One-stop talent service method, device and equipment and storage medium

Abstract

The invention discloses a one-stop talent service method and device, equipment and a storage medium, and the method comprises the steps: obtaining a personal resume of a user, and extracting the basic information of the user from the personal resume; performing preliminary ability evaluation on the user to generate a user evaluation result; performing data fusion on the user basic information and the user evaluation result, and constructing a comprehensive information file including a user ability portrait, an occupational target and a development demand; performing structured processing on a plurality of preset talent policy texts to form a policy information base; consultation information input by a user is acquired, and a user intention policy type is identified through a semantic understanding algorithm; matching the comprehensive information file with a policy information base, screening in the policy information base according to the intentional policy type, and generating a target policy and an associated post list; and according to the post capability requirement corresponding to each associated post in the associated post list and the user evaluation result, generating a capability difference list and a promotion suggestion so as to complete personalized training of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence and human resource services, in particular to a one-stop talent service method, device, equipment and storage medium. BACKGROUND

[0002] In the traditional talent service field, users need to complete resume input, policy inquiry, post matching and training application processes through different platforms respectively, and there are problems such as service fragmentation, information islandization and low matching accuracy. The existing technology mainly has the following defects: Limited resume information extraction capability: traditional methods can only extract explicit information such as education background and skills, lack depth analysis of implicit information such as project technology stack and certificate validity period, resulting in incomplete user portrait; Low policy matching efficiency: relying on manual screening or simple keyword matching, unable to intelligently adapt to user ability portrait, career inclination and policy dynamic update, low policy utilization rate; Lack of personalized training service: training programs are mostly standardized, without accurate recommendation based on post ability gap and user preferences, making it difficult to guarantee training effectiveness; Service process is broken: each link from ability assessment to post delivery is independent, lacking closed-loop management, users need to manually complete cross-system operations, resulting in low experience efficiency.

[0003] With the complexity of talent policies and the refinement of job requirements, the existing technology cannot meet the user's demand for "accurate matching and one-stop service". There is an urgent need for a systematic solution integrating intelligent algorithms to realize full-process automation and personalization from resume processing to post delivery, and to improve the efficiency and quality of talent service.

[0004] Therefore, there is an urgent need for a method to solve at least one of the above problems. SUMMARY

[0005] The present application provides a one-stop talent service method, device, equipment and storage medium, aiming to solve the problem that with the complexity of talent policies and the refinement of job requirements, the existing technology cannot meet the user's demand for "accurate matching and one-stop service". There is an urgent need for a systematic solution integrating intelligent algorithms to realize full-process automation and personalization from resume processing to post delivery, and to improve the efficiency and quality of talent service.

[0006] In a first aspect, the present application provides a one-stop talent service method, comprising: obtaining a user's personal resume, extracting user basic information from the personal resume, the user basic information at least including education background, professional skills, work experience and project experience; The user is preliminarily assessed, and the user evaluation result is generated; the preliminary ability assessment at least includes occupation tendency analysis and skill proficiency evaluation; The user basic information is data fused with the user evaluation result, and a comprehensive information file containing user ability portrait, occupation target and development demand is constructed; The preset multiple talent policy texts are structured, the policy core elements are extracted, the core elements include policy applicable object, post demand, ability requirement and incentive measure, and a dynamic updated policy information library is formed; The user input consultation information is obtained, the user intention policy type is identified through semantic understanding algorithm, the consultation information includes user active question, historical browsing record and preference setting; the comprehensive information file is matched with the policy information library, the target policy and the associated post list are generated according to the intention policy type in the policy information library; According to the post ability requirement corresponding to each associated post in the associated post list and the user evaluation result, the ability gap list and the promotion suggestion are generated, and the individualized training of the user is completed according to the ability gap list and the promotion suggestion.

[0007] In some embodiments, the user basic information is data fused with the user evaluation result, and a comprehensive information file containing user ability portrait, occupation target and development demand is constructed, including: adopting a multi-dimensional vector space modeling method, the user basic information is mapped as an ability dimension vector, the evaluation result is mapped as an occupation tendency dimension vector, the weight coefficient of each dimension data is calculated through attention mechanism, and the weight coefficient is dynamically adjusted according to the post ability demand of the industry to which the user belongs; the user ability portrait is generated based on the fused vector space, and the user occupation target is deduced stage by stage, forming a dynamic development demand analysis report containing ability short board prediction.

[0008] In some embodiments, the multiple talent policy texts are structured, the policy core elements are extracted, including: based on the preset extraction rule, the statutory number, the effective time in the policy text are identified as metadata; the semantic role labeling is carried out on the policy clauses corresponding to the policy text, the regional restriction of policy applicable object, the professional subdivision direction of post demand, the grade division standard of ability requirement and the quantitative index of incentive measure are extracted; the policy core elements are generated according to the quantitative index and the metadata.

[0009] In some embodiments, the identifying the user intention policy type through the semantic understanding algorithm comprises: constructing a user consultation intention identification model, the model comprising a convolutional neural network branch for processing text consultation content, a recurrent neural network branch for analyzing historical browsing record time sequence features, and a rule matching module for analyzing preference settings; fusing the output results of the three branches through a multi-task learning mechanism, calculating the semantic weight of policy keywords in the user consultation information through an attention mechanism, and realizing fine-grained classification of policy types such as talent subsidy, employment support, and entrepreneurship support, and the classification accuracy is continuously optimized through a mistake set composed of historical consultation data.

[0010] In some embodiments, the matching the comprehensive information archive with the policy information library, screening in the policy information library according to the intention policy type, and generating a target policy and an associated post list comprises: adopting a multi-level matching strategy to screen in the policy information library according to the intention policy type, and generating a target policy and an associated post list; wherein the multi-level matching strategy comprises: first-level coarse screening: extracting a corresponding policy subset in the policy information library according to the intention policy type, the policy subset containing all policies associated with the type and associated posts; second-level accurate matching: constructing a similarity calculation of a post capability demand vector and a user capability portrait vector; third-level dynamic sorting: generating a post list combined with the similarity calculation result and obtaining feedback information corresponding to the post list from the user, and according to the feedback information, optimizing the weight information corresponding to the similarity calculation.

[0011] In some embodiments, the completing the individualized training of the user according to the capability gap list and the promotion suggestion comprises: according to the capability gap list, combining user preferences, intelligently recommending a training scheme from a preset training resource library, the training scheme at least including an online course, an offline course, and a customized tutorial, and tracking a training process to generate a progress report; when the progress report shows that the user's capability meets the requirements and meets the requirements of the post, automatically generating a resume and a job application letter for the post according to user authorization, and assisting the user in submitting a job application to the target post.

[0012] In some embodiments, after the matching the comprehensive information archive with the policy information library, the method further comprises: screening in the policy information library according to the intention policy type, generating a target policy and corresponding related field information; generating a qualification gap list and promotion suggestions according to the field qualification requirements corresponding to the related field information and the user evaluation results, so as to complete the individualized training of the user according to the qualification gap list and the promotion suggestions.

[0013] In a second aspect, the application also provides a one-stop talent service device, which comprises: The resume acquisition unit is configured to acquire a personal resume of a user, and extract user basic information from the personal resume, the user basic information including at least educational background, professional skill, work experience and project experience; The capability evaluation unit is configured to perform preliminary capability evaluation on the user, and generate a user evaluation result; the preliminary capability evaluation includes at least professional tendency analysis and skill proficiency evaluation; The data fusion unit is configured to perform data fusion on the user basic information and the user evaluation result, and construct a comprehensive information file including user capability portrait, career goal and development demand; The information generation unit is configured to perform structured processing on a plurality of preset talent policy texts, and extract policy core elements, the policy core elements including policy applicable object, post demand, capability requirement and incentive measure, and form a dynamically updated policy information library; The list generation unit is configured to acquire user inputted consultation information, identify user intended policy type through a semantic understanding algorithm, the consultation information including user active questions, historical browsing records and preference settings; match the comprehensive information file and the policy information library, filter in the policy information library according to the intended policy type, and generate a target policy and an associated post list; The training completion unit is configured to generate a capability gap list and an improvement suggestion according to the post capability requirement corresponding to each associated post in the associated post list and the user evaluation result, and complete personalized training on the user according to the capability gap list and the improvement suggestion.

[0014] In a third aspect, the present application further provides a computer device, which comprises a memory and a processor; the memory is configured to store a computer program; the processor is configured to execute the computer program and realize the one-stop talent service method as described above when executing the computer program.

[0015] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program; the computer program is executed by a processor to make the processor realize the one-stop talent service method as described above.

[0016] The one-stop talent service method realizes full-process intelligent service through the following core steps: multi-dimensional information collection and processing: extracting explicit and implicit basic information in the resume through natural language processing technology, combining a multi-dimensional evaluation model to generate evaluation results containing career inclination and skill proficiency, and constructing a dynamically updated user comprehensive information file; intelligent analysis of policy information: structuring the talent policy text, extracting core elements such as policy applicable objects and post requirements, and forming a dynamically updated policy information library; intention recognition and intelligent matching: identifying user policy intentions based on semantic understanding algorithms, and accurately matching user comprehensive information with the policy library through multi-level matching strategies to generate a target policy and associated post list; ability gap analysis and closed-loop service: comparing post ability requirements with user actual ability to generate a gap list, recommending personalized training programs, and automatically assisting post delivery after the ability meets the requirements, forming a closed-loop service of “evaluation-matching-training-employment”.

[0017] The provided method includes the following beneficial effects: 1. Deep information processing: through natural language processing models and multi-dimensional evaluation models, deep analysis and ability portrait construction of user information are realized, solving the problem of incomplete extraction of traditional resume information; 2. Accurate policy matching: based on semantic understanding and multi-level matching algorithms, combined with user intentions and dynamic policy library, accurate adaptation is realized, and the policy and post matching efficiency is improved by more than 30%; 3. Personalized training service: according to the ability gap and user preferences, intelligent training programs are recommended, supporting online and offline integrated training mode, and the training content matching degree is improved by 40%; 4. Closed-loop service process: full-process automation from information collection to post delivery, reducing user manual operation links, service efficiency is improved by 60%, forming a one-stop service system covering the whole cycle of career development.

[0018] In summary, through the above technical solutions, the present application breaks through the fragmentation bottleneck of traditional talent service, constructs a closed-loop service system based on intelligent algorithms, significantly improves the accuracy, efficiency and user experience of talent service, and has significant technical progress and practical application value.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a step schematic flowchart of a one-stop talent service method provided by an embodiment of the present application; Figure 2 is a schematic block diagram of a one-stop talent service device provided by an embodiment of the present application; Figure 3 is a structural schematic block diagram of a computer device provided by an embodiment of the present application.

[0022] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not intended to limit the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0024] The flowchart shown in the accompanying drawings is only illustrative, and is not necessarily required to include all the contents and operations / steps, nor is it necessarily required to be executed in the order described. For example, some operations / steps can be further divided, combined or partially combined, so that the actual execution order can be changed according to the actual situation.

[0025] It should be understood that the terms used in this specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, unless otherwise clear from the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0026] It should also be understood that the term "and / or" used in the specification and the appended claims of the present application means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0027] Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features in the embodiments can be combined with each other without conflict.

[0028] In the traditional talent service field, users need to complete resume input, policy inquiry, job matching and training application processes through different platforms respectively, and there are problems such as service fragmentation, information islandization and low matching accuracy. The existing technology mainly has the following defects: Limited resume information extraction capability: traditional methods can only extract explicit information such as education, skills, etc., lack deep analysis of implicit information such as project technology stack, certificate validity period, etc., resulting in incomplete user portrait; Low policy matching efficiency: relying on manual screening or simple keyword matching, unable to combine user ability portrait, career inclination and policy dynamic update for intelligent adaptation, low policy utilization rate; Lack of personalized training services: training programs are mostly standardized, without precise recommendation based on job ability gap and user preferences, making it difficult to guarantee training effectiveness; Service process is broken: each link from ability assessment to job delivery is independent, lacking closed-loop management, users need to manually complete cross-system operations, resulting in low efficiency.

[0029] With the complexity of talent policies and the refinement of job requirements, existing technologies cannot meet users' demand for "precise matching and one-stop service". There is an urgent need for a systematic solution integrating intelligent algorithms to realize full-process automation and personalization from resume processing to job delivery, improving the efficiency and quality of talent services.

[0030] Therefore, there is an urgent need for a method to solve at least one of the above problems.

[0031] To solve the above problems, please refer to Figure 1 , Figure 1 is a step schematic flowchart of the one-stop talent service method provided by an embodiment of the present application. The method can be implemented by a computer device, which can be deployed on a single server or a server cluster. It can also be deployed on a handheld terminal, a notebook computer, a wearable device, or a robot, etc.

[0032] At the same time, the collection and use of user information involved in the method provided by the present application are carried out with the authorization and permission of the relevant users, without infringing on the privacy of the users.

[0033] As shown in Figure 1 , the one-stop talent service method specifically includes steps S101 to S106: Step S101, obtaining a user's personal resume, extracting user basic information from the personal resume, the user basic information at least including education background, professional skills, work experience and project experience.

[0034] Specifically, by natural language processing (NLP) technology, the user's resume is analyzed to extract explicit information (education, skills, work experience) and implicit information (project technology stack, certificate validity period, task weight, etc.), and a multi-dimensional user basic information library is constructed. The core technologies include named entity recognition (NER), information extraction (IE), and text semantic analysis, which solve the problem of insufficient implicit information analysis in traditional methods. Resume format adaptation: support for multiple formats such as PDF, DOC, and pictures, and convert them into parseable text through OCR technology; Layered information extraction: explicit information layer: use rule engine + dictionary matching to extract education, skill keywords, and work timeline; implicit information layer: use deep learning models (such as BERT, GPT) to analyze project experience and extract technology stack (such as "developing distributed systems using Spring Boot + MySQL"), certificate validity period (such as "PMP certificate expires in 2024-12"), and project contribution (such as "leading the development of XX module, optimizing efficiency by 30%"); data verification: combined with industry knowledge base (such as skill standard library, job title mapping table) to calibrate information accuracy (such as unified "front-end development" and "Web development" as the same label).

[0035] The method breaks through the limitation of traditional resumes that only extract explicit information, and excavates implicit features such as technical depth and certificate timeliness in project experience, providing a richer data foundation for subsequent ability assessment and policy matching; converts unstructured resumes into structured data, facilitating subsequent intelligent matching with policy library and job library.

[0036] Step S102, performing a preliminary ability assessment on the user to generate a user assessment result; the preliminary ability assessment at least includes career aptitude analysis and skill proficiency evaluation.

[0037] Specifically, by combining the evaluation tool (psychological evaluation + skill assessment), the user's career aptitude and skill proficiency are quantified, and the internal ability dimensions not covered by the resume are supplemented. Career aptitude analysis is based on psychological theory (such as Holland's career interest test), and skill assessment combines job requirements to build an ability matrix, solving the problem of missing ability assessment in traditional services.

[0038] Career aptitude analysis: provide a lightweight online questionnaire (10-15 questions), through the user's selection of work scenarios and task preferences, use a classification model (such as random forest) to generate career aptitude labels (such as "technical research and development type", "management and coordination type", "innovation and creativity type"); combine the work content in the resume (such as "led cross-department collaboration multiple times") to automatically correct the questionnaire results and improve accuracy.

[0039] Skill proficiency assessment: Based on the skill tags extracted from the resume, match the industry standard skill level model (such as junior / middle / senior), and evaluate the proficiency through text similarity calculation (such as TF-IDF, cosine similarity) (such as "Java development" marked as senior, based on the use of project experience, complexity of project experience); Optional module: Open skill test question bank (such as programming questions, case analysis), supplemented by automatic scoring system to supplement quantitative data.

[0040] From "static resume information" to "dynamic ability tendency", a double-layer evaluation system of "explicit background + implicit characteristics" is constructed; key parameters such as occupation preference and ability short board are provided for policy adaptation and job recommendation, solving the one-sidedness problem of traditional matching relying on resume keywords.

[0041] Step S103, data fusion of the user basic information and the user evaluation results, constructing a comprehensive information file containing user ability portrait, career goal and development needs.

[0042] Specifically, the basic information extracted from the resume and the ability evaluation results are fused with multi-source data to construct a three-dimensional user model containing "ability portrait (skill matrix), career goal (tendency label), and development needs (ability short board)". Knowledge graph or label system is used to realize data association and solve the problem of information island.

[0043] Label system construction: Define three levels of labels: first-level label (education, skill category), second-level label (specific technology stack, career tendency type), and third-level label (certificate validity period, skill proficiency level); Example: User A's label is "education - master" "skill - Java (senior)" "career tendency - technology research and development type" "development needs - distributed architecture experience (lack)".

[0044] Data fusion algorithm: Use weighted fusion strategy to perform conflict verification (such as "3 years of Python experience" in the resume and "Python junior" in the evaluation, trigger manual review or supplementary test) on resume information (weight 60%) and evaluation results (40%); Generate user comprehensive file JSON structure, including basic information, ability matrix, and career goal description field.

[0045] Integrate scattered data to form a unified view, provide "single data source" for subsequent policy matching and training recommendation, and avoid cross-system information fragmentation; Demand accurate positioning: Through the development needs label, the user's ability gap is clear, and direct basis is provided for personalized services (such as training programs).

[0046] Step S104, the pre-set multi-talent policy text is structured and processed, and policy core elements are extracted, including policy applicable objects, post demands, capability requirements and incentive measures, to form a dynamically updated policy information library.

[0047] Specifically, talent policy texts (such as government documents and enterprise recruitment brochures) are analyzed, core elements are extracted, and a dynamically updated structured policy library is constructed. The core technology includes text segmentation, element extraction, and entity relationship modeling, solving the low efficiency problem of traditional policy matching relying on manual screening.

[0048] Policy text analysis process: preprocessing: obtaining policy original text through web crawler, using text cleaning tool to remove noise (such as format symbols, advertisements); element extraction: using domain-specific NER model to extract “applicable objects” (such as “master degree graduates”), “post demands” (such as “algorithm engineer”), “capability requirements” (such as “mastering deep learning framework”), “incentive measures” (such as “talent subsidy 50,000 yuan”) and other entities; relationship modeling: constructing policy element association graph (such as “applicable objects → post demands → capability requirements”), labeling policy effective time, regional range and other dynamic attributes.

[0049] Dynamic updating mechanism: set up a timed crawler task (daily / weekly) to grab the latest policies, identify new or changed elements through incremental learning model, and automatically synchronize to the policy library; support manual audit entry to ensure the accuracy of sensitive policy information.

[0050] Unstructured text is converted into structured data that can be processed by machines, providing a “policy portrait” basis for intelligent matching; through real-time updating mechanism to solve the policy lag problem, ensuring that users can obtain the latest subsidy, post demand and other information, and improving the utilization rate of policy.

[0051] Step S105, obtaining user input consultation information, identifying user intention policy type through semantic understanding algorithm, the consultation information including user active questioning, historical browsing records and preference settings; matching the comprehensive information file with the policy information library, screening in the policy information library according to the intention policy type, generating target policy and associated post list.

[0052] Specifically, the user's consultation intention is identified through semantic understanding technology, and multi-dimensional matching is performed combining the comprehensive file and the policy library to generate the target policy and the associated post list. The core technology includes intention classification and semantic matching algorithm (such as BM25, vector similarity), solving the extensive problem of traditional keyword matching.

[0053] User intention recognition: Multi-source input processing: Integrate user-initiated questions (such as "Which policies are suitable for Java development engineers?"), historical browsing records (such as multiple views of the "Hangzhou Talent Subsidy" page), and preference settings (such as regional and job type filtering); intent classification model: Use FastText or BERT model to identify consultation types (policy inquiry, job matching, training consultation), and extract key entities (such as region, skill, and education).

[0054] Intelligent matching algorithm: Policy preliminary screening: Filter the policy library according to the intention type (such as "subsidy policy") to narrow the matching range; precise matching: Calculate the similarity between user labels and policy elements (such as the cosine similarity between user skill labels and policy "ability requirements"), and combine with career inclination labels (such as the policy job preference "research and development type" and user inclination matching degree) for weighted sorting; result generation: Output the target policy list in descending order of matching degree, and generate a job list associated with the job requirements in the policy.

[0055] Upgrade from "keyword matching" to "semantic + portrait matching", combine user ability, career inclination, and policy dynamic requirements to solve the "policy inapplicability" problem of traditional methods; users do not need to query across platforms, the system automatically associates policies and jobs, reducing manual operation costs.

[0056] Step S106, according to the job ability requirements corresponding to each associated job in the associated job list and the user evaluation results, generate an ability gap list and improvement suggestions to complete personalized training for the user according to the ability gap list and improvement suggestions.

[0057] Specifically, compare job ability requirements with user evaluation results to identify ability gaps and recommend customized training programs. Use gap analysis models and recommendation algorithms to address the lack of targetedness in traditional training "standardized push".

[0058] Generate a "ability requirement vector" for each associated job (such as "Python (advanced), data analysis (intermediate), and communication skills (beginner)"), and compare it with the user's "ability vector" dimension by dimension to mark missing or insufficient skill points; Example: Job requirement "Distributed architecture experience (advanced)", user evaluation result is "missing", then generate gap item "Distributed architecture design ability".

[0059] Training recommendation strategy: Content matching: Filter courses in the training library according to gap skills (such as "Distributed System Practical Course"), combined with user preferences (such as "online courses" and "weekend time slots"); recommendation algorithm: Use collaborative filtering (combine similar user learning records) + content-based recommendation (course content similarity with gap skills) to generate an individualized list, including course links and learning path planning (such as "learn Java concurrency programming first, then learn distributed framework").

[0060] Based on the precise gap analysis of "job demand-user ability", avoid invalid course pushing, improve the training input-output ratio; service process closed loop: from "ability assessment → job matching → training improvement" to form a complete service link, solve the problem of traditional process fracture, users do not need to manually jump the system, the experience efficiency is significantly improved.

[0061] In some embodiments, the extracting user basic information from the personal resume comprises: performing unstructured text analysis on the personal resume through a natural language processing model, the natural language processing model being constructed based on a bidirectional long short-term memory network combined with a conditional random field and being pre-trained with a domain dictionary containing industry terms and skill keywords, realizing deep extraction of implicit information such as education certificate number, skill certificate validity period, project technology stack, and forming a structured basic information data frame.

[0062] By using a classic sequence labeling model of bidirectional long short-term memory network (Bi-LSTM) + conditional random field (CRF), combined with industry domain dictionary to enhance semantic understanding ability. Bi-LSTM is responsible for capturing the context semantic dependency of the text, and CRF is used to optimize the label sequence prediction to avoid illegal label combination (such as the reasonable order constraint of "certificate number" followed by "validity period").

[0063] The pre-trained domain dictionary includes: industry term library (such as "blockchain consensus algorithm" "digital signal processing"), skill keyword library (such as "TensorFlow" "PMP certification"), time format library (such as "YYYY-MM" "validity period to XXXX year XX month"); Constructing a "certificate number-certificate type" mapping table (such as "CN123456" corresponding to "information system project manager"), "technology stack-post category" association rules (such as "Spring Boot+MySQL" pointing to "backend development").

[0064] Unstructured text parsing process: preprocessing: convert resumes to plain text through PDF parsing tools (such as PyPDF2), OCR engines (such as Tesseract), and use regular expressions to remove noise (such as redundant punctuation, special symbols); word segmentation and labeling: combine domain dictionaries for word segmentation (such as cutting "3 years of Java development experience, holding PMP certificate (valid until 2024-12)" into "3 years" "Java" "development" "experience" "hold" "PMP" "certificate" "(" "valid period" "to" "2024-12" ")") and manually label training data (label tags include education certificate number "EDU_ID", skill certificate validity period "CERT_DATE", project technology stack "TECH_STACK", etc.); model inference: input the segmented text sequence, Bi-LSTM outputs the hidden state vector of each word, and CRF generates the final label sequence according to the domain dictionary constraints and label transition probability, extracting implicit information (such as extracting technology stack "Vue.js" "Element UI" from "participate in XX project, use Vue.js + Element UI to realize front-end development"); Structured output: map the extracted results to the preset data frame (DataFrame), fields include "education certificate number", "skill certificate name", "validity period", "project name", "technology stack list", "project responsibilities", etc., to form standardized basic information.

[0065] Traditional methods can only extract explicit information such as "education" and "skill keywords". This embodiment enhances the domain dictionary and uses a sequence labeling model to achieve deep analysis of implicit information such as "certificate validity period" (avoiding recommending expired certificate-related policies), "project technology stack" (precise matching of technical job requirements), and "certificate number" (which may be required for subsequent policy reporting). User portrait fields are expanded from 10+ to 30+, with a 200% increase in integrity. Data standardization front: directly output structured data frames, no secondary processing is required to access subsequent ability assessment and data fusion modules, reducing data conversion costs between systems and improving overall process efficiency.

[0066] In some embodiments, the data fusion of the user basic information and the user evaluation results to construct a comprehensive information file containing user ability portrait, career goals and development needs includes: using a multi-dimensional vector space modeling method to map the user basic information into an ability dimension vector and the evaluation results into a career inclination dimension vector, calculating the weight coefficients of each dimension data through an attention mechanism, and the weight coefficients are dynamically adjusted according to the job ability requirements of the industry to which the user belongs; generating a user ability portrait based on the fused vector space and performing a stage-by-stage deduction of the user's career goals to form a dynamic development needs analysis report containing ability short board prediction.

[0067] Multi-dimensional vector space modeling: Ability dimension vector construction: Map user's basic information (education, skills, project experience) into a 100-dimensional vector, each dimension corresponds to a subdivided skill (such as "Java", "distributed architecture", "data analysis"), and the value is the proficiency score (0-10 points, from resume analysis and subsequent evaluation); Career inclination dimension vector construction: Convert the test results (Holland code, career preference label) into a 50-dimensional vector, including "realistic", "research-oriented", "artistic" and other dimensions, and the value is the inclination probability (such as "research-oriented = 0.8"); Dynamic weight calculation: Introduce the industry job ability demand matrix through the attention mechanism (such as the Internet industry pays more attention to "programming ability" and "algorithm design", with a weight ratio of 30%; the financial industry pays more attention to "risk management" and "compliance knowledge", with a weight ratio of 25%), dynamically adjust the weight of each dimension according to the user's industry (identified from the "work unit" field in the resume) (formula: w i=softmax(QK T )×industry ability matrix, where Q and K are query and key vectors).

[0068] Ability profile and development needs generation: Gaussian Mixture Model (GMM) clustering: input the fused 150-dimensional vector into GMM to generate "technical expert type", "management potential type", "cross-domain composite type" and other ability profile labels, and output the probability distribution of each skill dimension (such as "technical expert type" in "algorithm" dimension probability ≥0.7); Time series prediction: based on user's work experience and historical job promotion path, use LSTM model to predict future 1-3 years career goal (such as "from junior developer → intermediate architect"), identify the gap between current ability and goal (such as "lack of microservice governance experience"), and generate a dynamic analysis report containing "short-term shortcomings" (skills to be mastered within 6 months) and "long-term needs" (development direction for more than 1 year).

[0069] Traditional data fusion uses fixed weights (such as 60% for resume and 40% for evaluation), and this embodiment dynamically adjusts the weights through the industry ability matrix (such as the "compliance knowledge" weight in the financial industry is increased to 30%), making the matching more in line with industry characteristics; not only presents the current ability gap, but also predicts future development needs (such as predicting that the target job will be "AI product manager" in 3 years, and marking "machine learning foundation" and "product design" for improvement skills in advance), helping users to develop long-term growth plan, the service value extends from "immediate matching" to "career development accompaniment".

[0070] In some embodiments, the pre-set multi-personnel policy text is structured and processed to extract policy core elements, including: identifying the statutory number and effective time in the policy text as metadata based on pre-set extraction rules; performing semantic role labeling on the policy provisions corresponding to the policy text to extract regional restrictions of policy application objects, professional subdivision directions of post demands, grading standards of ability requirements, and quantitative indicators of incentive measures; and generating the policy core elements according to the quantitative indicators and the metadata.

[0071] Metadata extraction (rule engine driven): Regular expressions are used to match the statutory number (such as "Human Resources and Social Security

[2023] No. 15") and effective time (such as "effective on May 1, 2023") and expiration time (such as "this policy is valid until December 31, 2025") in the policy text to generate policy metadata fields; a "regional name library" (containing provincial, municipal, and district names and abbreviations, such as "Hangzhou" mapping "Hangzhou") is constructed, and regular expressions are used to identify the policy application region (such as "Beijing-registered enterprises" mapping "Beijing").

[0072] Core element extraction (deep learning driven): A pre-trained cross-language model (such as XLM-Roberta) is used to perform semantic role labeling on the policy provisions to identify: application objects: extract "age" "education" "hukou" "work experience" and other restrictions (such as "graduated within 5 years of a master's degree"); post demand: analyze "professional subdivision direction" (such as "computer science and technology (artificial intelligence direction)") and "post level" (junior / middle / senior); ability requirement: label "skill level" (such as "English six-level ≥425 points" "PMP certification") and "experience requirement" (such as "more than 3 years of cross-border e-commerce operation experience"); incentive measures: extract quantitative indicators (such as "rental subsidy 1500 yuan per month for 3 years" "one-time entrepreneurship subsidy 100,000 yuan"); and structured conversion of unstructured provisions (such as converting "qualified talents can enjoy the convenience of settling down and preferential education for their children" to "incentive measure type = public service; specific content = settlement convenience, children's education").

[0073] Element verification and storage: Rule engine is used to verify data integrity (such as "effective time" cannot be empty), and knowledge base is used to map and calibrate regional and professional names (such as "computer application technology" is unified as "computer class"), and finally generate policy structured data containing metadata and core elements, and store in the dynamic policy information library.

[0074] The traditional method only extracts coarse-grained information such as "policy name" and "release time". The present embodiment realizes the expansion of policy element fields from 5 to 20+ to provide rich dimensions for accurate matching, such as "applicable object regional restriction to district level" (such as "Pudong New Area" instead of "Shanghai City"), "post demand professional subdivision to research direction" (such as "machine learning direction"), and "incentive measure quantification to specific amount and time limit". Combining rule engine processing of clear structured information (such as document number and time) and deep learning processing of semantic complex clauses (such as ambiguous policy description), efficiency and accuracy are taken into account, and multi-language policy text analysis is supported (relying on cross-language model) to adapt to international talent service scenarios.

[0075] In some embodiments, the user intention policy type is identified by a semantic understanding algorithm, including: constructing a user consultation intention recognition model, the model including a convolutional neural network branch for processing text consultation content, a recurrent neural network branch for analyzing historical browsing record time sequence features, and a rule matching module for analyzing preference settings; the output results of the three branches are fused through a multi-task learning mechanism, the semantic weight of the policy keywords in the user consultation information is calculated by combining the attention mechanism, and the fine-grained classification of policy types such as talent subsidy, employment support and entrepreneurship support is realized, and the classification accuracy is continuously optimized through the error set composed of historical consultation data.

[0076] Three-branch input processing: CNN branch: processing user-initiated question text, using one-dimensional convolution kernel to extract local features of keywords (such as "subsidy", "settle down", "entrepreneurship"), outputting text semantic vector; RNN branch: analyzing historical browsing record time sequence data (such as "2025-05-20 browsing 'Shenzhen talent subsidy policy', 2025-05-21 browsing 'Nanshan entrepreneurship support policy'"), capturing preference trends in time series through LSTM, outputting browsing mode vector; rule matching module: analyzing user preference settings (such as "region = Guangzhou", "post type = R&D", "policy type = subsidy type"), directly generating rule matching vector. Multi-task learning fusion: the output vectors of the three branches are spliced into a comprehensive feature vector, which is input into the full connection layer for intention classification, and the attention mechanism is introduced to calculate the weight of each branch (such as when the user first consults, the text question weight accounts for 60%; the browsing record weight of the old user is increased to 50%).

[0077] Classification optimization mechanism: build error set continuous learning: automatically collect classification error cases (such as misclassifying "entrepreneurship guarantee loan policy" as "employment support type"), manually label correct labels, update training data every week, and use focal loss (Focal Loss) to improve the training weight of difficult samples; Support dynamic expansion policy type tags (such as adding "digital economy talent special policy"), quickly adapt to new categories through few-shot learning, and avoid retraining the entire model.

[0078] Traditional keyword matching can only identify coarse-grained categories such as "subsidy" and "policy". The embodiment realizes fine-grained classification such as "talent subsidy class -> rent subsidy / housing subsidy" and "employment support class -> internship post / vocational skill training subsidy", and the classification accuracy is improved from 75% to 92% (based on historical data test). Combined with real-time questioning, historical behavior and preset preferences, it solves the problem of ambiguous user intent (such as the user asking "Is there a policy for programmers?", and by browsing the "Hangzhou" page, it is inferred as "Hangzhou programmer talent subsidy policy"), reduces invalid matching, and improves interaction experience.

[0079] In some embodiments, the matching of the comprehensive information file with the policy information library, filtering according to the intended policy type in the policy information library, generating a target policy and associated post list, comprises: adopting a multi-level matching strategy to filter according to the intended policy type in the policy information library, generating a target policy and associated post list; wherein the multi-level matching strategy comprises: first coarse screening: extracting a policy subset corresponding to the intended policy type from the policy information library, the policy subset containing all policies associated with the type and their associated posts; second precise matching: constructing a similarity calculation between the post ability demand vector and the user ability portrait vector; third dynamic sorting: combining the similarity calculation result to generate a post list and obtaining feedback information corresponding to the post list from the user, and according to the feedback information, the weight information corresponding to the similarity calculation is optimized.

[0080] First coarse screening: policy subset fast screening According to the user's intended policy type (such as "talent subsidy class" and "entrepreneurship support class"), extract all associated policies and corresponding posts from the policy information library, exclude irrelevant types (such as filtering "entrepreneurship policy" if the user is interested in "subsidy"), and reduce the matching range from 100,000+ policies in the entire library to 10,000+ in the subdivided field, improving the efficiency of subsequent matching.

[0081] Second precise matching: cosine similarity calculation with industry weight Post ability demand vector construction: Extract "skill requirements", "experience requirements", "education requirements", etc. from policy associated posts and convert them into vectors of the same dimension as the user's ability portrait (such as "Java (senior) = 10, distributed architecture (intermediate) = 7"). Industry-specific weight factor: According to the field of the post (extracted from policy text or job description, such as "Internet", "Finance", "Manufacturing"), introduce industry skill shortage weight table as weight information (such as Internet industry "algorithm design" weight 1.5, manufacturing industry "mechanical design" weight 1.8), the calculation formula is: Windustry is the industry weight vector, highlighting the impact of scarce skills on matching degree.

[0082] Three-level dynamic sorting: Reinforcement learning optimizes the definition of recommendation sequence state: the state includes user portrait vector, job matching degree, and historical application record (whether to deliver, whether to pass the screening); Action space: adjust the sorting of matching results; Reward function: set according to user feedback data (such as user clicks on the post +1 point, applies for the post +5 points, and does not feedback -0.5 points), use deep reinforcement learning algorithm (such as DQN) to dynamically optimize the sorting strategy, generate a comprehensive score including "policy matching degree (40%), job competitiveness (30%: such as job competition ratio), user adaptation degree (30%: such as regional preference)", and output the recommendation list in descending order of score.

[0083] First-level screening reduces the amount of calculation (time consumption from 10 seconds to 2 seconds), second-level matching solves the problem of "general skill matching ignoring field characteristics" (such as financial posts paying more attention to "compliance" rather than simply "programming ability"), and third-level sorting dynamically optimizes through user feedback, avoiding "high matching degree but low success rate posts" being displayed first. The actual user job application conversion rate is improved by 40%; For different user types (fresh graduates / senior engineers), dynamically adjust the sorting strategy (such as fresh graduates paying more attention to "training opportunities" weight, and senior engineers paying more attention to "salary incentive" weight), realize "thousand people thousand sequences" accurate recommendation, and also can optimize the weight vector value according to user selection results.

[0084] In some embodiments, the individualized training of the user according to the ability gap list and promotion suggestions includes: according to the ability gap list, combining user preferences, intelligently recommending training programs from a pre-set training resource library, the training programs at least including online courses, offline courses and customized tutoring, and tracking the training process to generate progress reports; When the progress report shows that the user's ability meets the requirements of the post, automatically generate a resume and a job application letter that adapt to the post according to user authorization, and assist the user in submitting a job application to the target post.

[0085] Training program intelligent recommendation: gap analysis: compare the post ability demand vector and the user ability portrait vector to generate a "ability gap list" (such as "missing: Python data analysis (intermediate); insufficient: communication skills (primary to intermediate)"); resource matching: select matching items from the training resource library (including 5000+ online courses, 200+ offline training, and 100+ customized tutoring): online courses: filter according to "skill gap + learning time preference (such as ≤10 hours) + learning method (video / text)", recommend popular courses for similar users; offline training: match by region (such as "Shanghai"), time (weekend class), and training theme (such as "AI practical camp"); customized tutoring: assign senior engineers for 1-on-1 tutoring based on difficulty of gap (such as "advanced architecture design") and develop an 8-week learning plan. Program generation: output personalized programs including course links, learning calendar, and assessment goals (such as "pass Python data analysis practical exam after completing the course").

[0086] Training process tracking and job application assistance: progress report: obtain user course completion and test scores through learning platform API and generate a progress report every two weeks (such as "Python data analysis mastery reaches 80%, expected to meet the standard within 2 weeks"); standard achievement triggering mechanism: when the user's skills meet the standard and meet the job requirements, automatically start the job application process: resume generation: based on the user's latest information (new training experience, skill improvement), call the resume template generator to highlight the key abilities that match the job (such as emphasizing "Python practical project" for data analysis positions); customize cover letter: use NLP generation tools to write a personalized cover letter based on the job JD (such as "acquire data analysis skills through XX training and highly match the job requirements"); automatic submission: after user authorization, submit applications to target positions in batches and send notifications to user email / APP.

[0087] The traditional standardized training qualification rate is only 60%, and the present embodiment makes the training content and target post highly relevant through the "post demand → ability gap → customized program" closed loop, and the actual measured skill standard achievement rate is improved to 85%; from "finding ability gap → improving ability → applying for post", there is no need for users to manually switch systems, solving the broken problem of "preparing resumes after training" in traditional services, reducing user operation steps from an average of 15 steps to 3 steps (confirming training → checking progress → authorizing submission), greatly improving service efficiency and experience continuity.

[0088] In some embodiments, after the matching of the comprehensive information profile and the policy information library, further comprising: screening according to the intended policy type in the policy information library, generating target policies and corresponding related field information; generating a qualification gap list and improvement suggestions according to the corresponding field qualification requirements and user evaluation results of the related field information, to complete personalized training for the user according to the qualification gap list and improvement suggestions.

[0089] By meeting the requirements of the corresponding talent policy system, such as certificate requirements and skill requirements, when the user has relevant interest in a certain industry, the user can be assisted to complete the corresponding system training or certification, which helps the user to quickly enter the related industry. The training method is not limited by the embodiments of the present application.

[0090] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a one-stop talent service device 200 provided by the embodiments of the present application. The one-stop talent service device 200 is used to execute the steps of the one-stop talent service method shown in each of the above embodiments. The one-stop talent service device 200 can be a single server or a server cluster, or the one-stop talent service device 200 can be a terminal, which can be a handheld terminal, a notebook computer, a wearable device, or a robot, etc.

[0091] As shown in Figure 2 , the one-stop talent service device 200 comprises: A resume acquisition unit 201 is configured to acquire a personal resume of a user, and extract user basic information from the personal resume, wherein the user basic information at least includes educational background, professional skill, work experience and project experience. A capability evaluation unit 202 is configured to perform a preliminary capability evaluation on the user, and generate a user evaluation result, wherein the preliminary capability evaluation at least includes a career inclination analysis and a skill proficiency assessment. A data fusion unit 203 is configured to perform data fusion on the user basic information and the user evaluation result, and construct a comprehensive information profile containing a user capability profile, a career goal and development needs. An information generation unit 204 is configured to perform structured processing on a plurality of preset talent policy texts, extract policy core elements, and form a dynamically updated policy information library, wherein the policy core elements include policy applicable objects, post requirements, capability requirements and incentive measures. A list generation unit 205 is configured to acquire consultation information input by a user, identify an intended policy type of the user by a semantic understanding algorithm, wherein the consultation information includes user active questions, historical browsing records and preference settings; match the comprehensive information profile with the policy information library, screen according to the intended policy type in the policy information library, and generate a target policy and an associated post list. The training completion unit 206 is configured to generate a capability gap list and improvement suggestions according to the post capability requirements corresponding to each associated post in the associated post list and the user evaluation results, and complete the personalized training of the user according to the capability gap list and the improvement suggestions.

[0092] It should be noted that, for the convenience and brevity of description, the specific working processes of the above-described device and modules can be referred to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0093] The above-described device can be implemented in the form of a computer program, which can run on a computer device such as the one shown in Figure 3 .

[0094] Please refer to Figure 3 , Figure 3 for a structural schematic block diagram of the computer device in an embodiment. The computer device can be a server.

[0095] Please refer to Figure 3 , the computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory can include a non-volatile storage medium and an internal memory.

[0096] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions which, when executed, can cause the processor to perform any one of the one-stop talent service methods.

[0097] The processor is configured to provide computing and control capabilities to support the operation of the entire computer device.

[0098] The internal memory provides an environment for the running of the computer program in the non-volatile storage medium, which, when executed by the processor, can cause the processor to perform any one of the one-stop talent service methods.

[0099] The network interface is configured to perform network communication, such as sending assigned tasks. Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0100] It should be understood that the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0101] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps: Obtaining a personal resume of a user, extracting user basic information from the personal resume, the user basic information at least including educational background, professional skill, work experience and project experience; Performing a preliminary ability assessment on the user to generate a user assessment result; the preliminary ability assessment at least including professional inclination analysis and skill proficiency evaluation; Fusing the user basic information and the user assessment result to construct a comprehensive information file containing user ability profile, career goal and development needs; Performing structural processing on a plurality of preset talent policy texts to extract policy core elements, the core elements including policy applicable objects, post requirements, ability requirements and incentive measures, and forming a dynamically updated policy information library; Obtaining user inputted consultation information, identifying a user intended policy type through a semantic understanding algorithm, the consultation information including user active questions, historical browsing records and preference settings; matching the comprehensive information file and the policy information library, screening in the policy information library according to the intended policy type to generate a target policy and an associated post list; Generating an ability gap list and improvement suggestions according to the post ability requirements corresponding to each associated post in the associated post list and the user assessment result, to complete personalized training on the user according to the ability gap list and the improvement suggestions.

[0102] The embodiment of the present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program includes program instructions, and the processor executes the program instructions to implement any one of the one-stop talent service methods provided by the embodiments of the present application.

[0103] The computer readable storage medium can be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The computer readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like.

[0104] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements shall be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A one-stop talent service method, characterized in that: include: Obtain the user's resume and extract the user's basic information from the resume, the user's basic information at least including educational background, professional skills, work experience and project experience; Conducting a preliminary competency assessment on the user and generating user assessment results; the preliminary competency assessment includes at least a career orientation analysis and skill proficiency assessment; Fusing the user's basic information with the user's assessment results to construct a comprehensive information profile containing the user's ability profile, career goals, and development needs; Structural processing is performed on multiple preset talent policy texts to extract the core elements of the policies, including policy applicable targets, job requirements, ability requirements, and incentive measures, to form a dynamically updated policy information database; Obtaining consultation information input by the user and identifying the user's intended policy type through a semantic understanding algorithm. The consultation information includes the user's proactive questions, historical browsing history, and preference settings; matching the comprehensive information archive with the policy information database, screening the policy information database based on the intended policy type, and generating a list of target policies and related positions; Based on the job competency requirements and user assessment results corresponding to each associated job in the associated job list, a competency gap list and improvement suggestions are generated to complete personalized training for the user based on the competency gap list and improvement suggestions.

2. The method according to claim 1, characterized in that The data fusion of the user basic information and the user assessment results to construct a comprehensive information file containing the user's ability profile, career goals and development needs includes: Using a multidimensional vector space modeling method, the user's basic information is mapped into a capability dimension vector, and the assessment results are mapped into a career orientation dimension vector. The weight coefficient of each dimension data is calculated through an attention mechanism. The weight coefficient is dynamically adjusted according to the job capability requirements of the user's industry. Based on the fused vector space, a user capability profile is generated, and the user's career goals are deduced in stages to form a dynamic development needs analysis report that includes predictions of capability shortcomings.

3. The method according to claim 1, characterized in that The aforementioned structural processing of multiple preset talent policy texts and extraction of policy core elements include: Identify the legal document number and effective date in the policy text as metadata based on preset extraction rules; Conduct semantic role annotation on the policy clauses corresponding to the policy text, extract the geographical restrictions on policy applicability, the professional subdivision of job requirements, the grading standards of competency requirements, and the quantitative indicators of incentive measures; The policy core elements are generated according to the quantitative indicators and metadata.

4. The method according to claim 1, wherein The identification of user intent policy types through semantic understanding algorithms includes: Building a user consultation intent recognition model, which includes a convolutional neural network branch for processing text consultation content, a recurrent neural network branch for analyzing the temporal characteristics of historical browsing records, and a rule matching module for parsing preference settings; The output results of the three branches are integrated through a multi-task learning mechanism, and the semantic weights of policy keywords in user consultation information are calculated in combination with the attention mechanism to achieve fine-grained classification of policy types such as talent subsidies, employment support, and entrepreneurship support. The classification accuracy is continuously optimized through a set of wrong questions composed of historical consultation data.

5. The method according to claim 1, wherein The matching of the comprehensive information file with the policy information database, screening the policy information database according to the intended policy type, and generating a list of target policies and related positions includes: A multi-level matching strategy is adopted to screen the policy information library according to the intended policy type, and generate a list of target policies and related positions; wherein, the multi-level matching strategy includes: first-level coarse screening: extracting the corresponding policy subset in the policy information library according to the intended policy type, and the policy subset contains all policies associated with this type and their related positions; second-level precise matching: constructing a similarity calculation between the job capability requirement vector and the user capability portrait vector; third-level dynamic sorting: generating a job list based on the results of the similarity calculation and obtaining the user's feedback information corresponding to the job list, and optimizing the weight information corresponding to the similarity calculation based on the feedback information.

6. The method according to claim 1, characterized in that After matching the comprehensive information archive with the policy information database, the method further includes: Screening the policy information database according to the intended policy type to generate target policies and corresponding related field information; Based on the field qualification requirements corresponding to the relevant field information and the user evaluation results, a qualification gap list and improvement suggestions are generated to complete personalized training for the user based on the qualification gap list and improvement suggestions.

7. The method according to claim 1, characterized in that The personalized training for the user is completed based on the capability gap list and improvement suggestions, including: Based on the competency gap list and user preferences, the system intelligently recommends training programs from a pre-set training resource library. The training programs include at least online courses, offline courses, and customized coaching. The system also tracks the training process and generates progress reports. When the progress report shows that the user's capabilities meet the standards and job requirements, a resume and cover letter suitable for the position will be automatically generated based on the user's authorization to assist the user in submitting an application for the target position.

8. A one-stop talent service device, characterized in that: The one-stop talent service device includes: A resume acquisition unit, configured to acquire a user's resume and extract basic user information from the resume, wherein the basic user information includes at least educational background, professional skills, work experience, and project experience; A capability assessment unit, configured to conduct a preliminary capability assessment on the user and generate a user assessment result; the preliminary capability assessment includes at least a career orientation analysis and a skill proficiency assessment; A data fusion unit is used to fuse the user's basic information with the user's assessment results to construct a comprehensive information file including the user's ability profile, career goals and development needs; An information generation unit is used to perform structural processing on multiple preset talent policy texts, extract the core elements of the policies, including policy applicable objects, job requirements, ability requirements and incentive measures, and form a dynamically updated policy information database; a list generation unit configured to obtain inquiry information input by a user and identify the user's intended policy type through a semantic understanding algorithm, wherein the inquiry information includes user-initiated questions, historical browsing history, and preference settings; match the comprehensive information archive with the policy information database, filter the policy information database based on the intended policy type, and generate a list of target policies and associated positions; The training completion unit is used to generate a capability gap list and improvement suggestions based on the job capability requirements corresponding to each associated position in the associated position list and the user assessment results, so as to complete personalized training for the user based on the capability gap list and improvement suggestions.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the one-stop talent service method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program. When the computer-readable instructions are executed by the processor, one or more processors are caused to execute the steps of the one-stop talent service method according to any one of claims 1 to 7.

Images