53 results about "Career planning" patented technology

The invention discloses an employment and entrepreneurship support system based on artificial intelligence, relates to the technical field of occupational planning, and aims to solve the problem that a traditional support mode is insufficient in individuation and accuracy. The system comprises a multi-modal data acquisition module used for acquiring facial expressions, whole body dynamics and voice data of a user in at least 30 minutes of video dialogue in real time; a dialogue text is processed through a large language model based on a Transform architecture, continuous time sequence modeling is carried out on multi-modal dynamic behaviors by applying a liquid time constant network, and deep fusion reasoning is carried out in combination with multiple groups of multi-head Transform attention mechanisms. The core analyzes the real thought, psychological state, behavior pattern and core ability of the user through consistency verification, generates a structured dynamic user insight abstract, and customizes personalized vocational development or entrepreneurship planning according to the structured dynamic user insight abstract. According to the method, the potential of the user can be deeply informed, high-precision personalized planning is provided, the decision-making quality and success rate are improved, and the method has dynamic adaptation and continuous learning capabilities.

The present invention discloses an educational evaluation method for dynamic vocational ability and job matching based on a knowledge graph. S1. Construct a vocational ability knowledge graph; S2. Construct a job requirement knowledge graph; S3. Design and train a double-layer generative adversarial network model; S4. Match the vocational ability enhancement plan and job requirement changes generated by the double-layer generative adversarial network model with the existing vocational ability knowledge graph and job requirement knowledge graph; S5. Based on the dynamic changes in market demand and the development of individual vocational abilities, update the vocational ability knowledge graph and job requirement knowledge graph in real time, and use the double-layer generative adversarial network model to generate new matching relationships to continuously optimize the matching results; S6. Generate an educational evaluation report and career planning suggestions according to the optimized matching results. The present invention significantly improves the dynamic adaptability and matching accuracy of vocational ability and job matching, and realizes the precise docking between job seekers and job requirements.

The invention belongs to the technical field of employment market management, and provides an employment matching and occupational development planning system, which comprises a user information acquisition module, a post information analysis module, a matching algorithm module and an occupational planning suggestion module, the user information acquisition module, the post information analysis module, the matching algorithm module and the occupational planning suggestion module are electrically connected in sequence; according to the invention, the user information acquisition module is constructed to comprehensively acquire user information, the post information analysis module is combined to analyze post requirements in real time, and the advanced matching algorithm module is adopted to realize accurate post matching; a machine learning algorithm is introduced through the occupational planning suggestion module, so that a personalized occupational development path and a training improvement scheme are provided for the user; according to the design, the matching accuracy and the user satisfaction degree are remarkably improved, meanwhile, the user is helped to achieve precise planning of occupational development, and the user experience is optimized.

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.

The invention discloses a career planning evaluation method and system based on an AI large model. According to the method, multi-dimensional data of a user is collected and preprocessed, and an occupational planning evaluation analysis model is constructed. And performing occupational interest and cognitive competence evaluation on the user by using the model, and generating a personalized occupational planning report. The user can select or upload a resume, participate in evaluation and explore different professions and industries to customize the own occupational development path. In addition, the method also comprises the step of regularly updating the job application influence factors to adapt to the change of the employment market and ensure the scientificity and practicability of the occupational planning suggestions. According to the invention, through intelligent evaluation and planning, the efficiency and accuracy of occupational planning are improved, and powerful support is provided for occupational development of users.

The invention discloses an AI large model-based occupational evaluation method and system. The method comprises the following steps: capturing network recruitment data by utilizing a search engine to capture a current recruitment hotspot trend and post talent portrait labels, training a bottom layer model by utilizing structured resume data uploaded by a user, and obtaining a user occupational development model under a multi-factor dimension; secondly, evaluating the user based on a traditional character evaluation model, and determining a basic character type of the user; and finally inputting the character type and the personal resume data of the user into the trained model, and outputting a personalized occupational evaluation result in combination with a recruitment hotspot trend and a talent portrait label. In addition, the evaluation result of the user serves as incremental data feedback and is used for further training and optimizing the model. According to the method, the accuracy, timeliness and model iteration capability of occupational evaluation are improved, and a scientific basis is provided for personal occupational planning and enterprise talent selection.

The invention relates to the technical field of occupational management planning, in particular to an AI technology-based student occupational direction intelligent planning system and method. The method comprises the following steps: acquiring multi-source data of a student and mapping the multi-source data to a COMET comprehensive vocational ability model to generate a student ability vector; performing text analysis on the intentional occupational post information of the student, generating an occupational ability vector, and extracting a core ability dimension of each occupational; the system constructs a directed acyclic graph by taking a student portrait node as a starting point, a core capability node as an inevitable path, a non-core capability node as an optional path and an intentional occupational node as a terminal point, and constructs an occupational migration path based on capability difference and similarity; and calculating an optimal migration path by adopting an optimal priority search algorithm, and outputting an occupational evolution route of the trainee. According to the system, quantitative mapping of trainee ability and occupational requirements, visualization of core ability and scientific planning of occupational paths are realized, and individuation, dynamics and operability of occupational planning are improved.

The invention relates to the technical field of artificial intelligence, in particular to a post recommendation method based on a knowledge graph and a large language model and a related product, and the method comprises the steps: obtaining and processing recruitment data, and generating a special training data set; constructing a dynamic vocational knowledge graph, training a large language model in the recruitment field, and establishing an interaction mechanism between the large language model and the dynamic vocational knowledge graph; according to the method, the fairness and objectivity of the final recommendation result are improved, more timely occupational development suggestions are provided for job seekers, and the accuracy and reliability of post recommendation and occupational planning suggestions are enhanced.

The invention discloses a career planning analysis system and method based on multi-modal data fusion, belongs to the technical field of data analysis, and solves the problems that an existing method performs iteration and correlation analysis on original learning condition data, the data source is single, and the method cannot dynamically adapt to the change of interest of students. The method comprises the steps of pre-constructing an occupational interest knowledge graph based on original graph data, generating a student three-dimensional career portrait based on multi-modal data fusion, traversing the occupational interest knowledge graph by an interest recommendation model based on a frequent subgraph mining algorithm in combination with an interaction point set, and generating a personalized occupational interest matching path; according to the method, normalization fusion processing is carried out in combination with the student associated data, the students can be comprehensively known from multiple dimensions, and a personalized occupational interest matching path can be dynamically generated by adopting multi-modal data fusion, the occupational interest knowledge graph, the improved NLP model and the personalized recommendation model, so that the method can better adapt to the interest and ability change of the students.

The invention discloses a career planning simulation system, and relates to the technical field of career planning management. Comprising a user information collection management module which collects user information, determines occupational interest types and preference fields of users, generates an evaluation report and provides occupational interest portraits for the users; the occupational information database module is used for collecting and sorting detailed occupational information covering each industry and each field and carrying out classification processing; and carrying out visual information display. According to the system, deep analysis is carried out based on occupational interests, skills, abilities and values of users, personalized occupational interest portraits and occupational planning suggestions are generated, and the accuracy and effectiveness of recommendation are ensured; an intelligent algorithm and a data analysis model are applied to recommend a potential occupational target list matched with the occupational interest, skill ability and value of the user for the user, and a decision simulation and risk assessment function is provided to help the user to make a more intelligent occupational choice.

The invention relates to the technical field of work adaptation, in particular to a cancer surviver work adaptation method and system, and the method comprises the following steps: based on the occupational data of cancer survivors, uniformly processing a data format, carrying out the occupational path analysis of a data set, and recognizing key occupational path change nodes. According to the invention, by analyzing the occupational data of cancer survivors, the identification capability of occupational path change nodes is improved, and occupational transition periods related to health condition changes are screened more accurately, so that personalized occupational planning suggestions are provided for the cancer survivors, and through a time window and sliding window technology, the cancer survivors can be more accurately planned. According to the method, career change frequency and amplitude are finely calculated, and career change trend characteristics are extracted, so that prediction of future career paths is more accurate, analysis of work adaptability problems is further refined, association between work performance and health data is clarified, and a scientific basis is provided for dynamic work allocation and configuration.

The invention relates to a course recommendation method for multi-level memory enhancement based on a large language model, and belongs to the field of intelligent education and recommendation systems. The method comprises the following steps: acquiring interaction data between a user and a course, and dividing the interaction data into multi-level memories such as feeling, work and long-term memories based on a cognitive psychology principle; constructing three types of cue word templates, and driving a large language model to perform semantic knowledge enhancement on user interaction data, course attribute information and multi-level memory comparison; and the enhanced semantic knowledge is encoded into vectors through a pre-training model, then enhancement processing is performed through a hierarchical attention expert network, and the output enhanced vectors are used for improving the performance of a downstream recommendation model. The problems that a recommendation system is insufficient in semantic understanding, inaccurate in cognitive memory modeling and limited in personalized recommendation ability are solved, the instant demand, the current learning state and the long-term occupational planning of the user are accurately understood, the course recommendation precision is remarkably improved, and the method is suitable for online education, occupational training and lifelong learning scenes.

The invention provides an environmental art design course recommendation method and system, and relates to the technical field of intelligent education, and the method comprises the steps: screening a candidate course set adaptive to the current ability of a student according to the ability matching degree in a multi-dimensional student portrait and the pre-repair relation between courses in a dynamic course knowledge graph; setting double target points in the candidate course set, wherein the double target points comprise the highest matching value of course basic requirements and student abilities and the highest association value of course high-order abilities and occupational planning; constructing a linear evaluation axis, calculating a deviation angle and a midpoint slope of an axis direction and a reference direction, and generating a course display correction parameter; dynamically adjusting the multi-modal display content of the candidate course set according to the course display correction parameters; and generating an interactive recommendation list based on the multi-modal display content. The course selection efficiency can be improved.

The invention belongs to the technical field of education artificial intelligence, and particularly relates to an adaptive learning path generation method based on a knowledge graph, which comprises a basic construction stage of constructing a double-layer knowledge graph and acquiring and standardizing multi-source dynamic data, and is used for constructing a course and cross-domain double-layer knowledge system and a data base, a three-dimensional learning condition analysis stage including knowledge dimension, dynamic learning condition dimension and cognitive development dimension; the cross-domain knowledge migration mechanism breaks course barriers, associates current learning with core course knowledge points of the same major, helps students establish a complete professional knowledge network, avoids knowledge fragmentation, embeds cross-domain knowledge points in combination with professional planning and competition requirements, realizes connection of current learning and future application, improves the practical value and migration ability of knowledge, and improves the learning efficiency. The knowledge association weight is dynamically optimized, so that path planning follows domain knowledge logic and fits individual learning feedback, students are assisted in understanding internal association among knowledge, and knowledge construction logicality is enhanced.

The invention relates to the field of occupational planning, and discloses an ideological and political and employment fusion-oriented college student occupational planning auxiliary system, which realizes personalized occupational path planning through an optimization algorithm, a Bellman optimality principle and a dynamic feedback mechanism. The system comprises a data acquisition module, an occupational planning module, an optimal control module, a dynamic feedback module and a user interaction platform, can acquire data such as interests, capabilities and social responsibility of students in real time, and performs dynamic adjustment in combination with changes of an external environment. By integrating the demands of students and the trend of social development, the occupational path is ensured to meet the personal goal, and the performance of social responsibility can be promoted. The system provides a flexible, accurate and sustainable occupational development scheme for students, and solves the problem that a traditional occupational planning system cannot flexibly cope with student changes and external environment fluctuations.

The application discloses a talent information analysis method based on deep learning, and relates to the technical field of human resource management, comprising the following steps: candidate data collection; generating a high-dimensional feature vector of the candidate; constructing a knowledge graph of the field to generate an enhanced feature vector of the candidate; dividing the candidate into multiple groups by using a clustering algorithm, and defining the category name of clustering for each group; generating a demand feature vector according to the demand of the enterprise and selecting a group; and sorting and recommending the candidate in the group based on similarity calculation. The application is based on the organic combination of a large model, a knowledge graph and clustering analysis, can fully excavate the skill connection and development potential hidden in massive data, has high scalability in each link of feature representation and matching degree calculation, and expands the application potential of talent data analysis in intelligent recruitment, career planning and human resource management and the like.

1. Name of the designed product: game sand table (career planning). 2. Use of the designed product: toy for simulating career planning. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

The invention discloses a learning plan analysis system based on beauty teaching, which comprises an education server, a learning analysis module and a learning strategy generation module, and is characterized in that the education server receives a learning progress result of a learner, and the learning analysis module identifies vulnerable knowledge elements by analyzing the result and generates a learning strategy; the learning strategy generation module generates supplementary learning content in a targeted manner; and meanwhile, functional modules such as a learning psychological analysis unit, a learning career analysis unit and an art learning evaluation unit are integrated, and algorithms such as deep learning, natural language processing and reinforcement learning are combined, so that functions such as learning mode recognition, beauty ability multi-dimensional evaluation, personalized learning strategy generation and career path recommendation are realized. According to the system, knowledge weak points and learning requirements of the learner can be accurately positioned, customized learning planning and feedback are provided in combination with beauty teaching characteristics, the learning efficiency and beauty literacy are effectively improved, and support is provided for comprehensive development and occupational planning of the learner.

The invention discloses a tourism internship and employment system based on big data sharing, and aims to solve the technical problems that talent training and industrial requirements in the current tourism industry are disjointed, internship and employment information is asymmetric, and vocational development lacks data support. According to the system, multi-source data of colleges, enterprises, students and government / industry associations are integrated by constructing a'government-enterprise-school-bank 'multi-dimensional data sharing and fusion architecture; high-precision bidirectional recommendation of students and posts is realized by utilizing an intelligent matching engine driven by dynamic ability portraits and deep learning; a full life cycle tracking and feedback closed loop throughout'practice-employment-occupational development 'is established, and industry talent demand prediction and personalized occupational planning are provided based on big data analysis. The method effectively breaks the information island, improves the man-post matching efficiency, meets the production and study gap, enables the personalized development of students, provides data support for talent strategy decision-making in the tourism industry, and is suitable for talent cultivation and human resource allocation scenes in the tourism industry.

The application discloses a kind of based on artificial intelligence's youth career development planning method and system, belong to educational informatization technical field, by the standardization processing and data fusion processing of the multi-source career related data of target user, construct the multi-source fusion, including multiple portrait dimensions target user career portrait, so that the target user career portrait that constructs is more accurate and comprehensive;And based on the target user career portrait of target user, target user career reasoning chain is constructed to form interpretable reasoning path, on the basis of target user career reasoning chain in target user career reasoning chain, each youth career development direction in the youth career development direction library is matched with target user career development direction and target user development career planning scheme generation, to make the target user development career planning scheme finally obtained more accurate, comprehensive, and have explainability.

The invention provides a reinforcement learning-based university student personalized occupational planning method, which comprises the following steps of: firstly, acquiring and normalizing multi-source individual data of university students, and constructing capability vectors based on multi-modal information such as texts and videos; and further obtaining post portrait data, generating post capability demand vectors and calculating capability gap characterization. On the basis, a reinforcement learning environment with capability vectors, capability gap vectors and time constraints as states is constructed, a micro-intervention task package composed of a micro course task, a project training task and a simulation interview training task is used as an action, and the capability gap reduction effect and the task cost are comprehensively evaluated through a reward function; and training to obtain an occupational planning strategy. And finally, a personalized occupational planning result is generated based on the trained planning strategy, and the planning is continuously updated and dynamically corrected according to time change and ability gap change in the execution process, so that the scientificity and suitability of occupational planning are improved.

The invention discloses a multivariate intelligent evaluation and career planning consultation system based on multi-modal data fusion, and belongs to the crossing field of artificial intelligence and education evaluation. The system comprises a multi-modal data acquisition module, a preprocessing module, a multivariate intelligent evaluation module, a cross combination analysis module, a dynamic updating module and a personalized recommendation module. The acquisition module integrates structured data (questionnaires, academic data and genes) and unstructured data (behaviors, voices and images); the preprocessing module cleans, converts and fuses data; the evaluation module constructs an'intelligence-capability 'secondary model, and quantifies 8 items of intelligence and 40 items of core capability; the cross analysis module generates a potential label; the dynamic updating module iterates the result in real time; and the recommendation module outputs interest, academic, professional and career planning suggestions. The system solves the problems of single traditional evaluation data, rough evaluation, poor dynamics and the like, and improves the planning precision and timeliness.

1. The name of the design product: Accompanying intelligent machine (career planning). 2. The use of the design product: Voice assistants for providing intelligent voice interaction, career planning, leisure companionship, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1. 5. Other circumstances that need to be explained: A is the screen display of the intelligent machine.

The invention relates to the technical field of intelligent education, and particularly discloses an intelligent education support method, system and device based on deep learning, and a medium, and the method comprises the steps: S1, data collection; s2, constructing a model; s3, identifying and classifying service requests; s4, analyzing a resource library; s5, data interaction; s6, carrying out model iteration; according to the digital intelligence tutor APP designed by the invention, three core requirements of academic guidance (examination research, insurance research and graduation theses), scientific research competition guidance and career planning are integrated into a whole for the first time, and the application limitation of a general digital intelligence education tool is broken through; a professional exclusive digital intelligence guidance normal form taking nursing subject requirements as the core and taking undergraduate personalized growth as the guide is constructed, the theoretical and practical blank of a full-chain digital intelligence growth support system in the nursing education field is filled, and a non-universal theoretical reference is provided for digital intelligence education tool development of highly specialized subjects.

The invention relates to the technical field of informatization systems, in particular to an occupational scene simulation system and method for promoting employment and entrepreneurship, and the system comprises a scene generation module, a multi-modal interaction module, a multi-modal fusion module, a dynamic evaluation module, a dynamic evaluation module, a dynamic evaluation module and a dynamic evaluation module, the system comprises a dynamic evaluation module which is in communication connection with the multi-modal fusion module, a decision optimization module which is in communication connection with the dynamic evaluation module, a path planning module which is in communication connection with the decision optimization module, and a visualization module which is in communication connection with the path planning module and receives the optimal occupational development path. Based on a three-dimensional situation projection technology, generating a visual representation of a user capability development track; and sending the visual representation to the multi-modal interaction module for presentation in a virtual reality environment. The problems existing in an existing occupational planning and ability evaluation system are effectively solved.

The invention provides an artificial intelligence education planning and matching method and system, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a skill portrait and target post information of a target user in response to an occupational planning request; querying and extracting a corresponding target skill set from a preset post skill reference library, calculating a mastery degree difference between the skill portrait and the target skill set to obtain a skill difference list, obtaining a planning skill learning sequence based on the skill difference list and a preset skill constraint rule set, and obtaining a planning skill learning result; on the basis of the skill learning sequence and the skill portrait of the target user, the corresponding post is matched from the post skill reference library, and the occupational planning path is generated, so that the problem that the skill gap and the real-time post demand are difficult to accurately quantify due to dependence on a static database and subjective single user evaluation in the existing scheme is solved; the technical problem is solved, and the method and the device have the effect of improving the career planning accuracy.

The invention relates to the technical field of planning evaluation, in particular to a college volunteer filling and college career planning information interaction system. Comprising a career planning module, a willingness filling module, an evaluation result summarizing unit and an interconnection module, the career planning module comprises an identification module and a willingness module, and the identification module and the willingness module transmit final data to the evaluation result summarizing unit. The evaluation result summarization unit and the volunteer filling module transmit final data to the interconnection module together, and the identification module comprises an answer proofreading unit and a scoring structure unit. The college volunteer filling and college career planning information interaction system has the beneficial effects that the volunteer filling is evaluated in a targeted manner through the volunteer filling module and the career planning module by comparing recent scores with previous college score lines, the career planning is evaluated through the identification module and the volunteer module, and the college volunteer filling and college career planning information interaction system is high in accuracy. And accurate reference data can be conveniently provided for volunteer filling and career planning.

The invention discloses a border AI portrait-driven career planning cooperation system, and the system comprises the following modules: a multi-source data collection module which is used for collecting college educational administration system data, Internet recruitment platform post data, user social media behavior data and psychological assessment results in real time; the dynamic portrait modeling module is used for fusing structured data and unstructured data on the basis of a graph neural network and a BERT semantic analysis technology, and constructing a student multi-dimensional dynamic portrait and post demand graph; the mixed recommendation engine module integrates a collaborative filtering algorithm and a content recommendation algorithm, and generates a personalized recommendation result through a two-way scoring mechanism (user portrait similarity + post hot trend); and a closed-loop feedback learning module. Compared with a traditional static scale evaluation method, the matching precision is improved, the matching F1 value of the post and the student is improved by 29.4%, the accuracy of occupational recommendation is greatly improved, and the user can obtain occupational recommendation better meeting the requirements of the user.

The invention relates to the technical field of employment planning, in particular to an AI and character analysis-based occupational planning method and a terminal, which can efficiently screen massive occupational information by issuing character questionnaires, generating character feature vectors and calling an occupational feature library constructed by expert annotations and machine learning. The modal difference between characters and occupational characteristics is eliminated through cross-modal alignment, dynamic attention weights are generated to highlight occupational key character requirements, recommendation is performed according to weighted similarity, the adaptation degree of occupations and user characteristics can be greatly improved, effective occupational recommendation conforming to the user characteristics is provided, short-term demission caused by inconsistent characters is reduced, and the user experience is improved. Not only is job hunting time cost of students reduced, but also recruitment burden of enterprises is reduced.

The invention discloses a precise pairing method and system, and relates to the fields of recruitment, dating and dating, occupational planning, volunteer filling service and the like. The method comprises the following steps: firstly, establishing a factor standard library, then carrying out measurement test and evaluation on objects participating in pairing, quantifying factors or numbering and valuing according to the factor standard library, and dividing the factors into core factors and non-core factors. Quantized or serialized factors are accurately paired, and the strict degrees of pairing algorithms for core factors and non-core factors are different, so that an accurate pairing result can be output and reliability can be ensured. The job hunting time of job hunters in the recruitment and job hunting fields is reduced, so that recruitment units can quickly obtain qualified people choices with low cost; in the field of dating and friend making, the phenomena that impulse type friend making is caused by surface information, and consequently the family is out of the marriage and the like are avoided, and genetic diseases can be avoided; in the field of occupational planning and volunteer filling service, examinees can be helped to obtain matched schools and majors, and the situation that the examinees enter wrong lines at the beginning is avoided.