539 results about "Skill sets" patented technology

The invention relates to the technical field of crowdsourcing task allocation for multi-person cooperation of complex tasks, and discloses a crowdsourcing task allocation method for multi-person cooperation of complex tasks, which comprises the following steps: acquiring all persons in a talent pool and marking and numbering the persons in sequence, thereby facilitating subsequent management and information retrieval, acquiring a skill label of each person, and improving the efficiency of crowdsourcing task allocation. Multiple skills are considered, so that task allocation is more accurate, through skill label classification, the system can quickly match task requirements and personnel skills, obtain a historical task list to provide reference for task disassembly and estimation, and decompose tasks into small sub-tasks, and the system can accurately estimate task load and personnel allocation. According to the method, archives of the split tasks are recorded to provide data reference for follow-up, the average workload and the completion time of the historical tasks provide a basis for task allocation, uncertainty is reduced through data-driven estimation, and it is ensured that task load estimation is flexible and accurate through a similarity evaluation mechanism, so that the overall task execution quality and efficiency are improved.

The invention provides an intelligent human resource training recommendation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining post data, skill data, performance data, training records and development target data of a target, building an employee database, and constructing a knowledge graph; post data, skill levels, performance expressions, training records and individual development targets of the employees are integrated to form a comprehensive portrait; dividing all courses according to difficulty levels, selecting courses with corresponding difficulty according to post requirements and performance data of the employees, and generating a group of preliminary course lists matched with current skill levels and post requirements of the employees; semantic analysis is carried out on skill requirements of employee portraits and recommended course contents, a semantic analysis result is obtained, and according to employee portrait data, a semantic matching algorithm is used to calculate the matching degree between the employees and each training content; and according to the matching degree scores of the employees and the training contents, generating a course list subjected to semantic matching optimization.

Apparatuses, systems, and methods are provided for training employees are provided. The method comprises: (1) receiving, by one or more processors from a user device associated with an employee, an answer associated with a question; (2) generating, by the one or more processors via a chatbot, values associated with one or more metrics of the answer; and / or (3) performing, by the one or more processors, at least one of the following: (i) determining a skill level of the employee based upon the values associated with the one or more metrics of the answer; (ii) presenting, to the employee via the user device, a second question based upon the values associated with the one or more metrics of the answer; and / or (iii) presenting, to the employee via the user device, an explanation based upon the values associated with the one or more metrics of the answer.

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 discloses a driving skill training method constructed based on a diversified virtual examination room, and relates to the technical field of data processing, and the method comprises the steps: constructing a system based on the diversified virtual examination room, constructing a virtual examination room scene to carry out the driving training of a training user, outputting a driving behavior data set in real time, and carrying out the scoring, and analyzing the data according to the driving score, generating a personalized training path in combination with historical data when the driving score does not reach the standard, and training the user to carry out training step by step according to the training path until a preset skill scoring target is achieved. The technical problems that in the prior art, a traditional driving training method cannot dynamically adjust a training path to meet the actual skill requirements of trainees, consequently, training content lacks pertinence and is low in efficiency are solved, the individuation and pertinence of driving skill training are improved, it is ensured that the training content is highly matched with the actual skill requirements of the trainees, and the driving skill training efficiency is improved. And the training efficiency and effect 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 visual information monitoring method and system based on talent management, and relates to the technical field of talent management, and the method comprises the steps: collecting and preprocessing talent data, extracting talent features, carrying out dynamic label updating, and constructing cross-industry skill mapping based on dynamic labels. And generating a talent portrait based on a mapping result, predicting a talent loss risk, optimizing the talent portrait based on a prediction result, and performing talent recommendation. According to the method, semantic features in dynamic data are accurately extracted by introducing a BERT model and a dynamic exit mechanism and combining distillation optimization and a multi-level feature fusion method, cross-industry skill mapping based on a knowledge graph is constructed, the ability of versatility analysis and migration path modeling on skill nodes is improved, and the method is suitable for mass production. Through organic combination of a dynamic instrument panel and three-dimensional display, panoramic dynamic visual monitoring of talent distribution, post matching and industry migration is realized, and the intelligent level of an enterprise in talent management is remarkably improved.

Techniques for selecting a skill to execute in response to a natural language input are described. A system may receive a natural language input, determine profile data associated with the natural language input, and determine the profile data indicates a locale and at least first language and second languages. The system determines first and second sets of skills corresponding to the locale / first language and locale / second language, respectively. The system determines a first group of skill candidates corresponding to a portion of the first set of skills, and determines a second group of skill candidates corresponding to a portion of the second set of skills. The system performs ranking processing across the first and second groups of skills to determine a best skill for responding to the natural language input. Thus, in some situations, the skill invoked may not correspond to the same language represented in the natural language input.

An AI-based training recommendation system (100) for personalized employee development, which includes the following: a module for creating user profiles that is configured to collect and analyze employee data, including job role, skills, learning history, performance indicators, and career aspirations; a module for real-time skills gap analysis that uses artificial intelligence to assess employee skills and identify areas for improvement; a training recommendation engine that uses machine learning algorithms to create personalized training suggestions based on employee data, company goals, and industry trends; a learning path optimization module configured to adaptively modify training recommendations based on employee progress, feedback, and evolving qualification requirements; an integration module for connecting to existing enterprise systems, including Learning Management Systems (LMS), HR platforms and performance management tools; a module for monitoring engagement and progress, designed to track employee participation in training, assess knowledge retention, and provide managers with insights; and an automated training planner configured to assign and prioritize training sessions based on employee availability and business requirements.

The invention relates to a temporary worker flexible employment information matching method, and the method comprises the steps: constructing a temporary worker skill portrait according to the historical employment information of a temporary worker; classifying the skills of the temporary workers according to the temporary worker skill portraits to obtain temporary worker subsets of different skill types; constructing a skill attenuation model for the temporary worker subset of each skill type; by analyzing the historical academic record and the training participation degree of the temporary worker, the learning ability coefficient of the temporary worker is calculated to serve as an input parameter of the attenuation model; according to the skill attenuation model, predicting the skill proficiency degree change condition of the temporary worker in a period of time in the future; dynamically acquiring latest skill use information of temporary workers, and updating input parameters; selecting a corresponding attenuation curve template to obtain a skill attenuation prediction result; combining various information to obtain a comprehensive matching degree; the prediction accuracy of the skill attenuation model is evaluated regularly; according to the invention, through the precise modeling skill attenuation process, the matching precision and timeliness of temporary workers and post demands are improved.

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 multi-dimensional talent recommendation method based on human resource big data, and the method comprises the steps: obtaining post demand data issued by an enterprise, the post demand data comprising post responsibilities, skill requirements, working experience and educational background requirements of posts required by the enterprise; performing feature extraction on the post demand data, determining post feature data, performing feature mapping on the post feature data to obtain a post demand vector, generating a post portrait of a post required by an enterprise according to the post demand vector, and adding the post portrait into a post library; the post demand vector has the same dimension as the talent evaluation vector of the talent portrait of each job seeker in the talent pool; the talent portraits in the talent pool are matched with the post portraits of the posts required by the enterprise, and the adaptation degree between the talent portraits of the job seekers and the post portraits of the posts required by the enterprise is determined; and generating a talent recommendation list based on the adaptation degree between the talent portrait of the job seeker and the post portrait of the post required by the enterprise, and recommending the job seeker to the enterprise based on the talent recommendation list.

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 relates to a robot skill continuous learning method and device based on imitation learning, and the method comprises the steps: sequentially aiming at each task in a plurality of tasks; before a preset termination condition is met, circularly executing the following operations: inputting the observation image, the state information, a pre-acquired text instruction of the current task and a skill code book of the current task into a simulation learning strategy network, and predicting to obtain action information of the robot at the current moment, the skill code book of the current task is a plurality of pre-initialized learnable sub-skill vectors, and the skill code book comprises skill information of the skill code book of the previous task under the condition that the previous task exists; calculating loss based on the predicted action information of the robot and the real action information of the robot in the expert demonstration data; and updating the imitation learning strategy network and the skill code book of the current task based on the loss. The problems that skill selection is not flexible, and it is difficult to abstract the sub-skill hierarchical structure through manual skill design are solved.

The invention relates to the field of intelligent occupational planning, and provides an occupational planning auxiliary system and method based on a knowledge graph, and the method comprises the steps: firstly constructing an occupational knowledge graph, then extracting a first alternative occupational entity, and carrying out the semantic analysis of the first alternative occupational entity, so as to obtain a semantic feature of the first alternative occupational entity; meanwhile, obtaining personal information, work experience, skill specialty and job hunting intention input by the user to construct a user portrait, and performing semantic analysis on the user portrait to obtain semantic features of the user portrait; and matching the obtained semantic features of the first alternative occupational entity with the semantic features of the user portrait so as to judge whether the first alternative occupational entity is returned or not, namely whether occupational information related to the first alternative occupational entity is matched with the user or not. Therefore, suitable occupational information can be screened for the user from a more comprehensive and objective angle, and the problems that suggestions are not comprehensive and are greatly influenced by artificial factors in traditional occupational planning services can be 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.

A method and system for dynamic skills assessment. Digital indicators are used with dynamic skill assessment indicators to indicate plurals type of skill sets assessed, plural levels of skill sets completed, plural expiration times and dates for the plurals type of skill set assessment completed and plurals levels of skill assessment completed, plural unique identifiers for plural users who completed the plural skill set assessments and plural identifiers for plural network devices on which the plurals skill sets were assessed for the plural users. The dynamic skill assessment indicators include plural different indicators including graphical, audio, video, text and / or haptic dynamic skill assessment indicators that dynamically are changed to different colors, shapes, sizes, sounds, videos, feelings, etc., when new skills sets are achieved and when existing skill sets expire.

Methods, systems, and apparatuses for a career management co-op platform. A method includes receiving, from a device associated with a prospective job candidate, a request for consideration for a first job type for the job candidate; and determining, based on aggregated job candidate data of a plurality of job candidates having the first job type, one or more relevant skills possessed by the plurality job candidates. The method further includes determining, via a machine learning inference based on a comparison of current skills possessed by the prospective job candidate and the relevant skills, a job seeker skillset match score; and based on the skillset match score satisfying a threshold, providing, to the device, a recommendation of a second job type relevant to at least one of the one or more relevant skills missing from the current skills of the prospective job candidate.

Machine learning models are used to generate a plan that responds to a user request. The plan includes one or more skills selected from a list of available skills. The prompt may be written in natural language, enabling the user to express their intent without having to know which skills are available or their intricacies. In some configurations, a skill is included in the plan if an embedding representation of an example prompt associated with the skill is within a defined distance of an embedding representation of the user request. Additionally, or alternatively, the embedding distance computations are used to narrow the list of available skills, which is then used to construct a meta-prompt that selects a skill. Skills listed in the meta-prompt may include data types of parameters and return values. This allows the model that processes the meta-prompt to order skills based on data type compatibility.

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.

A system is described for using large language models for labor upskilling. The system includes the steps of identifying required skills, generating personalized training materials based on the learners' existing knowledge and skills, and evaluating the effectiveness of the training materials. This system can be applied to both hard skills and soft skills.

The present invention discloses a system and method for recommending workforce and associated skillset for a task. The method includes receiving a description of the task from a user along with details of current participants to determine roles associated with the task. Next, the method includes determining skills and required number of candidates for handling the determined roles. Next, the method includes filtering candidates for handling the roles based on the skills and the required number of candidates for determining psychometric qualities of the filtered candidates and current participants. Next, the method includes determining psychometric compatibility score of the filtered candidates with current participants based on corresponding psychometric qualities. Thereafter, the method includes recommending potential team including the filtered candidates based on the psychometric compatibility score and the required number of candidates.

Embodiments are directed to managing skill proficiencies by using multiple evaluators to identify overlapping skills. A first job description and a second job description that each respectively describe features of a first job and a second job may be provided. Activities associated with the first job may be determined based on the first job description and activities associated with the second job may be determined based on the second job description. The first activities may be compared with the second activities based on matching models such that each activity that may be included in the first job description and included in the second job description may be considered shared activities. Shared skills may be determined based on the shared activities and may be associated with the job seeker, the first job, the second job, or the like.