An all-process employment precise helping system and method based on multi-modal data fusion

Abstract

The application relates to the technical field of the cross of artificial intelligence and human resource management, and discloses a full-process employment precise helping system and method based on multi-modal data fusion, wherein the full-process employment precise helping system based on multi-modal data fusion comprises a multi-modal information acquisition module, a multi-modal data fusion engine, a time-sequencing dynamic occupation portrait construction unit, a hidden demand deep mining engine, a human-post precise matching recommendation unit, an AI resume intelligent guidance unit, an AI simulated interview training unit, a job-seeking psychological counseling unit, a one-life-one-strategy account management unit and a whole-process escort closed-loop feedback unit; through the systematic architecture, full-process cooperation from information acquisition to job-seeking success is realized, closed-loop services are formed through data flow and strategy cooperation between the modules, and the technical defects that the functional modules are isolated from each other in the prior art are overcome.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of artificial intelligence and human resource management, specifically a full-process precise employment assistance system and method based on multimodal data fusion. Background Technology

[0002] With the rapid development of artificial intelligence technology, AI is being applied more and more widely in the field of employment assistance. Among existing technologies, AI-based resume analysis and generation, AI-simulated interviews, and job matching recommendations have already achieved initial technological accumulation. For example, some platforms can automatically parse resume content and generate optimization suggestions, some systems can conduct interview simulation training through virtual scenarios, and some recommendation engines can push job recommendations based on keyword matching.

[0003] However, existing technologies have the following shortcomings: 1) The functional modules are isolated from each other and lack a systematic end-to-end collaborative mechanism. In existing technologies, functions such as resume analysis, mock interviews, and job recommendations usually exist as independent application modules. There is a lack of data flow and strategy coordination between modules, which means that job seekers need to repeatedly input information at different stages. The support strategies cannot be dynamically adjusted according to the data throughout the entire process, making it difficult to form a closed-loop service from information collection to job search success.

[0004] 2) The construction of professional profiles is static and singular, lacking multimodal data fusion and time-series dynamic updates. Existing professional profiles are usually built based on static data such as resume text, which are single-modal, and fail to integrate multimodal data such as the job seeker's voice, image, and behavior. Moreover, once the profile is built, it lacks a dynamic update mechanism and cannot reflect the job seeker's ability growth, changes in interests, and psychological state evolution during the job search process.

[0005] 3) Recommendation strategies rely solely on matching explicit conditions, lacking in-depth exploration of implicit needs. Existing job recommendation technologies typically match only explicit conditions such as job seekers' clearly stated job intentions and skill tags, failing to leverage behavioral data analysis to uncover implicit needs such as job preferences, regional preferences, and career path preferences. This results in a discrepancy between the recommendation results and the job seekers' true expectations.

[0006] 4) The psychological support mechanism is lacking or disconnected from the employment process. The psychological counseling function in the existing technology usually exists as an independent auxiliary tool and has not been deeply integrated with the job search process. It cannot provide timely and accurate psychological support based on the changes in the psychological state of job seekers at key nodes such as resume submission, interview preparation, and waiting for results. Furthermore, it lacks a graded counseling strategy and an effect evaluation mechanism.

[0007] 5) The multimodal data fusion process lacks targeted processing of data characteristics. Existing multimodal fusion methods usually adopt a unified preprocessing and feature extraction process, which fails to fully consider the differences in distribution characteristics, sparsity, noise level and data quality of different modal data. As a result, the feature representation after fusion fails to fully retain the essential characteristics of each modal data, affecting the accuracy and reliability of subsequent occupational profile construction.

[0008] Based on this, a full-process precise employment assistance system and method based on multimodal data fusion is designed. Summary of the Invention

[0009] In order to overcome the shortcomings of the prior art, this invention provides a full-process precise employment assistance system and method based on multimodal data fusion, which effectively solves the problems mentioned in the background.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a full-process precise employment assistance system based on multimodal data fusion, the system comprising: The multimodal information acquisition module is used to collect job seekers' text data, voice data, image data, behavioral data, and structured data. The text data includes resume text, cover letter text, and social media text; the voice data includes interview voice and consultation voice; the image data includes ID document images and video frame images; the behavioral data includes operation behavior logs and browsing behavior trajectories; and the structured data includes educational background information, work experience information, and skills certificate information. A multimodal data fusion engine is communicatively connected to the multimodal information acquisition module and is used to perform data feature perception analysis, preprocessing, modality alignment, feature extraction, cross-modal fusion, and unified representation generation on the acquired multimodal data to obtain a multimodal fusion feature vector. The time-series dynamic career profile construction unit is connected to the multimodal data fusion engine and is used to construct an initial career profile of job seekers based on the multimodal fusion feature vector. Through time-series data access, time-series feature extraction, dynamic update mechanism, profile version management and profile evolution analysis, a time-series dynamic career profile containing explicit ability dimension, implicit trait dimension, psychological state dimension and development potential dimension is generated. The latent demand deep mining engine is communicatively connected to the time-series dynamic professional profile construction unit. It is used to perform behavior pattern recognition, potential preference inference, demand intensity quantification, and mapping of latent and explicit demands based on user behavior logs, and generate personalized recommendation strategies. The job matching recommendation unit is connected to the implicit demand deep mining engine. It is used to calculate the matching degree between job seekers and job resources based on the time-series dynamic professional profile and the personalized recommendation strategy, and generate a job resource precise push list through a multi-objective optimization algorithm. The AI ​​resume intelligent guidance unit is communicatively connected to the time-series dynamic career profile building unit, and is used to perform intelligent diagnosis, content optimization suggestions, keyword matching degree analysis, and personalized layout guidance on job seekers' resumes based on the time-series dynamic career profile. The AI-powered simulated interview training unit is connected to the AI-powered resume intelligent guidance unit. It is used to generate personalized interview questions based on the time-series dynamic career profile and the precise job resource push list, conduct simulated interviews through multimodal interaction, and evaluate the interview performance in multiple dimensions. The job-seeking psychological counseling unit is connected in communication with the time-series dynamic career profile construction unit. It is used to monitor the psychological state of job seekers in real time and execute graded psychological counseling strategies based on the anxiety level assessment results, including AI self-help counseling, AI and human collaborative counseling, and human expert intervention counseling. The "One Life, One Policy" ledger management unit communicates with the "Precise Matching and Recommendation Unit for Job Seekers," the "AI Resume Intelligent Guidance Unit," the "AI Simulated Interview Training Unit," and the "Job Seeking Psychological Counseling Unit." It is used to record the job seeker's full-process support data, generate a personalized support strategy ledger, and track the execution status of the support strategies. The full-process support closed-loop feedback unit communicates with the life-one-policy ledger management unit to collect data on the effectiveness of assistance, evaluate the effectiveness of assistance strategies, and feed the evaluation results back to the time-series dynamic occupational profile construction unit and the implicit needs deep mining engine, forming a closed-loop optimization mechanism.

[0011] Preferably, the multimodal data fusion engine includes: The data feature perception and analysis subunit is used to analyze the data features of each collected modality, identify the distribution characteristics, sparsity features, noise level and data quality level of each modality, and generate data feature description vectors. The data preprocessing subunit is used to clean, denoise, standardize the format, and fill in missing values ​​for the collected multimodal data. The modality alignment subunit is used to align data from different modalities on a unified time axis based on a timestamp alignment mechanism, and to establish cross-modal data associations. The feature extraction subunit is used to extract deep features from each modality of data. Text data is extracted with semantic features through a pre-trained language model, speech data is extracted with voiceprint and emotion features through an acoustic model, image data is extracted with visual features through a convolutional neural network, behavioral data is extracted with behavioral pattern features through a sequence model, and structured data is extracted with structured features through an embedding layer. The cross-modal fusion subunit is used to perform weighted fusion of the deep features of each modality based on an attention mechanism to generate a cross-modal joint representation; A unified representation generation subunit is used to map the cross-modal joint representation to a unified high-dimensional vector space to generate the multimodal fusion feature vector.

[0012] Preferably, the time-series dynamic professional profile construction unit includes: The initial profile construction subunit is used to generate an initial professional profile of a job seeker based on the multimodal fusion feature vector. The initial professional profile includes basic attribute information, educational background information, skill and ability information, and job intention information. The time-series data access subunit is used to access job seekers’ behavioral data, interaction data, and feedback data at different time points according to a preset time granularity. The temporal feature extraction subunit is used to extract temporal dependency features from the time series data through a long short-term memory network, and to identify the job seeker’s ability evolution trend, interest transfer pattern and psychological state change pattern. The dynamic update mechanism subunit is used to update the initial professional profile in real time based on the incremental learning algorithm, and the update cycle is adaptively adjusted according to the frequency of data changes. The portrait version management sub-unit is used to assign version numbers, timestamps, and change logs to each updated professional portrait, and supports historical version backtracking and comparative analysis. The profile evolution analysis subunit is used to generate a career development evolution report for job seekers based on the historical version data recorded by the profile version management subunit, using a trend analysis algorithm.

[0013] Preferably, the implicit demand deep mining engine includes: The behavior pattern recognition subunit is used to identify typical behavior patterns of job seekers based on the user behavior logs using a clustering algorithm, including browsing patterns, search patterns, interaction patterns, and abandonment patterns. The potential preference inference subunit is used to infer job seekers' potential job preferences, regional preferences, salary preferences, and career path preferences based on the typical behavioral patterns using a probabilistic graphical model. The demand intensity quantification subunit is used to calculate the demand intensity value of each potential preference based on the potential preferences and behavioral indicators such as behavior frequency, dwell time, and operation depth. The implicit and explicit needs mapping subunit is used to establish a mapping relationship between the potential preferences and the explicit needs clearly expressed by job seekers, and to identify the deviation between explicit and implicit needs; The personalized recommendation strategy generation subunit is used to generate a personalized recommendation strategy based on the demand intensity value and the mapping relationship, which includes job recommendation priority, recommendation timing, recommendation method and recommendation reason.

[0014] Preferably, the precise job matching recommendation unit includes: The job resource library management subunit is used to store, classify, index, and update job resources in real time. The job resources include job description information, job requirements information, company information, and salary and benefits information. The matching degree calculation subunit is used to calculate the explicit matching degree based on the explicit ability dimension in the time-series dynamic professional profile and the job requirement information of the job resource, calculate the implicit matching degree based on the implicit trait dimension and the job description information of the job resource, and calculate the development matching degree based on the development potential dimension and the development path information of the job resource. The multi-objective optimization subunit is used to solve for the Pareto optimal solution set by using the explicit matching degree, the implicit matching degree, the developmental matching degree, and the job seeker's psychological fit degree as optimization objectives; The recommendation list generation subunit is used to generate a precise push list of job resources based on the Pareto optimal solution set. The push list includes job information, matching score, recommendation reason and application suggestions.

[0015] Preferably, the job-seeking psychological counseling unit includes: The real-time psychological state monitoring subunit is used to assess the job seeker's psychological state in real time through a multimodal emotion fusion model based on the voice emotion features, facial expression features, text emotion features, and behavioral stress indicators collected by the multimodal information acquisition module. Anxiety level assessment subunit is used to classify job seekers’ anxiety levels into mild anxiety level, moderate anxiety level and severe anxiety level based on the psychological state assessment results and through a preset anxiety level classification standard. The AI ​​self-help subunit is used to provide cognitive behavioral therapy guidance, relaxation training guidance, and positive psychological suggestions through an intelligent dialogue robot when the anxiety level is the mild anxiety level. The AI ​​and human collaborative guidance subunit is used to provide initial guidance and generate a guidance summary by the intelligent dialogue robot when the anxiety level is the moderate anxiety level, while transferring the job seeker to a human psychological counselor for in-depth intervention. The artificial expert intervention and guidance subunit is used to directly trigger the artificial psychological guidance expert intervention mechanism when the anxiety level is the severe anxiety level, so that a qualified psychological counselor can provide one-on-one professional guidance. The counseling effect evaluation subunit is used to evaluate the counseling effect by comparing pre- and post-tests after each counseling session, and to feed the evaluation results back to the time-series dynamic occupational profile construction unit.

[0016] Preferably, the one-person-one-policy ledger management unit includes: The data recording sub-unit for job seekers' assistance is used to record the structured data generated by job seekers in each assistance stage, including profile building data, matching and recommendation data, resume guidance data, interview training data, psychological counseling data, and job application data. The personalized strategy generation subunit is used to generate personalized support strategies for individual job seekers based on the full-process data recorded by the support data recording subunit and through a decision tree algorithm. The personalized support strategy includes support objectives, support stage division, support measures at each stage, and expected performance indicators. The execution status tracking subunit is used to track the execution progress, completion status, and execution deviation of each assistance measure in the personalized assistance strategy in real time. The strategy adjustment subunit is used to automatically trigger the strategy adjustment process and regenerate the adjusted personalized assistance strategy when the execution deviation is detected to exceed a preset threshold.

[0017] Preferably, the full-process escort closed-loop feedback unit includes: The results data collection subunit is used to collect job seekers' job search results data, interview feedback data, onboarding adaptation data, and satisfaction evaluation data. The strategy effectiveness evaluation subunit is used to evaluate the causal effect value of each assistance strategy on job search effectiveness based on the data collected by the effectiveness data collection subunit and through a causal inference model. The profile feedback update subunit is used to feed back the evaluation results of the strategy effectiveness evaluation subunit to the time-series dynamic occupation profile construction unit, thereby driving the optimization and update of the time-series dynamic occupation profile. The strategy optimization feedback subunit is used to feed back the causal effect value to the implicit demand deep mining engine to optimize the generation rules of the personalized recommendation strategy.

[0018] A method for precise employment assistance across the entire process based on multimodal data fusion, the method comprising the following steps: S1: Multimodal information acquisition, which collects job seekers' text data, voice data, image data, behavioral data, and structured data through the multimodal information acquisition module; S2: Multimodal data preprocessing and fusion, the multimodal data fusion engine performs data feature perception analysis, preprocessing, modality alignment, feature extraction, cross-modal fusion and unified representation generation on the collected multimodal data to obtain multimodal fusion feature vectors; S3: Construct a time-series dynamic career profile. The time-series dynamic career profile construction unit constructs an initial career profile of the job seeker based on the multimodal fusion feature vector. Through time-series data access, time-series feature extraction, dynamic update mechanism, profile version management, and profile evolution analysis, a time-series dynamic career profile is generated, which includes explicit ability dimension, implicit trait dimension, psychological state dimension, and development potential dimension. S4: Deep mining of implicit needs. The implicit needs deep mining engine performs behavioral pattern recognition, potential preference inference, demand intensity quantification, and mapping of implicit and explicit needs based on user behavior logs to generate personalized recommendation strategies. S5: Precise matching and recommendation of job seekers. Based on the time-series dynamic professional profile and the personalized recommendation strategy, the precise matching and recommendation unit of job seekers calculates the matching degree between job seekers and job resources through a multi-objective optimization algorithm and generates a precise list of job resources to be pushed. S6: AI Resume Intelligent Guidance, which uses the AI ​​Resume Intelligent Guidance Unit to perform intelligent diagnosis, content optimization suggestions, keyword matching analysis, and personalized layout guidance on job seekers' resumes based on the time-series dynamic career profile; S7: AI simulated interview training, which generates personalized interview questions based on the time-series dynamic career profile and the precise job resource push list through the AI ​​simulated interview training unit, conducts simulated interviews through multimodal interaction, and evaluates the interview performance in multiple dimensions; S8: Job-seeking psychological counseling, which monitors the job seeker's psychological state in real time through the job-seeking psychological counseling unit and implements a graded psychological counseling strategy based on the anxiety level assessment results; S9: Personalized Support Plan Management: The Personalized Support Plan Management Unit records the full-process support data of job seekers, generates personalized support strategy ledgers, and tracks the implementation status of support strategies. S10: Full-process escort closed-loop feedback. The full-process escort closed-loop feedback unit collects data on the effectiveness of assistance, evaluates the effectiveness of assistance strategies, and feeds back the evaluation results to steps S3 and S4 to form a closed-loop optimization mechanism.

[0019] Preferably, step S2 specifically includes: S20: Perform data characteristic perception analysis on the collected modal data, identify the distribution characteristics, sparsity features, noise level and data quality level of each modal data, and generate data characteristic description vectors; S21: Clean, denoise, standardize the format, and fill in missing values ​​for the collected multimodal data; S22: Based on the timestamp alignment mechanism, data from different modalities are aligned on a unified time axis to establish cross-modal data association; S23: Extract deep features for each modality of data respectively. Text data is extracted using a pre-trained language model to extract semantic features, speech data is extracted using an acoustic model to extract voiceprint and emotion features, image data is extracted using a convolutional neural network to extract visual features, behavioral data is extracted using a sequence model to extract behavioral pattern features, and structured data is extracted using an embedding layer to extract structured features. S24: Based on the attention mechanism, the deep features of each modality are weighted and fused to generate a cross-modal joint representation; S25: Map the cross-modal joint representation to a unified high-dimensional vector space to generate the multimodal fusion feature vector.

[0020] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a systematic architecture that includes a multimodal information collection module, a multimodal data fusion engine, a time-series dynamic career profile construction unit, a latent demand deep mining engine, a precise job matching recommendation unit, an AI resume intelligent guidance unit, an AI mock interview training unit, a job-seeking psychological counseling unit, a life-one-policy ledger management unit, and a full-process escort closed-loop feedback unit. This achieves full-process collaboration from information collection to job-seeking success. The modules form a closed-loop service through data flow and strategy collaboration, overcoming the technical defects of isolated functional modules in the prior art. 2. This invention integrates job seekers' text, voice, images, behavior, and structured data through a multimodal data fusion engine, and realizes the time-series dynamic updating of the profile through a time-series dynamic career profile construction unit. This enables the career profile to reflect the job seeker's ability growth, interest changes, and psychological state evolution during the job search process, overcoming the technical defects of static and single career profile construction methods in the prior art. 3. This invention adds a data characteristic perception and analysis subunit to the multimodal data fusion engine. Before data preprocessing, it quantitatively analyzes the distribution characteristics, sparsity features, noise levels, and data quality levels of each modality of data, generating a data characteristic description vector. Based on this description vector, it guides the adaptive adjustment of subsequent preprocessing parameters, dynamic selection of feature extraction models, and optimization of cross-modal fusion weights. This enables the data fusion process to differentiate based on the inherent characteristics of different modalities, overcoming the technical shortcomings of existing multimodal fusion methods that use a uniform process and ignore the differences in data characteristics. 4. This invention uses a latent demand deep mining engine to identify behavioral patterns, infer potential preferences, quantify demand intensity, and map latent and explicit demands based on user behavior logs to generate personalized recommendation strategies. This enables job recommendations to not only be based on explicit condition matching but also to deeply mine the latent demands of job seekers, overcoming the technical deficiency of existing technologies that only rely on explicit condition matching for recommendation strategies. 5. This invention monitors the psychological state of job seekers in real time through a job-seeking psychological counseling unit, and implements a graded psychological counseling strategy based on the anxiety level assessment results, including AI self-help counseling, AI and human collaborative counseling, and human expert intervention counseling. This deeply integrates psychological support with the employment process and overcomes the technical defects of existing technologies, such as the lack of psychological support mechanisms or their disconnect from the employment process. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0022] In the attached diagram: Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention; Figure 2 This is a structural block diagram of the multimodal data fusion engine of the present invention; Figure 3 This is a structural block diagram of the time-series dynamic occupational profile construction unit of the present invention; Figure 4 This is a structural block diagram of the job-seeking psychological counseling unit of the present invention; Figure 5 This is a flowchart of the method of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Depend on Figures 1-5 The present invention relates to a full-process precise employment assistance system based on multimodal data fusion. The system includes: a multimodal information collection module, a multimodal data fusion engine, a time-series dynamic career profile construction unit, a latent demand deep mining engine, a person-job precise matching recommendation unit, an AI resume intelligent guidance unit, an AI simulated interview training unit, a job-seeking psychological counseling unit, a life-one-policy ledger management unit, and a full-process escort closed-loop feedback unit.

[0025] in: The multimodal information acquisition module is used to collect job seekers' text data, voice data, image data, behavioral data, and structured data. The text data includes resume text, cover letter text, and social media text. The voice data includes interview voice and consultation voice. The image data includes ID card images and video frame images. The behavioral data includes operation behavior logs and browsing behavior trajectories. The structured data includes educational background information, work experience information, and skills certificate information.

[0026] The multimodal data fusion engine is communicatively connected to the multimodal information acquisition module and is used to perform data feature perception analysis, preprocessing, modality alignment, feature extraction, cross-modal fusion, and unified representation generation on the acquired multimodal data to obtain a multimodal fusion feature vector.

[0027] The multimodal data fusion engine includes: a data feature perception and analysis subunit, a data preprocessing subunit, a modality alignment subunit, a feature extraction subunit, a cross-modal fusion subunit, and a unified representation generation subunit.

[0028] The data feature perception and analysis subunit is used to perform data feature analysis on the collected modal data, identify the distribution characteristics, sparsity features, noise levels, and data quality levels of each modal data, and generate data feature description vectors. Specifically, for text data, it identifies text sparsity and information density through word frequency statistics and text length distribution analysis; for speech data, it identifies noise levels and sound quality features through signal-to-noise ratio calculation and spectral distribution analysis; for image data, it identifies image quality levels through pixel value distribution statistics and sharpness assessment; for behavioral data, it identifies data sparsity and missing patterns through behavioral frequency distribution and sequence integrity analysis; for structured data, it identifies data integrity and distribution characteristics through field fill rate statistics and numerical distribution analysis. The feature analysis results of the above modal data are encoded into data feature description vectors, and the dimension of the data feature description vectors is the sum of the number of modal feature indicators.

[0029] The data preprocessing subunit is used to clean, denoise, standardize the format, and fill in missing values ​​for the collected multimodal data. Specifically, the data preprocessing subunit adaptively adjusts the preprocessing parameters based on the data characteristic description vector. When the sparsity index of text data exceeds a preset sparsity threshold, an intelligent filling strategy based on contextual semantic understanding is used to fill in missing content. When the noise level index of speech data exceeds a preset noise threshold, a speech enhancement algorithm based on deep learning is used for noise reduction. When the image data quality level is lower than a preset quality threshold, an image super-resolution reconstruction algorithm based on generative adversarial networks is used for image quality enhancement. When the missing pattern of behavioral data is random, a sequence interpolation method is used for filling; when the missing pattern is blocky, a generative model based on behavioral pattern prediction is used for filling. When the filling rate of structured data fields is lower than a preset filling threshold, a filling method based on association rule reasoning is used for missing value filling.

[0030] The modal alignment subunit is used to align data of different modalities on a unified time axis based on a timestamp alignment mechanism, and establish cross-modal data association. Specifically, based on the unified timestamp of the system, the data of each modality are arranged in the order of collection time. For multimodal data generated by the same event, they are associated and bound by event identifiers to generate cross-modal data pairs.

[0031] The feature extraction subunit is used to extract deep features from each modality of data. Specifically, text data is processed by a pre-trained language model to extract semantic features. This pre-trained language model employs a bidirectional encoder model based on a Transformer architecture, outputting a contextual semantic representation vector of the text. Speech data is processed by an acoustic model to extract voiceprint and emotion features. This acoustic model employs a voiceprint recognition model based on a time-delay neural network and a speech emotion recognition model based on a convolutional recurrent neural network, outputting a voiceprint embedding vector and an emotion category probability distribution, respectively. Image data is processed by a convolutional neural network to extract visual features. This convolutional neural network employs a residual network architecture, outputting a deep visual feature vector of the image. Behavioral data is processed by a sequence model to extract behavioral pattern features. This sequence model employs a bidirectional long short-term memory network, outputting a temporal pattern feature vector of the behavioral sequence. Structured data is processed by an embedding layer to extract structured features. This embedding layer employs a graph embedding algorithm based on an entity-relationship graph, outputting a low-dimensional dense vector representation of the structured data.

[0032] The cross-modal fusion subunit is used to perform weighted fusion of the deep features of each modality based on an attention mechanism to generate a cross-modal joint representation. Specifically, firstly, the attention weight matrix between each modality feature is calculated. The attention weight matrix is ​​obtained by scaling dot product attention mechanism. At the same time, a modality reliability coefficient based on the data feature description vector is introduced to adaptively correct the attention weight. The modality reliability coefficient is dynamically adjusted according to the data quality level of each modality. The higher the data quality level, the larger the modality reliability coefficient. The corrected attention weight is the product of the original attention weight and the modality reliability coefficient. Then, the attention weight matrix is ​​used to perform weighted summation of each modality feature to generate a fused cross-modal joint representation vector.

[0033] The unified representation generation subunit is used to map the cross-modal joint representation to a unified high-dimensional vector space to generate the multimodal fusion feature vector. Specifically, the cross-modal joint representation vector is mapped to a unified vector space of a preset dimension (768 dimensions) through a fully connected layer, and the mapped vector is subjected to L2 normalization to obtain the multimodal fusion feature vector.

[0034] The time-series dynamic career profile construction unit is communicatively connected to the multimodal data fusion engine. It is used to construct an initial career profile of job seekers based on the multimodal fusion feature vector, and generate a time-series dynamic career profile that includes explicit ability dimensions, implicit trait dimensions, psychological state dimensions, and development potential dimensions through time-series data access, time-series feature extraction, dynamic update mechanism, profile version management, and profile evolution analysis.

[0035] The time-series dynamic professional profile construction unit includes: an initial profile construction subunit, a time-series data access subunit, a time-series feature extraction subunit, a dynamic update mechanism subunit, a profile version management subunit, and a profile evolution analysis subunit.

[0036] The initial profile construction subunit is used to generate an initial professional profile of the job seeker based on the multimodal fusion feature vector. The initial professional profile includes basic attribute information, educational background information, skill and ability information, and job intention information. Specifically, the multimodal fusion feature vector is input into a preset classifier network, which outputs profile labels for each dimension. The classifier network adopts a multilayer perceptron structure, and each dimension includes professional field, skill level, years of work experience, expected salary, expected region, and expected job type.

[0037] The time-series data access subunit is used to access job seekers' behavioral data, interaction data, and feedback data at different time points according to a preset time granularity. The preset time granularity is daily, and the accessed data includes the day's resume submission records, job browsing records, mock interview participation records, psychological counseling interaction records, and system feedback records.

[0038] The time-series feature extraction subunit is used to extract time-series dependency features from the time-series data through a long short-term memory network. Specifically, the daily data vector arranged in chronological order is input into a recurrent neural network containing two layers of long short-term memory units with a hidden layer dimension of 256, and the time-series dependency features in the time-series data are output. The time-series dependency features include a capability evolution trend vector, an interest migration pattern vector, and a psychological state change pattern vector.

[0039] The dynamic update mechanism subunit is used to update the initial professional profile in real time based on an incremental learning algorithm. Specifically, when the difference between the newly accessed time series data and the current profile exceeds a preset threshold, the profile update process is triggered. The difference is obtained by calculating the cosine distance between the new data vector and the current profile vector. The preset threshold is 0.15. During the update, an incremental learning algorithm based on experience playback is used to integrate new knowledge while retaining historical knowledge. The update cycle is adaptively adjusted according to the data change frequency. When the data change frequency is higher than the preset frequency threshold, the update cycle is shortened to daily updates; otherwise, weekly updates are maintained.

[0040] The portrait version management subunit is used to assign version numbers, timestamps, and record change logs for each updated professional portrait. Specifically, a new portrait version is generated after each update. The version number adopts the format of a major version number plus a minor version number. When the portrait structure changes, the major version number increments. When the portrait content changes, the minor version number increments. At the same time, the change timestamp and change content summary are recorded, supporting historical version backtracking and comparative analysis.

[0041] The profile evolution analysis subunit is used to generate a career development evolution report for job seekers based on the historical version data recorded by the profile version management subunit, using a trend analysis algorithm. Specifically, a sliding window-based linear regression algorithm is used to analyze the historical change trends of profile tags in each dimension, calculate the trend slope and confidence interval, and generate a career development evolution report that includes ability growth trajectory, interest evolution path, psychological state fluctuation curve, and development potential prediction.

[0042] The implicit demand deep mining engine is communicatively connected to the time-series dynamic professional profile construction unit, and is used to perform behavior pattern recognition, potential preference inference, demand intensity quantification, and mapping of implicit and explicit demands based on user behavior logs to generate personalized recommendation strategies.

[0043] The implicit demand deep mining engine includes: a behavior pattern recognition subunit, a potential preference inference subunit, a demand intensity quantification subunit, an implicit and explicit demand mapping subunit, and a personalized recommendation strategy generation subunit.

[0044] The behavior pattern recognition subunit is used to identify typical behavior patterns of job seekers based on the user behavior logs using a clustering algorithm. Specifically, the behavior sequences in the user behavior logs are converted into fixed-length behavior feature vectors, which include behavior type encoding, behavior duration, behavior result, and behavior interval time. Then, a density peak-based clustering algorithm is used to cluster the behavior feature vectors to identify four typical behavior patterns: browsing pattern, search pattern, interaction pattern, and abandonment pattern.

[0045] The latent preference inference subunit is used to infer job seekers' potential job preferences, regional preferences, salary preferences, and career path preferences based on the typical behavioral patterns using a probabilistic graphical model. Specifically, a Bayesian network with a three-layer structure including behavioral pattern nodes, preference nodes, and observation nodes is constructed. The network parameters are trained based on historical behavioral data, and then the typical behavioral patterns of the current user are input. The posterior probability of each preference node is calculated using a belief propagation algorithm, and preferences with posterior probabilities exceeding a preset probability threshold are output as latent preferences.

[0046] The demand intensity quantification subunit is used to calculate the demand intensity value of each potential preference based on the potential preferences and behavioral indicators such as behavior frequency, dwell time, and operation depth. Specifically, the demand intensity value is calculated using the formula I=w1*f+w2*t+w3*d, where I is the demand intensity value, f is the normalized value of behavior frequency, t is the normalized value of dwell time, d is the normalized value of operation depth, and w1, w2, and w3 are preset weight coefficients with values ​​of 0.4, 0.3, and 0.3, respectively.

[0047] The implicit and explicit demand mapping subunit is used to establish a mapping relationship between the potential preferences and the explicit demands expressed by job seekers. Specifically, by calculating the cosine similarity between the potential preference vector and the explicit demand vector, the deviation between explicit and implicit demands is identified. When the cosine similarity is lower than a preset similarity threshold, it is determined that there is a demand deviation, and a deviation analysis report is generated.

[0048] The personalized recommendation strategy generation subunit is used to generate a personalized recommendation strategy based on the demand intensity value and the mapping relationship, which includes job recommendation priority, recommendation timing, recommendation method, and recommendation reason. Specifically, the job recommendation priority is determined by sorting the job in descending order of demand intensity value, the recommendation timing is determined by the job seeker's active time period, the recommendation method is determined by the job seeker's information receiving preferences, and the recommendation reason is generated based on the matching degree analysis results.

[0049] The precise job matching recommendation unit is communicatively connected to the implicit demand deep mining engine. It is used to calculate the matching degree between job seekers and job resources based on the time-series dynamic professional profile and the personalized recommendation strategy, and generate a precise job resource push list.

[0050] The precise matching and recommendation unit for people and jobs includes: a job resource database management subunit, a matching degree calculation subunit, a multi-objective optimization subunit, and a recommendation list generation subunit.

[0051] The job resource library management subunit is used for structured storage, categorized indexing, and real-time updates of job resources.

[0052] The job resources include job descriptions, job requirements, company information, and salary and benefits information. Specifically, a distributed search engine based on Elasticsearch is used to index and store the job resources, supporting full-text search, fuzzy matching, and multi-condition combination queries. The update frequency is daily, synchronizing with the latest job information released by the company.

[0053] The matching degree calculation subunit is used to calculate the explicit matching degree based on the explicit ability dimension in the time-series dynamic career profile and the job requirement information of the job resource, to calculate the implicit matching degree based on the implicit trait dimension and the job description information of the job resource, and to calculate the development matching degree based on the development potential dimension and the development path information of the job resource. Specifically, the explicit matching degree is obtained by calculating the cosine similarity between the job seeker's skill vector and the job skill requirement vector, the implicit matching degree is obtained by calculating the Euclidean distance between the job seeker's trait vector and the job culture fit vector, and the development matching degree is obtained by calculating the Pearson correlation coefficient between the job seeker's development potential vector and the job promotion path vector.

[0054] The multi-objective optimization subunit is used to solve for the Pareto optimal solution set by using the explicit matching degree, the implicit matching degree, the developmental matching degree, and the job seeker's psychological fit as optimization objectives. Specifically, a multi-objective optimization framework based on the NSGA-II algorithm is adopted, with a population size of 100, 200 iterations, a crossover probability of 0.9, and a mutation probability of 0.1 to obtain the Pareto optimal solution set.

[0055] The recommendation list generation subunit is used to generate the precise push list of job resources based on the Pareto optimal solution set; the push list includes job information, matching score, recommendation reason and application suggestions; specifically, the top N jobs with the highest comprehensive matching score are selected from the Pareto optimal solution set as the recommendation results, and the value of N is dynamically adjusted according to the job seeker's receiving preferences, with a default value of 10.

[0056] The AI ​​resume intelligent guidance unit is communicatively connected to the time-series dynamic career profile construction unit. It is used to perform intelligent diagnosis, content optimization suggestion generation, keyword matching degree analysis, and personalized layout guidance on job seekers' resumes based on the time-series dynamic career profile. Specifically, the AI ​​resume intelligent guidance unit first parses the resume document uploaded by the job seeker, extracts the content of each module in the resume, and then compares the extracted content with the information of each dimension in the time-series dynamic career profile to identify missing content, weak descriptions, and insufficient keyword matching. Based on the identification results, it generates content optimization suggestions, including supplementary suggestions, description optimization suggestions, and keyword replacement suggestions. Simultaneously, based on the job seeker's career profile characteristics, it generates a personalized layout scheme, including module sorting suggestions, key point highlighting suggestions, and visual style suggestions.

[0057] The AI-powered simulated interview training unit is communicatively connected to the AI-powered resume intelligent guidance unit. It generates personalized interview questions based on the time-series dynamic career profile and the precise job resource push list. Simulated interviews are conducted through multimodal interaction, and interview performance is evaluated from multiple dimensions. Specifically, the AI-powered simulated interview training unit first selects a target job from the precise job resource push list. Based on the job requirements and skill information in the time-series dynamic career profile, it generates personalized interview questions using a retrieval-enhanced generation technology-based interview question generation model. These interview questions cover three categories: professional skills questions, behavioral interview questions, and situational simulation questions. During the simulated interview, multimodal interaction is conducted through text, voice, and video. The candidate's answer text, voice features, and video behavioral features are collected in real time. After the interview, the performance is evaluated from five dimensions—content quality, expression ability, logical thinking, emotional control, and job suitability—based on the collected multimodal data, generating an interview evaluation report.

[0058] The job-seeking psychological counseling unit is communicatively connected to the time-series dynamic career profile construction unit, and is used to monitor the job seeker's psychological state in real time and execute a graded psychological counseling strategy based on the anxiety level assessment results.

[0059] The job-seeking psychological counseling unit includes: a real-time psychological state monitoring subunit, an anxiety level assessment subunit, an AI self-help counseling subunit, an AI and human collaborative counseling subunit, human expert intervention counseling subunit, and a counseling effect evaluation subunit.

[0060] The real-time psychological state monitoring subunit is used to assess the job seeker's psychological state in real time through a multimodal emotion fusion model, based on the voice emotion features, facial expression features, text emotion features, and behavioral stress indicators collected by the multimodal information acquisition module. Specifically, the emotional features of each modality are input into a multimodal emotion fusion model based on a multilayer perceptron. The multimodal emotion fusion model includes an input layer, a modality fusion layer, an emotion classification layer, and an output layer, outputting the job seeker's psychological state assessment results, which include three categories: positive, neutral, and negative states.

[0061] The anxiety level assessment subunit is used to classify job seekers' anxiety levels into mild, moderate, and severe levels based on the psychological state assessment results and a preset anxiety level classification standard. Specifically, when the psychological state is negative and the duration of the negative state exceeds a first preset time threshold, it is determined to be a mild anxiety level; when the duration of the negative state exceeds a second preset time threshold and is accompanied by abnormal behavioral indicators, it is determined to be a moderate anxiety level; when the duration of the negative state exceeds a third preset time threshold and is accompanied by abnormal physiological indicators, it is determined to be a severe anxiety level. The first preset time threshold is 24 hours, the second preset time threshold is 72 hours, and the third preset time threshold is 168 hours.

[0062] The AI ​​self-help subunit is used to provide cognitive behavioral therapy guidance, relaxation training instruction, and positive psychological cues through an intelligent chatbot when the anxiety level is at the mild anxiety level. Specifically, the intelligent chatbot interacts with job seekers based on a preset cognitive behavioral therapy dialogue process, guiding them to identify and adjust irrational cognitions, while providing audio guidance on breathing relaxation training and pushing positive psychological cues.

[0063] The AI ​​and human collaborative counseling subunit is used to provide initial counseling and generate a counseling summary when the anxiety level is at the moderate anxiety level. At the same time, the job seeker is transferred to a human psychological counselor for in-depth intervention. Specifically, the intelligent dialogue robot first conducts an initial counseling session of 30 minutes, records the key dialogue content during the counseling process and generates a counseling summary. Then, the job seeker and the counseling summary are transferred to a human psychological counselor, who conducts targeted in-depth intervention based on the counseling summary.

[0064] The artificial expert intervention and guidance subunit is used to directly trigger the artificial psychological guidance expert intervention mechanism when the anxiety level reaches the severe anxiety level, so that a qualified psychological counselor can provide one-on-one professional guidance. Specifically, the system immediately sends an intervention request to the qualified psychological counselor and pushes an expert appointment link to the job seeker. After the job seeker confirms the appointment, one-on-one professional psychological guidance is carried out through video call.

[0065] The counseling effect evaluation subunit is used to evaluate the counseling effect by comparing pre- and post-test results after each counseling session. Specifically, the psychological state evaluation results of job seekers are collected before and after the counseling session, and the improvement of psychological state before and after the counseling session is calculated. When the improvement exceeds the preset improvement threshold, the counseling is deemed effective; otherwise, it is marked as needing follow-up counseling. The evaluation results are then fed back to the time-series dynamic career profile construction unit to update the psychological state dimension information of job seekers.

[0066] The "One Life, One Policy" ledger management unit is connected to the "Precise Matching and Recommendation Unit for Jobs and Personnel", the "AI Resume Intelligent Guidance Unit", the "AI Simulated Interview Training Unit", and the "Job Seeking Psychological Counseling Unit" to record the job seeker's full-process support data, generate a personalized support strategy ledger, and track the execution status of the support strategies.

[0067] The "One Life, One Policy" ledger management unit includes: a support data recording subunit, a personalized strategy generation subunit, an execution status tracking subunit, and a strategy adjustment subunit.

[0068] The support data recording subunit is used to record the structured data generated by job seekers in each support stage, including profile building data, matching recommendation data, resume guidance data, interview training data, psychological counseling data, and job application data. Specifically, a structured storage method based on a relational database is adopted to create an independent support data table for each job seeker. The table contains data type fields, data content fields, timestamp fields, and data source fields.

[0069] The personalized strategy generation subunit is used to generate personalized support strategies for individual job seekers based on the full-process data recorded by the support data recording subunit. Specifically, the subunit uses the job seeker's profile features, historical support effects, and current job-seeking status as input features, and the combination of support measures as output labels. It trains a decision tree model based on historical support cases, with a model depth of 10 and a minimum sample size of 5 for leaf nodes. The trained decision tree model generates personalized support strategies for each job seeker. The personalized support strategies include support objectives, support stage divisions, support measures for each stage, and expected performance indicators.

[0070] The execution status tracking subunit is used to track the execution progress, completion status, and execution deviation of each assistance measure in the personalized assistance strategy in real time. Specifically, a planned start time, a planned completion time, an actual start time, and an actual completion time are set for each assistance measure, and the execution deviation value is obtained by calculating the difference between the actual progress and the planned progress.

[0071] The strategy adjustment subunit is used to automatically trigger the strategy adjustment process when the execution deviation is detected to exceed a preset threshold. Specifically, when the execution deviation value exceeds the preset threshold of 20%, the strategy adjustment process is triggered to re-collect the job seeker's current status data and regenerate the adjusted personalized assistance strategy through the decision tree model.

[0072] The full-process escort closed-loop feedback unit is communicatively connected to the life-one-policy ledger management unit. It is used to collect data on the effectiveness of assistance, evaluate the effectiveness of assistance strategies, and feed the evaluation results back to the time-series dynamic occupational profile construction unit and the implicit needs deep mining engine to form a closed-loop optimization mechanism.

[0073] The full-process support closed-loop feedback unit includes: an effectiveness data collection subunit, a strategy effectiveness evaluation subunit, a profile feedback update subunit, and a strategy optimization feedback subunit.

[0074] The effectiveness data collection subunit is used to collect job seekers' job search results data, interview feedback data, onboarding adaptation data, and satisfaction evaluation data. Specifically, job search results data includes the number of resumes submitted, the number of interview invitations, the number of offer letters, and the final onboarding status; interview feedback data includes interviewer evaluations, whether the interview was passed or not, and the reasons for failure; onboarding adaptation data includes an assessment of the adaptation situation three months after onboarding; and satisfaction evaluation data includes job seekers' satisfaction ratings with the support services.

[0075] The strategy effectiveness evaluation subunit is used to evaluate the causal effect value of each support strategy on job search effectiveness based on the data collected by the effectiveness data collection subunit and through a causal inference model. Specifically, a causal inference method based on propensity score matching is adopted, in which job seekers who receive a specific support strategy are taken as the treatment group and job seekers who do not receive the strategy but have similar profile characteristics are taken as the control group. The causal effect value is obtained by calculating the difference in job search effectiveness between the two groups.

[0076] The profile feedback update subunit is used to feed back the evaluation results of the strategy effectiveness evaluation subunit to the time-series dynamic occupation profile construction unit, driving the optimization and update of the time-series dynamic occupation profile; specifically, the causal effect value is used as the weight parameter of the profile optimization objective function. When the causal effect value is positive, the weight of the corresponding profile dimension in the matching calculation is increased, and when the causal effect value is negative, the weight of the corresponding profile dimension in the matching calculation is decreased.

[0077] The strategy optimization feedback subunit is used to feed back the causal effect value to the implicit demand deep mining engine to optimize the generation rules of the personalized recommendation strategy. Specifically, the weight coefficient in the demand intensity quantification subunit is adjusted according to the causal effect value. When the causal effect value of a certain type of preference is significantly positive, the weight coefficient corresponding to that type of preference is increased, and vice versa.

[0078] This invention also provides a method for precise employment assistance throughout the entire process based on multimodal data fusion, the method comprising the following steps: S1: Multimodal information acquisition, which collects job seekers' text data, voice data, image data, behavioral data, and structured data through the multimodal information acquisition module.

[0079] Specifically, job seekers upload their resumes, cover letters, and ID photos through the system's front-end interface. The system automatically collects job seekers' operation logs and browsing behavior patterns in the background. During mock interviews and psychological counseling sessions, it collects job seekers' voice data and video frame images. At the same time, it receives job seekers' manually filled-in educational background information, work experience information, and skills certificate information.

[0080] S2: Multimodal data preprocessing and fusion: The multimodal data fusion engine performs data feature perception analysis, preprocessing, modality alignment, feature extraction, cross-modal fusion, and unified representation generation on the collected multimodal data to obtain a multimodal fusion feature vector.

[0081] Specifically, step S2 includes: S20: Perform data characteristic perception analysis on the collected modal data, identify the distribution characteristics, sparsity features, noise level and data quality level of each modal data, and generate data characteristic description vectors.

[0082] Specifically, for text data, word frequency statistics and text length distribution analysis are used to identify text sparsity and information density; for speech data, signal-to-noise ratio calculation and spectral distribution analysis are used to identify noise level and sound quality characteristics; for image data, pixel value distribution statistics and sharpness assessment are used to identify image quality levels; for behavioral data, behavior frequency distribution and sequence integrity analysis are used to identify data sparsity and missing patterns; for structured data, field fill rate statistics and numerical distribution analysis are used to identify data integrity and distribution characteristics; and the characteristic analysis results of the above modalities are encoded into data characteristic description vectors.

[0083] S21: Clean, denoise, standardize the format, and fill in missing values ​​for the collected multimodal data.

[0084] Specifically, based on the data feature description vector generated in step S20, the preprocessing parameters are adaptively adjusted. When the sparsity index of text data exceeds the preset sparsity threshold, an intelligent filling strategy based on contextual semantic understanding is used to complete the missing content. When the noise level index of speech data exceeds the preset noise threshold, a speech enhancement algorithm based on deep learning is used for noise reduction. When the image data quality level is lower than the preset quality threshold, an image super-resolution reconstruction algorithm based on generative adversarial networks is used for image quality enhancement. When the missing pattern of behavioral data is random, a sequence interpolation method is used for filling. When the missing pattern is blocky, a generative model based on behavioral pattern prediction is used for filling. When the filling rate of structured data fields is lower than the preset filling threshold, a completion method based on association rule reasoning is used to fill the missing values.

[0085] S22: Based on the timestamp alignment mechanism, data from different modalities are aligned on a unified time axis to establish cross-modal data association.

[0086] S23: Extract deep features for each modality of data. For text data, extract semantic features through a pre-trained language model. For speech data, extract voiceprint and emotion features through an acoustic model. For image data, extract visual features through a convolutional neural network. For behavioral data, extract behavioral pattern features through a sequence model. For structured data, extract structured features through an embedding layer.

[0087] S24: Based on the attention mechanism, the deep features of each modality are weighted and fused to generate a cross-modal joint representation.

[0088] Specifically, when calculating the attention weight matrix, a modal reliability coefficient based on the data feature description vector generated in step S20 is introduced to adaptively correct the attention weight. The modal reliability coefficient is dynamically adjusted according to the data quality level of each modality. The higher the data quality level, the larger the modal reliability coefficient. The corrected attention weight is the product of the original attention weight and the modal reliability coefficient. Then, based on the corrected attention weight matrix, the modal features are weighted and summed to generate the fused cross-modal joint representation vector.

[0089] S25: Map the cross-modal joint representation to a unified high-dimensional vector space to generate the multimodal fusion feature vector.

[0090] S3: Construct a time-series dynamic career profile. The time-series dynamic career profile construction unit constructs an initial career profile of the job seeker based on the multimodal fusion feature vector. Through time-series data access, time-series feature extraction, dynamic update mechanism, profile version management, and profile evolution analysis, a time-series dynamic career profile is generated, which includes explicit ability dimension, implicit trait dimension, psychological state dimension, and development potential dimension.

[0091] Specifically, firstly, an initial career profile of the job seeker is generated based on the multimodal fusion feature vector generated in step S2. Then, behavioral data, interaction data, and feedback data of the job seeker at different time points are accessed at a daily granularity. Temporal dependent features are extracted through a long short-term memory network to identify the job seeker's ability evolution trend, interest migration pattern, and psychological state change pattern. The initial career profile is updated in real time based on an incremental learning algorithm. Each updated profile is marked with a version number and timestamp. Finally, a career development evolution report is generated based on historical version data.

[0092] S4: Deep mining of implicit needs. Based on user behavior logs, the implicit needs deep mining engine performs behavior pattern recognition, potential preference inference, demand intensity quantification, and mapping of implicit and explicit needs to generate personalized recommendation strategies.

[0093] Specifically, the algorithm first identifies typical behavioral patterns of job seekers through clustering, including browsing, searching, interaction, and abandonment patterns. Then, it infers job seekers' potential job preferences, regional preferences, salary preferences, and career path preferences based on Bayesian networks. It calculates the demand intensity values ​​of each potential preference based on behavioral indicators such as frequency of behavior, dwell time, and depth of operation, and establishes a mapping relationship between potential preferences and explicit needs. Finally, it generates a personalized recommendation strategy that includes job recommendation priority, recommendation timing, recommendation method, and recommendation reasons.

[0094] S5: Precise matching and recommendation of job seekers. Based on the time-series dynamic professional profile and the personalized recommendation strategy, the precise matching and recommendation unit calculates the matching degree between job seekers and job resources through a multi-objective optimization algorithm, and generates a precise job resource push list.

[0095] Specifically, firstly, explicit matching degree is calculated based on the explicit ability dimension in the time-series dynamic career profile and the job requirements information of the job resources; implicit matching degree is calculated based on the implicit trait dimension and the job description information; and development matching degree is calculated based on the development potential dimension and the development path information. Then, with explicit matching degree, implicit matching degree, development matching degree, and psychological fit degree as optimization objectives, the Pareto optimal solution set is solved through the NSGA-II multi-objective optimization algorithm. Finally, based on the Pareto optimal solution set, a precise job resource push list containing job information, matching degree score, recommendation reasons, and application suggestions is generated.

[0096] S6: AI Resume Intelligent Guidance. The AI ​​Resume Intelligent Guidance Unit performs intelligent diagnosis, generates content optimization suggestions, analyzes keyword matching, and provides personalized layout guidance for job seekers' resumes based on the time-series dynamic career profile.

[0097] Specifically, the system analyzes the resumes uploaded by job seekers, extracts the content of each module, compares it with a time-series dynamic professional profile, identifies missing content, weak descriptions, and insufficient keyword matching, and generates content optimization suggestions and personalized layout schemes.

[0098] S7: AI-powered mock interview training. The AI-powered mock interview training unit generates personalized interview questions based on the time-series dynamic career profile and the precise job resource push list. The mock interview is conducted through multimodal interaction, and the interview performance is evaluated from multiple dimensions.

[0099] Specifically, target positions are selected from the precise job resource push list. Personalized interview questions are generated based on the job requirements information of the target positions and the skills and abilities information in the time-series dynamic career profile. Simulated interviews are conducted through three modes of interaction: text, voice and video. The interview performance is evaluated from five dimensions: content quality, expression ability, logical thinking, emotional control and job matching, and an interview evaluation report is generated.

[0100] S8: Job-seeking psychological counseling, which monitors the job seeker's psychological state in real time through the job-seeking psychological counseling unit and implements a graded psychological counseling strategy based on the anxiety level assessment results.

[0101] Specifically, the system assesses job seekers' psychological state in real time based on a multimodal emotion fusion model. According to a preset anxiety level classification standard, the anxiety level is divided into three levels: mild, moderate, and severe. When the anxiety level is mild, an AI self-help guidance is provided through an intelligent dialogue robot. When the anxiety level is moderate, the intelligent dialogue robot provides initial guidance and then transfers the candidate to a human psychological counselor for in-depth intervention. When the anxiety level is severe, a human psychological counselor intervention mechanism is directly triggered. After each guidance session, the guidance effect is evaluated by comparing pre- and post-test results, and the evaluation results are fed back to the time-series dynamic career profile construction unit.

[0102] S9: Personalized Support Plan Management: The Personalized Support Plan Management Unit records the job seeker's full-process support data, generates a personalized support strategy ledger, and tracks the implementation status of the support strategy.

[0103] Specifically, it records structured data generated by job seekers in each stage of profile building, matching and recommendation, resume guidance, interview training, psychological counseling and job application. Based on the decision tree algorithm, it generates personalized support strategies that include support goals, support stage divisions, support measures at each stage and expected performance indicators. It tracks the execution progress, completion status and execution deviation of each support measure in real time. When the execution deviation exceeds the preset threshold, it automatically triggers the strategy adjustment process.

[0104] S10: Full-process escort closed-loop feedback. The full-process escort closed-loop feedback unit collects data on the effectiveness of assistance, evaluates the effectiveness of assistance strategies, and feeds back the evaluation results to steps S3 and S4 to form a closed-loop optimization mechanism.

[0105] Specifically, job seekers' job search results, interview feedback, onboarding adaptation, and satisfaction evaluation data are collected. The causal effect value of each support strategy on job search effectiveness is evaluated through a causal inference model based on propensity score matching. The causal effect value is fed back to step S3 to optimize the update strategy of the time-series dynamic career profile, and then fed back to step S4 to optimize the generation rules of the personalized recommendation strategy, forming a complete closed loop from information collection to strategy optimization.

[0106] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0107] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A full-process precise employment assistance system based on multimodal data fusion, characterized in that, The system includes: The multimodal information acquisition module is used to collect job seekers' text data, voice data, image data, behavioral data, and structured data. The text data includes resume text, cover letter text, and social media text; the voice data includes interview voice and consultation voice; the image data includes ID document images and video frame images; the behavioral data includes operation behavior logs and browsing behavior trajectories; and the structured data includes educational background information, work experience information, and skills certificate information. A multimodal data fusion engine is communicatively connected to the multimodal information acquisition module and is used to perform data feature perception analysis, preprocessing, modality alignment, feature extraction, cross-modal fusion, and unified representation generation on the acquired multimodal data to obtain a multimodal fusion feature vector. The time-series dynamic career profile construction unit is connected to the multimodal data fusion engine and is used to construct an initial career profile of job seekers based on the multimodal fusion feature vector. Through time-series data access, time-series feature extraction, dynamic update mechanism, profile version management and profile evolution analysis, a time-series dynamic career profile containing explicit ability dimension, implicit trait dimension, psychological state dimension and development potential dimension is generated. The latent demand deep mining engine is communicatively connected to the time-series dynamic professional profile construction unit. It is used to perform behavior pattern recognition, potential preference inference, demand intensity quantification, and mapping of latent and explicit demands based on user behavior logs, and generate personalized recommendation strategies. The job matching recommendation unit is connected to the implicit demand deep mining engine. It is used to calculate the matching degree between job seekers and job resources based on the time-series dynamic professional profile and the personalized recommendation strategy, and generate a job resource precise push list through a multi-objective optimization algorithm. The AI ​​resume intelligent guidance unit is communicatively connected to the time-series dynamic career profile building unit, and is used to perform intelligent diagnosis, content optimization suggestions, keyword matching degree analysis, and personalized layout guidance on job seekers' resumes based on the time-series dynamic career profile. The AI-powered simulated interview training unit is connected to the AI-powered resume intelligent guidance unit. It is used to generate personalized interview questions based on the time-series dynamic career profile and the precise job resource push list, conduct simulated interviews through multimodal interaction, and evaluate the interview performance in multiple dimensions. The job-seeking psychological counseling unit is connected in communication with the time-series dynamic career profile construction unit. It is used to monitor the psychological state of job seekers in real time and execute graded psychological counseling strategies based on the anxiety level assessment results, including AI self-help counseling, AI and human collaborative counseling, and human expert intervention counseling. The "One Life, One Policy" ledger management unit communicates with the "Precise Matching and Recommendation Unit for Job Seekers," the "AI Resume Intelligent Guidance Unit," the "AI Simulated Interview Training Unit," and the "Job Seeking Psychological Counseling Unit." It is used to record the job seeker's full-process support data, generate a personalized support strategy ledger, and track the execution status of the support strategies. The full-process support closed-loop feedback unit communicates with the life-one-policy ledger management unit to collect data on the effectiveness of assistance, evaluate the effectiveness of assistance strategies, and feed the evaluation results back to the time-series dynamic occupational profile construction unit and the implicit needs deep mining engine, forming a closed-loop optimization mechanism.

2. The full-process precise employment assistance system based on multimodal data fusion according to claim 1, characterized in that, The multimodal data fusion engine includes: The data feature perception and analysis subunit is used to analyze the data features of each collected modality, identify the distribution characteristics, sparsity features, noise level and data quality level of each modality, and generate data feature description vectors. The data preprocessing subunit is used to clean, denoise, standardize the format, and fill in missing values ​​for the collected multimodal data. The modality alignment subunit is used to align data from different modalities on a unified time axis based on a timestamp alignment mechanism, and to establish cross-modal data associations. The feature extraction subunit is used to extract deep features from each modality of data. Text data is extracted with semantic features through a pre-trained language model, speech data is extracted with voiceprint and emotion features through an acoustic model, image data is extracted with visual features through a convolutional neural network, behavioral data is extracted with behavioral pattern features through a sequence model, and structured data is extracted with structured features through an embedding layer. The cross-modal fusion subunit is used to perform weighted fusion of the deep features of each modality based on an attention mechanism to generate a cross-modal joint representation; A unified representation generation subunit is used to map the cross-modal joint representation to a unified high-dimensional vector space to generate the multimodal fusion feature vector.

3. The full-process precise employment assistance system based on multimodal data fusion according to claim 1, characterized in that, The time-series dynamic professional profile construction unit includes: The initial profile construction subunit is used to generate an initial professional profile of a job seeker based on the multimodal fusion feature vector. The initial professional profile includes basic attribute information, educational background information, skill and ability information, and job intention information. The time-series data access subunit is used to access job seekers’ behavioral data, interaction data, and feedback data at different time points according to a preset time granularity. The temporal feature extraction subunit is used to extract temporal dependency features from the time series data through a long short-term memory network, and to identify the job seeker’s ability evolution trend, interest transfer pattern and psychological state change pattern. The dynamic update mechanism subunit is used to update the initial professional profile in real time based on the incremental learning algorithm, and the update cycle is adaptively adjusted according to the frequency of data changes. The portrait version management sub-unit is used to assign version numbers, timestamps, and change logs to each updated professional portrait, and supports historical version backtracking and comparative analysis. The profile evolution analysis subunit is used to generate a career development evolution report for job seekers based on the historical version data recorded by the profile version management subunit, using a trend analysis algorithm.

4. The full-process precise employment assistance system based on multimodal data fusion according to claim 1, characterized in that, The implicit demand deep mining engine includes: The behavior pattern recognition subunit is used to identify typical behavior patterns of job seekers based on the user behavior logs using a clustering algorithm, including browsing patterns, search patterns, interaction patterns, and abandonment patterns. The potential preference inference subunit is used to infer job seekers' potential job preferences, regional preferences, salary preferences, and career path preferences based on the typical behavioral patterns using a probabilistic graphical model. The demand intensity quantification subunit is used to calculate the demand intensity value of each potential preference based on the potential preferences and behavioral indicators such as behavior frequency, dwell time, and operation depth. The implicit and explicit needs mapping subunit is used to establish a mapping relationship between the potential preferences and the explicit needs clearly expressed by job seekers, and to identify the deviation between explicit and implicit needs; The personalized recommendation strategy generation subunit is used to generate a personalized recommendation strategy based on the demand intensity value and the mapping relationship, which includes job recommendation priority, recommendation timing, recommendation method and recommendation reason.

5. The full-process precise employment assistance system based on multimodal data fusion according to claim 1, characterized in that, The precise job matching and recommendation unit includes: The job resource library management subunit is used to store, classify, index, and update job resources in real time. The job resources include job description information, job requirements information, company information, and salary and benefits information. The matching degree calculation subunit is used to calculate the explicit matching degree based on the explicit ability dimension in the time-series dynamic professional profile and the job requirement information of the job resource, calculate the implicit matching degree based on the implicit trait dimension and the job description information of the job resource, and calculate the development matching degree based on the development potential dimension and the development path information of the job resource. The multi-objective optimization subunit is used to solve for the Pareto optimal solution set by using the explicit matching degree, the implicit matching degree, the developmental matching degree, and the job seeker's psychological fit degree as optimization objectives; The recommendation list generation subunit is used to generate a precise push list of job resources based on the Pareto optimal solution set. The push list includes job information, matching score, recommendation reason and application suggestions.

6. The full-process precise employment assistance system based on multimodal data fusion according to claim 1, characterized in that, The job-seeking psychological counseling unit includes: The real-time psychological state monitoring subunit is used to assess the job seeker's psychological state in real time through a multimodal emotion fusion model based on the voice emotion features, facial expression features, text emotion features, and behavioral stress indicators collected by the multimodal information acquisition module. Anxiety level assessment subunit is used to classify job seekers’ anxiety levels into mild anxiety level, moderate anxiety level and severe anxiety level based on the psychological state assessment results and through a preset anxiety level classification standard. The AI ​​self-help subunit is used to provide cognitive behavioral therapy guidance, relaxation training guidance, and positive psychological suggestions through an intelligent dialogue robot when the anxiety level is the mild anxiety level. The AI ​​and human collaborative guidance subunit is used to provide initial guidance and generate a guidance summary by the intelligent dialogue robot when the anxiety level is the moderate anxiety level, while transferring the job seeker to a human psychological counselor for in-depth intervention. The artificial expert intervention and guidance subunit is used to directly trigger the artificial psychological guidance expert intervention mechanism when the anxiety level is the severe anxiety level, so that a qualified psychological counselor can provide one-on-one professional guidance. The counseling effect evaluation subunit is used to evaluate the counseling effect by comparing pre- and post-tests after each counseling session, and to feed the evaluation results back to the time-series dynamic occupational profile construction unit.

7. The full-process precise employment assistance system based on multimodal data fusion according to claim 1, characterized in that, The "One Life, One Policy" ledger management unit includes: The data recording sub-unit for job seekers' assistance is used to record the structured data generated by job seekers in each assistance stage, including profile building data, matching and recommendation data, resume guidance data, interview training data, psychological counseling data, and job application data. The personalized strategy generation subunit is used to generate personalized support strategies for individual job seekers based on the full-process data recorded by the support data recording subunit and through a decision tree algorithm. The personalized support strategy includes support objectives, support stage division, support measures at each stage, and expected performance indicators. The execution status tracking subunit is used to track the execution progress, completion status, and execution deviation of each assistance measure in the personalized assistance strategy in real time. The strategy adjustment subunit is used to automatically trigger the strategy adjustment process and regenerate the adjusted personalized assistance strategy when the execution deviation is detected to exceed a preset threshold.

8. The full-process precise employment assistance system based on multimodal data fusion according to claim 1, characterized in that, The full-process escort closed-loop feedback unit includes: The results data collection subunit is used to collect job seekers' job search results data, interview feedback data, onboarding adaptation data, and satisfaction evaluation data. The strategy effectiveness evaluation subunit is used to evaluate the causal effect value of each assistance strategy on job search effectiveness based on the data collected by the effectiveness data collection subunit and through a causal inference model. The profile feedback update subunit is used to feed back the evaluation results of the strategy effectiveness evaluation subunit to the time-series dynamic occupation profile construction unit, thereby driving the optimization and update of the time-series dynamic occupation profile. The strategy optimization feedback subunit is used to feed back the causal effect value to the implicit demand deep mining engine to optimize the generation rules of the personalized recommendation strategy.

9. A method for precise employment assistance throughout the entire process based on multimodal data fusion, characterized in that, The method includes the following steps: S1: Multimodal information acquisition, which collects job seekers' text data, voice data, image data, behavioral data, and structured data through the multimodal information acquisition module; S2: Multimodal data preprocessing and fusion, the multimodal data fusion engine performs data feature perception analysis, preprocessing, modality alignment, feature extraction, cross-modal fusion and unified representation generation on the collected multimodal data to obtain multimodal fusion feature vectors; S3: Construct a time-series dynamic career profile. The time-series dynamic career profile construction unit constructs an initial career profile of the job seeker based on the multimodal fusion feature vector. Through time-series data access, time-series feature extraction, dynamic update mechanism, profile version management, and profile evolution analysis, a time-series dynamic career profile is generated, which includes explicit ability dimension, implicit trait dimension, psychological state dimension, and development potential dimension. S4: Deep mining of implicit needs. The implicit needs deep mining engine performs behavioral pattern recognition, potential preference inference, demand intensity quantification, and mapping of implicit and explicit needs based on user behavior logs to generate personalized recommendation strategies. S5: Precise matching and recommendation of job seekers. Based on the time-series dynamic professional profile and the personalized recommendation strategy, the precise matching and recommendation unit of job seekers calculates the matching degree between job seekers and job resources through a multi-objective optimization algorithm and generates a precise list of job resources to be pushed. S6: AI Resume Intelligent Guidance, which uses the AI ​​Resume Intelligent Guidance Unit to perform intelligent diagnosis, content optimization suggestions, keyword matching analysis, and personalized layout guidance on job seekers' resumes based on the time-series dynamic career profile; S7: AI simulated interview training, which generates personalized interview questions based on the time-series dynamic career profile and the precise job resource push list through the AI ​​simulated interview training unit, conducts simulated interviews through multimodal interaction, and evaluates the interview performance in multiple dimensions; S8: Job-seeking psychological counseling steps, which involves real-time monitoring of the job seeker's psychological state through the job-seeking psychological counseling unit and implementing a graded psychological counseling strategy based on the anxiety level assessment results. S9: Personalized Support Plan Management: The Personalized Support Plan Management Unit records the full-process support data of job seekers, generates personalized support strategy ledgers, and tracks the implementation status of support strategies. S10: Full-process escort closed-loop feedback. The full-process escort closed-loop feedback unit collects data on the effectiveness of assistance, evaluates the effectiveness of assistance strategies, and feeds back the evaluation results to steps S3 and S4 to form a closed-loop optimization mechanism.

10. The method according to claim 9, characterized in that, Step S2 specifically includes: S20: Perform data characteristic perception analysis on the collected modal data, identify the distribution characteristics, sparsity features, noise level and data quality level of each modal data, and generate data characteristic description vectors; S21: Clean, denoise, standardize the format, and fill in missing values ​​for the collected multimodal data; S22: Based on the timestamp alignment mechanism, data from different modalities are aligned on a unified time axis to establish cross-modal data association; S23: Extract deep features for each modality of data respectively. Text data is extracted using a pre-trained language model to extract semantic features, speech data is extracted using an acoustic model to extract voiceprint and emotion features, image data is extracted using a convolutional neural network to extract visual features, behavioral data is extracted using a sequence model to extract behavioral pattern features, and structured data is extracted using an embedding layer to extract structured features. S24: Based on the attention mechanism, the deep features of each modality are weighted and fused to generate a cross-modal joint representation; S25: Map the cross-modal joint representation to a unified high-dimensional vector space to generate the multimodal fusion feature vector.

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