Multi-modal occupational portrait construction and intelligent post matching method based on simulation model and federal learning

Abstract

The invention relates to the technical field of occupational portrait construction, in particular to a multi-mode occupational portrait construction and intelligent post matching method based on a simulation model and federal learning. Comprising the following steps: collecting and preprocessing multi-modal occupational data; carrying out asymmetric federation simulation modeling on vocational ability; constructing a multi-mode occupational portrait; and intelligent post matching and feedback. According to the method, a vocational ability map and a transfer matrix are constructed based on national vocational skill standards, skill association strength is calculated through historical skill co-occurrence data, an enterprise skill gap acceptable threshold is quantified in combination with a dynamic tolerance function, and standardized support is provided for post matching. In federated training, a weighted federated average algorithm is combined with an entropy weight method to aggregate weights to update a map and a matrix, model parameters, a map structure and feature dimensions are optimized through a full-link feedback mechanism, dynamic iteration is realized, and post matching suitability and reliability are improved.

Description

Technical Field

[0001] This invention relates to the field of occupational profile construction technology, specifically to a method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning. Background Technology

[0002] In digital human resource management and intelligent recruitment, professional profiling and precise job matching are key to improving allocation efficiency. The explosive growth of multimodal data makes traditional single-dimensional matching insufficient to meet the demands for precision and dynamism; furthermore, personal and corporate data involve privacy and trade secrets, and centralized processing can easily lead to leakage risks. Therefore, integrating the advantages of multimodal data with privacy protection technologies to build dynamic and accurate professional profiles and achieve efficient matching has become a critical issue that the industry urgently needs to address.

[0003] In existing technologies, relevant patents have explored the fields of professional profile generation and job matching. For example, invention patent CN202411330402.8 discloses a method and system for generating recruitment talent profiles based on multi-dimensional information. It collects candidate resume information, cleans and transforms it, extracts behavioral patterns and points of interest through natural language processing, trains a model to generate a profile containing skills, career potential, and cultural fit, and then provides job matching recommendations and career advice. Another example is invention patent CN202411530537.9, which discloses an intelligent job matching and employee growth path planning system, method, and storage medium. The system collects information such as employee performance and educational background, constructs a comprehensive profile through clustering and factor analysis, uses recommendation algorithms to achieve job matching, and provides personalized career development paths and training suggestions in conjunction with industry trends.

[0004] Despite the design advantages of the aforementioned technical solutions, they also suffer from the following technical defects: First, data processing cannot balance privacy and security with dynamic adaptability. Both invention patents CN202411330402.8 and CN202411530537.9 adopt a centralized data collection and processing mode without setting up data privacy protection related technical designs. Personal occupational data and enterprise demand data are prone to privacy leakage or trade secret leakage during centralized storage and model training. At the same time, both CN202411330402.8 and CN202411530537.9 build profiles and matching models based on historical static data, only capturing the current ability status of individuals and fixed needs of enterprises. They do not consider the evolution of personal skills over time, nor do they adapt to the elastic changes in job requirements brought about by fluctuations in enterprise business, making it difficult for the profiles and matching results to dynamically fit the actual scenario. Secondly, the matching logic lacks skill association support and dynamic optimization mechanisms: Neither CN202411330402.8 nor CN202411530537.9 establishes a standardized occupational skill association system. They only construct profiles and perform matching calculations based on a single skill dimension or independent feature, failing to reflect the strength of association, hierarchical relationships, and conversion logic between different skills. Furthermore, they do not quantify the acceptable range of skill gaps for enterprises, resulting in limited matching accuracy. In addition, neither CN202411330402.8 nor CN202411530537.9 designs feedback optimization paths for matching results on model parameters and feature dimensions. Once the model and profile are constructed, they remain fixed and difficult to adjust and optimize based on actual matching results, resulting in insufficient adaptive capability. Therefore, we propose a multimodal occupational profile construction and intelligent job matching method based on simulation models and federated learning. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning, so as to solve the problems mentioned in the background art, such as the inability of data processing to take into account both privacy and security and dynamic adaptability, and the lack of skill association support and dynamic optimization mechanism in the matching logic.

[0006] To address the aforementioned technical problems, this invention provides a method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning, comprising the following steps: S1. Multimodal Occupational Data Acquisition and Preprocessing: Multimodal occupational data is acquired through multi-source heterogeneous data acquisition technology. Data cleaning, feature annotation, and normalization techniques are used to preprocess the raw data to generate a standardized data subset that includes individual data subsets and enterprise data subsets. The standardized data subset is output to S2 through a data interaction interface. At the same time, the graph iteration feedback signal from S2 is received to dynamically adjust the preprocessed feature dimensions. S2. Asymmetric Federated Simulation Modeling of Occupational Competencies: Receives the standardized data subset output from S1. Based on the dynamic characteristics of occupational competencies, it builds an asymmetric federated simulation framework, initializes the individual terminal-side competency evolution simulation model, the enterprise terminal-side job requirement simulation model, and corresponding modeling logic; through the individual terminal-side competency evolution simulation model and the enterprise terminal-side job requirement simulation model, it performs simulation processing on the individual terminal-side and enterprise terminal-side data subsets, respectively, generating competency vectors representing the dynamic changes in skill status, and representations including job skill requirement vectors and dynamic tolerance functions. The dynamic tolerance function quantifies the acceptable threshold for skill gaps and the range of business flexibility adjustments for enterprises; it maintains industry-based... The professional competence graph and transition matrix constructed by the standard skills framework are aggregated with the incremental correlation parameters and graph correction signals output by the individual simulation terminal and the enterprise simulation terminal during the federated training iteration process. The professional competence graph and transition matrix are updated and the updated graph parameters are distributed to the individual simulation terminal and the enterprise simulation terminal. The individual simulation terminal and the enterprise simulation terminal calibrate the logic of the competence evolution simulation model and optimize the logic of the job requirement simulation model according to the updated graph. The competence vector, job skill requirement vector and the updated professional competence graph are output to S3. At the same time, the matching result feedback signal from S4 is received to adjust the dynamic parameters and federated aggregation rules of the individual terminal-side competence evolution simulation model and the enterprise terminal-side job requirement simulation model. S3, Multimodal Career Profile Construction: Receives the ability vector, job skill requirement vector and updated career ability map output from S2, constructs an individual multimodal career profile and an enterprise job requirement profile, and outputs the individual multimodal career profile and the enterprise job requirement profile to S4. S4. Intelligent Job Matching and Feedback: Receives the individual multimodal career profile and enterprise job requirement profile output by S3, calculates the matching degree and outputs the matching result, and feeds back the matching result as a matching result feedback signal to S2.

[0007] As a further improvement to this technical solution, the multimodal occupational data collection and preprocessing in S1 includes the following steps: S11. Collect multimodal occupational data from individuals and enterprises, and perform format conversion and unified storage on the collected raw data; S12. Clean the stored raw data, use the 3σ principle to remove outliers in the personal skill status data, use the box plot method to filter extreme values ​​in the enterprise job requirement data, use the K-nearest neighbor imputation method to fill missing data, and remove duplicate data based on timestamp priority. S13. Based on a pre-set industry standard skill dictionary, the cleaned data is automatically labeled with features. The labeling dimensions for personal data include skill type, proficiency level, and application scenario. The labeling dimensions for enterprise data include core job skills, skill requirement priority, and business relevance. Labeling errors are corrected through manual sampling and verification. S14. The labeled data are processed using a differential normalization algorithm. Personal data is mapped to the [0,1] interval using Min-Max normalization, and enterprise data is converted to standard normal distribution data using Z-Score normalization. S15. Divide the normalized data into blocks according to data ownership and time dimension to generate a standardized data subset including personal data subset and enterprise data subset. The standardized data subset is stored in Parquet format and is accompanied by data collection timestamp and preprocessing process traceability identifier. S16. Output the standardized data subset to S2 through the encrypted RESTful API interface. The data transmission is encrypted using SSL and TLS protocols and is verified for integrity. S17. Receive the graph iteration feedback signal from S2, extract the graph node correlation parameters, and when the parameter change exceeds the preset threshold, retain the features with correlation higher than the set value with the professional ability graph node, and remove redundant features to adjust the preprocessed feature dimension.

[0008] As a further improvement to this technical solution, the process of asymmetric federated simulation modeling of professional competence in S2 includes the following steps: S21. Receive the standardized data subset output by S1, complete integrity and format verification, build an asymmetric federated simulation framework based on the dynamic characteristics of occupational abilities, initialize the individual terminal-side ability evolution simulation model, the enterprise terminal-side job requirement simulation model and corresponding modeling logic, and determine the core parameter as the skill evolution rate threshold. Demand fluctuation coefficient ; S22. Using the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model, respectively, simulation processing is performed on the corresponding terminal-side personal data subsets and enterprise data subsets to generate capability vectors representing the dynamic changes in skill status. and includes job skill requirement vectors and dynamic tolerance functions. The representation; S23. Maintain a professional competency graph and transition matrix based on an industry-standard skills framework. Through skill association strength Define the graph rules and initialize the matrix state transition logic based on the probability distribution to provide basic support for the simulation model of personal terminal-side capability evolution and the simulation model of enterprise terminal-side job requirements. S24. In the federated training iteration, a weighted aggregation algorithm is used to process the parameter increments of the simulation model for the evolution of individual terminal capabilities. Incremental parameters of enterprise terminal-side job requirement simulation model Update the professional ability graph and transition matrix with the graph correction signal. Simulation models for the evolution of capabilities of post-encrypted distribution to personal terminals and simulation models for job requirements on enterprise terminals; S25. Based on the updated graph, calibrate the simulation model logic for the evolution of personal terminal-side capabilities and optimize the simulation model logic for enterprise terminal-side job requirements. Output core data to S3, receive the matching result feedback signal from S4, and analyze the fluctuation amplitude. Adjust the parameters of the simulation model for the evolution of individual terminal capabilities and the simulation model for the job requirements of enterprise terminal positions to form an adaptive closed loop.

[0009] As a further improvement to this technical solution, the process of receiving a standardized data subset and completing verification, building an asymmetric federated simulation framework, and initializing the model in S2.1 includes the following steps: S21.1. Verify the integrity of the standardized data subset using the SHA-256 hash algorithm, and perform format verification on the data subset based on the JSONSchema specification. If the verification fails, return to S1 for reprocessing. S21.2 After establishing the asymmetric federated simulation framework, clarify the attribution of core parameters: skill evolution rate threshold. Corresponding to the simulation model of the evolution of personal terminal capabilities, the demand fluctuation coefficient The corresponding enterprise terminal-side job demand simulation model has parameter values ​​that are associated with historical occupational skill evolution data and enterprise demand change data. S21.3. Fit the two data sources separately using a time series fitting algorithm, combined with time decay weights. Sure and The values ​​are used to synchronously initialize the basic structural parameters of the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model. The algorithm is executed automatically throughout the process, avoiding parameter deviation problems caused by a single data source, and providing accurate support for subsequent model simulations.

[0010] As a further improvement to this technical solution, the process of processing data through corresponding model simulation and generating capability vectors and demand representations in S2.2 includes the following steps: S22.1. The personal terminal side calls the personal terminal side capability evolution simulation model to perform time series modeling on the skill status change data of the personal data subset and extract the core features of skill proficiency and evolution trend. S22.2, Generate capability vector based on extracted features Vector data is synchronously stored in the personal terminal-side capability evolution simulation model; S22.3, The enterprise terminal side calls the enterprise terminal side job demand simulation model to fit the enterprise's historical skills gap matching data and business fluctuation data. Construct a dynamic tolerance function ; S22.4, Through dynamic tolerance function Quantify the acceptable threshold for skills gaps and the range of business flexibility adjustments, and generate a vector containing job skill requirements. The representation of.

[0011] As a further improvement to this technical solution, the process of maintaining the professional competence map and transition matrix and adapting it to the corresponding model in S2.3 includes the following steps: S23.1. Adopt the national vocational skills standards as the industry standard skills framework, and define the nodes of the vocational ability map as skills ID, level, and application scenario; S23.2 Analyze historical skill co-occurrence data and calculate the correlation strength between skills using a similarity algorithm. ,based on Define the edge attributes of the graph with the skill evolution path definition; S23.3 Statistically analyze historical skill conversion data and initialize the transition matrix based on the data probability distribution. State transition logic; S23.4, Complete the transition matrix After initialization, the basic data of the graph and matrix are synchronized to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model.

[0012] As a further improvement to this technical solution, the process of aggregating model parameter increments and correction signals and updating the distribution map matrix in S2.4 includes the following steps: S24.1. Invoke the weighted federated average algorithm to increment the parameters of the simulation model for the evolution of personal terminal capabilities. Incremental parameters of enterprise terminal-side job requirement simulation model And perform aggregation processing on the spectral correction signal; S24.2 Calculate the data volume weight and model contribution weight of the personal terminal-side capability evolution simulation model, and the data volume weight and model contribution weight of the enterprise terminal-side job requirement simulation model using the entropy weight method; then, weight the data volume weight and model contribution weight of the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model to obtain the final aggregate weight. , ; S24.3, Based on Aggregate Weights , and correction factor Update the correlation strength of the professional ability graph With transition matrix ; S24.4 The updated map and matrix parameters are encrypted using SSL and TLS protocols. After passing hash verification, they are distributed to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model, respectively.

[0013] As a further improvement to this technical solution, the process of calibrating and optimizing the model logic and receiving feedback adjustment parameters in S2.5 includes the following steps: S25.1 The personal terminal side calibrates the personal terminal side capability evolution simulation model logic based on the updated map, and uses the mean absolute error (MAE) and root mean square error (RMSE) as dual indicators to verify the model output accuracy. When the error exceeds the preset threshold, the structural parameters of the personal terminal side capability evolution simulation model are adjusted. S25.2. On the enterprise terminal side, the simulation model logic for job requirements is optimized based on the updated graph. The accuracy of requirement matching is statistically analyzed. When the accuracy is lower than the benchmark value, the dynamic tolerance function is adjusted. The coefficient; S25.3, Transfer the capability vector The job skill requirement vector and the updated map are output to S3, and the matching result feedback signal is received from S4. The feedback data is synchronized to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model. S25.4 Calculate the fluctuation range of the matching results using the sliding window algorithm. When the fluctuation amplitude exceeds a preset threshold, the dynamic parameters of the personal terminal-side capability evolution simulation model are automatically adjusted. Dynamic parameters of the enterprise terminal-side job demand simulation model and federal aggregation weight , .

[0014] As a further improvement to this technical solution, the process of receiving the output data of S2 and constructing the personal multimodal occupational profile and the enterprise job requirement profile in S3 includes the following steps: S31.1, Capability vector for receiving output from S2 Job skill requirement vector and updated occupational competency map; S31.2, Transfer the capability vector Mapping to a professional competence graph, associating graph node attributes, and extracting individual multimodal features; S31.3 Construct a personal multimodal occupational profile based on the extracted personal multimodal features; S31.4, Linking the job skill requirement vector to the skill correlation strength of the occupational ability map. Extract multimodal features of enterprise job requirements; S31.5. Based on the extracted multimodal features of enterprise job requirements, construct a profile of enterprise job requirements; S31.6 Output the individual multimodal career profile and the enterprise job requirement profile to S4.

[0015] As a further improvement to this technical solution, the process of receiving the output image from S3 and calculating the matching degree, output result, and feedback signal in S4 includes the following steps: S41.1 Receive the personal multimodal career profile and enterprise job requirement profile output by S3; S41.2 Skill Association Strength Based on Occupational Ability Map Calculate the matching degree between an individual's multimodal professional profile and the company's job requirement profile; S41.3 Output the matching results between the individual's multimodal career profile and the company's job requirement profile; S41.4 The matching results are used as a matching result feedback signal and are simultaneously sent to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention establishes an asymmetric federated simulation framework, enabling personal and enterprise data to undergo simulation processing on their respective terminal sides. It aggregates only the incremental correlation parameters and graph correction signals, and uses encrypted RESTful API interfaces and SSL and TLS protocols for encrypted transmission and integrity verification. This avoids the risks of centralized storage and processing of raw data, ensuring the security of personal privacy and enterprise trade secrets. Simultaneously, it generates capability vectors by using a personal terminal-side capability evolution simulation model to perform time-series modeling of changes in personal skill status, and uses an enterprise terminal-side job requirement simulation model to fit business fluctuation data to construct a dynamic tolerance function. This can capture the dynamic evolution of personal skills and the elastic fluctuations of enterprise job requirements, making the career profile and job matching results more closely reflect the dynamic changes of actual application scenarios. 2. This invention constructs a vocational competency graph and transition matrix based on national vocational skill standards. By statistically analyzing historical skill co-occurrence data, it calculates the strength of skill associations, clarifies the hierarchical relationships, association logic, and transformation paths between skills, and utilizes a dynamic tolerance function to quantify the acceptable threshold for skill gaps and the range of business flexibility adjustments for enterprises, providing standardized skill association support for job matching. Furthermore, during the federated training iteration process, the vocational competency graph and transition matrix are updated using aggregation weights calculated by a weighted federated average algorithm combined with the entropy weight method. Through a full-link feedback mechanism, job matching results are fed back to the simulation model to adjust dynamic parameters and federated aggregation rules. Simultaneously, graph iteration signals are fed back to the data preprocessing stage to optimize feature dimensions, achieving dynamic optimization of model logic, graph structure, and feature dimensions, thereby improving the adaptability and reliability of job matching. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method steps of the present invention; Figure 2 This is a schematic diagram of the process for asymmetric federated simulation modeling of professional competence in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figures 1-2 As shown, this embodiment provides a method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning, including the following steps: S1. Multimodal Occupational Data Acquisition and Preprocessing: Multimodal occupational data is acquired through multi-source heterogeneous data acquisition technology. Data cleaning, feature annotation, and normalization techniques are used to preprocess the raw data to generate a standardized data subset that includes individual data subsets and enterprise data subsets. The standardized data subset is output to S2 through a data interaction interface. At the same time, the graph iteration feedback signal from S2 is received to dynamically adjust the preprocessed feature dimensions. In this step, the multimodal occupational data collection and preprocessing in S1 includes the following steps: S11. Collect multimodal occupational data from individuals and enterprises, and perform format conversion and unified storage on the collected raw data; Specifically, the multimodal occupational data collection, format conversion, and unified storage in S11 are used to achieve standardized access and centralized management of multi-source heterogeneous data, laying the foundation for subsequent preprocessing stages. The specific implementation is as follows: The specific types of multimodal occupational data include resume texts, scanned copies of skill certificates (including electronic certificates), online course learning records, work experience forms, and project experience descriptions from the individual side, as well as job descriptions, recruitment requirement documents, business scenario requirement descriptions, historical job matching data, and industry skill standard documents from the enterprise side.

[0020] The format conversion adopts differentiated logic: unstructured data in PDF and Word formats are extracted into text content and converted into JSON format using the ApacheTika tool; structured data in Excel is directly mapped to JSON fields; real-time data collected by API interfaces (such as online course records) is directly received in JSON format and the integrity of the fields is verified; scanned copies of skill certificates are extracted into text information using an OCR tool and standardized into unified fields of "certificate name, issuing institution, acquisition time, and skill level".

[0021] Meanwhile, the storage adopts a combined architecture of "distributed file storage + database". Unstructured raw data is stored in the HDFS distributed file system and classified by "personal / enterprise-data type-collection date" directory. The converted structured JSON data is stored in the MongoDB database, and the data index (including storage path, data ownership, and collection timestamp) is stored in the MySQL database to support fast retrieval.

[0022] S12. Clean the stored raw data, use the 3σ principle to remove outliers in the personal skill status data, use the box plot method to filter extreme values ​​in the enterprise job requirement data, use the K-nearest neighbor imputation method to fill missing data, and remove duplicate data based on timestamp priority. Specifically, the data cleaning process in S12 aims to remove data noise, supplement missing information, and eliminate duplicate data to ensure that the data quality meets the requirements of subsequent processing. The specific implementation is as follows: Outlier removal for individual skill status data employs the 3σ principle. First, the mean μ and standard deviation σ of the feature dimension are calculated, and data falling outside the interval [μ-3σ, μ+3σ] are identified as outliers. If the data distribution deviates from a normal distribution, it is normalized by logarithmic transformation before applying the 3σ principle.

[0023] Extreme value filtering for enterprise job requirement data uses the box plot method. The quartiles Q1 (25th quartile), Q3 (75th quartile), and interquartile range IQR = Q3 - Q1 are calculated. Values ​​less than Q1 - 1.5IQR or greater than Q3 + 1.5IQR are identified as extreme values. After verification in conjunction with the business scenario, the data is filtered (for example, extreme values ​​of temporary job requirements during promotional periods can be retained after manual confirmation).

[0024] Missing data were filled using the K-nearest neighbor imputation method. The value of K was adaptively selected based on the sample size. When the sample size was ≥1000, K was 10, and when the sample size was <1000, K was 5. Before imputation, the data was standardized. The similarity between the target sample and other samples was calculated by Euclidean distance. The median of the feature of the K most similar samples was selected as the imputation value.

[0025] The deduplication process uses "data ownership (personal ID / enterprise ID) + core field (such as skill name, job number)" as a unique identifier. Multiple records with the same identifier are compared, and the record with the latest timestamp is retained. If the timestamps are the same, the record with higher field completeness and no null values ​​is retained. If the core fields are completely the same and only the auxiliary fields are different, the valid auxiliary fields are merged and only one record is retained.

[0026] S13. Based on a pre-set industry standard skill dictionary, the cleaned data is automatically labeled with features. The labeling dimensions for personal data include skill type, proficiency level, and application scenario. The labeling dimensions for enterprise data include core job skills, skill requirement priority, and business relevance. Labeling errors are corrected through manual sampling and verification. Specifically, the feature annotation and manual review in S13 aim to standardize the annotation of data features, ensuring uniform feature dimensions and accurate annotation results. The specific implementation is as follows: The industry standard skills dictionary is built based on the "Occupational Classification of the People's Republic of China" (2022 edition) and corresponding industry skills specifications. For example, the information technology industry supplements the core skills terms in the "Skills Specifications for Information Technology Service Practitioners". It is organized in a hierarchical structure of "skill category - skill subcategory - skill name - proficiency level" and is regularly synchronized with the updated content of industry standards. Automatic feature annotation adopts a combination algorithm of "dictionary matching + semantic similarity verification". First, it accurately locates the skill type through string matching, and then uses the BERT semantic model to calculate the similarity between the text and the dictionary terms (the threshold is set to 0.8) to correct the annotation deviation of synonyms and near-synonyms. Individual data is annotated according to "skill type - proficiency level (beginner / intermediate / advanced / expert) - application scenario (office automation / project development / data analysis, etc.)" and enterprise data is annotated according to "core job skills - skill requirement priority (P1 / P2 / P3) - business relevance (high / medium / low)".

[0027] Meanwhile, manual sampling verification selects labeled data at a sampling ratio of 10%, and two people with industry experience independently verify the data. If the two people's labeling results are consistent, the labeling is confirmed to be valid. If they are inconsistent, the data is submitted to a third party for arbitration, and the labeling model parameters are updated after the labeling error is corrected.

[0028] S14. The labeled data are processed using a differential normalization algorithm. Personal data is mapped to the [0,1] interval using Min-Max normalization, and enterprise data is converted to standard normal distribution data using Z-Score normalization. Specifically, the differential normalization process in S14 aims to eliminate the influence of different feature dimensions' units, ensuring that the data distribution meets the requirements of subsequent modeling. The specific implementation is as follows: Personal data is normalized using Min-Max, the formula is as follows: ,in These are the normalized individual data values. The original values ​​of personal data. This is the minimum value of this feature dimension. The maximum value of this feature dimension is used to map features such as skill proficiency and application scenario frequency to the [0,1] interval. Before normalization, outliers processed by S12 are removed.

[0029] Enterprise data typically uses Z-Score normalization, with the formula being: ,in The normalized enterprise data value. The original values ​​of enterprise data. This is the mean of this feature dimension. The standard deviation of this feature dimension; Meanwhile, for cases with extreme values ​​that need to be retained based on business verification, robust Z-Score normalization is adopted, with the formula as follows: ,in For robustly normalized enterprise data values, This is the median of this feature dimension. The median of the absolute deviation of this feature dimension ( ), For the th feature dimension (Data value).

[0030] S15. Divide the normalized data into blocks according to data ownership and time dimension to generate a standardized data subset including personal data subset and enterprise data subset. The standardized data subset is stored in Parquet format and is accompanied by data collection timestamp and preprocessing process traceability identifier. Specifically, the data block partitioning and storage in S15 aims to achieve data classification management, improve storage efficiency and retrieval convenience, and ensure data traceability. The specific implementation is as follows: Data is divided into two levels based on “data ownership (personal / enterprise) + time dimension (monthly)”. Personal data subsets are further subdivided by personal ID, and enterprise data subsets are subdivided by enterprise department + job type, such as “personal data subset-202401-ID1001” and “enterprise data subset-202401-technology department-algorithm job”.

[0031] Meanwhile, the storage uses the Parquet format and is compressed using the Snappy compression algorithm. The row group size is set to 128MB to facilitate parallel reading. Each Parquet file comes with metadata (including data field descriptions, data volume statistics, compression methods, etc.).

[0032] Meanwhile, the traceability identifier is a unique string consisting of "data acquisition device ID - acquisition timestamp (accurate to the second) - cleaning step number - annotation personnel ID - normalization algorithm version number", such as "DEVICE001-20240115103025-CLEAN002-LABEL003-NORM001", which can be used to trace the entire data preprocessing process.

[0033] S16. Output the standardized data subset to S2 through the encrypted RESTful API interface. The data transmission is encrypted using SSL and TLS protocols and is verified for integrity. Specifically, the encrypted transmission and integrity verification in S16 aim to ensure the security and integrity of standardized data subsets during transmission, preventing data leakage or tampering. The specific implementation is as follows: The encrypted RESTful API interface follows the RESTful specification, uses the HTTPS protocol, and has the interface path " / api / v1 / data / submit". The request method is POST. The request header includes the interface version number, data subset type, and signature information (generated based on request parameters and a key). The request body is a JSON string of a standardized data subset. The response status codes are defined according to the HTTP standard: 200 indicates successful transmission, 400 indicates incorrect parameters, 403 indicates insufficient permissions, 500 indicates server error, 409 indicates integrity verification failure, and 415 indicates decryption failure.

[0034] Meanwhile, the transmission uses the AES-256-CBC symmetric encryption algorithm to encrypt business data, and the key is obtained through negotiation using the RSA-2048 asymmetric encryption algorithm. It also combines TLS 1.3 and SSL protocols to build a dual encryption channel. Before data transmission, the JSON string is Base64 encoded and then encrypted using AES-256.

[0035] Furthermore, the integrity check uses the SHA-256 hash algorithm. The sender calculates the hash value of a standardized subset of data and appends it to the "Integrity-Check" field in the request header. After receiving the data, the receiver recalculates the hash value and compares it. If they match, the data is confirmed to be complete; otherwise, the receiver rejects the data and returns a 409 response and a verification failure message.

[0036] In addition, if a network interruption or 30-second timeout occurs during transmission, the sender will automatically retry 3 times at intervals of 2 seconds, 5 seconds, and 10 seconds. If the retry fails, the data will be cached in a local preset directory (retained for 72 hours). During this period, network connectivity will be checked every hour, and retransmission will be prioritized after recovery. If the transmission fails to succeed after a timeout, an alarm will be triggered to the administrator. When a 400 response is received, the sender will correct the error parameters indicated in the response body and retransmit. If the transmission fails 3 times in a row, the log will be logged and the parameter configuration will be checked. When a 403 response is received, the sender will re-negotiate the key via RSA-2048 and generate a new signature. If the negotiation fails twice, an alarm will be triggered to check permissions. When a 500 response is received, the sender will retry 3 times at intervals of 5 seconds, 10 seconds, and 15 seconds. If the retry fails, the data will be cached locally and the health status of the receiver's server will be checked every 30 minutes. If the health status of the receiver's server does not recover within 24 hours, an alarm will be triggered. If encryption or decryption fails, the sender will verify the JSON format and encoding validity and retry. If the encryption or decryption fails twice in a row, an alarm will be triggered to check the key and algorithm configuration. If the receiver fails to decrypt, a 415 response will be returned prompting for retransmission.

[0037] S17. Receive the graph iteration feedback signal from S2, extract the graph node correlation parameters, and when the parameter change exceeds the preset threshold, retain the features with correlation higher than the set value with the professional ability graph node, and remove redundant features to adjust the preprocessed feature dimension.

[0038] Specifically, the feedback loop and feature dimension adjustment in S17 aim to optimize the preprocessed feature dimensions, eliminate redundant features, and improve the data-modeling compatibility based on iterative feedback from subsequent modeling stages. The specific implementation is as follows: Receive the JSON-formatted graph iteration feedback signal sent by S2, parse it, extract the graph node correlation parameter corresponding to each preprocessed feature, and form a "feature name-correlation parameter" mapping table.

[0039] Meanwhile, the preset threshold is determined based on the statistical analysis of parameter changes over 10 historical map iterations, and is set at the 75th percentile of the historical changes; the feature retention setting is determined based on the industry skill correlation benchmark, and is set at 0.6 in this embodiment.

[0040] Threshold determination formula (in For the first The magnitude of change in the correlation parameter of each feature The correlation parameter is the one used in the latest iteration. (The correlation parameter after the previous iteration), when ( (for the preset threshold) and ( When reserving the set value for a feature, the first one is removed. One characteristic.

[0041] After removing redundant features, update the preprocessing feature configuration file. The next round of data preprocessing will extract features according to the new configuration file. At the same time, record the feature adjustment log (including adjustment time, adjustment reason, and name of the redundant feature removed).

[0042] S2. Asymmetric Federated Simulation Modeling of Occupational Competencies: Receives the standardized data subset output from S1. Based on the dynamic characteristics of occupational competencies, it builds an asymmetric federated simulation framework, initializes the individual terminal-side competency evolution simulation model, the enterprise terminal-side job requirement simulation model, and the corresponding modeling logic; Through the individual terminal-side competency evolution simulation model and the enterprise terminal-side job requirement simulation model, it performs simulation processing on the individual terminal-side and enterprise terminal-side data subsets, respectively, generating competency vectors representing the dynamic changes in skill status, as well as representations including job skill requirement vectors and dynamic tolerance functions. The dynamic tolerance function quantifies the acceptable threshold for skill gaps and the range of business flexibility adjustments for enterprises; it maintains an occupational competency graph constructed based on an industry standard skills framework. In the federated training iteration process, the spectrum and transition matrix are aggregated with the incremental correlation parameters and spectrum correction signals output by the individual simulation terminal and the enterprise simulation terminal. The professional ability spectrum and transition matrix are updated, and the updated spectrum parameters are distributed to the individual simulation terminal and the enterprise simulation terminal. The individual simulation terminal and the enterprise simulation terminal calibrate the ability evolution simulation model logic and optimize the job requirement simulation model logic according to the updated spectrum. The ability vector, job skill requirement vector and the updated professional ability spectrum are output to S3, and the matching result feedback signal from S4 is received to adjust the dynamic parameters and federated aggregation rules of the individual terminal-side ability evolution simulation model and the enterprise terminal-side job requirement simulation model. The process of asymmetric federated simulation modeling of professional ability in S2 includes the following steps: In this step, S21 receives the standardized data subset output by S1, performs integrity and format verification, builds an asymmetric federated simulation framework based on the dynamic characteristics of occupational abilities, initializes the individual terminal-side ability evolution simulation model, the enterprise terminal-side job requirement simulation model and corresponding modeling logic, and determines the core parameter as the skill evolution rate threshold. Demand fluctuation coefficient The process of receiving a standardized data subset, performing verification, building an asymmetric federated simulation framework, and initializing the model in S2.1 includes the following steps: S21.1. Verify the integrity of the standardized data subset using the SHA-256 hash algorithm, and perform format verification on the data subset based on the JSONSchema specification. If the verification fails, return to S1 for reprocessing. Specifically, the data verification in S21.1 is used to ensure that the standardized data accessed is tamper-proof and that its format meets the input requirements for simulation modeling. The specific implementation is as follows: Integrity verification: The normalized subset of data output by S1 is processed using the SHA-256 hash algorithm. Perform verification, verification value The calculation formula is: ; Will The hash check value that is included with the S1 transmission (Compare the "Integrity-Check" field from S16) If If the check passes, the validation will pass; otherwise, it will fail.

[0043] Format validation: A validation template is built based on the JSON Schema specification. The template includes the data field type (e.g., personal data must include a "skill type" string field and a "proficiency level" enumeration field), required fields, and value range; If all fields meet the requirements when compared with the template, the validation passes. If the validation fails, a "data validation failed" signal is returned to S1, triggering S1 to reprocess.

[0044] Furthermore, if data verification fails, the scope of re-preprocessing is limited to repeating the cleaning / labeling steps (without needing to re-collect data); the verification failure feedback returned by the receiver is in JSON format, including the error code and the specific reason.

[0045] S21.2 After establishing the asymmetric federated simulation framework, clarify the attribution of core parameters: skill evolution rate threshold. Corresponding to the simulation model of the evolution of personal terminal capabilities, the demand fluctuation coefficient The corresponding enterprise terminal-side job demand simulation model has parameter values ​​that are associated with historical occupational skill evolution data and enterprise demand change data. Specifically, the framework and parameter configuration in step S21.2 aims to build a distributed framework that adapts to the differences between individual and enterprise ends, and to clarify the application scenarios of core parameters. The specific implementation is as follows: Unlike traditional symmetric federated frameworks (which use the same model structure and parameters at both ends), the asymmetric framework in this step deploys heterogeneous models to address the different dynamic characteristics of "dynamic evolution of individual skills" and "elastic fluctuations in enterprise demand": the individual terminal side uses a "capability evolution simulation model" (focusing on the temporal changes of skills), while the enterprise terminal side uses a "job demand simulation model" (focusing on the correlation between demand and business fluctuations); the framework only aggregates parameter increments / spectral signals and does not transmit raw data to achieve privacy protection.

[0046] Core parameter attribution: skill evolution rate threshold : Corresponds to the personal terminal side model, used to determine whether the rate of change in an individual's skill status exceeds the normal range; Demand volatility coefficient : Corresponds to the enterprise terminal-side model, used to quantify the degree of fluctuation in job requirements as business changes occur; Initial data source for parameters: Historical evolution data of similar skills in related industries Historical demand fluctuation data for the industries in which the related companies operate.

[0047] Furthermore, the federated server, personal terminals, and enterprise terminals adopt a star topology (with the federated server as the central node); the communication configuration is as follows: port number range 8080-8090, data transmission timeout threshold 30 seconds; terminal access requires authorization verification through a two-way authentication rule of "device ID + key", and only after successful authentication can it access the federated framework.

[0048] S21.3. Fit the two data sources separately using a time series fitting algorithm, combined with time decay weights. Sure and The values ​​are used to synchronously initialize the basic structural parameters of the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model. The algorithm is executed automatically throughout the process, avoiding parameter deviation problems caused by a single data source, and providing accurate support for subsequent model simulations.

[0049] Specifically, the parameter calculation and model initialization in S21.3 aim to obtain accurate parameters through fitting from two data sources, avoiding the bias of a single data source. The specific implementation is as follows: Time decay weight : Used to measure the timeliness of historical data, the formula is: ; in: Indicates the first The weight of each historical data point, with a value of (0,1]. This represents the attenuation coefficient (industry-specific, e.g., 0.1 for the IT industry). This represents the difference between the historical data collection time and the current time (unit: days).

[0050] skill evolution rate threshold Calculation: An ARIMA(p,d,q) model is used to fit historical skill evolution data from an individual's perspective. The order of the ARIMA(p,d,q) model is determined using the AIC (Akaike Information Criterion) (calculating the AIC values ​​for different order combinations and selecting the combination with the smallest AIC). In this example, the values ​​are p=2, d=1, and q=1. Based on this order, historical skill evolution data from individuals and historical enterprise demand data are fitted to calculate the skill evolution rate threshold. With demand volatility coefficient ; combination After weighting, take the 75th quantile. The formula is: ; in: The first step in fitting the ARIMA model is to represent the... The rate of skill evolution for each data point; This indicates the sample size of historical data on the individual's end. Indicates retrieving the dataset The 75th percentile.

[0051] Demand volatility coefficient Calculation: Similarly, the formula for fitting historical demand data from enterprises is: ; in: The first step in fitting the ARIMA model is to represent the... The fluctuation range of demand for each piece of data; This indicates the sample size of historical data from the enterprise side.

[0052] Model initialization: based on , Initialize the personal-side LSTM time series modeling module (input dimension corresponding to the number of skill features) and the enterprise-side GARCH fluctuation fitting module (variance parameter adaptation). The entire process is automated, avoiding human error.

[0053] It should be noted that the historical data required for fitting the ARIMA model must meet the following requirements: at least 6 months of individual skill evolution data and at least 12 months of enterprise demand fluctuation data, to ensure... , Statistical reliability of the calculation.

[0054] In this step, S22, the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model are used to simulate and process the personal data subset and the enterprise data subset on the corresponding terminal side, respectively, to generate capability vectors representing the dynamic changes in skill status. and includes job skill requirement vectors and dynamic tolerance functions. The representation of requirements; the process of processing data through corresponding model simulation and generating capability vectors and requirement representations in S2.2 includes the following steps: S22.1. The personal terminal side calls the personal terminal side capability evolution simulation model to perform time series modeling on the skill status change data of the personal data subset and extract the core features of skill proficiency and evolution trend. Specifically, the purpose of setting up S22.1 is to explore the dynamic changes in individual skill status and extract the core features supporting the generation of ability vectors. The specific implementation is as follows: On the individual terminal side, the LSTM time-series modeling module is called (initialized in S21.3; the LSTM model uses tanh as the activation function for the hidden layers; the termination condition for model training is mean squared error (MSE) ≤ 0.001; when the MSE remains ≤ 0.001 for three consecutive iterations during the iteration process, training stops and features are extracted). This module models the skill status change data (including time-series data such as skill proficiency, learning duration, and application frequency) of a subset of the individual data. Data Input: Sort a subset of personal data by timestamp, with a length of [length value missing]. A sliding window (L is the sample size corresponding to the data of the past 3 months) is used to extract time segments as the input sequence of LSTM; Modeling process: The input dimension of the LSTM model is the number of individual skill features. The hidden layer dimension is set to The output layer outputs the predicted value of the skill state; the model training uses the mean squared error (MSE) between the predicted value and the actual value as the loss function, and iterates until the loss function converges; Feature extraction: Two types of core features are extracted from the hidden layer output of the LSTM: Skill proficiency trait: The normalized proficiency value of the skill at the current moment (same as S14). ); Evolutionary trend characteristics: the difference between the predicted value and the current value output by the LSTM. (Similar to the state change of the capability vector in S2), combined with the skill evolution rate threshold. The system filters out skill features with significant changing trends. After feature extraction, redundant features unrelated to skill state changes are automatically filtered out, and core features are retained for subsequent capability vector generation.

[0055] S22.2, Generate capability vector based on extracted features Vector data is synchronously stored in the personal terminal-side capability evolution simulation model; Specifically, S22.2 is used to quantify the dynamic features of individual skills into standardized vectors, and simultaneously realize the synchronous storage of vector data within the model. The specific implementation is as follows: Ability vector generation: Based on the core features extracted in S22.1, construct dimensional capability vector , of which dimension The calculation follows the formula below (same as the ability vector formula in S2): ; In the formula: Indicates the first Personal Moment The ability value of each skill; Indicates the first The skill's stats at any given time; This refers to the skill state change extracted in S22.1; This represents the skill evolution rate threshold defined in S21.2; This is an indicator function (it takes 1 if the condition is met, otherwise it takes 0).

[0056] Vector storage: generated Identified by “personal ID-timestamp-vector data”, it is synchronously stored in the local vector database of the personal terminal-side capability evolution simulation model; the storage format is JSON, which includes vector dimensions, the corresponding skill names for each dimension, and the generation timestamp, so as to facilitate quick reading during subsequent model calls and federated aggregation.

[0057] S22.3, The enterprise terminal side calls the enterprise terminal side job demand simulation model to fit the enterprise's historical skills gap matching data and business fluctuation data. Construct a dynamic tolerance function ; Specifically, the purpose of setting up S22.3 is to establish the correlation between enterprise skill gaps and business fluctuations, and to construct a tolerance function that adapts to business elasticity. The specific implementation is as follows: The enterprise terminal calls the GARCH fluctuation fitting module (which has been initialized in S21.3; in this solution, the GARCH fluctuation fitting module uses a GARCH(1,1) order configuration) to fit the enterprise's historical skill gap adaptation data and business fluctuation data. The specific steps are as follows: Data preparation: Historical skills gap matching data: the difference between the number of job skills required by companies and the actual skills required by candidates over the past 12 months. (The same as the benchmark skill gap for the dynamic tolerance function in S2). Business fluctuation data The growth rate of a company's business volume during the same period (such as the monthly order volume growth rate or the project number growth rate), with a value range of [−0.3, 0.3] (negative values ​​indicate business contraction, and positive values ​​indicate business expansion).

[0058] Fitting process: Linear regression algorithm is used for fitting. and The correlation relationship is used to obtain the initial fitting equation. ( The slope (This is the intercept); then combined with the demand fluctuation coefficient. (As defined in S21.2), the fitting results are corrected to adapt to business flexibility, and the dynamic tolerance function is finally obtained. The formula (same as the tolerance function formula in S2): ; In the formula: Indicates business fluctuations The acceptable threshold range for the skills gap at that time; This is the demand fluctuation coefficient.

[0059] Function verification: Substitute the company's actual business fluctuation data for the past 3 months into the function. Verify whether the output threshold range covers the actual skill gap matching situation. If the coverage is less than 90%, readjust it. The values ​​are then adjusted and refitted until the coverage meets the requirements.

[0060] It should be added that this embodiment uses the least squares method to solve for the linear regression coefficients. (Slope) and (Intercept), the specific implementation process is as follows: based on historical skills gap adaptation data With business fluctuation data Matching sample set Construct the error function: ; in: This indicates the number of matching samples between historical skills gap matching data and business fluctuation data. Indicates the first Historical skills gap matching data corresponding to the sample group (i.e., the difference between the number of job skill requirements and the actual number of skills of the candidates). Indicates the first Business fluctuation data corresponding to the group sample (i.e., the growth rate of the enterprise's business volume in the same period). The sum of squares of the fitting error is used to quantify the degree of deviation between the initial fitting equation and the actual sample.

[0061] For error function Regarding respectively , Find the partial derivative and set it to 0 (to minimize the error). The solution is: ; ; The linear regression coefficients are obtained through the above calculations. , Complete the initial fitting equation The construction of.

[0062] S22.4, Through dynamic tolerance function Quantify the acceptable threshold for skills gaps and the range of business flexibility adjustments, and generate a vector containing job skill requirements. The representation of.

[0063] Specifically, the purpose of setting S22.4 is to quantify the acceptable range of skills gaps through a dynamic tolerance function, generating a standardized representation that includes a demand vector and a tolerance function, as implemented below: Quantifying the skills gap threshold: This involves analyzing current business fluctuation data within the enterprise. (e.g., monthly business growth rate) Substitute This yields the acceptable threshold range for skill gaps in the current business scenario. ,in: ; ; This range indicates: when the candidate's skills gap falls within... When available, it can be adapted to the current job requirements.

[0064] Job Skill Requirement Vector Generation: Based on core job skills and requirement priorities from a subset of enterprise data, construct... Dimensional job skill requirement vector ,in For the first The required intensity of a core skill (values ​​[0,1], where 1 indicates that it is mandatory); Demand representation integration: Vectorizing job skill requirements With dynamic tolerance function The data is integrated into a standardized representation in JSON format, containing vector dimensions, the corresponding skill names for each dimension, demand intensity, and current business fluctuation data. Skills gap threshold range The data is synchronously stored in the local database of the job requirement simulation model on the enterprise's terminal side.

[0065] In this step, S23, maintain the professional competency graph and transition matrix based on the industry standard skills framework. Through skill association strength Define the graph rules and initialize the matrix state transition logic based on probability distribution to provide basic support for the simulation model of personal terminal-side capability evolution and the simulation model of enterprise terminal-side job requirements; the process of maintaining the professional capability graph and transition matrix and adapting it to the corresponding model in S2.3 includes the following steps: S23.1. Adopt the national vocational skills standards as the industry standard skills framework, and define the nodes of the vocational ability map as skills ID, level, and application scenario; Specifically, the purpose of S23.1 is to rely on authoritative industry standards to unify the node attributes of the occupational competency map, ensuring the map's standardization and industry adaptability. This is achieved as follows: The "Occupational Classification of the People's Republic of China" (2022 edition) and corresponding national occupational skill standards (such as the "National Occupational Skill Standard for Computer Programmers" for information technology occupations) are adopted as the industry standard skill framework to define the node attributes of the occupational competency map: Skill ID: Uses the skill code in the national vocational skill standard (e.g., "IT-PY-001" represents the beginner level of Python skills in the information technology category). Skill levels: Corresponding to the national vocational skill standards, they are divided into primary (level 5), intermediate (level 4), advanced (level 3), technician (level 2), and senior technician (level 1). Application scenario: Associating skills with corresponding job types in the standards (e.g., Python basic skills correspond to "junior developer" or "data analysis assistant"). All nodes are organized in a hierarchical structure of "skill category - skill subcategory - skill ID" to form the basic node system of the career competency graph.

[0066] S23.2 Analyze historical skill co-occurrence data and calculate the correlation strength between skills using a similarity algorithm. ,based on Define the edge attributes of the graph with the skill evolution path definition; Specifically, the purpose of setting up S23.2 is to quantify the degree of correlation between different skills, define the edge logic of the graph, and support skill correlation analysis. The specific implementation is as follows: Historical Skill Co-occurrence Data Statistics: The industry talent pool must contain no fewer than 50,000 skill data entries. Based on this sample pool, personal skill data from the past three years will be collected to statistically analyze "simultaneous possession of skills". With skills The number of people (referred to as "Possessing skills" The number of people (referred to as "Possessing skills" The number of people (referred to as ); Skill correlation strength calculate: The co-occurrence similarity algorithm is used, and the formula is the same as in S2: ; In the formula, Representation skills With skills The correlation strength, with a value range of [0,1], indicates a stronger correlation with a larger value. Graph edge attribute definition: [will...] As "skills" in the diagram →Skills The weight of the edge, combined with the skill evolution path (such as "basic Python skill → intermediate Python skill"), defines the direction of the edge, forming a complete graph structure of "node (skill) - edge (association strength + evolution direction)".

[0067] S23.3 Statistically analyze historical skill conversion data and initialize the transition matrix based on the data probability distribution. State transition logic; Specifically, the purpose of setting up S23.3 is to define the state transition probabilities between skills and initialize the logic of the transition matrix based on historical skill transition data. The specific implementation is as follows: Historical Skills Transition Data Statistics: Data on employee skills upgrades and job changes over the past three years is collected from the industry talent pool to statistically analyze "skills transition data." Convert into skills The number of people (referred to as ), "from skills" The total number of people who convert to any skill (denoted as) , (Total number of skills) Transition matrix initialization: for The square array, elements Indicates "from skills" Convert into skills The probability of [the outcome] is given by the same formula as in S2: ; In the formula: Representation skills To skills The conversion probability has a value range of [0,1], and the sum of elements in the same row is 1; Probability distribution verification: for the initialized... Verify that the sum of the elements in each row is 1 (error ≤ 0.01). If not, recalculate and adjust the data to ensure that the matrix conforms to the probability distribution logic.

[0068] S23.4, Complete the transition matrix After initialization, the basic data of the graph and matrix are synchronized to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model.

[0069] Specifically, the purpose of setting up S23.4 is to synchronize the standardized professional competency map and transition matrix to both ends of the model (individual terminal-side competency evolution simulation model and enterprise terminal-side job requirement simulation model), ensuring that the model is based on a unified skill system for simulation processing. The specific implementation is as follows: Data packaging: This involves packaging the "node attribute-edge attribute" data and transition matrix of the professional competence graph. The initial values ​​are packaged into a standardized data file in Parquet format (the same storage format as S15), and a SHA-256 hash check value is generated. Encrypted synchronization: It adopts the same encrypted RESTful API interface as S16 (HTTPS protocol + TLS1.3 encryption) to synchronize data files to personal terminals and enterprise terminals; Receive verification: After receiving the data, both ends of the model first verify the SHA-256 hash value (to ensure that the data is complete and has not been tampered with), and then verify whether the graph node / edge attributes and the format of the transition matrix meet the model input requirements; Local storage: After successful verification, the graph and matrix data are stored in the local configuration database of both models as the basic skill system support for simulation processing.

[0070] In this step, during S24, the federated training iteration uses a weighted aggregation algorithm to process the parameter increments of the simulation model for the evolution of individual terminal capabilities. Incremental parameters of enterprise terminal-side job requirement simulation model Update the professional ability graph and transition matrix with the graph correction signal. The encrypted distribution is then sent to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model. The process in S2.4 of aggregating model parameter increments and correction signals and updating the distribution map matrix includes the following steps: S24.1. Invoke the weighted federated average algorithm to increment the parameters of the simulation model for the evolution of personal terminal capabilities. Incremental parameters of enterprise terminal-side job requirement simulation model And perform aggregation processing on the spectral correction signal; Specifically, the purpose of setting S24.1 is to aggregate the parameter increments and spectral correction signals of the two models through an improved federated averaging algorithm, avoiding single-model bias, while adapting to the heterogeneous model characteristics of asymmetric frameworks. The specific implementation is as follows: The input content specifically includes: Personal terminal side parameter increment The update amount of parameters such as weights and biases in the simulation model of personal terminal capability evolution (LSTM module); Enterprise terminal side parameter increment The update amount of parameters such as variance and coefficients in the enterprise terminal-side job demand simulation model (GARCH module); Graph correction signals: Structured signals such as "suggestions for adjusting the strength of skill association" and "correction values ​​for the probability of the transition matrix" fed back from both ends of the model; Algorithm improvements: Unlike the ordinary federated average algorithm (equal-weighted aggregation), this step uses a weighted federated average algorithm, first... , Align the parameter dimensions (by padding with zeros to accommodate parameter differences in heterogeneous models), and then combine this with the aggregated weights calculated subsequently. , Weighted aggregation is performed, and the aggregation formula is: ; ; In the formula: This represents the increment of model parameters after aggregation; This represents the corrected signal after aggregation; This represents the spectral correction signal fed back from the personal terminal side; This indicates the graph correction signal fed back from the enterprise's terminal side; , The aggregate weights calculated for S24.2 (satisfying) ).

[0071] S24.2 Calculate the data volume weight and model contribution weight of the personal terminal-side capability evolution simulation model, and the data volume weight and model contribution weight of the enterprise terminal-side job requirement simulation model using the entropy weight method; then, weight the data volume weight and model contribution weight of the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model to obtain the final aggregate weight. , ; Specifically, S24.2 objectively quantifies the data volume and contribution of both models using the entropy weight method to obtain reasonable aggregation weights, avoiding the bias of subjective manual assignment. The specific implementation is as follows: Weight dimension decomposition: The aggregate weight is divided into data volume weight (reflecting the sample size of the data at both ends) and model contribution weight (reflecting the matching effect contribution of the models at both ends), which are calculated separately and then weighted and combined. Data volume weight calculation: Let the sample size of the data on the individual terminal be... The sample size of enterprise terminal data is Data volume weight , The formula is: ; ; Model contribution weight calculation (entropy weight method): Using "matching accuracy" as the contribution indicator, let the matching accuracy of the simulation model for the evolution of individual terminal capabilities be denoted as . The matching accuracy rate of the enterprise terminal-side job requirement simulation model is First, the indicators are standardized using the following formula: ; ; in, This represents the matching accuracy of the simulation model for the evolution of personal terminal capabilities (the statistical period is monthly, calculated based on the matching data of all positions and talents in the current month, and the match rate between the model output matching results and the actual recruitment results), with a value range of [0,1]. This represents the matching accuracy of the enterprise's terminal-side job demand simulation model (the statistical period is monthly, calculated based on the matching data of all job positions and talents in the current month, and the match rate between the model output matching results and the actual recruitment results), with a value range of [0,1]. Indicates taking and The smaller value in the middle is used to eliminate dimensional differences. Indicates taking and The larger value in the range is used to eliminate dimensional differences. The standardized value representing the matching accuracy of the simulation model for the evolution of personal terminal capabilities is... Mapping to the [0,1] interval eliminates the influence of different model dimensionality on accuracy. The standardized value representing the matching accuracy of the simulation model of job requirements on the enterprise's terminal side is mapped to the [0,1] interval to eliminate the influence of the units of different model accuracies; Next, calculate the information entropy of the indicator, using the following formula: ; ; in, Information entropy represents the matching accuracy of the simulation model of personal terminal capability evolution, and measures the uncertainty of this accuracy indicator (the smaller the entropy value, the higher the indicator's discriminative power). Information entropy represents the accuracy of the simulation model matching the job requirements on the enterprise's end-user side, and measures the uncertainty of this accuracy indicator (the smaller the entropy value, the higher the indicator's discriminative power). This represents the natural logarithm function, used in the core calculation of information entropy; Finally, the model contribution weights are obtained. , : ; ; in, The model contribution weight represents the model contribution of the simulation model of the personal terminal capability evolution, reflecting the degree of contribution of the model's accuracy to federated aggregation, and the value range is [0,1]. This represents the model contribution weight of the enterprise terminal-side job demand simulation model, reflecting the degree of contribution of the model's accuracy to federated aggregation, with a value range of [0,1]. The degree of difference in the accuracy of the simulation model of the evolution of personal terminal capabilities (the smaller the entropy value, the greater the degree of difference, and the higher the weight). This indicates the degree of difference in the accuracy of the simulation model of job requirements on the enterprise's end side (the smaller the entropy value, the greater the degree of difference, and the higher the weight). Final aggregated weight combination: The data volume weight and the model contribution weight are each assigned a weight of 0.5, and the combination formula is as follows: ; ; in: The weights representing the data volume of the individual terminal-side model are determined by the ratio of the individual terminal sample size to the total sample size from both terminals. , For individual sample size, (Sample size for enterprise clients). The weight of the data volume in the enterprise terminal model is determined by the ratio of the enterprise terminal sample size to the total sample size at both ends. ); This represents the allocation coefficient between the data volume weight and the model contribution weight, indicating that each accounts for 50% in the final weight, balancing the impact of data scale and model performance. Final verification (Error ≤ 0.01) to ensure the weighting is reasonable.

[0072] S24.3, Based on Aggregate Weights , and correction factor Update the correlation strength of the professional ability graph With transition matrix ; Specifically, S24.3 dynamically adjusts the skill association strength and transition probability based on the aggregated signal and weights, making the graph / matrix adapt to the actual simulation requirements of both end models. The specific implementation is as follows: Skill correlation strength Update: Combined with the aggregated spectrum correction signal With correction factor (Value range [0.1, 0.9], balancing existing parameters and increments), the update formula is: ; In the formula: , These represent the skills before and after the update, respectively. and The strength of the association; This indicates feedback from the individual terminal. Correction increment; This indicates feedback from the enterprise's terminal side. Correction increment; Transition matrix Update: Transition matrix elements The update formula is: ; In the formula: , This indicates the difference between the skills after the update and before the update. arrive The transition probability; This indicates feedback from the individual terminal. Correction increment; This indicates feedback from the enterprise's terminal side. Correction increment; Update verification: Verify ( (Total number of skills), ensuring the graph / matrix conforms to logical constraints.

[0073] S24.4 The updated map and matrix parameters are encrypted using SSL and TLS protocols. After passing hash verification, they are distributed to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model, respectively.

[0074] Specifically, the purpose of setting S24.4 is to ensure the security and integrity of the updated parameters during transmission, ensuring that both ends of the model receive valid and untampered data. The specific implementation is as follows: Data packaging: This will update the data. , Package the file into Parquet format and generate the SHA-256 hash checksum of the file; Encryption processing: A dual encryption channel is built using SSL 3.0 protocol + TLS 1.3 protocol. The Parquet file is first Base64 encoded and then encrypted using AES-256-CBC symmetric encryption algorithm (the key is negotiated through RSA-2048 asymmetric algorithm). Distribution and Verification: The encrypted file is distributed to both ends of the model via the same encrypted RESTful API interface as S16. After receiving the file, both ends of the model first decrypt it, then calculate the SHA-256 hash value and compare it with the attached verification value. If they match, the verification is successful. Local Update: After successful verification, both ends of the model will replace the locally stored professional ability graph and transition matrix with the updated version, and synchronously update the skill association / conversion logic within the model.

[0075] In this step, S25 calibrates the simulation model logic for the evolution of personal terminal-side capabilities based on the updated map, optimizes the simulation model logic for enterprise terminal-side job requirements, outputs core data to S3, receives the matching result feedback signal from S4, and analyzes the fluctuation amplitude. The parameters of the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model are adjusted to form an adaptive closed loop. The process of calibrating and optimizing the model logic and receiving feedback to adjust the parameters in S2.5 includes the following steps: S25.1 The personal terminal side calibrates the personal terminal side capability evolution simulation model logic based on the updated map, and uses the mean absolute error (MAE) and root mean square error (RMSE) as dual indicators to verify the model output accuracy. When the error exceeds the preset threshold, the structural parameters of the personal terminal side capability evolution simulation model are adjusted. Specifically, S25.1 calibrates the skill evolution logic of the individual terminal-side capability evolution simulation model based on the updated professional capability map, and verifies the accuracy of the model output through dual indicators to ensure the accuracy of the capability vector. The specific implementation is as follows: Model logic calibration: The individual terminal side calls the updated professional ability graph to determine the correlation strength of skills within the model. Transition matrix Synchronize to the LSTM time series modeling module and adjust the weights of the skill evolution path (e.g., closely related skills participate in the evolution calculation first). Accuracy verification index calculation: using mean absolute error (MAE) ) and root mean square error ( Two metrics are used to verify the capability vector output by the model. The deviation from the actual skill level is expressed by the formula: ; ; In the formula: The model predicts the first The ability value of each skill; Indicates the number of actual data collected. The ability value of each skill; Represents the number of skill features; Model parameter adjustment: Preset The threshold is 0.1. The threshold is 0.15 (determined based on the error tolerance of industry skills assessment); if or If the threshold is exceeded, adjust the structural parameters of the personal terminal capability evolution simulation model (such as increasing the LSTM hidden layer dimension by 20% and adjusting the sliding window length to the data of the last two months), and retrain the model until the error meets the threshold requirements.

[0076] S25.2. On the enterprise terminal side, the simulation model logic for job requirements is optimized based on the updated graph. The accuracy of requirement matching is statistically analyzed. When the accuracy is lower than the benchmark value, the dynamic tolerance function is adjusted. The coefficient; Specifically, S25.2 optimizes the demand matching logic of the enterprise terminal-side job demand simulation model based on the updated occupational competency map, and adjusts the coefficients of the dynamic tolerance function through accuracy verification to improve the adaptability of demand representation. The specific implementation is as follows: Model logic optimization: The enterprise terminal side will be updated with the following: , Synchronize with the GARCH fluctuation fitting module and adjust the job skill requirement vector. Feature weights (e.g., prioritizing skills with high correlation strength). Job matching accuracy calculation: The accuracy rate is calculated by counting the number of "matching results that meet the company's actual hiring standards" in the last 50 job matches. The formula is as follows: ; Dynamic tolerance function adjustment: The preset accuracy benchmark is 80% (determined based on the company's historical recruitment matching success rate); if the accuracy is lower than the benchmark, the dynamic tolerance function will be adjusted. Demand volatility coefficient (For example, for every 5% decrease in accuracy, Increase by 0.05), refit The accuracy rate meets the benchmark requirements.

[0077] S25.3, Transfer the capability vector The job skill requirement vector and the updated map are output to S3, and the matching result feedback signal is received from S4. The feedback data is synchronized to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model. Specifically, S25.3 is used to output the core representation data generated by the two end models to S3, while receiving the matching result feedback from S4, providing a basis for subsequent parameter adjustments. The specific implementation is as follows: Core data output: Capability vectors from the individual terminal side. Job skill requirements vector at the enterprise terminal side And the updated career ability map (including , The data is packaged in JSON format (with data generation timestamp and model version number) and output to S3 via an encrypted interface. Feedback signal reception: Receives the matching result feedback signal sent by S4. The signal includes: the matching degree of a single match (value [0,1]); the matching success / failure indicator; and the company's evaluation label for the matching result (such as "significant skills gap" or "high suitability"). Feedback data synchronization: The feedback signals are synchronously stored in the local feedback database of the individual and enterprise terminal models in the format of "matching ID-timestamp-feedback content" as the input source for parameter adjustment.

[0078] S25.4 Calculate the fluctuation range of the matching results using the sliding window algorithm. When the fluctuation amplitude exceeds a preset threshold, the dynamic parameters of the personal terminal-side capability evolution simulation model are automatically adjusted. Dynamic parameters of the enterprise terminal-side job demand simulation model and federal aggregation weight , .

[0079] Specifically, the purpose of setting S25.4 is to quantify the fluctuation of matching results through the sliding window algorithm, dynamically adjust the model dynamics parameters and aggregation weights, and achieve adaptive closed-loop optimization. The specific implementation is as follows: Matching fluctuation amplitude calculate: Using a sliding window algorithm (window size) =10, which represents the last 10 matching results), calculate the standard deviation of the matching degree using the following formula: ; In the formula: Indicates the first The matching degree of this match; This represents the mean of the matching degree within the window; Parameter adjustment trigger condition: Preset The threshold is 0.2 (determined based on the company's acceptable matching stability requirements); if If the threshold is exceeded, parameter adjustment will be triggered; Specific parameter adjustments: Dynamic parameters of the simulation model for the evolution of personal terminal capabilities : ; Dynamic parameters of enterprise terminal-side job requirement simulation model : ; Federated Aggregation Weights , The entropy weight method calculation in S24.2 was re-executed to adapt to the latest matching contribution. After adjustment, the simulation process was re-executed based on the new parameters for both end models to continuously optimize the matching stability.

[0080] S3, Multimodal Career Profile Construction: Receives the ability vector, job skill requirement vector and updated career ability map output from S2, constructs an individual multimodal career profile and an enterprise job requirement profile, and outputs the individual multimodal career profile and the enterprise job requirement profile to S4. In this step, the process of receiving the output data from S2 and constructing the personal multimodal professional profile and the enterprise job requirement profile in S3 includes the following steps: S31.1, Capability vector for receiving output from S2 Job skill requirement vector and updated occupational competency map; Specifically, S31.1 ensures that the received S2 output data is complete, formatted correctly, and consistent with the feature information from previous technical stages, providing reliable foundational data for subsequent profile construction. The specific implementation is as follows: Capability vector for receiving S2 output The job skill requirement vector and the updated professional competency map are validated using the JSON Schema specification (matching the field rules output by S2). At the same time, the version number of the professional competency map is validated to ensure that it is consistent with the version updated by S24, so as to ensure the timeliness of the data. If the validation fails, the corresponding data is returned to S2 and re-output.

[0081] S31.2, Transfer the capability vector Mapping to a professional competence graph, associating graph node attributes, and extracting individual multimodal features; Specifically, S31.2 associates abstract ability vectors with standardized career ability maps to extract multi-dimensional personal skill features, providing support for building rich personal multimodal career profiles. The specific implementation is as follows: Mapping and feature extraction of ability vectors to occupational ability maps: mapping ability vectors to occupational ability maps. Each dimension (corresponding to a single skill) is mapped to a corresponding node in the professional ability graph, and associated with the node's "skill ID, level, and application scenario" attributes; Based on this mapping relationship, three types of personal multimodal features are extracted: skill state features (ability vector values ​​+ skill level of graph nodes), skill association features (the strength of the skill association). The top 3 related skills and their corresponding strengths; if related skills exist, their corresponding strengths... In the case of ties, select skills in ascending order by skill ID to ensure uniqueness of the filtering results; skill evolution characteristics (combined with the transition matrix). (Potential skill upgrade paths obtained).

[0082] S31.3 Construct a personal multimodal occupational profile based on the extracted personal multimodal features; Specifically, S31.3 is used to integrate the extracted multimodal features to form a structured personal career profile, clearly presenting the status, relationships, and evolution of an individual's skills. The specific implementation of constructing a personal multimodal career profile is as follows: Based on extracted personal multimodal features, a hierarchical personal profile is constructed, comprising three core modules: a basic skills module (skill ID, level, proficiency, and application scenario), and a skills association module (a list of associated skills and their corresponding attributes). The skill evolution module (potential skill upgrade paths and corresponding transfer probabilities) is used; the profile is stored in JSON format, with a generation timestamp and map version number to ensure information traceability.

[0083] S31.4, Linking the job skill requirement vector to the skill correlation strength of the occupational ability map. Extract multimodal features of enterprise job requirements; Specifically, S31.4 combines the skill correlation strength of the occupational ability graph to expand the multi-dimensional features of job requirements, avoiding the limitations of describing requirements in a single skill dimension. The specific implementation of the correlation between job requirement vector and skill correlation strength and feature extraction is as follows: The correlation strength between each dimension of the job skill requirement vector (corresponding to the core skills of the job) and that skill in the professional ability map is determined. ; Based on this relationship, three types of multimodal features of enterprise job requirements are extracted: core requirement features (requirement vector values ​​+ skill requirement priority), related requirement features (a list of related skills for core skills, with selection rules consistent with S31.2, i.e., extracting the correlation strength). The top 3 related skills, when tied, are filtered in ascending order by skill ID), and flexible demand characteristics (combined with dynamic tolerance functions). (The skills gap is within an acceptable range).

[0084] S31.5. Based on the extracted multimodal features of enterprise job requirements, construct a profile of enterprise job requirements; Specifically, S31.5 is used to integrate the multimodal characteristics of enterprise needs to form a structured job requirement profile, which is matched with the individual career profile to form a corresponding information structure. The specific implementation of enterprise job requirement profile construction is as follows: Based on the extracted multimodal features of enterprise job requirements, a hierarchical job requirement profile is constructed, comprising three core modules: a core skills requirement module (skill ID, requirement intensity, requirement priority), and a related skills requirement module (a list of related skills and their corresponding attributes). The module includes a flexible demand module (acceptable range of skill gaps + explanation of business fluctuation adaptation); the profile format is consistent with the individual professional profile to ensure the compatibility of subsequent matching fields.

[0085] S31.6 Output the individual multimodal career profile and the enterprise job requirement profile to S4.

[0086] Specifically, S31.6 is used to securely transmit the completed individual and enterprise profiles to S4, providing a structured matching basis for intelligent job matching. The specific implementation is as follows: The individual's multimodal professional profile and the company's job requirement profile are packaged into a Parquet format file, encrypted using SSL and TLS protocols, and the data integrity is ensured by SHA-256 hash verification. The data is then output to S4 via an encrypted RESTful API interface. After receiving the data, S4 verifies the hash value and, after confirming that the data is complete and has not been tampered with, proceeds to the matching process.

[0087] S4. Intelligent Job Matching and Feedback: Receives the individual multimodal career profile and enterprise job requirement profile output by S3, calculates the matching degree and outputs the matching result, and feeds back the matching result as a matching result feedback signal to S2.

[0088] In this step, the process of receiving the output image from S3 and calculating the matching degree, output result, and feedback signal in S4 includes the following steps: S41.1 Receive the personal multimodal career profile and enterprise job requirement profile output by S3; Specifically, S41.1 ensures that the received profile data is complete, tamper-proof, and in a compliant format, and is consistent with the feature information output by S3, providing a reliable data foundation for matching degree calculation. The specific implementation is as follows: Data received: Receive personal multimodal career profiles (including basic skills module, skills association module, and skills evolution module) and enterprise job requirement profiles (including core skills requirement module, related skills requirement module, and flexible requirements module) output by S3, along with the timestamp and map version number generated by S3. Integrity verification: The SHA-256 hash algorithm is used to calculate the verification value of the received image data. The checksum accompanying the S3 transmission In comparison, if The integrity check then passes. Consistency verification: Based on the JSONSchema specification, a verification template is built to verify whether the skill ID of the individual profile and the core skill ID of the enterprise profile are consistent with the node ID of the professional ability graph, so as to ensure that the profile and the graph's skill system match. Verification process: If the integrity or consistency verification fails, a "Portrait data verification failed" signal is returned to S3, triggering S3 to re-output the corresponding portrait data.

[0089] S41.2 Skill Association Strength Based on Occupational Ability Map Calculate the matching degree between an individual's multimodal professional profile and the company's job requirement profile; Specifically, S41.2 combines the skill association logic of the professional competency map to comprehensively quantify the fit between individual skills and job requirements. It highlights the matching weight of core skills while also taking into account the implicit requirements of related skills, making the matching results more in line with the actual employment scenarios of enterprises. The specific implementation is as follows: The matching degree calculation adopts the weighted sum logic of "core skill matching score + related skill matching score". The core idea is to prioritize ensuring the fit of core skills, then supplement the evaluation through related skills, and finally make adjustments based on flexible requirements. The formula is as follows: ; in, The initial matching degree (value range [0,1]) This is the core skills weighting coefficient, set at 0.7 based on the priority of enterprise needs, meaning that the matching contribution of core skills is higher than that of related skills; Matching scores based on core skills The score is for matching related skills, and both scores take values ​​of [0,1].

[0090] The specific calculation process for each parameter in the above formula is as follows: Core Skill Matching Score First, clarify the core skills set for each job position within the company. ( (Number of core skills), corresponding to the proficiency level of that skill in the individual profile. (From Ability Vector) ), and the degree of job requirements for this skill. (From the job skills requirement vector) The core skill matching score is obtained by averaging the "proficiency × demand intensity" of each core skill. The formula is as follows: If an individual lacks a core skill, their corresponding proficiency level will be... The value is 0.

[0091] Related skill matching score For each core skill Based on the skill correlation strength of the occupational ability map (in , For related skills, extract the top two most closely related skills. This constitutes a set of related skills. This set of related skills is defined as follows: (Right now , According to the correlation strength The first two related skills after sorting) are selected according to the same rules as in S31.2 (if the correlation strength is the same, filter by skill ID in ascending order). Based on correlation strength... As the implicit requirement for this related skill in the job, combined with the individual's proficiency in the corresponding related skill. The score is calculated by weighted summation and then normalization, using the following formula: ; The total correlation strength of the denominator is used to ensure that the score falls within the [0,1] interval.

[0092] Elastic demand adaptation correction: Based on the acceptable range of skill gaps in the flexible demand module of enterprise job requirements profiles First, calculate the number of individual core skill gaps. —When a certain core skill proficiency Below demand intensity A gap is considered significant and counted when it reaches 50% (this 50% threshold is a generally accepted industry default and can be adjusted according to different industry characteristics, with the adjustment range limited to between 40% and 60%, and the specific value determined by the enterprise based on its business scenario). If the gap number... If the match is within an acceptable range, it indicates that the individual meets the company's flexible hiring requirements. A 5% positive adjustment is made to the initial match rate, resulting in the final match rate. To balance adaptability and objectivity.

[0093] S41.3 Output the matching results between the individual's multimodal career profile and the company's job requirement profile; Specifically, S41.3 is used to output clear and actionable matching results, providing HR with quantitative data for recruitment decisions and allowing candidates to intuitively understand the details of their skills' suitability for the position, avoiding vague matching conclusions. The specific implementation is as follows: Output core content: Basic matching information: includes personal ID, job ID, final match score (MatchScorefinal), and match level (high match:). ; Matching: Low match: ); Detailed Matching Details: The core skills dimension lists the proficiency level of each skill. Job demand intensity and single-item matching score Related Skill Dimension Description: Related skill name, skill correlation strength and individual proficiency This allows both parties to clearly see the strengths of the fit and the weaknesses that need improvement; Additional notes on adaptation: If the flexible demand adjustment is triggered, it will be clearly marked "Meets the enterprise's flexible skill gap demand, and the matching degree has been optimized by 5%"; if it is a low matching level, the top 3 key mismatched core skills will be listed first, combined with the skill correlation strength. Provide suggestions on potentially suitable related skills to assist in subsequent adjustments.

[0094] Output format and distribution: The matching results are packaged and stored in JSON format (archived by "matching time-job ID-person ID" for easy traceability), and a PDF version of the visual report is generated, which includes a skills matching radar chart and a pie chart of matching degree to intuitively present the matching details; the PDF report is distributed to the enterprise HR terminal and the candidate's authorized terminal through the same encrypted interface as above to ensure information security.

[0095] S41.4 The matching results are used as a matching result feedback signal and are simultaneously sent to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model.

[0096] Specifically, S41.4 is used to accurately feed back key information from the matching results to both ends of the S2 model, providing specific data support for model parameter adjustment and logic optimization, ensuring the complete closed loop of the "profile building - matching - feedback - model optimization" process. The specific implementation is as follows: Core content of feedback signals: Core decision parameter: final matching degree Matching level, and match success / failure indicators (matching level ≥ medium is considered successful, low match is considered a failure); Optimized reference information: Number of core skills gaps The top 3 key mismatched skill IDs, and the corresponding skill proficiency levels. Job demand intensity Skill-related strength This provides a clear direction for adjusting the models at both ends; Data traceability information: Match the version number of the professional competency map and the timestamp of the profile generation to ensure that the feedback data is traceable and verifiable, and consistent with the map iteration logic of S2.

[0097] Transmission and Acknowledgment Mechanism: Feedback signals are packaged in JSON format, using the same encryption scheme as described above—a dual-encryption channel is built using SSL and TLS 1.3 protocols, coupled with SHA-256 hash verification to ensure data integrity. The signals are then synchronously sent to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model via an encrypted RESTful API interface. The interface follows the encryption specifications of S16, with the unified interface path being " / api / v1 / match / feedback", the request method being POST, and the data format being JSON. Both models must return a "successful reception" acknowledgment message upon receiving the data. If no acknowledgment is received, S4 will automatically retry sending (up to 3 times) to ensure effective delivery of the feedback signal and support the adaptive closed-loop optimization of S2.

[0098] Those skilled in the art will understand that the process of implementing all or part of the steps of the above embodiments can be carried out by hardware or by a program instructing the relevant hardware.

[0099] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning, characterized in that: Includes the following steps: S1. Multimodal Occupational Data Acquisition and Preprocessing: Multimodal occupational data is acquired through multi-source heterogeneous data acquisition technology. Data cleaning, feature annotation, and normalization techniques are used to preprocess the raw data to generate a standardized data subset that includes individual data subsets and enterprise data subsets. The standardized data subset is output to S2 through a data interaction interface. At the same time, the graph iteration feedback signal from S2 is received to dynamically adjust the preprocessed feature dimensions. S2, Asymmetric Federation Simulation Modeling of Occupational Abilities: Receives a standardized subset of data output from S1, builds an asymmetric federated simulation framework based on the dynamic characteristics of occupational abilities, and initializes the personal terminal-side capability evolution simulation model, the enterprise terminal-side job requirement simulation model, and the corresponding modeling logic. The simulation process utilizes both a personal terminal-side capability evolution simulation model and an enterprise terminal-side job requirement simulation model. Simulations are performed on subsets of personal and enterprise data on both sides to generate capability vectors representing dynamic changes in skill status, as well as representations including job skill requirement vectors and dynamic tolerance functions. The dynamic tolerance function quantifies the acceptable threshold for skill gaps and the range of business flexibility adjustments for enterprises. A professional capability graph and transition matrix based on an industry-standard skill framework are maintained. During federated training iterations, the incremental correlation parameters and graph correction signals output from the personal and enterprise simulation ends are aggregated to update the professional capability graph and transition matrix. The updated graph parameters are then distributed to the personal and enterprise simulation ends. The personal and enterprise simulation ends calibrate the capability evolution simulation model logic and optimize the job requirement simulation model logic based on the updated graphs, respectively. Output the capability vector, job skill requirement vector, and updated career capability map to S3, and receive matching result feedback signals from S4 to adjust the dynamic parameters and federated aggregation rules of the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model. S3, Multimodal Career Profile Construction: Receives the ability vector, job skill requirement vector and updated career ability map output from S2, constructs an individual multimodal career profile and an enterprise job requirement profile, and outputs the individual multimodal career profile and the enterprise job requirement profile to S4. S4. Intelligent Job Matching and Feedback: Receives the individual multimodal career profile and enterprise job requirement profile output by S3, calculates the matching degree and outputs the matching result, and feeds back the matching result as a matching result feedback signal to S2.

2. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 1, characterized in that, The multimodal occupational data collection and preprocessing in S1 includes the following steps: S11. Collect multimodal occupational data from individuals and enterprises, and perform format conversion and unified storage on the collected raw data; S12. Clean the stored raw data, use the 3σ principle to remove outliers in the personal skill status data, use the box plot method to filter extreme values ​​in the enterprise job requirement data, use the K-nearest neighbor imputation method to fill missing data, and remove duplicate data based on timestamp priority. S13. Based on a pre-set industry standard skill dictionary, the cleaned data is automatically labeled with features. The labeling dimensions for personal data include skill type, proficiency level, and application scenario. The labeling dimensions for enterprise data include core job skills, skill requirement priority, and business relevance. Labeling errors are corrected through manual sampling and verification. S14. The labeled data are processed using a differential normalization algorithm. Personal data is mapped to the [0,1] interval using Min-Max normalization, and enterprise data is converted to standard normal distribution data using Z-Score normalization. S15. Divide the normalized data into blocks according to data ownership and time dimension to generate a standardized data subset including personal data subset and enterprise data subset. The standardized data subset is stored in Parquet format and is accompanied by data collection timestamp and preprocessing process traceability identifier. S16. Output the standardized data subset to S2 through the encrypted RESTful API interface. The data transmission is encrypted using SSL and TLS protocols and is verified for integrity. S17. Receive the graph iteration feedback signal from S2, extract the graph node correlation parameters, and when the parameter change exceeds the preset threshold, retain the features with correlation higher than the set value with the professional ability graph node, and remove redundant features to adjust the preprocessed feature dimension.

3. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 2, characterized in that, The process of asymmetric federated simulation modeling of professional competence in S2 includes the following steps: S21. Receive the standardized data subset output by S1, complete integrity and format verification, build an asymmetric federated simulation framework based on the dynamic characteristics of occupational abilities, initialize the individual terminal-side ability evolution simulation model, the enterprise terminal-side job requirement simulation model and corresponding modeling logic, and determine the core parameter as the skill evolution rate threshold. Demand fluctuation coefficient ; S22. Using the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model, respectively, simulation processing is performed on the corresponding terminal-side personal data subsets and enterprise data subsets to generate capability vectors representing the dynamic changes in skill status. and includes job skill requirement vectors and dynamic tolerance functions. The representation; S23. Maintain a professional competency graph and transition matrix based on an industry-standard skills framework. Through skill association strength Define the graph rules and initialize the matrix state transition logic based on the probability distribution to provide basic support for the simulation model of personal terminal-side capability evolution and the simulation model of enterprise terminal-side job requirements. S24. In the federated training iteration, a weighted aggregation algorithm is used to process the parameter increments of the simulation model for the evolution of individual terminal capabilities. Incremental parameters of enterprise terminal-side job requirement simulation model Update the professional ability graph and transition matrix with the graph correction signal. Simulation models for the evolution of capabilities of post-encrypted distribution to personal terminals and simulation models for job requirements on enterprise terminals; S25. Based on the updated graph, calibrate the simulation model logic for the evolution of personal terminal-side capabilities and optimize the simulation model logic for enterprise terminal-side job requirements. Output core data to S3, receive the matching result feedback signal from S4, and analyze the fluctuation amplitude. Adjust the parameters of the simulation model for the evolution of individual terminal capabilities and the simulation model for the job requirements of enterprise terminal positions to form an adaptive closed loop.

4. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 3, characterized in that, The process of receiving a standardized data subset, performing verification, building an asymmetric federated simulation framework, and initializing the model in S2.1 includes the following steps: S21.

1. Verify the integrity of the standardized data subset using the SHA-256 hash algorithm, and perform format verification on the data subset based on the JSONSchema specification. If the verification fails, return to S1 for reprocessing. S21.2 After establishing the asymmetric federated simulation framework, clarify the attribution of core parameters: skill evolution rate threshold. Corresponding to the simulation model of the evolution of personal terminal capabilities, the demand fluctuation coefficient The corresponding enterprise terminal-side job demand simulation model has parameter values ​​that are associated with historical occupational skill evolution data and enterprise demand change data. S21.

3. Fit the two data sources separately using a time series fitting algorithm, combined with time decay weights. Sure and The value of is used to synchronously initialize the basic structural parameters of the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model.

5. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 4, characterized in that, The process of processing data through simulation using the corresponding model and generating capability vectors and demand representations in S2.2 includes the following steps: S22.

1. The personal terminal side calls the personal terminal side capability evolution simulation model to perform time series modeling on the skill status change data of the personal data subset and extract the core features of skill proficiency and evolution trend. S22.2, Generate capability vector based on extracted features Vector data is synchronously stored in the personal terminal-side capability evolution simulation model; S22.3, The enterprise terminal side calls the enterprise terminal side job demand simulation model to fit the enterprise's historical skills gap matching data and business fluctuation data. Construct a dynamic tolerance function ; S22.4, Through dynamic tolerance function Quantify the acceptable threshold for skills gaps and the range of business flexibility adjustments, and generate a vector containing job skill requirements. The representation of.

6. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 5, characterized in that, The process of maintaining the professional competence map and transition matrix and adapting it to the corresponding model in S2.3 includes the following steps: S23.

1. Adopt the national vocational skills standards as the industry standard skills framework, and define the nodes of the vocational ability map as skills ID, level, and application scenario; S23.2 Analyze historical skill co-occurrence data and calculate the correlation strength between skills using a similarity algorithm. ,based on Define the edge attributes of the graph with the skill evolution path definition; S23.3 Statistically analyze historical skill conversion data and initialize the transition matrix based on the data probability distribution. State transition logic; S23.4, Complete the transition matrix After initialization, the basic data of the graph and matrix are synchronized to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model.

7. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 6, characterized in that, The process of aggregating model parameter increments and correction signals and updating the distribution map matrix in S2.4 includes the following steps: S24.

1. Invoke the weighted federated average algorithm to increment the parameters of the simulation model for the evolution of personal terminal capabilities. Incremental parameters of enterprise terminal-side job requirement simulation model And perform aggregation processing on the spectral correction signal; S24.2 Calculate the data volume weight and model contribution weight of the personal terminal-side capability evolution simulation model, and the data volume weight and model contribution weight of the enterprise terminal-side job requirement simulation model using the entropy weight method. The final aggregate weight is obtained by weighting the data volume weight and model contribution weight of the simulation model of personal terminal capability evolution and the simulation model of enterprise terminal job requirements. , ; S24.3, Based on Aggregate Weight , and correction factor Update the correlation strength of the professional ability graph With transition matrix ; S24.4 The updated map and matrix parameters are encrypted using SSL and TLS protocols. After passing hash verification, they are distributed to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model, respectively.

8. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 7, characterized in that, The process of calibrating and optimizing the model logic and receiving feedback to adjust parameters in S2.5 includes the following steps: S25.1 The personal terminal side calibrates the personal terminal side capability evolution simulation model logic based on the updated map, and uses the mean absolute error (MAE) and root mean square error (RMSE) as dual indicators to verify the model output accuracy. When the error exceeds the preset threshold, the structural parameters of the personal terminal side capability evolution simulation model are adjusted. S25.

2. On the enterprise terminal side, the simulation model logic for job requirements is optimized based on the updated graph. The accuracy of requirement matching is statistically analyzed. When the accuracy is lower than the benchmark value, the dynamic tolerance function is adjusted. The coefficient; S25.3, Transfer the capability vector The job skill requirement vector and the updated map are output to S3, and the matching result feedback signal is received from S4. The feedback data is synchronized to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model. S25.4 Calculate the fluctuation range of the matching results using the sliding window algorithm. When the fluctuation amplitude exceeds a preset threshold, the dynamic parameters of the personal terminal-side capability evolution simulation model are automatically adjusted. Dynamic parameters of the enterprise terminal-side job demand simulation model and federal aggregation weight , .

9. The method for constructing multimodal professional profiles and intelligent job matching based on simulation models and federated learning according to claim 8, characterized in that, The process of receiving the output data from S2 and constructing a personal multimodal professional profile and an enterprise job requirement profile in S3 includes the following steps: S31.1, Capability vector for receiving output from S2 Job skill requirement vector and updated occupational competency map; S31.2, Transfer the capability vector Mapping to a professional competence graph, associating graph node attributes, and extracting individual multimodal features; S31.3 Construct a personal multimodal occupational profile based on the extracted personal multimodal features; S31.4, Linking the job skill requirement vector to the skill correlation strength of the occupational ability map. Extract multimodal features of enterprise job requirements; S31.

5. Based on the extracted multimodal features of enterprise job requirements, construct a profile of enterprise job requirements; S31.6 Output the individual multimodal career profile and the enterprise job requirement profile to S4.

10. The method for constructing multimodal occupational profiles and intelligent job matching based on simulation models and federated learning according to claim 9, characterized in that, The process of receiving the output image from S3 and calculating the matching degree, output result, and feedback signal in S4 includes the following steps: S41.1 Receive the personal multimodal career profile and enterprise job requirement profile output by S3; S41.2 Skill Association Strength Based on Occupational Ability Map Calculate the matching degree between an individual's multimodal professional profile and the company's job requirement profile; S41.3 Output the matching results between the individual's multimodal career profile and the company's job requirement profile; S41.4 The matching results are used as a matching result feedback signal and are simultaneously sent to the personal terminal-side capability evolution simulation model and the enterprise terminal-side job requirement simulation model.

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