to a practical training teaching and evaluation management system, and aims to ensure that each task strictly corresponds to a teaching target by acquiring practical training task parameters and establishing a mapping relationship between practical training tasks and a teaching outline through a knowledge graph, and to realize practical training teaching and evaluation management by utilizing integration and mapping of multi-source data. A data basis is provided for subsequent analysis; a collaborative filtering algorithm is utilized to match student ability and training task requirements, and a matching degree score is output, so that the rationality of training task allocation is improved; the training process of the students is monitored and evaluated in real time through a multi-modal data intelligent analysis technology, and timely feedback is provided for teachers and the students; the skill tree is dynamically constructed and updated through a multi-modal evaluation result, a personalized practical training task recommendation sequence is generated, and the structure and node attributes of the skill tree are adjusted in time according to the actual learning condition and ability development of students, so that the skill tree has better pertinence and adaptability, and the learning efficiency is improved.

The invention discloses an employment information matching method and system based on data analysis, and particularly relates to the field of employment matching, and the method comprises the steps: collecting structured and unstructured data, and carrying out the semantic feature extraction through employing a BERT model and BiLSTM-CRF; in the preprocessing stage, entity standardization is realized through a knowledge graph, and a job seeker portrait and post model including a skill matrix and an occupational development trajectory is constructed; in the feature engineering stage, extracting four core features of skill matching degree, salary expectation integrating degree, commuting tolerance and occupational development goodness of fit; the salary integrating degree is quantitatively evaluated through a bidirectional tolerance model, a random forest model with time decay is used for dynamic weight, feature weight is generated based on historical successful cases, and a real-time feedback mechanism is introduced to adjust a weight coefficient; finally, the matching degree function fuses the weighted features and the industry trend factors, and the model is continuously optimized through a three-level updating mechanism.

The embodiment of the invention provides an intelligent learning path recommendation method and system based on a dynamic state. The method is applied to the technical field of intelligent learning recommendation, and comprises the following steps: calculating a skill adaptation weight, a load weight and an emergency degree weight in real time based on a dynamic state of a target employee, including a current skill improvement condition, a workload and a task emergency degree; combining the skill matching degree, the learning strength and the task association degree of each learning unit in the candidate learning path, and performing comprehensive evaluation by using a multi-weight scoring function; and sorting the paths according to the comprehensive score, generating a personalized recommended learning path set and sending the personalized recommended learning path set to an employee terminal, thereby realizing intelligent learning path recommendation with dynamic adaptation and accurate matching. According to the scheme, personalized learning path pushing aiming at actual post requirements and working states of the employees can be realized, correlation, urgency and acceptability of learning contents are improved, learning efficiency and task adaptability are remarkably enhanced, and the employees are helped to quickly compete with post targets.

The invention discloses a multi-intention control method and device based on a large model, a storage medium and an electronic device, and relates to the technical field of smart home, and the method comprises the steps: carrying out the intention recognition of a voice instruction corresponding to a target object, so as to determine a plurality of to-be-executed intentions indicated by the voice instruction; each to-be-executed intention is input into a preset large language model, so that the preset large language model extracts a keyword corresponding to each to-be-executed intention, the intention type corresponding to each to-be-executed intention is recognized according to the keyword corresponding to each to-be-executed intention, and the intention type comprises an equipment control type intention and a skill type intention; the conversion mode of each to-be-executed intention is determined according to the intention type, and each to-be-executed intention is converted into each target instruction according to the conversion mode, so that each target to-be-executed device executes each target instruction, and the target instructions at least comprise one of a control type instruction and a skill type instruction.

The invention provides a vocational ability assessment system based on a skill map, and relates to the field of human resource technology and knowledge map application. The evaluation main system comprises a skill star map generation module, an occupational trajectory simulation module, a skill challenge module, a skill authentication chain module, a future skill navigation module, a skill collaborative ecological module, a skill evolutionary tree module, an occupational story generation module, a skill energy pool module, an occupational time capsule module and a control flow management module; according to the invention, the dynamic skill star map is constructed and the 5G network is combined to transmit data in real time, so that the occupational experience, learning records and test data of the user can be collected in time, and the node weight is dynamically adjusted through the semantic analysis algorithm and the weighted scoring algorithm; the problems that traditional manual evaluation and online testing are high in subjectivity and low in efficiency, and the capability cannot be accurately quantified are effectively solved.

The invention discloses human resource software for a supply chain, and relates to the technical field of human resource allocation, and the human resource software comprises the steps of S1, database construction, S2, model establishment, S3, matching degree matrix calculation, S4, weight allocation, S5, real-time optimization, S6, dynamic adjustment, and S7, scheme output. According to the method, the ability evaluation value is dynamically adjusted based on the employee historical task execution time through the skill attenuation factor, the natural decline rule of the employee skill along with time is reflected, and the mismatching problem caused by outdated skill evaluation is avoided; in combination with a five-dimensional scoring system and skill threshold constraints, the system can automatically filter substandard persons, and meanwhile, the comprehensive level of multiple skills is considered during matching; according to the design, the personnel ability evaluation is always kept synchronous with the real-time state, the accuracy of personnel and post matching is improved in a complex and changeable supply chain environment, and the subjective error of artificial evaluation is reduced while the task execution quality is guaranteed.

The invention discloses an industry post skill intelligent evaluation method and system based on an AI knowledge graph, and relates to the technical field of post skill data processing. The industry post skill intelligent evaluation method and system based on the AI knowledge graph comprises the following steps: S1, acquiring post skill graph data, and preprocessing the post skill graph data; s2, generating candidate post skill connection edges, evaluating post skill map data credibility, and constructing an effective post skill node set; s3, constructing a local post skill subgraph, removing abnormal connecting edges, performing post skill consistency evaluation on the remaining connecting edges, and implementing a hierarchical connecting edge input strategy; and S4, executing post skill map version updating, evaluating the evolutionary stability of the map, and implementing hierarchical updating control. The problem that according to an existing post skill knowledge graph, due to abbreviation of terms during post information filling, information scarcity, mistaken linking and low-quality connected-edge writing are caused, and therefore the reliability and stability of the graph are reduced is solved.

The invention discloses a personalized intelligent interview system, and relates to the technical field of artificial intelligence and recruitment interview, and the system comprises a position information analysis module which is used for inputting an original position description text, carrying out the analysis through employing a natural language processing NLP technology, and extracting structured position demand data containing skill items, characteristic items and quantification levels; the resume / position intelligent matching module is used for receiving the structured position demand data, receiving a job seeker resume text, performing intelligent matching analysis by constructing a skill semantic vector space, and outputting a multi-dimensional matching score matrix and an investigation key index set; according to the job demand and the job seeker resume, personalized interview topic screening and optimization are realized, and the interview pertinence and accuracy are improved; the job seeker is comprehensively evaluated, and the resume / position matching information is combined, so that a more comprehensive evaluation and scoring basis is provided, and an enterprise is helped to make a more accurate recruitment decision.

This skill management system comprises: a generation reception unit that receives, from a user device, a generation request for one or more first evaluation items for measuring the skill of human resources in a first organization; a generation unit that, on the basis of the generation request, inputs, to a first language model, a generation instruction for the first evaluation items and recruitment information that indicates each of one or more human resource recruitments performed in the first organization and includes the skill requested in the recruitments, and generates a first evaluation item group including the one or more first evaluation items; and an output unit that causes the user device to output the generated first evaluation item group.

A method for generating a résumé may receive at least an experience parameter and a job title parameter. Related job titles in a historical résumé dataset may be selected that are semantically related to the job title parameter by using a semantically similar job title selector. The historical résumé dataset may be filtered based on at least the experience parameter and the related job titles to obtain a filtered résumé dataset. A list of user skills may be obtained by selecting skills from the filtered résumé dataset. A list of curated skills may be obtained by selecting curated skills from a curated skills dataset based on at least the experience parameter and the one or more related job titles. A list of candidate skills may be obtained by mapping skills from the list of user skills to corresponding curated skills. Selected candidate skills may be incorporated into a résumé.

According to another embodiment, a method may include: receiving, at a centralized controller, a play data for players using a plurality of basketball hoop devices, the play data comprising shot data comprising ball flight trajectory data, shot angle data, shot distance data, and shot success data; calculating, by the centralized controller, a skill level for each of the plurality of players using the play data; applying, by the centralized controller, a skill leveling algorithm to the shot data to determine parameters for each basketball hoop device based on the skill level of each of the players, wherein the parameters comprise a hoop height, a hoop angle, and a hoop distance; and communicating, by the centralized controller, the parameters to local controllers at the respective basketball hoop device, wherein the local controllers control actuators at the respective basketball hoop devices to implement the parameters.

Various embodiments set forth a techniques for generating computer-aided design (CAD) models that includes receiving a plurality of inputs via a user interface, generating a plurality of geometric prompts based on the plurality of inputs, executing a trained machine-learning model on the geometric prompts to generate CAD data, and generating at least one CAD model based on the CAD data. Advantageously, the disclosed techniques can substantially facilitate the overall process of designing CAD objects and CAD models of differing levels of complexity, thereby increasing the accessibility of CAD software and applications to a wider array of users with differing skill sets.

The invention discloses a worker personalized training and self-adaptive evaluation method and system based on an AI large model, and relates to the technical field of building construction worker training and management. Constructing a post skill library based on the knowledge graph; constructing a to-be-trained personnel post matching database based on the knowledge graph; entity pairs of the post skill library and the post matching database of the to-be-trained personnel are screened through a heuristic rule, linking is completed, the matching degree of the entity pairs is calculated through a similarity algorithm, and personalized training content is generated; designing a self-adaptive evaluation mechanism according to personalized training content, analyzing performance data through a machine learning algorithm, predicting learning ability and knowledge mastering degree, dynamically adjusting an evaluation standard, and customizing an assessment index for an operator; and training operators, collecting performance data in real time, performing evaluation according to an adaptive evaluation mechanism, and presenting an evaluation result in a multi-dimensional form. The safety management level of a construction site and the skill level of constructors are improved.

The invention relates to an employment matching method and system based on intelligent data analysis. According to the method, multi-modal recessive skill data of candidates in an occupational cooperation platform, professional field contribution and occupational evaluation are collected, recessive ability vectors of communication cooperation, problem solving and the like are extracted through structured processing and an emotion analysis algorithm, and cooperation compactness and skill complementarity among members are modeled in combination with a team skill relation graph. And based on a game theory multi-objective optimization algorithm, enterprise employment cost, team efficiency and individual development demands are dynamically balanced, employment matching of accurate quantification of recessive skills, team structure suitability optimization and multi-party benefit balance is finally realized, and the precision of employment matching and the sustainability of long-term occupational development are significantly improved.

Systems and methods for interacting with a skill repository are provided herein. In some examples, a generative large model (GLM) is used to process a task to coordinate skills for performing the task. The coordinated skills include a plurality of skills related to the task. At least one of the coordinated skills is determined to be unavailable to the GLM, and an indication corresponding to the at least one skill is sent to a remote skill repository. The indication may be associated with a description of at least one skill based on which a similarity for retrieving the skill from a remote skill repository may be determined. Remote skills are received from a remote skill repository corresponding to the sent indication, and the task is executed using a generative LLM. The generative LLM uses remote skills to perform tasks.

The invention provides a post recommendation method and system based on resume analysis, and relates to the technical field of big data analysis, and the method comprises the steps: obtaining resume data of a job seeker; performing natural language processing on the resume data to obtain a plurality of keywords, wherein the keywords comprise skill keywords and job application intention keywords; determining the job application field of the job seeker; performing prediction to obtain future market recruitment information and future market demands; a dynamic market label is generated; determining an expected matching degree according to the skill keyword and the job-hunting intention keyword in combination with a dynamic market tag; and according to the expected matching degree of each post, generating a post recommendation strategy of the job seeker. According to the method, the expected matching degree is calculated in a three-dimensional mode through skill keywords, job hunting intention keywords and dynamic market tags, the defects that a traditional method neglects tag semantic association and only depends on static data are effectively overcome, and the man-post matching accuracy is remarkably improved.

The invention discloses an employee skill atlas construction system based on artificial intelligence, and relates to the technical field of atlas construction.The employee skill atlas construction system comprises the steps that firstly, multi-source skill data are obtained, a data processing algorithm based on deep learning is adopted to process the multi-source skill data to mine skill phrases in the multi-source skill data; and further coding the skill phrases into a high-dimensional semantic space, capturing a cross-skill description semantic cooperation relationship in combination with a semantic interaction mechanism, and realizing semantic similarity discrimination of the skill phrases based on the semantic cooperation relationship between the skill phrases. According to the method, a standardized skill system with strong semantic generalization ability is constructed, so that the defect of a traditional method in the aspect of semantic ambiguity elimination of skill data is overcome, the accuracy of employee skill portrait construction is improved, and accurate decision support is provided for an enterprise talent strategy.

The invention relates to a skill grade evaluation method and system for talent management. The skill grade evaluation method comprises the steps of obtaining target occupational information, corresponding occupational element information and assessment index information; constructing a multi-channel evaluation model based on the occupational element information and the assessment index information; obtaining target skill data and performing preliminary evaluation to generate a standardized evaluation result; obtaining target historical behavior data and extracting skill mode features and implicit capability features; inputting the skill mode features and the implicit capability features into a multi-channel evaluation model to generate a personalized evaluation result; in conclusion, according to the skill grade evaluation method and system for talent management provided by the invention, standardized evaluation is carried out on skill data by constructing a multi-channel evaluation model, and dynamic correction is carried out in combination with implicit features extracted from historical behavior data; the method has the advantages of dynamically capturing skill demand changes, identifying skill synergistic effects, mining recessive ability characteristics and providing timeliness occupational planning.

A dialogue system, comprising:an input, configured to receive input data from a user, wherein the input data comprises one or more of text data, speech data, image data and motion data; an output, configured to output data to the user; and one or more processors, configured to:obtain information identifying a skill and obtain information identifying a proficiency level of the user for the identified skill from stored proficiency level information; and execute at least one iteration of a coaching session, each iteration comprising performing one or more dialogue interactions, wherein each dialogue interaction comprises:receiving first input data from the user via the input;generating a first language model prompt and providing the first language model prompt to a language model, said first language model prompt comprising the first input data, the information identifying a skill, the information identifying a proficiency level of the user for the identified skill and a request to generate coaching information based on the first input data, the information identifying a skill and the information identifying a proficiency level; and generating first output data based on a first language model response to the first language model prompt and outputting, via the output, the first output data to the user;wherein the at least one iteration of the coaching session further comprises, after the one or more dialogue interactions:generating a second language model prompt and providing the second language model prompt to the language model, said second language model prompt comprising the information identifying a skill, the information identifying a proficiency level of the user for the identified skill, the first input data and the first output data, and a request to generate at least one proficiency update assessment based on the first input data, the first output data, the identified skill and the information identifying a proficiency level; generating second output data based on a second language model response to the second language model prompt and outputting, via the output, the second output data to the user; receiving second input data from the user via the input; determining a revised proficiency level of the user for the identified skill based on the second input data; andupdating the stored proficiency level information based on the revised proficiency level.

Embodiments of the present disclosure enable system(s) and method(s) for creating and deploying an electronic skill-based activity, including implementing a matchup tool to determine a projected performance score for participants in real-world events based at least in part on historical performance data of each participant. The matchup tool creates suggested matchups for inclusion in a skill-based game by selecting, for each suggested matchup, at least two components, formed from one or more participants, expected to produce substantially similar scores in the skill-based game based on the projected performance score of each participant. The matchup tool renders for display to a game operator the suggested matchups to enable the game operator to interactively select suggested matchups for inclusion within the skill-based game. The matchup tool transmits the selected matchup to presentation devices of users to enable the users to interactively select components as predicted winning components of the skill-based game.

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 discloses an operation automation generation method and device and a storage medium, and relates to the technical field of automatic operation, and discloses an operation automation generation method, which comprises the following steps: generating a skill model based on an operation behavior sequence of a user in a business system, the skill model comprising a plurality of atomic operations, the execution condition and the sequence dependency relationship of the atomic operation are determined; in response to the automatic execution request, obtaining a current interface of the target service system, and comparing the current interface with a historical interface associated with the corresponding atomic operation in the skill model; according to a comparison result, performing adaptive adjustment on the corresponding atomic operation in the skill model to obtain an adjusted skill model; and based on the adjusted skill model, executing automatic operation in the target business system. Through an adaptive adjustment mechanism, the automatic process can dynamically adapt to the interface change of the target business system, and the maintainability of cross-system operation automation is effectively improved.

Various embodiments set forth a techniques for generating computer-aided design (CAD) models that includes receiving a plurality of inputs via a user interface, generating a plurality of geometric prompts based on the plurality of inputs, executing a trained machine-learning model on the geometric prompts to generate CAD data, and generating at least one CAD model based on the CAD data. Advantageously, the disclosed techniques can substantially facilitate the overall process of designing CAD objects and CAD models of differing levels of complexity, thereby increasing the accessibility of CAD software and applications to a wider array of users with differing skill sets.

The invention discloses a skill application vulnerability presentation method and related equipment. The skill application vulnerability presentation method comprises the following steps: displaying a skill visual interface; in response to a reading operation on the skill application historical file, analyzing the recorded skill application historical file to obtain time sequence information and parameter information of the skill application key points; displaying key point identifiers of the corresponding skill release key points in a skill key point display area based on the time sequence information to obtain a key point identifier sequence; displaying parameter information corresponding to the key point identification sequence in a skill key point data display area to obtain skill release parameters; and determining skill release vulnerabilities based on the key point identification sequence and the skill release parameters. According to the method and the device, when the timeline where the skill vulnerabilities occur is analyzed, observation is more intuitive, so that the time consumed for clearing the timeline in the searching process of the skill vulnerabilities can be reduced, and the efficiency of checking the skill vulnerabilities can be improved.

The invention discloses a skill grade evaluation system for talent management. The skill grade evaluation system comprises a demand analysis module, a data collection and preprocessing module, a skill mapping module, a skill level analysis module, a development path planning module and a system optimization module. The invention belongs to the technical field of human resource management, and particularly relates to a skill grade evaluation system for talent management, which is characterized in that skill data are converted into feature vectors through a data preparation unit, a Gaussian mixture model is defined, and model parameters are iteratively optimized through a parameter estimation unit; calculating the posterior probability that a new data point belongs to each skill category through the trained model, and achieving the classification of skill levels; the skill level is analyzed based on a decision tree and an entropy theory, accumulated residual entropy is calculated to evaluate the uncertainty of overall skill distribution, the influence degree of residual uncertainty analysis features on skill evaluation is calculated, and optimal features are selected according to information gains to achieve skill level evaluation.

The invention provides a post recommendation and learning path generation method based on skill level entity extraction, and relates to the technical field of person and post matching, and the method comprises the steps: obtaining job seeker data and post data; extracting skills and skill levels of job seekers and posts from the data by using a large language model (LLM), and confirming the extracted skill data through an ESCO classification method; on the basis of the extracted skill data, according to the defined similarity constraint and calculation formula of the job seekers and the posts, similarity calculation is carried out on the job seekers and the posts, and post recommendation is carried out on the basis of the similarity; and analyzing the extracted skill data of the job seekers and the posts to form a visual learning path diagram. By the adoption of the scheme, accurate matching and recommendation of the job seeker and the post are achieved.

The invention relates to the technical field of occupational skill evaluation, and particularly discloses an online evaluation system and method based on occupational skills, and the system comprises the steps: building a multi-task learning framework comprising a shared feature extraction module and a task exclusive head through collecting the multi-modal behavior data of a user in the process of executing an occupational skill task; jointly training a plurality of related tasks to improve the generalization ability of the model; a pre-training teacher model is introduced, cross-domain knowledge is migrated through a knowledge distillation mechanism, and lightweight student model performance is optimized in combination with a regularization strategy; calculating a cognitive load fluctuation characteristic value and a skill migration consistency characteristic value based on student model output, and fusing to generate a composite risk assessment vector; an asynchronous parallel optimization model is input for comprehensive evaluation, and a structured evaluation report and personalized comments are output in combination with an SHAP interpretability analysis and natural language generation module.

Embodiments are directed to managing data for skill extraction pipeline. Skill spans may be determined based on skill information associated with job descriptions such that each skill span includes words included in the skill information. A comparison of the skill spans with other skill spans associated with a skill knowledge graph may be generated. Skill nodes may be generated for the unmatched skill spans. The new skill nodes may be integrated into the skill knowledge graph. Accordingly, the skill graph may be automatically updated for use in job / career applications including report generation.

A method for performing a dialog session including receiving a user input from a chatbot application wherein the user input is indicative of a user intent associated with one of a plurality of skills, generating an input vector in response to the user input, generating a list of skills in response to a semantic search comparing the input vector to a plurality of skills vectors where each of the plurality of skills vectors is associated with one of the plurality of skills with the vector to determine a list of skills matching the vector, identifying an intended skill from the list of skills in response to the list of skills and the input vector using a large language model, and performing the intended skill in response to the identification of the intended skill.