37 results about "Career planning" patented technology

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 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 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 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 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 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 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.

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

The invention discloses a multi-theory fusion-based career planning large model construction method and system, and relates to the technical field of data mining and career planning, and the method comprises the specific steps: firstly integrating multiple theories to construct a career development heterogeneous causal graph, and defining an element relationship; constructing a dynamic heterogeneous graph neural network and training a large model by taking the dynamic heterogeneous graph as input; establishing a channel to collect control intervention variable data; reasoning and visually explaining the career path; and finally, providing a double-deployment mode, utilizing a dynamic incremental updating algorithm and combining with multi-party data to realize full-process linkage optimization of the model, and providing precise learning career planning for the user. According to the method, a structured heterogeneous causal graph is constructed, a career element association rule is defined, interference is weakened in combination with a related mechanism, accurate reasoning is guaranteed by means of long-term data training, and traditional problems are solved; and a multivariate data acquisition channel is also established, and a double deployment mode and a dynamic increment updating algorithm are adopted, so that static limitation is broken through.

The invention discloses a student career planning management method and system based on big data, and relates to the technical field of education big data analysis, and the method comprises the steps: collecting multi-source data of students, constructing a weighted feature matrix, carrying out the dimension reduction processing through employing UMAP, dividing a gray scale image into three classes through employing a method for maximizing the inter-class variance among the three classes, defining the variance in a weighted cluster, and carrying out the calculation of the multi-source data. Carrying out iterative optimization by using an improved difference algorithm; constructing an adjacent matrix, distributing an initial community, optimizing community division by using an F2 modular function, deducing a dimension reduction tree by using a single cell trajectory to generate a pseudo time trajectory, and constructing a dynamic potential portrait; a weighted feature matrix is combined with a UMAP dimension reduction technology to process multi-source high-dimensional data, clustering of a three-class inter-class variance method and a DBSCAN clustering algorithm is maximized, a single cell trajectory inference technology dimension reduction tree is used to generate a pseudo time trajectory, a dynamic potential portrait of a student is constructed, and accurate evaluation of the system on the development potential of the student is enhanced.

1. The name of the design product: career planning sand table. 2. The use of the design product: sand table for career planning. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention discloses an intelligent AI-based occupational planning method and system, and particularly relates to the technical field of electric digital data processing. The method is used for solving the problem of insufficient planning suggestion accuracy caused by difficulty in distinguishing real causal correlation and false statistical correlation due to a large number of hybrid variables in student behavior data when occupational planning is carried out by using artificial intelligence at present. Historical behavior sequence data of a target college student and a corresponding historical occupational development result are obtained and processed to form a feature-result pair, cross-sequence consistency verification analysis is performed based on the feature-result pair, and causal verification information for constraining a false causal relationship is generated. The information is used as a constraint to construct a causal structure diagram, causal contribution dispersion of each student is calculated based on the causal structure diagram, clustering layering is performed on a student group according to the causal contribution dispersion, a causal effect of a characteristic variable and an occupational development result is estimated for each layered sub-group, and an occupational planning suggestion is generated based on the effect.

The application relates to the technical field of career planning, and particularly discloses a kind of individualized career development path dynamic planning systems, through the multi-modal time series data of daily learning behavior of user is collected and is organized as original behavior sequence;Through time series coding structure, skill mastery, cognitive load and learning motivation factor are separated, and decoupling state representation is output;Constructing positive and negative sample pairs for comparative prediction, obtaining comparative enhancement representation;Establishing action score query table, combining long-term sparse reward and intrinsic exploration reward training strategy function, outputting optimal learning action sequence;Periodically adjust the independence constraint of hidden variable by using the latest behavior data, re-plan the subsequent stage not executed, realize the dynamic update of career development path;The application can adaptively generate individualized growth path in line with the real learning state of user.

The invention provides a higher education occupational planning information pushing method and system based on a particle swarm big language model, and relates to the technical field of occupational planning. According to the method, multi-source heterogeneous information such as school status data, post requirements and industry development is fused, node representation vectors are constructed, and student portraits, occupational features and college resource vectors are generated based on improved BERT-DeepWalk. And dynamic optimization of a parameter space is realized by introducing a large language model of quantum particle swarm optimization. Finally, model output and node representation vector reasoning are combined, and a self-adaptive and personalized occupational planning recommendation strategy is formed. According to the invention, scientificity, precision and timeliness of professional planning information pushing are improved, and intelligent employment guidance requirements of college students are met.

The invention discloses a university student personalized career planning system based on big data analysis, and belongs to the technical field of education assistance and career planning. Comprising a data acquisition module, an occupational information database module, a data analysis module, a personalized occupational planning module and a user interaction interface, the data acquisition module is used for acquiring personal basic information, academic performance data, hobbies and interests data and campus activity data of college students; the occupational information database module is used for storing occupational classification and introduction information, updating industry dynamic data and establishing an occupational skill requirement database; the data analysis module is used for preprocessing the data acquired by the data acquisition module, analyzing the correlation between the preprocessed data and the data stored in the occupational information database module, and performing clustering analysis on the college student group; and the personalized occupational planning module recommends occupations to the college students through an occupational matching algorithm based on the analysis result of the data analysis module, and customizes a personalized occupational planning scheme.

The invention discloses an occupational planning method and terminal based on industry trend and integrating degree analysis, a complete technical framework of user feature modeling-occupational feature matching-industry value evaluation-comprehensive recommendation is constructed, and multi-dimensional fusion is performed on individual suitability of user characters and skills, industry current competitiveness and future trend. The limitation that traditional occupational planning singly depends on subjective experience or local features is broken through, standardization and interpretability of the recommendation process are achieved through structured vectors and quantitative scoring, inherent adaptability of recommendation results and users is guaranteed, market value and development potential of occupations are taken into consideration, and the recommendation method is suitable for popularization and application. And scientificity and practicability of occupational planning are obviously improved.

The invention relates to a method for communication between a human user and a digital AI-supported computer system, in which the system can generate suggestions, particularly regarding job offers and career paths, by recognizing data collected from a user. The system comprises video and audio sensors and communicates on the computer side via an AI-supported avatar.