A talent supply and demand matching method and system for regional industrial demand

Abstract

The present application belongs to the technical field of talent supply and demand matching, and discloses a talent supply and demand matching method and system for regional industrial demand. By building an industry layer, an industry chain layer, an enterprise layer and a technology layer four-layer industry data system, space-time tags are bound for each architecture node to record the industrial time sequence development track and regional distribution characteristics. The entropy weight and analytic hierarchy process fusion algorithm is used to divide the industry chain level and calibrate the talent demand priority. The industrial life cycle research and judgment model is used to quantify the industrial development trend and the talent demand structural fluctuation coefficient. The talent demand iteration trend of different types of industries can be defined, and the talent allocation demand of the whole link of the industrial chain is coordinated. By collecting the talent whole life cycle static qualification and dynamic growth data, a standardized talent information database is built, and the multi-modal feature fusion technology is used to disassemble the two dimensions of talent explicit ability and implicit ability, and the five-level quantitative rating of implicit ability is completed.

Description

Technical Field

[0001] This invention belongs to the field of talent supply and demand matching technology, specifically a talent supply and demand matching method and system oriented towards regional industrial needs. Background Technology

[0002] Currently, regional talent supply and demand matching is mainly conducted through talent websites, third-party recruitment platforms, and offline job fairs. The mainstream matching method is based on keyword text comparison algorithms, which compare the keyword similarity between talents and job positions to complete the matching and screening. Some technologies combine basic machine learning algorithms to optimize the matching and ranking results, but they can only meet the basic recruitment and adaptation needs of companies for scattered positions. The existing technologies still have the following technical problems:

[0003] Existing talent supply and demand matching technologies only match the immediate job needs of a single enterprise, lacking the ability to systematically analyze the overall layout of regional industries, the collaborative architecture of the industrial chain, and the development trend of industries in sequence. They have not established a structural and forward-looking talent matching system that is suitable for the development of regional industrial clusters.

[0004] Existing technologies cannot combine the characteristics of the industrial chain hierarchy and the laws of the industrial life cycle to judge the medium- and long-term talent demand trends, making it difficult to locate the talent gap in leading industries and emerging industries. They can only achieve single-point and short-term job matching.

[0005] Existing technologies only match supply and demand based on explicit, fixed characteristics such as education, major, work experience, and skills certificates, which cannot meet the talent requirements of cross-border and integrated emerging industries. At the same time, existing technologies cannot break down and quantify the implicit comprehensive qualities and full-cycle growth potential of talents. They can only achieve a superficial match between talents and basic entry conditions for positions, and it is difficult to match talents' comprehensive abilities and growth characteristics with the core positions in the industrial chain and the long-term development needs of regional industries. The problem of homogeneous matching is prominent, and the quality of matching is poor.

[0006] Once existing technologies complete a single person-job matching, they cannot dynamically update the matching results in line with regional industrial restructuring, iterative enterprise job requirements, industrial technology upgrades, and the growth of talent's own capabilities. The effectiveness of the matching continuously diminishes over time. At the same time, existing technologies lack the ability to dynamically monitor the supply and demand of talent in regional industries, identify imbalance risks, and implement targeted regulation. They also lack the ability to adaptively iterate and optimize, as well as the ability to allocate and regulate talent at the macro level. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for matching talent supply and demand in response to regional industrial needs, so as to solve one or more problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for matching talent supply and demand to regional industrial needs, comprising the following specific steps:

[0009] Furthermore, the map construction phase collects enterprise registration, business registration changes, patent applications, project bidding, industrial import and export data, and government industrial planning documents within the region. It binds dedicated spatiotemporal tags to all map nodes within the four-layer architecture of industry layer, industrial chain layer, enterprise layer, and technology layer, recording the temporal development trajectory and regional distribution characteristics of each industry, enterprise, and technology link.

[0010] Using an entropy weight and hierarchical analysis fusion algorithm, the various links of the industrial chain are quantitatively evaluated from multiple dimensions such as strategic priority, technological barrier level, and regional economic contribution. Based on the evaluation results, the industrial chain is divided into three levels: core key industrial chain links, supporting and complementary industrial chain links, and auxiliary and redundant industrial chain links. The talent demand priority standards corresponding to each level are marked.

[0011] Simultaneously, an industry life cycle assessment model is constructed, using industry growth rate, patent iteration rate, and policy support as core quantitative indicators. Combined with time series data, the model completes indicator fitting analysis, defines the scope of leading industries, emerging industries, and declining industries in the region, quantifies the talent demand iteration trend of various types of industries and various links in the industrial chain, and calculates the structural fluctuation coefficient of talent demand in various links of the industrial chain.

[0012] Furthermore, the demand mining stage collects talent demand data from enterprise recruitment information, enterprise annual reports, industry research reports, and government industrial policies, extracts explicit demand parameters such as job titles, professional requirements, skill requirements, work experience, and salary levels, and matches talent demand standards with the attributes of the three levels of the industrial chain. The core and key industrial chain links correspond to the demand standards for innovation capabilities, technical breakthrough capabilities, and cross-domain integration capabilities; the supporting and complementary industrial chain links correspond to the demand standards for practical skills and team collaboration capabilities; and the auxiliary and redundant industrial chain links correspond to the demand standards for standardized job performance capabilities.

[0013] By building a technology integration demand decomposition algorithm using knowledge graph reasoning technology, and targeting the cross-border integration characteristics of emerging regional industries, the algorithm decomposes the cross-domain composite skill requirements needed for industrial development and refines the talent capability requirements in multi-technology cross-scenarios.

[0014] At the same time, a dynamic update mechanism for industry talent demand will be established. Based on the rate of technological iteration in each industry, a corresponding demand decay and update cycle will be set. Outdated job skill standards will be periodically eliminated. Combining the results of industry life cycle analysis and time-series development data, the quantity, structural proportion, and comprehensive ability standards of talent demand in each link of the industrial chain will be iteratively updated.

[0015] Furthermore, the talent modeling stage collects static data such as basic talent information, educational background, work experience, project experience, skill certificates, patent achievements, and paper publications, while simultaneously collecting dynamic data such as the career growth trajectory of talents, cross-project technology integration experience, job flow and adaptation records, and vocational training iteration status, to construct a talent full life cycle data chain and build a standardized talent basic information database.

[0016] Using multimodal feature fusion technology, the talent competency system is deconstructed into two dimensions: explicit competency and implicit competency. The explicit competency includes practical skills such as professional skills, language skills, and computer operation skills, while the implicit competency includes comprehensive qualities such as learning ability, innovation ability, communication ability, stress resistance ability, cross-domain collaboration ability, and technology iteration ability. A five-level quantitative rating standard of poor, average, good, excellent, and outstanding is established for the implicit competency indicators.

[0017] Simultaneously, a talent growth potential prediction model is constructed, embedding the potential coefficient of job matching in the industrial chain. Combined with the talent's full-cycle growth trajectory data, the long-term development potential of talent in different industrial types and different levels of industrial chain links is quantified, forming a dual quantitative result of talent's short-term job matching ability and long-term industrial development matching ability, thus completing the construction of talent capability profile.

[0018] Furthermore, the supply and demand matching stage constructs a dynamic and standardized supply and demand matching feature vector space based on the talent capability profile and the quantitative data of industry adaptation potential, unifies the quantitative dimensions and data format of industry demand parameters and talent capability parameters, and synchronously accesses industry demand time-series iterative data and talent growth dynamic data in the vector space.

[0019] The attention mechanism deep learning model is set up to complete the matching degree calculation. The attention mechanism deep learning model autonomously adjusts the weight parameters of each feature dimension according to the hierarchical attributes of the industrial chain links. For the core key industrial chain links, the weight is preferentially allocated to the implicit core features, including talent innovation ability, cross-domain integration ability, and growth potential. For the supporting and auxiliary redundant industrial chain links, the weight is evenly allocated to the explicit features.

[0020] Based on the matching degree values ​​calculated by the model, and according to the multi-dimensional ranking rules of prioritizing core industries, adapting to industrial chain synergy, and adapting to long-term development, the matching combinations of talents and positions are ranked in a hierarchical manner, and a talent supply and demand matching list is output to complete the preliminary matching and screening of talents and regional industry positions.

[0021] Furthermore, the collaborative optimization stage, based on the talent supply and demand matching list, sorts out the talent complementarity relationship, job collaboration logic and normal talent flow pattern in the upstream and downstream of the industrial chain within the regional industrial map, sets out the talent matching collaboration constraint rules of the industrial chain, and clarifies the talent adaptation priority, flow adaptation standard and resource allocation norms of each level of the industrial chain.

[0022] An integer linear programming algorithm is set up with the core objective of maximizing the efficiency of talent resource allocation across the entire industry chain. The talent supply and demand matching list is globally optimized and adjusted to standardize the flow path of talent in upstream and downstream positions of the industry chain.

[0023] Furthermore, the early warning and intervention phase constructs a talent supply and demand monitoring indicator system, taking the supply and demand ratio, gap rate, surplus rate, talent structure mismatch rate, and talent mobility activity of each industry and each link of the industrial chain as core monitoring indicators, and sets three levels of early warning thresholds: mild, moderate, and severe. The system compares the monitoring indicator values ​​with the corresponding early warning thresholds in real time, and automatically triggers the corresponding level of early warning signal when the indicator value reaches the threshold.

[0024] Differentiated regulation strategies are matched based on the different development attributes of leading industries, emerging industries, and declining industries. Talent introduction and special skills training programs are set up for emerging industries, talent flow guidance and skills transformation training programs are set up for declining industries, and special talent policy support programs are set up for core industrial chain links.

[0025] At the same time, an industrial policy linkage mechanism will be established to link talent supply and demand monitoring data, early warning results and targeted regulation plans with regional industrial planning and talent policy formulation.

[0026] Furthermore, the closed-loop iteration phase collects the full-cycle growth data of matched talents in their positions, including work performance, job promotion, employee turnover, and skills improvement. Simultaneously, it collects industry iteration data on enterprise production efficiency, technological output, market competitiveness, and changes in job requirements to build a basic database of matching effects.

[0027] Establish a standardized matching effect evaluation index system, integrate two-way feedback information from talent employment experience and enterprise talent hiring experience, quantify the matching effect between talent and job and between industry and talent, and generate quantitative data on matching effect;

[0028] By matching effect monitoring data and two-way feedback data, the internal parameters of the industry demand mining model, talent capability modeling model, supply and demand matching calculation model, and industry map weight judgment model are adjusted in layers.

[0029] This embodiment also provides a talent supply and demand matching system oriented towards regional industry needs, based on the above method, and includes the following modules:

[0030] The industry map construction module is used to collect regional industry data, bind spatiotemporal labels to the nodes of the four-layer industry architecture, use the entropy weight and hierarchical analysis fusion algorithm to divide the industry chain levels, and combine the industry life cycle model to quantify the development trend and talent demand fluctuation coefficient to build a four-layer industry data system.

[0031] The hierarchical topology and constraint rules established within the four-layer industrial architecture are as follows: the industrial layer coordinates the overall regional industrial layout and forms a top-down affiliation binding relationship with the industrial chain layer, clearly defining the industrial scope to which each industrial chain belongs; the industrial chain layer forms a business support relationship with the enterprise layer, limiting enterprises to only the industrial chain links corresponding to their core businesses; the enterprise layer forms an application implementation relationship with the technology layer, only including the technologies actually used and developed by enterprises in the corresponding technology nodes; all node relationships must pass four verifications: industrial synergy, technological relevance, business matching, and regional layout rationality, while data linkage channels are established between nodes, so that data updates in any node can be synchronously transmitted to related nodes.

[0032] The industry demand mining module, based on the full-domain industry map, collects talent demand data from multiple channels, analyzes explicit industry demand and implicit cross-domain composite talent demand, matches differentiated demand standards of the industrial chain, dynamically iterates and updates talent demand parameters of each link of the industrial chain, and builds an industry talent demand database.

[0033] The talent holographic modeling module is used to collect data on the static qualifications and dynamic growth cycle of talents, build a standardized talent information database, decompose the explicit and implicit abilities of talents through multimodal feature fusion technology and complete the five-level quantitative rating of implicit abilities, and quantify the long-term industrial adaptation potential of talents by embedding the industrial chain adaptation coefficient into the growth potential prediction model, thus completing the construction of talent profiles.

[0034] The intelligent supply and demand matching module builds a standardized feature vector space, accesses time-series iterative data to update parameters in real time, dynamically adjusts feature weights through an attention mechanism deep learning model, completes talent job matching sorting through multi-dimensional rules, and outputs a talent supply and demand matching list.

[0035] The supply chain collaboration optimization module, based on the preliminary matching results, sorts out the collaborative patterns of talent flow in the supply chain and sets constraint rules. It uses an integer linear programming algorithm to globally adjust the matching results and achieve the optimal allocation of talent resources across the entire domain.

[0036] The supply and demand early warning and intervention module establishes a multi-dimensional supply and demand monitoring indicator and a multi-level early warning threshold system to identify the risk of supply and demand imbalance and match corresponding industrial regulation plans, and link regional industrial and talent policies to complete the intervention.

[0037] The closed-loop iterative optimization module collects feedback data from both talent and enterprises, constructs a matching effect evaluation system, and adjusts the parameters of each core model in layers.

[0038] The beneficial effects of this invention are as follows:

[0039] 1. This invention establishes a four-layer industrial data system comprising the industry layer, supply chain layer, enterprise layer, and technology layer, binding spatiotemporal tags to each architectural node to record the temporal development trajectory and regional distribution characteristics of the industry. It uses a fusion algorithm of entropy weighting and hierarchical analysis to divide the supply chain levels and prioritize talent demand. An industry life cycle analysis model quantifies the structural fluctuation coefficients of industry development trends and talent demand, enabling a systematic and forward-looking assessment of regional industrial talent demand. This allows for the definition of iterative trends in talent demand across different types of industries and the coordinated allocation of talent across the entire supply chain.

[0040] 2. This invention establishes a standardized talent information database by collecting static qualification and dynamic growth data throughout the entire talent lifecycle. It uses multimodal feature fusion technology to decompose the explicit and implicit abilities of talents, and completes a five-level quantitative rating of implicit abilities. By embedding an industry chain adaptation coefficient into a talent growth potential prediction model, it quantifies the talent's industry adaptation potential and constructs a comprehensive and three-dimensional talent capability profile. Through a dynamic standardized supply and demand feature vector space and an attention mechanism deep learning model, it dynamically adjusts the matching feature weights, completes hierarchical sorting according to multidimensional rules, and outputs a hierarchical matching list to achieve the matching of talents with positions and industries.

[0041] 3. This invention analyzes the patterns of talent collaboration and flow within the industry chain, sets standardized matching constraints, and uses an integer linear programming algorithm to achieve global optimization and adjustment of talent resources across the entire domain; it constructs a talent supply and demand monitoring indicator and multi-level early warning threshold system to automatically identify the risk of supply and demand imbalance, match differentiated control solutions, and coordinate with industrial policies to complete intervention; at the same time, it collects two-way data on talent growth and enterprise operation, builds a matching effect evaluation system, and continuously adjusts the core algorithm model through two-way feedback. Attached Figure Description

[0042] Figure 1 This invention provides a complete flowchart of the supply and demand matching process for regional industry talents.

[0043] Figure 2 A flowchart for constructing a talent capability profile for this invention. Detailed Implementation

[0044] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] like Figures 1 to 2As shown, this embodiment of the invention provides a method for matching talent supply and demand to regional industrial needs, including the following specific steps:

[0046] In this embodiment of the invention, the map construction stage collects enterprise registration, business change data, patent applications, project bidding, industrial import and export data and government industrial planning documents within the region over the past 5-10 years. It binds dedicated spatiotemporal tags to all map nodes within the four-layer architecture of industry layer, industrial chain layer, enterprise layer and technology layer, and records the temporal development trajectory and regional distribution characteristics of each industry, enterprise and technology link.

[0047] The spatiotemporal tags adopt a unified coding rule and are composed of four parts: regional code, timestamp, industry level code, and unique node identifier. The regional code records the geographical distribution information at the provincial, municipal, and county levels. The timestamp records the time sequence nodes of industrial development at the year, quarter, and month levels. The industry level code distinguishes four node types: industry layer, industrial chain layer, enterprise layer, and technology layer. The unique node identifier is used to accurately locate a single industry, enterprise, or technological link. The spatiotemporal tags are bound to industry data in real time, and the dynamic data such as industrial and commercial changes, patent applications, project implementation, and production capacity changes are updated synchronously. The time sequence development trajectory is automatically updated to form a traceable, queryable, and analyzable spatiotemporal data system for the industry.

[0048] Using an entropy weight and hierarchical analysis fusion algorithm, the various links of the industrial chain are quantitatively evaluated from multiple dimensions such as strategic priority, technological barrier level, and regional economic contribution. Based on the evaluation results, the industrial chain is divided into three levels: core key industrial chain links, supporting and complementary industrial chain links, and auxiliary and redundant industrial chain links. The talent demand priority standards corresponding to each level are marked.

[0049] Simultaneously, an industry life cycle assessment model is constructed, using industry growth rate, patent iteration rate, and policy support as core quantitative indicators. Combined with time series data, the model completes indicator fitting analysis, defines the scope of leading industries, emerging industries, and declining industries in the region, quantifies the talent demand iteration trend of various types of industries and various links in the industrial chain, and calculates the structural fluctuation coefficient of talent demand in various links of the industrial chain in the next 3-5 years.

[0050] The entropy weight and analytic hierarchy process (AHP) fusion algorithm used in the map construction phase consists of three parts: an entropy weight method submodule, an AHP judgment matrix submodule, and a weight fusion submodule. The entropy weight method is used to calculate the objective weights of three indicators: strategic priority, technological level, and regional economic contribution. The core calculation process is as follows: first, the original indicator data is standardized; then, the information entropy of each indicator is calculated using the entropy weight method's information entropy calculation formula; finally, the entropy weight is obtained using the entropy weight method's indicator weight calculation formula. The AHP method constructs a third-order pairwise comparison judgment matrix, calculates the weight vector using the square root method, and determines the matrix's validity if the consistency ratio CR < 0.1. Finally, the fused weights are calculated using a weighted average method, with the formula as follows: Entropy weight hierarchical analysis applies the calculation results to the classification of industrial chain levels, distinguishing three categories of links: core and key industrial chain links, supporting and complementary industrial chain links, and auxiliary and redundant industrial chain links. The industrial life cycle judgment model is a multivariate time series fitting model, using industrial growth rate, patent iteration rate, and policy support as core input indicators. The least squares method is used to complete the time series data fitting, and the fitting formula is the industrial life cycle comprehensive score fitting formula. Among them, industrial growth rate ≥15% is judged as emerging industry, 5%-15% is judged as leading industry, and <5% is judged as declining industry. Industrial chain links with Y≥2.5 are defined as core industries, thereby quantifying the industrial development trend and talent demand fluctuation pattern.

[0051] Formula for fitting the comprehensive score of the industry life cycle:

[0052]

[0053] This indicates the comprehensive assessment score for the industry life cycle;

[0054] The weighting coefficient representing the industry growth rate indicator;

[0055] This represents the annual growth rate of the industry.

[0056] The weighting coefficients representing the patent iteration rate index;

[0057] This indicates the annual update frequency of core technology patents in the industry.

[0058] The weighting coefficient representing the level of policy support;

[0059] This represents a quantitative score indicating the policy support received by the industry.

[0060] The formula for calculating information entropy using the entropy weight method is as follows:

[0061]

[0062] Indicates the first The information entropy value of the evaluation indicator;

[0063] This indicates the total number of links in the industrial chain participating in the evaluation;

[0064] The operator represents the natural logarithm operator;

[0065] Indicates the sequence number of a link in the industrial chain;

[0066] The index indicates the sequence number of the evaluation indicator;

[0067] This represents the standardization percentage of the i-th link in the industrial chain under the j-th indicator.

[0068] Entropy weight method index weight calculation formula:

[0069]

[0070] Indicates the first The entropy weighting method weight values ​​of the evaluation indicators;

[0071] Indicates the first The information entropy value of the evaluation indicator;

[0072] This indicates the total number of evaluation indicators involved in the calculation;

[0073] The index indicates the serial number of the evaluation indicator.

[0074] Formula for calculating the integration weight of the industry chain hierarchy:

[0075]

[0076] This represents the final integration weight value for the hierarchical division of the industry chain;

[0077] The weighting coefficients represent the weights in the entropy weighting method;

[0078] This represents the index weight value calculated using the entropy weight method;

[0079] The weighting coefficients represent the weights in the analytic hierarchy process (AHP).

[0080] This represents the index weight values ​​calculated using the analytic hierarchy process.

[0081] A dynamic talent demand coupling and transmission mechanism is established between various industrial chain nodes. The core and key industrial chain links serve as the source of demand transmission. The increase or decrease in talent demand will be transmitted step by step to the upstream and downstream supporting and auxiliary redundant industrial chain links according to the degree of technological relevance, industrial synergy, and capacity matching. The system will automatically calculate the transmission coefficient based on the correlation strength between nodes and adjust the predicted value of talent demand in the related links in a synchronous manner. At the same time, the system will cross-verify the demand data after transmission and correct the transmission deviation by combining the overall regional industrial planning, project implementation progress, and technology iteration rhythm.

[0082] In this embodiment of the invention, the demand mining stage collects talent demand data from enterprise recruitment information, enterprise annual reports, industry research reports, and government industrial policies, extracts explicit demand parameters such as job title, professional requirements, skill requirements, work experience, and salary level, and matches talent demand standards with the three-level node hierarchy attributes of the industrial chain. The core and key links of the industrial chain correspond to the demand standards for innovation capability, technical breakthrough capability, and cross-domain integration capability. The supporting and complementary links of the industrial chain correspond to the demand standards for practical ability and team collaboration capability. The auxiliary and redundant links of the industrial chain correspond to the demand standards for standardized job performance capability.

[0083] By building a technology integration demand decomposition algorithm using knowledge graph reasoning technology, and targeting the cross-border integration characteristics of emerging regional industries, the algorithm decomposes the cross-domain composite skill requirements needed for industrial development and refines the talent capability requirements in multi-technology cross-scenarios.

[0084] The knowledge graph reasoning technology used in the demand mining phase consists of a four-level structure: entity layer, relation layer, reasoning layer, and output layer. The entity layer includes four types of entity nodes: industry type, core technology, job type, and skill requirements. The relation layer defines four types of entity relationships: dependency, correspondence, adaptation, and coupling. The reasoning layer integrates rule-based reasoning and path-based reasoning engines. Rule-based reasoning pre-sets rules for determining technical coupling degree and cross-domain skill matching rules. Path-based reasoning uses the TransE algorithm to mine the relationship paths between entities. Among them, technical coupling degree is calculated by combining the frequency of skill co-occurrence and the degree of industry integration. Technology combinations with a coupling degree ≥ 0.7 are judged as strongly coupled technologies, corresponding to the demand for cross-domain composite talents, while technology combinations with a coupling degree < 0.7 are judged as weakly coupled technologies, corresponding to the demand for single-skill talents. The output results of the reasoning layer are standardized and then imported into the industry talent demand database to realize the automated decomposition and standardized output of implicit composite talent demands.

[0085] At the same time, a dynamic update mechanism for industry talent demand will be established. Based on the rate of technological iteration in each industry, a corresponding demand decay and update cycle will be set. Outdated job skill standards will be periodically eliminated. Combining the results of industry life cycle analysis and time-series development data, the quantity, structural proportion, and comprehensive ability standards of talent demand in the industrial chain links will be iteratively updated for the next 3-5 years.

[0086] The industry talent demand database employs a dual-track mechanism combining regular and triggered updates. Regular updates are conducted quarterly, synchronizing the latest industry policies, industry research data, and enterprise recruitment needs and job changes. Triggered updates are initiated immediately upon adjustments to regional industrial planning, iterations of core technologies, the implementation of major projects, or significant changes in enterprise employment structures. The data update process automatically eliminates outdated skills, removes ineffective job positions, and discards obsolete standards, while simultaneously adding cross-disciplinary skills, requirements for emerging positions, and upgraded competency standards.

[0087] In this embodiment of the invention, the talent modeling stage collects static data such as basic talent information, educational background, work experience, project experience, skill certificates, patent achievements, and paper publications, and simultaneously collects dynamic data such as the career growth trajectory of talents, cross-project technology integration experience, job flow and adaptation records, and vocational training iteration status, to construct a talent full life cycle data chain and build a standardized talent basic information database.

[0088] The talent database is configured with fixed data fields and dynamically expanded fields. The fixed fields include six categories: basic personal information, educational background, work experience, skills certificates, project achievements, and professional evaluation. The dynamically expanded fields include four categories: skills iteration records, cross-job adaptation performance, training and improvement effects, and career growth trajectory. Data is collected from multiple sources, including government talent databases, enterprise employment files, vocational training platforms, and project application systems. Encrypted storage and anonymization rules are used to ensure the security of talent information. The database is updated daily with static qualification data and monthly with dynamic growth data, forming a complete and continuous data chain for the entire talent lifecycle.

[0089] Using multimodal feature fusion technology, the talent competency system is deconstructed into two dimensions: explicit competency and implicit competency. The explicit competency includes practical skills such as professional skills, language skills, and computer operation skills, while the implicit competency includes comprehensive qualities such as learning ability, innovation ability, communication ability, stress resistance ability, cross-domain collaboration ability, and technology iteration ability. A five-level quantitative rating standard of poor, average, good, excellent, and outstanding is established for the implicit competency indicators.

[0090] The implicit competency five-level rating is based on a comprehensive judgment of multi-dimensional performance data and behavioral behavior. The poor level corresponds to not meeting the basic competency requirements of the position and being unable to complete routine work tasks independently; the average level corresponds to meeting the basic job performance requirements; the good level corresponds to completing the job duties and having the ability to learn and solve problems proactively; the excellent level corresponds to outstanding competency performance; and the outstanding level corresponds to having industry-leading competency levels.

[0091] The multimodal feature fusion technology used in the talent modeling stage is an end-to-end feature fusion network, consisting of a five-level structure: a text feature extraction module, a numerical feature encoding module, a temporal feature embedding module, a feature concatenation layer, and a fully connected fusion layer. The text feature extraction module uses a BERT pre-trained model to extract 768-dimensional features from text data such as talent project experience and published papers. The numerical feature encoding module uses one-hot encoding to convert static numerical data such as academic qualifications and skill certificates into fixed-dimensional vectors. The temporal feature embedding module uses an LSTM network to extract temporal features from dynamic data such as career growth trajectories and training iterations. The feature concatenation layer concatenates the three types of modal features in terms of dimensions. The fully connected fusion layer has a 128-dimensional hidden layer and uses the ReLU activation function to complete the nonlinear fusion of features, outputting a standardized fusion feature vector of two dimensions: explicit and implicit talent abilities.

[0092] Simultaneously, a talent growth potential prediction model is constructed, embedding the potential coefficient of job matching in the industrial chain. Combined with the talent's full-cycle growth trajectory data, the long-term development potential of talent in different industrial types and different levels of industrial chain links is quantified, forming a dual quantitative result of talent's short-term job matching ability and long-term industrial development matching ability, thus completing the construction of talent capability profile.

[0093] The system automatically compares talent competency profiles with the demand standards of target industry chain positions to accurately identify the types of talent competency gaps. These gaps are categorized into four main types: professional skills gaps, project experience gaps, innovation capability gaps, and cross-domain collaboration gaps. Each type is further classified into three levels based on severity: minor, moderate, and severe. For different types and levels of competency gaps, the system automatically matches customized competency-building paths. Skill gaps are connected to regional vocational skills training platforms and online professional course resources; experience gaps are matched with practical training projects and on-the-job training opportunities within the industry chain; and competency gaps are pushed with cutting-edge industry technical materials, innovative thinking training materials, and collaboration ability enhancement plans. The system tracks the progress of talent competency building throughout the process and dynamically updates talent competency profiles and industry adaptability potential.

[0094] The talent growth potential prediction model constructed in the talent modeling stage is a LightGBM regression model. The model consists of a feature input layer, a decision tree ensemble layer, and an output layer. The input layer incorporates 28 core features, including explicit ability scores, implicit ability ratings, years of professional experience, number of projects, and skill iteration frequency. The decision tree ensemble layer uses 100 decision trees with a maximum depth of 8 and a minimum of 2 leaf nodes. The model training process is as follows: first, the model is divided into an 80% training set and a 20% validation set. Five-fold cross-validation is used to complete the model training. The learning rate is set to 0.05, the loss function is mean squared error (MSE), and the training termination condition is that the validation set R² ≥ 0.85. The output layer generates industry adaptation potential values ​​in the range of 0-1. These industry adaptation potential values ​​are directly embedded into the talent profile and synchronously associated with the industry chain level adaptation standards.

[0095] In this embodiment of the invention, the supply and demand matching stage constructs a dynamic and standardized supply and demand matching feature vector space based on the talent capability profile and potential quantification data, unifies the quantification dimensions and data format of industry demand parameters and talent capability parameters, realizes the connection between industry demand characteristics and talent capability characteristics, and synchronously accesses industry demand time-series iterative data and talent growth dynamic data in the vector space to achieve real-time updates of matching feature parameters around the clock.

[0096] The dynamic standardized supply and demand feature vector space is divided into feature items according to a unified dimension. On the industry demand side, feature items such as job requirements, skill standards, ability levels, and industry fit types are set. On the talent ability side, feature items such as explicit skill scores, implicit ability ratings, growth potential values, and industry chain fit coefficients are set. All feature items are converted into numerical features of a unified dimension, outliers and missing values ​​are removed, and standardization and normalization processing is completed. A real-time synchronization mechanism is established in the vector space. When industry demand changes or talent ability is updated, the feature data is automatically synchronized to the vector space.

[0097] The attention mechanism deep learning model is set up to complete the matching degree calculation. The attention mechanism deep learning model autonomously adjusts the weight parameters of each feature dimension according to the hierarchical attributes of the industrial chain links. For the core key industrial chain links, the weight is preferentially allocated to the implicit core features, including talent innovation ability, cross-domain integration ability, and growth potential. For the supporting and auxiliary redundant industrial chain links, the weight is evenly allocated to the explicit features.

[0098] The feature weight allocation of the attention mechanism model is triggered by the system automatically reading the industry chain level calibration results. In core and key industry chain links, the system automatically elevates the weight of implicit core features such as innovation capability, cross-domain integration capability, and growth potential to a dominant position. For supporting and complementary industry chain links, the system allocates the weight of explicit skill features and implicit capability features in a balanced proportion. For auxiliary and redundant industry chain links, the system elevates the weight of explicit practical features such as professional skills and work experience to a dominant position. The weight allocation is completed in real time.

[0099] Based on the matching degree values ​​calculated by the model, and according to the multi-dimensional ranking rules of prioritizing core industries, adapting to industrial chain synergy, and adapting to long-term development, the matching combinations of talents and positions are ranked in a hierarchical manner, and a talent supply and demand matching list is output to complete the preliminary matching and screening of talents and regional industry positions.

[0100] After the initial matching list is generated, the system adapts and fine-tunes the matching results by taking into account the development stage, team configuration, technological environment, and work mode of the target positions' respective companies. For innovative companies in their early stages, the system increases the weighting of talent's innovation capabilities, self-learning abilities, and resilience. For mature, large-scale production companies, the system increases the weighting of talent's practical skills, process execution capabilities, and team collaboration stability. For research-oriented companies, the system focuses on strengthening the adaptation and verification of talent's technical problem-solving capabilities, cross-domain integration capabilities, and patent development capabilities. After fine-tuning, the matching score is recalculated, and the matching ranking results are optimized.

[0101] The deep learning model using the attention mechanism employed in the supply and demand matching phase is a lightweight TransformerEncoder structure, consisting of four modules: an embedding layer, a multi-head attention layer, a feedforward neural network layer, and an output matching layer. The model is deeply integrated with regional industrial scenarios. The embedding layer converts industry demand characteristics and talent capability characteristics into 128-dimensional standardized vectors. The multi-head attention layer sets up eight attention heads to autonomously capture the correlation between implicit talent capabilities and core industry needs. For core and key industrial chain links, it automatically increases the weight of implicit characteristics such as innovation capability, cross-domain integration capability, and growth potential. For supporting and auxiliary redundant industrial chain links, it evenly distributes the weight of explicit characteristics such as professional skills and work experience. The feedforward neural network layer adopts a two-layer fully connected structure with the GELU activation function. The output matching layer generates a 0-1 matching score using the Sigmoid function. The model training parameters are set as follows: batch size 32, optimizer Adam, learning rate 1e-4, and training epochs 50. Combinations with a matching score ≥ 0.7 are considered valid matches. The models are sorted according to rules prioritizing core industries, adapting to industrial chain collaboration, and adapting to long-term development, outputting a hierarchical matching list.

[0102] In this embodiment of the invention, the collaborative optimization stage is based on the talent supply and demand matching list, sorts out the talent complementarity relationship, job collaboration logic and normal talent flow pattern of the upstream and downstream of the industrial chain in the regional industrial map, sets the talent matching collaboration constraint rules of the industrial chain, and clarifies the talent adaptation priority, flow adaptation standard and resource allocation specification of each level of the industrial chain.

[0103] The talent matching and coordination constraints rules for the industrial chain include three core elements: First, hierarchical adaptation constraints, prioritizing the matching of high-growth potential and high-impact talent for core and key industrial chain positions, while supporting and auxiliary redundant positions can be matched based on explicit skill requirements; second, flow path constraints, talent can only flow within relevant links in the upstream and downstream of the industrial chain, prohibiting disorderly flow across unrelated industries and unrelated technology links; and third, resource allocation constraints, tilting talent staffing, training resources, and policy support towards core industrial chain links to ensure a sufficient supply of talent for key links; all constraint rules are embedded in the system execution process, and compliance is automatically verified during the matching process.

[0104] An integer linear programming algorithm is set up with the core objective of maximizing the efficiency of talent resource allocation across the entire industry chain. The algorithm optimizes and adjusts the talent supply and demand matching list globally, standardizes the flow path of talent in upstream and downstream positions of the industry chain, and achieves the optimal allocation of talent resources across the entire industry.

[0105] The integer linear programming algorithm used in the collaborative optimization phase is the core algorithm for global talent resource optimization. This algorithm is deeply integrated with the talent flow scenario across the industry chain and consists of four parts: defining decision variables, constructing the objective function, setting constraints, and solving the algorithm. The decision variable xij is a binary variable of 0-1, where xij=1 indicates a match between the i-th talent and the j-th position, and xij=0 indicates a mismatch. The objective function focuses on maximizing the efficiency of talent resource allocation across the entire industry chain, and its formula is the talent resource allocation efficiency maximization objective function, where xij is the matching score between talent and position. The constraints include three core rules: first, one person, one position constraint, meaning each talent can match at most one position ∑j=1mxij≤1; second, full staffing constraint, meaning the number of people matched for each position does not exceed the authorized number ∑i=1nxij≤Cj (Cj is the authorized number for position j); and third, integer constraint, meaning xij∈{0,1}. The algorithm uses the Gurobi solver to complete the global optimization calculation, outputting the optimal matching result, standardizing the talent flow path in the upstream and downstream of the industry chain, and ensuring efficient and collaborative talent allocation across the entire industry chain.

[0106] Objective function for maximizing the efficiency of overall talent resource allocation:

[0107]

[0108] The operator represents the maximization operation.

[0109] This represents the total score of the efficiency of talent resource allocation across the entire industry chain;

[0110] The index indicating the serial number of an individual talent;

[0111] This indicates the total number of talents participating in the matching process;

[0112] Indicates the position number subscript;

[0113] This indicates the total number of positions participating in the matching process;

[0114] This represents the matching score between the i-th talent and the j-th position;

[0115] A binary decision variable representing the matching status between talent and job.

[0116] To address the shortage of key positions and flexible employment needs in the core industrial chain, the system establishes a cross-enterprise talent sharing and transfer mechanism, clearly defining the applicable scenarios, scope, and compliance requirements for talent sharing. Talent sharing is only permitted between companies within the same upstream and downstream industrial chain with close technological ties. It requires triple verification: informed consent from the talent, qualification verification by both employing companies, and assessment of job suitability. The system also clearly defines the talent's responsibilities, compensation, ownership of results, and safety guarantees during the sharing period. The system automatically records the entire talent sharing process, tracks performance and suitability during the sharing period, and guides the talent back to their original company or completes the formal transfer process upon completion.

[0117] After the optimal matching result is output, it must pass three compliance checks. The first check is constraint condition check, confirming that all matching combinations comply with collaborative constraint rules such as one person, one position, maximum staffing limit, and talent mobility path. The second check is matching validity check, with a matching degree ≥ 0.7 as the criterion. The third check is industry collaboration check, verifying that the matching result meets the talent allocation collaboration requirements of upstream and downstream industries. Matching results that pass all checks become officially effective, and combinations that fail any check are automatically rolled back to the optimization module for recalculation.

[0118] In this embodiment of the invention, the early warning intervention stage constructs a talent supply and demand monitoring indicator system, taking the supply and demand ratio, gap rate, surplus rate, talent structure mismatch rate, and talent mobility activity of each industry and each link of the industrial chain as core monitoring indicators, and dividing the indicator into three levels of early warning thresholds: mild, moderate, and severe. The system compares the monitoring indicator values ​​with the corresponding early warning thresholds in real time, and automatically triggers the corresponding level of early warning signal when the indicator value reaches the threshold.

[0119] The core monitoring indicator system for talent supply and demand implements hierarchical data collection and real-time calculation rules. The supply-demand ratio, talent gap rate, and talent surplus rate are collected, calculated, and updated daily, while the talent structure mismatch rate and talent mobility activity are collected, calculated, and updated weekly. All indicator data are sourced from authoritative data sources such as real enterprise employment data, dynamic talent pool data, job turnover records, and industry operation statistics. The indicator values ​​are generated after data cleaning, outlier removal, and standardization.

[0120] The talent supply and demand monitoring and early warning model constructed in the early warning and intervention phase is a multi-level threshold triggering model. The model is deeply linked with the regional industrial development type. The core monitoring indicators include five items: supply-demand ratio, talent gap rate, talent surplus rate, talent structure mismatch rate, and talent mobility activity. The early warning thresholds are standardized into three levels: slight, moderate, and severe. A supply-demand ratio of <0.5 indicates a severe gap, 0.5-0.8 indicates a moderate gap, and 0.8-1 indicates a slight gap; a talent structure mismatch rate of >30% indicates a severe mismatch, 20%-30% indicates a moderate mismatch, and 10%-20% indicates a slight mismatch. After the early warning signal is triggered, the model automatically matches differentiated control strategies. For emerging industries, it triggers instructions for talent introduction and special training; for declining industries, it triggers instructions for talent mobility guidance and skills transformation; and for core industrial chain links, it triggers instructions for special policy support. The early warning results and control plans are simultaneously connected to the regional industrial planning and talent policy system.

[0121] Differentiated regulation strategies are matched based on the different development attributes of leading industries, emerging industries, and declining industries. Talent introduction and special skills training programs are set up for emerging industries, talent flow guidance and skills transformation training programs are set up for declining industries, and special talent policy support programs are set up for core industrial chain links.

[0122] The system responds to and allocates resources according to the level of warnings for talent shortages. For core industry shortages corresponding to severe warnings, priority is given to allocating core resources such as government special talent recruitment funds, high-end talent recruitment channels, university-enterprise cooperation targeted training quotas, and talent housing guarantees. For regular shortages corresponding to moderate warnings, basic resources such as public recruitment services, inclusive skills training, and talent employment guidance are allocated. For potential shortages corresponding to minor warnings, only guiding resources such as industry talent trend analysis and talent reserve planning suggestions are pushed. All resource allocation is made reasonably in combination with the total amount of regional resources, carrying capacity, and usage cycle.

[0123] All regulatory plans clearly define specific implementation measures and procedures. The regulatory plan for emerging industries includes specific measures such as subsidies for attracting high-end talent, targeted training for skills in short supply, and school-enterprise cooperation in order-based talent cultivation. The regulatory plan for declining industries includes implementation measures such as cross-industry retraining for talent, placement of surplus talent, and guidance for skills upgrading and transformation. The regulatory plan for core industrial chain links includes special support such as talent housing security, green channels for professional title evaluation, and incentives for project research and development. All regulatory plans are linked to government talent service platforms, enterprise employment departments, and vocational education institutions to form a joint implementation mechanism of government guidance, enterprise participation, and institutional support.

[0124] At the same time, an industrial policy linkage mechanism will be established to link talent supply and demand monitoring data, early warning results and targeted regulation plans with regional industrial planning and talent policy formulation.

[0125] In this embodiment of the invention, the closed-loop iteration stage collects the full-cycle growth data of talent job performance, promotion, employee turnover, and skills improvement after the pairing is completed, and simultaneously collects industry iteration data such as enterprise production efficiency, technological output, market competitiveness, and changes in job requirements to build a basic database of matching effects;

[0126] Establish a standardized matching effect evaluation index system, integrate two-way feedback information from talent employment experience and enterprise talent hiring experience, quantify the matching effect between talent and job and between industry and talent, and generate quantitative data on matching effect;

[0127] The matching effectiveness evaluation system sets up three categories of evaluation indicators: talent, enterprise, and industry. Talent indicators include job competence, job satisfaction, and growth rate; enterprise indicators include job performance efficiency, team collaboration effectiveness, and talent retention rate; and industry indicators include talent fit rate in the industrial chain, demand satisfaction, and contribution to capacity improvement. The evaluation adopts a combination of regular data collection and real-time feedback, generating short-term evaluation reports monthly and comprehensive evaluation reports quarterly. The evaluation results are directly used for model parameter adjustment and system optimization. Matching combinations with low evaluation scores are automatically marked for review, and targeted optimization of demand mining, capability modeling, and matching calculation processes is carried out to improve the overall matching quality.

[0128] By matching effect monitoring data and two-way feedback data, the internal parameters of the industry demand mining model, talent capability modeling model, supply and demand matching calculation model, and industry map weight judgment model are adjusted in layers to achieve adaptive iterative optimization of each core algorithm model and technical module.

[0129] When the model encounters problems such as decreased matching accuracy, supply and demand analysis deviations, and distorted adaptation results, the system automatically initiates a full-process deviation tracing process to pinpoint the links and causes of the deviations. The deviations are categorized into four types: data acquisition deviations, feature extraction deviations, weight calculation deviations, and scenario generalization deviations. Targeted corrections are implemented for different deviation types: for data deviations, the acquisition channels and verification rules are optimized; for feature deviations, key feature dimensions and extraction logic are supplemented; for weight deviations, algorithm parameters and allocation rules are adjusted; and for scenario deviations, training samples and adaptation logic for the corresponding scenarios are expanded. After the corrections are completed, a small-scale gray-scale test is conducted to verify that the deviations have been eliminated and the effect has improved before officially updating to the running model. At the same time, tracing records and correction logs are retained.

[0130] Model parameter tuning includes core dimensions such as feature weights, number of iterations, threshold boundaries, matching rules, and evaluation criteria. Tuning operations are performed in stages according to the matching effect evaluation scores. Models with excellent evaluation scores undergo minor parameter optimization; models with medium evaluation scores focus on adjusting feature weights and matching rules; models with low evaluation scores undergo a complete reconstruction of parameters and algorithm logic. After tuning, a small-scale gray-scale test is conducted first, and a full update is implemented after the test meets the standards, while retaining historical parameter versions.

[0131] The closed-loop iteration phase employs a Bayesian optimization algorithm to complete the model's closed-loop iteration, with an iteration cycle of quarterly. The algorithm consists of a four-level structure: a data acquisition layer, an effect evaluation layer, a parameter tuning layer, and a model update layer. The data acquisition layer collects growth data such as talent work performance, job promotion, and skill improvement, as well as operational data such as enterprise production efficiency, technological output, and employee satisfaction. The effect evaluation layer constructs a comprehensive evaluation index, calculating the overall fit effect score with a weight of 0.6 for talent performance and 0.4 for enterprise employee satisfaction. The parameter tuning layer automatically searches for the optimal parameter combination through Bayesian optimization for the core parameters of the industry demand mining model, talent capability modeling model, supply and demand matching calculation model, and industry graph weight judgment model, focusing on tuning key parameters such as feature weights, number of training rounds, learning rate, and threshold boundaries. After parameter replacement, the model update layer verifies the matching accuracy monthly to ensure monthly improvement in matching accuracy, thus constructing a full-cycle self-iteratory talent supply and demand matching operation system.

[0132] This invention also provides a talent supply and demand matching system oriented towards regional industrial needs, based on the above method, and including the following modules:

[0133] The industry mapping module is used to collect regional industry data over the past 5-10 years, bind spatiotemporal labels to nodes in the four-layer industry architecture, use an entropy weighting and hierarchical analysis fusion algorithm to divide the industry chain into levels, and combine an industry life cycle model to quantify development trends.

[0134] Based on the fluctuation coefficient of talent demand, a four-tier industrial structure is established;

[0135] The industry demand mining module, based on the full-domain industry map, collects talent demand data from multiple channels, analyzes explicit industry demand and implicit cross-domain composite talent demand, matches differentiated demand standards of the industrial chain, dynamically iterates and updates talent demand parameters of each link of the industrial chain, and builds an industry talent demand database.

[0136] The talent holographic modeling module is used to collect data on the static qualifications and dynamic growth cycle of talents, build a standardized talent information database, decompose the explicit and implicit abilities of talents through multimodal feature fusion technology and complete the five-level quantitative rating of implicit abilities, and quantify the long-term industrial adaptation potential of talents by embedding the industrial chain adaptation coefficient into the growth potential prediction model, thus completing the construction of talent profiles.

[0137] The intelligent supply and demand matching module builds a standardized feature vector space, accesses time-series iterative data to update parameters in real time, dynamically adjusts feature weights through an attention mechanism deep learning model, completes talent job matching sorting through multi-dimensional rules, and outputs a talent supply and demand matching list.

[0138] The supply chain collaboration optimization module, based on the preliminary matching results, sorts out the collaborative patterns of talent flow in the supply chain and sets constraint rules. It uses an integer linear programming algorithm to globally adjust the matching results and achieve the optimal allocation of talent resources across the entire domain.

[0139] The supply and demand early warning and intervention module establishes a multi-dimensional supply and demand monitoring indicator and a multi-level early warning threshold system to identify the risk of supply and demand imbalance and match corresponding industrial regulation plans, and link regional industrial and talent policies to complete the intervention.

[0140] The closed-loop iterative optimization module collects feedback data from both talent and enterprises, constructs a matching effect evaluation system, and adjusts the parameters of each core model in layers.

[0141] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0142] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for matching talent supply and demand to meet regional industrial needs, characterized in that, The specific steps include the following: During the map construction phase, regional industrial data is collected, a four-layer industrial architecture is built, spatiotemporal tags are bound to each node within the architecture, the industrial chain level and demand priority are marked, industrial development trends are analyzed, and the structural fluctuation coefficient of talent demand in each link of the industrial chain is calculated. In the demand mining stage, talent demand data is collected based on industry maps, the explicit and implicit cross-domain composite talent demand of industries is analyzed, the demand standards of the industrial chain are matched and a dynamic update mechanism is set up to build an industry talent demand database. In the talent modeling stage, data on the static qualifications and dynamic growth of talents throughout the entire life cycle are collected to build a talent information database. The two major ability dimensions of explicit and implicit capabilities are broken down and quantified and rated. The industry chain adaptability coefficient is embedded to quantify the talent's industry adaptability potential and complete the construction of talent profiles. In the supply and demand matching stage, a dynamic and standardized supply and demand feature vector space is built, the quantification standard of supply and demand parameters is unified and the supply and demand feature data is iterated in real time. The feature weights are dynamically adjusted according to the industrial chain level to calculate the matching degree. Combined with the matching rules, the sorting and filtering are performed to output a hierarchical matching list. In the collaborative optimization phase, based on the preliminary matching results, the patterns of talent collaborative flow in the industry chain are sorted out, standardized matching constraint rules are set, and a global optimization algorithm is used to adjust the matching results; During the early warning and intervention phase, a talent supply and demand monitoring indicator and multi-level early warning threshold system are constructed to automatically identify the risk of supply and demand imbalance, output corresponding control plans for different industry types and link industry talent policies. In the closed-loop iteration phase, data on talent development and business operations are collected to build a matching effect evaluation system to quantify the effectiveness of the adaptation. The core algorithm model parameters are then adjusted in layers through two-way feedback data to build a long-term operating system.

2. The talent supply and demand matching method oriented towards regional industrial needs as described in claim 1, characterized in that, The four-layer industrial architecture includes an industry layer, a supply chain layer, an enterprise layer, and a technology layer. The spatiotemporal tags bound to each node within the architecture are used to record the temporal development trajectory and regional distribution characteristics of each industry, enterprise, and technology link.

3. The talent supply and demand matching method oriented towards regional industrial needs as described in claim 2, characterized in that, The industrial chain hierarchy and demand priority identification adopts a fusion algorithm of entropy weight and hierarchical analysis, which conducts quantitative evaluation from multiple dimensions such as strategic priority, level of technological barriers, and regional economic contribution. The industrial chain is divided into three levels: core and key, supporting and complementary, and auxiliary and redundant, and the corresponding talent demand priority is identified.

4. The talent supply and demand matching method oriented towards regional industrial needs as described in claim 3, characterized in that, The industry development trend analysis is achieved through an industry life cycle analysis model, using industry growth rate, patent iteration rate, and policy support as core quantitative indicators to define the categories of leading, emerging, and declining industries, and to quantify the iteration trend of talent demand in various types of industries.

5. The talent supply and demand matching method oriented towards regional industrial needs according to claim 4, characterized in that, The demand mining phase collects talent demand data from enterprise recruitment information, annual reports, industry research reports, and government industrial policies. It extracts explicit job demand parameters and matches them with corresponding industry chain level demand standards. Core and key industry chain links correspond to innovation and cross-domain integration capability standards, supporting links correspond to practical and collaborative capability standards, and auxiliary and redundant links correspond to standardized job performance capability standards. Through knowledge graph reasoning technology, it deconstructs cross-domain implicit composite talent demand, sets demand update cycles according to the industry technology iteration rate, and periodically cleans up and eliminates outdated job skill standards.

6. The talent supply and demand matching method oriented towards regional industrial needs as described in claim 5, characterized in that, The talent modeling stage uses multimodal feature fusion technology to break down talent capabilities into two dimensions: explicit and implicit. It establishes a five-level quantitative rating standard for implicit ability indicators such as learning ability, innovation ability, and communication ability, categorized as poor, average, good, excellent, and outstanding. By embedding an industry chain fit coefficient into a talent growth potential prediction model, a dual quantitative result of short-term job fit and long-term industrial development fit is generated.

7. The talent supply and demand matching method oriented towards regional industrial needs as described in claim 6, characterized in that, The supply and demand matching stage uses an attention mechanism deep learning model to calculate the matching degree, prioritizing the allocation of implicit core feature weights to core and key industrial chain links, and evenly allocating explicit feature weights to supporting, auxiliary, and redundant links.

8. The talent supply and demand matching method oriented towards regional industrial needs as described in claim 7, characterized in that, The global optimization algorithm is an integer linear programming algorithm, with the core objective of maximizing the efficiency of talent resource allocation across the entire industry chain, and standardizing the flow path of talent in the upstream and downstream of the industry chain. The early warning and intervention stage constructs a core monitoring indicator system for talent supply and demand, including supply-demand ratio, gap rate, and talent structure mismatch rate, and sets three levels of early warning thresholds: mild, moderate, and severe, and matches differentiated control strategies for different industry types.

9. A talent supply and demand matching system oriented towards regional industrial needs, based on the method described in any one of claims 1-8, characterized in that, Includes the following modules: The industry mapping module is used to collect regional industry data, build a four-layer industry architecture, bind spatiotemporal tags to each node within the architecture, mark the industry chain level and demand priority, analyze industry development trends, and calculate the talent demand fluctuation coefficient. The industry demand mining module collects talent demand data based on industry maps, analyzes cross-domain implicit composite talent demand, dynamically updates talent demand parameters for each link in the industrial chain, and builds an industry talent demand database. The talent holographic modeling module is used to collect data on the static qualifications and dynamic growth of talents throughout the entire life cycle to build an information database. It breaks down the talents into two dimensions: explicit abilities and implicit abilities. Implicit abilities are quantitatively rated at five levels to quantify the talents' potential for industry adaptation and complete the talent profile. The intelligent supply and demand matching module builds a dynamic and standardized supply and demand feature vector space, unifies parameter quantification standards and iterates data in real time, dynamically adjusts feature weights for matching calculations, sorts and filters according to adaptation rules, and outputs a hierarchical matching list. The supply chain collaboration optimization module analyzes the patterns of talent collaboration and flow within the supply chain and sets constraint rules, then uses a global optimization algorithm to adjust the matching results. The supply and demand early warning and intervention module constructs a talent supply and demand monitoring indicator and multi-level early warning threshold system, identifies the risk of supply and demand imbalance, outputs control solutions, and links with industry talent policies. The closed-loop iterative optimization module collects bidirectional operational data on talent development and enterprise management, establishes a matching effect evaluation system, and adjusts the core algorithm model parameters in layers.

10. The talent supply and demand matching system oriented towards regional industrial needs according to claim 9, characterized in that, The industry map construction module uses an entropy weight and hierarchical analysis fusion algorithm to complete the division of the industry chain hierarchy, and quantifies the industry development trend through the industry life cycle judgment model; the talent holographic modeling module uses multimodal feature fusion technology to decompose the explicit and implicit ability dimensions of talent, and performs a five-level quantitative rating of the implicit ability.

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