Intelligent occupational planning matching system and method fusing innovation and entrepreneurship elements

Abstract

The invention provides an intelligent occupational planning matching system and method fusing innovation and entrepreneurship elements, and is applied to the technical field of data processing. According to the method, data such as personal occupational appeals and ability characteristics and external data such as innovation and entrepreneurship resources and industry trends are collected, redundancy is removed based on a feature screening algorithm, and a standardized occupational-resource feature sequence is generated; converting the data into a supply-demand matching relation network map, processing the data through a hybrid modeling tool, subdividing a scene through a hierarchical clustering algorithm, and constructing a dynamic supply-demand coupling model; based on the model, setting a matching adaptation degree function, establishing an optimization equation by using an improved genetic algorithm, and calculating a key matching gap value; index extraction and grading are carried out in combination with gap values, and a multi-dimensional matching feature matrix is established; comparing the real-time data with the benchmark data to generate an optimization potential coefficient; and combining the coefficient with a personal planning grouping path, screening key factors, establishing an adaptive matching optimization model, and finally outputting a real-time matching suggestion and an occupational planning optimization scheme.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent career planning and matching system and method that integrates elements of innovation and entrepreneurship. Background Technology

[0002] Existing systems often focus on a simple match between basic personal traits and job positions, failing to effectively integrate external core elements such as innovation and entrepreneurship resources and industry trends. This leads to a disconnect between career planning and market dynamics and entrepreneurial opportunities, making it difficult to meet the development needs of individuals driven by innovation and entrepreneurship. On the other hand, the lack of a systematic supply and demand coupling mechanism and the failure to accurately segment career development scenarios make it impossible to provide differentiated matching solutions for individual needs at different stages (start-up, growth, and maturity).

[0003] Meanwhile, existing methods are not accurate enough in identifying matching gaps, and fail to clarify the core discrepancies between individual abilities, resource supply and career paths through quantitative analysis. Furthermore, the optimization process lacks a dynamic adjustment mechanism, making it difficult to adapt to real-time changes in individual development dynamics and external resource supply. Ultimately, this results in insufficient relevance and practicality of career planning guidance information, which cannot support long-term stable development for individuals.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0006] According to one aspect of this application, an intelligent career planning matching method integrating innovation and entrepreneurship elements is provided, comprising: acquiring data on individual career aspirations, abilities, development vision, and the supply of innovation and entrepreneurship resources, industry trends, and job requirements; based on big data collection and multi-dimensional information integration technology, using a feature screening algorithm to remove invalid and redundant information, generating a standardized career-resource feature sequence; after converting the standardized career-resource feature sequence into a supply-demand matching relationship network graph, using a hierarchical clustering algorithm to subdivide career development scenarios, constructing a dynamic supply-demand coupling model; based on the dynamic supply-demand coupling model, setting the matching fit degree as an element-scenario dependent function, using an improved genetic algorithm coupled with an industry development prediction correction term to construct an optimization objective equation, extracting the fit weights of different innovation and entrepreneurship elements to construct a multi-dimensional matching matrix, and performing collaborative calculation on the fit relationship between individual traits, innovation resources, and career paths to obtain key matches. Gap values ​​are identified; based on key matching gap values, competency fit, resource accessibility, and path feasibility are extracted according to career development stages to classify matching levels and construct a multi-dimensional matching feature matrix containing personal characteristics, innovation elements, industry demands, and job standard information; based on the multi-dimensional matching feature matrix, real-time matching status is compared with industry benchmark data to generate abnormal signals of competency deficiencies, resource mismatches, and path deviations, and an optimization potential coefficient is generated based on the slope of the matching degree-development potential curve; the optimization potential coefficient is combined with personal development plans to group career paths, and a deep neural network algorithm is used to screen key adaptation factors, integrating personal traits, innovation resources, industry demands, and job standard information to construct an adaptive matching optimization model; based on the adaptation strategy output of the adaptive matching optimization model, combined with the correlation information between personal development dynamics and the supply of innovation and entrepreneurship resources, real-time matching suggestions and career planning optimization schemes are generated.

[0007] Another aspect of this application discloses an intelligent career planning matching device integrating innovation and entrepreneurship elements, comprising: a data acquisition module for acquiring data on individual career aspirations, abilities, development vision, and the supply of innovation and entrepreneurship resources, industry trends, and job requirements; using a feature filtering algorithm to remove invalid and redundant information and generate a standardized career-resource feature sequence; a processing module for converting the standardized career-resource feature sequence into a supply-demand matching relationship network graph; using a hierarchical clustering algorithm to subdivide career development scenarios and construct a dynamic supply-demand coupling model; based on the dynamic supply-demand coupling model, setting the matching fit degree as an element-scenario dependent function; using an improved genetic algorithm coupled with an industry development prediction correction term to construct an optimization objective equation; extracting the fit weights of different innovation and entrepreneurship elements to construct a multi-dimensional matching matrix; and performing collaborative calculations on the fit relationship between individual traits, innovation resources, and career paths to obtain key matching gap values. By combining key matching gap values, and extracting competency fit, resource accessibility, and path feasibility according to career development stages, matching levels are categorized, and a multi-dimensional matching feature matrix is ​​constructed, incorporating personal characteristics, innovation elements, industry demands, and job standards. Based on the multi-dimensional matching feature matrix, real-time matching status is compared with industry benchmark data to generate abnormal signals of competency gaps, resource mismatches, and path deviations. An optimization potential coefficient is generated based on the slope of the matching degree-development potential curve. The optimization potential coefficient is combined with personal development plans to group career paths, and a deep neural network algorithm is used to screen key adaptation factors. By integrating personal traits, innovation resources, industry demands, and job standards, an adaptive matching optimization model is constructed. Based on the adaptation strategy output of the adaptive matching optimization model, and combined with the correlation information between personal development dynamics and the supply of innovation and entrepreneurship resources, real-time matching suggestions and career planning optimization guidance information are generated.

[0008] According to another aspect of this application, an electronic device includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-described intelligent career planning matching method that integrates innovative entrepreneurship elements by executing the executable instructions.

[0009] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a second processor, implements the above-described intelligent career planning and matching method that integrates innovation and entrepreneurship elements.

[0010] This application provides an intelligent career planning matching system and method that integrates innovation and entrepreneurship elements. First, it collects data on individual career aspirations, abilities, and external data such as innovation and entrepreneurship resources and industry trends. Redundancy is removed using a feature filtering algorithm to generate a standardized career-resource feature sequence. This sequence is then converted into a supply-demand matching relationship network graph. A hierarchical clustering algorithm is used to subdivide scenarios and construct a dynamic supply-demand coupling model. Based on this model, key matching gap values ​​are calculated. Indicators are extracted and levels are assigned based on these gap values ​​to establish a multi-dimensional matching feature matrix. Real-time and benchmark data are compared to generate optimization potential coefficients. These coefficients are then integrated with individual plans, and a deep neural network algorithm is used to filter key matching factors, constructing an adaptive matching optimization model. Finally, real-time matching suggestions and career planning optimization schemes are output, improving the relevance and adaptability of career planning.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0012] Figure 1 The flowchart illustrates an embodiment of an intelligent career planning and matching method that integrates innovation and entrepreneurship elements, as provided in this application. Figure 2 The diagram shows a structural schematic of an intelligent career planning and matching device that integrates innovation and entrepreneurship elements, provided in an embodiment of this application. Detailed Implementation

[0013] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0014] In one implementation, Figure 1 A schematic flowchart illustrating an intelligent career planning and matching method integrating innovation and entrepreneurship elements according to an embodiment of this application is shown, including: S101 acquires data on individual career aspirations, abilities, development vision, innovation and entrepreneurship resource supply, industry trends, and job requirements. It then uses a feature filtering algorithm to remove invalid and redundant information and generates a standardized career-resource feature sequence.

[0015] In one implementation, structured questionnaires, interviews, forms, third-party tools, and channel connections are used to collect data from both individual (career aspirations, including direction, salary, work mode, and industry preference; ability traits, including hard and soft skills and personality; development vision, including short, medium, and long-term goals and value pursuit) and external (innovation and entrepreneurship resources, including policies, funding, venues, and mentors; industry trends, including market growth and technology direction; and job requirements, including job requirements, responsibilities, and compensation) perspectives. Corresponding data collection methods are then provided, such as job seekers selecting career directions related to smart hardware entrepreneurship and the system providing information on AI company subsidy policies.

[0016] The system employs feature selection algorithms such as variance thresholding, correlation analysis, and business logic validation to eliminate meaningless, duplicate, and irrelevant data related to career-resource matching, retaining core and effective features. Variance thresholding eliminates data lacking differentiation: For numerical data (such as salary expectations and ability scores), a variance threshold (e.g., 0.5) is set, and feature data with variances below the threshold are eliminated. For example, if all job seekers in a batch answered "yes" to "whether they require a first-tier city for their work location," this data has a variance of 0 and lacks differentiation, so the system eliminates it using variance thresholding; however, the variance for "salary expectations" is 2.8 (above the threshold), so this feature is retained.

[0017] The system calculates correlation coefficients (such as the Pearson correlation coefficient) between different features. If the correlation coefficient is higher than a set threshold (such as 0.85), it is considered redundant data, and only one core feature is retained. For example, the correlation coefficient between "startup project financing experience" and "number of successful financing cases" is 0.92, which the system considers highly correlated. Therefore, the "number of successful financing cases" feature is removed, and the core feature "startup project financing experience" is retained. Based on career planning matching business logic, data that does not conform to common sense or is logically contradictory is removed. For instance, if a job seeker fills in "career direction is early childhood education" but also checks "desired industry is heavy machinery manufacturing," the system determines this data is contradictory through business logic verification and removes invalid data matching industry demand and career direction. Similarly, if a job seeker fills in "no programming experience" but checks "proficient in programming languages ​​such as Java and Python," the system determines this as false and invalid data and removes it.

[0018] The selected valid data is standardized according to a unified format and measurement standards to generate a structured, standardized occupation-resource feature sequence, ensuring that the data format is consistent and can be directly used for subsequent map conversion and model building. Categorical data (such as career direction, industry sector, and job type) is converted into numerical features using one-hot coding, label coding, and other methods. "Career direction" is divided into technology research and development (code 1), entrepreneurship management (code 2), market operation (code 3), and functional support (code 4). A job seeker's career direction is "entrepreneurship management," which corresponds to the value 2 after coding. "Industry sector" is divided into artificial intelligence (code A01), new energy (code A02), and cross-border e-commerce (code A03). The job seeker's target industry, "new energy," is coded as A02.

[0019] For continuous numerical data (such as salary expectations, years of work experience, and ability scores), Min-Max normalization and Z-Score standardization are used to map the data to a unified interval (such as [0,1]). The "salary expectation" data (range 8000-50000 yuan) is processed using Min-Max normalization. For example, a job seeker expecting a monthly salary of 20000 yuan is calculated as (20000-8000) / (50000-8000) = 0.2857 after normalization. The "ability score" (1-10 points) is processed using Z-Score standardization to eliminate the dimensional differences between different scoring dimensions.

[0020] Following a fixed structure of "personal features + external features," standardized features are integrated into an ordered sequence. A job seeker's standardized career-resource feature sequence is: "[Career aspirations (code 2, normalized salary 0.2857, work mode code 3); Abilities (hard skills code: Python=1, patent application=1, normalized soft skills score: communication=0.8, innovation=0.75); Development vision (short-term goal code 3, medium-term goal code 2, long-term goal code 1); Innovation and entrepreneurship resource matching (policy subsidy matching degree 0.9, funding resource matching degree 0.7); Industry trend matching (market growth rate 0.82, technology direction matching degree 0.85); Job requirement matching (job requirement fit degree 0.78, salary matching degree 0.8)]", forming a standardized data sequence that can be directly used for subsequent processing.

[0021] S102, after converting the standardized occupation-resource feature sequence into a supply-demand matching relationship network graph, uses a hierarchical clustering algorithm to subdivide occupational development scenarios and construct a dynamic supply-demand coupling model.

[0022] In one implementation, a standardized occupational-resource feature sequence is transformed into a graph to generate a supply-demand matching network graph. The standardized occupational-resource feature sequence includes standardized information on individual career aspirations, competencies, development visions, innovation and entrepreneurship resource supply, industry trends, and job requirements. The graph transformation uses the standardized occupational-resource feature sequence as the core input. Through node definition, edge relationship construction, and graph visualization modeling, the structured feature data is transformed into a network graph that intuitively reflects the supply-demand relationship, clarifying the correspondence between individual and external elements. The core elements in the standardized occupational-resource feature sequence are defined as graph nodes, with each node associated with corresponding standardized attribute information.

[0023] The individual-level nodes include the "Career Aspiration Node" (related attributes: Career Direction Code 2, Normalized Salary 0.2857, Work Mode Code 3), the "Ability Trait Node" (related attributes: Hard Skills Code Python=1, Patent Application=1, Soft Skills Normalized Score Communication=0.8, Innovation=0.75), and the "Development Vision Node" (related attributes: Short-term Goal Code 3, Mid-term Goal Code 2, Long-term Goal Code 1). The external-level nodes include the "Innovation and Entrepreneurship Resource Node" (related attributes: Policy Subsidy Suitability 0.9, Funding Resource Suitability 0.7), the "Industry Trend Node" (related attributes: Market Growth Rate 0.82, Technology Direction Matching 0.85), and the "Job Requirements Node" (related attributes: Job Requirements Fit 0.78, Salary Matching 0.8).

[0024] Based on the logical connections and compatibility between various elements, edge relationships are constructed between nodes, and a connection strength value is assigned (range 0-1, higher values ​​indicate stronger connections). Example 1: The connection strength between the "Ability Trait Node" (including Python programming skills) and the "Innovation and Entrepreneurship Resource Node" (including funding support in the field of artificial intelligence) is 0.85, because programming skills are a core requirement for AI startups and have strong compatibility with funding resources. Example 2: The connection strength between the "Career Aspiration Node" (Entrepreneurial Management Direction) and the "Job Demand Node" (Partner Position in an AI Startup) is 0.79, because the entrepreneurial management direction and the partner position have a high degree of matching responsibility. Example 3: The connection strength between the "Development Vision Node" (mid-term goal: to establish an e-commerce startup) and the "Industry Trend Node" (18% growth rate in the cross-border e-commerce industry) is 0.83, because personal vision and industry growth trend are highly aligned.

[0025] By integrating nodes, attributes, and edge relationships using graph visualization tools (such as Neo4j and Gephi), a supply and demand matching network graph is generated. In the graph, node size is weighted according to attribute importance (e.g., career direction and industry trends are core attributes, so node size is larger), and edge thickness is adjusted according to association strength (association strength above 0.8 is thick edge, 0.6-0.8 is medium edge, and below 0.6 is thin edge), intuitively presenting the association pattern between individual and external elements.

[0026] The system models and analyzes the supply-demand matching network graph, generating element correlation analysis results. These results include correlation data between individual traits and innovation resources, and between industry demands and job standards. Based on the generated supply-demand matching network graph, the system analyzes the inherent correlation logic between individual and external elements through path mining, correlation type classification, and correlation strength statistics, outputting structured element correlation analysis results. The system identifies the core correlation paths from individual elements to external elements in the graph, clarifying the transmission logic of "individual-resource-industry-job".

[0027] The core path identified was "Ability Traits (Python programming + patent application) → Innovation and entrepreneurship resources (AI policy subsidies + funding support) → Industry trends (AI technology direction) → Job demand (AI product R&D positions)". This path fully presents the logical chain of how individual abilities are matched with resources to connect with industries and positions. The relationships were categorized into six types based on element attributes: "Individual-Resource Relationship", "Individual-Industry Relationship", "Individual-Job Relationship", "Resource-Industry Relationship", "Resource-Job Relationship", and "Industry-Job Relationship". The number and average strength of each type of relationship were statistically analyzed. The results show that there were 12 "Individual-Resource Relationships" with an average strength of 0.76; and 8 "Industry-Job Relationships" with an average strength of 0.81. Specifically, the "Individual Traits - Innovation Resources" relationship (e.g., programming skills and technology startup funding) belongs to the "Individual-Resource Relationship" category, while the "Industry Trends - Job Demand" relationship (e.g., the growth of the new energy industry and the demand for energy storage technology positions) belongs to the "Industry-Job Relationship" category.

[0028] Association Strength Statistics and Anomaly Association Identification: Statistical analysis is performed on the strength values ​​of various associations, calculating the mean, median, and standard deviation, while identifying anomaly associations (abnormal situations where the association strength is too high or too low). Example 1: The mean strength of the association between "personal traits and innovation resources" is 0.78, the median is 0.80, and the standard deviation is 0.05. There is no anomaly association, indicating that the association stability of this type of element is strong. Example 2: In the association between "career aspirations and industry trends," the association strength between a job seeker's "early childhood education" and "heavy machinery industry" is 0.12 (far lower than the mean of 0.65), which is judged as an anomaly association and labeled as "supply-demand mismatch association." Example 3: The final output of the element association analysis results includes "a list of association paths (including 3 core paths), a statistical table of association types (the number and average strength of 6 types of associations), and details of anomaly associations (2 mismatch associations)," where the core association data clearly covers the association information between personal traits and innovation resources, and industry needs and job standards.

[0029] Based on the results of element association analysis, a hierarchical clustering algorithm is used to subdivide career development scenarios, generating subdivided career development scenario categories. These categories are used to construct a dynamic supply-demand coupling model. The hierarchical clustering algorithm uses association strength, association path, and association type from the element association analysis results as clustering features. Following the logic of "first coarsely dividing into major categories, then subdividing into minor categories," career development scenarios are systematically subdivided to provide scenario support for constructing the dynamic supply-demand coupling model. Core clustering features are selected and standardized to ensure the objectivity of the clustering results. The selected features include four core features: "average individual-resource association strength," "maximum individual-job association strength," "number of industry-job association paths," and "innovation resource matching weight." Z-Score standardization is used to map each feature value to the same order of magnitude (mean 0, standard deviation 1). For example, a sample with "average individual-resource association strength 0.85" becomes 1.2 after standardization, and "number of industry-job association paths 3" becomes 0.9 after standardization.

[0030] The hierarchical clustering process (first aggregating large categories, then splitting into smaller categories) begins with the first step (aggregating large categories): based on feature similarity (using Euclidean distance), all samples are aggregated into three large categories: "Technology-Entrepreneurship Oriented," "Career Advancement Oriented," and "Resource-Dependent Entrepreneurship Oriented." Sample 1 (containing programming skills, AI entrepreneurship resource correlation strength of 0.85, and job requirement of technical R&D position) and Sample 2 (containing patent application experience, new energy entrepreneurship policy fit of 0.9, and job requirement of technical partner position) are aggregated into the "Technology-Entrepreneurship Oriented" category due to their "high personal-resource correlation strength and technically oriented job requirement."

[0031] Step 2 (Sub-categorization): Further subdivide each major category according to its specific characteristics to form specific career development scenario categories. Example 1: "Technology Entrepreneurship Oriented" is subdivided by industry into three subcategories: "Artificial Intelligence Technology Entrepreneurship Scenario," "New Energy Technology Entrepreneurship Scenario," and "Cross-border E-commerce Technology Entrepreneurship Scenario." The core characteristics of the "Artificial Intelligence Technology Entrepreneurship Scenario" are: "Personal-AI resource correlation strength ≥ 0.8, industry trend matching degree ≥ 0.82, and job requirements related to AI-related entrepreneurship." Example 2: "Career Advancement Oriented" is subdivided by job level into three subcategories: "Junior Technical Position Advancement Scenario," "Middle Management Position Advancement Scenario," and "Core Position Advancement Scenario in Startups." Example 3: "Resource-Dependent Entrepreneurship Oriented" is subdivided by resource type into three subcategories: "Policy-Dependent Entrepreneurship Scenario," "Funding-Dependent Entrepreneurship Scenario," and "Mentor Resource-Dependent Entrepreneurship Scenario."

[0032] The final output includes nine subcategories of career development scenarios, each with clearly defined core characteristics and target audiences, used to accurately construct a dynamic supply-demand coupling model. The core characteristics of the "Artificial Intelligence Technology Entrepreneurship Scenario" are defined as follows: "Individuals possess AI-related hard skills (programming, algorithms, etc.), have a correlation strength ≥0.8 with innovation and entrepreneurship resources in the field of artificial intelligence, an industry trend matching degree ≥0.82, and job requirements focus on AI startup projects' technology research and development, product implementation, and other aspects." The target audience is "individuals with an AI technology background who plan to start a business in the field of artificial intelligence."

[0033] S103, based on a dynamic supply and demand coupling model, performs collaborative calculations on the matching relationship between personal traits, innovative resources, and career paths to obtain key matching gap values.

[0034] In one implementation, a correlation function is set for the parameters of the dynamic supply and demand coupling model and the characteristic parameters of innovation and entrepreneurship elements to generate a matching and adaptable element-scenario dependent functional relationship. The dynamic supply and demand coupling model parameters include subdivided dimensions of career development scenarios and thresholds for element correlation strength. The characteristic parameters of innovation and entrepreneurship elements include types of innovation resource supply, adaptation conditions of entrepreneurial policies, and dynamic indicators of industry trends. The model is divided into 3 primary dimensions and 8 secondary dimensions based on industry, job level, and entrepreneurial type, using quantified label coding (e.g., Artificial Intelligence Industry = 1, New Energy Industry = 2; Entry-level Position = 1, Mid-level Position = 2, Entrepreneurial Position = 3). Example: The quantified value of the subdivided dimension of a certain "Artificial Intelligence Technology Entrepreneurship Scenario" is (Industry = 1, Job Level = 3, Entrepreneurship Type = Technical = 1). Values ​​are set in the range of 0-1 to determine the effectiveness of element correlation; different thresholds apply to different scenarios. Example: The element correlation strength threshold for the technical entrepreneurship scenario is set to 0.75, and for the career advancement scenario, it is set to 0.65.

[0035] Categorize resources by funding, location, technology, and mentorship, using one-hot coding for quantification (e.g., funding resources = (1,0,0,0), technology resources = (0,0,1,0)). Example: In a certain scenario, innovation resources are "artificial intelligence technology + startup funding," quantified as (1,0,1,0). Score the subsidy amount, tax breaks, and simplification of the approval process (0-10 points), then normalize and use as parameter values. Example: A province's artificial intelligence startup policy provides a subsidy of 500,000 yuan (score 10 points) and full tax exemption (score 10 points), with a normalized parameter value of 1.0.

[0036] The index is calculated using a weighted average of the market growth rate over the past three years, the technology iteration cycle, and the talent demand growth rate (with weights of 0.4, 0.3, and 0.3 respectively). Example: The artificial intelligence industry has a market growth rate of 22% over the past three years, a technology iteration cycle of 1.5 years, and a talent demand growth rate of 25%. The calculated index value is: 22% × 0.4 + (1 / 1.5) × 0.3 + 25% × 0.3 = 0.86.

[0037] The function design employs multiple linear regression combined with a scenario adjustment factor to construct the function, with the expression: M = α × (w1) P1+w2 P2+w3 P3+w4 P4+w5 P5)+βS where M is the matching fit degree (value 0-1), P1−P5 are the quantified values ​​of the above 5 core parameters respectively, w1−w5 are the parameter weights (summed to 1), α is the overall adjustment coefficient (0.8-1.2), and S is the scenario correction factor (determined by the subdivision of career development scenarios).

[0038] For the "AI technology startup scenario," the parameter weights are set as follows: w1=0.2 (scenario subdivision dimension), w2=0.15 (association strength threshold), w3=0.25 (resource type), w4=0.2 (policy conditions), and w5=0.2 (industry trend indicators). The overall adjustment coefficient α=1.1, and the scenario correction factor S=0.05. Substituting the parameter quantification values ​​corresponding to a job seeker, P1=(1,3,1) (comprehensive quantification of 0.9), P2=0.75, P3=(1,0,1,0) (comprehensive quantification of 0.8), P4=1.0, and P5=0.86, we calculate: M=1.1×(0.2×0.9+0.15×0.75+0.25×0.8+0.2×1.0+0.2×0.86)+0.05=0.89, meaning that the job seeker's matching suitability in this scenario is 0.89.

[0039] The factor-scenario dependent function is coupled with industry development forecast data to generate the foundational data for constructing the optimization objective equation. This foundational data includes the weights of the core function variables, industry development forecast correction coefficients, and factor adaptation priority ranking results. Industry forecast data for the next 3-5 years is obtained from sources such as iResearch, IDC, and government industry planning documents. This includes four core data categories: market size growth rate forecast, policy support forecast, technology development direction forecast, and talent demand structure forecast, all normalized to the 0-1 range. Example: For the artificial intelligence industry, the forecast data is: "Market size growth rate 25% (normalized 0.92), policy support increased by 30% (normalized 0.95), technology development focuses on generative AI (matching job seekers' technical directions 0.88), talent demand leans towards multi-skilled talents (matching job seekers' abilities 0.76)".

[0040] The core variable weights of the function are dynamically adjusted based on the importance of industry development forecast data. The weights w1-w5 of the core variables in the factor-scenario dependent function are strengthened, reinforcing the weights of variables highly correlated with future trends. Example: The original weight of "Industry Trend Dynamic Indicator" was 0.2. Because the forecast shows strong future growth in this industry, its weight is adjusted to 0.3. Simultaneously, the weight of "Factor Association Strength Threshold" is reduced to 0.05. The total weights after adjustment remain 1. The industry development forecast correction coefficient is calculated using the weighted average of the four types of forecast data (each with a weight of 0.25), used to correct the matching fit calculation results. Example: The weighted average of the above artificial intelligence industry forecast data = (0.92 + 0.95 + 0.88 + 0.76) × 0.25 = 0.8775, meaning the industry development forecast correction coefficient is 0.88 (rounded to two decimal places).

[0041] The priority ranking of factor adaptation is determined by the calculation result of "predicted data matching degree × original variable weight". Example: Calculate the priority score for each variable: scenario subdivision dimension (0.9 × 0.2 = 0.18), correlation strength threshold (0.75 × 0.05 = 0.0375), resource type (0.8 × 0.25 = 0.2), policy conditions (1.0 × 0.2 = 0.2), and industry trend indicator (0.86 × 0.3 = 0.258). The ranking result is: industry trend indicator > resource type = policy conditions > scenario subdivision dimension > correlation strength threshold. The basic data for constructing the final optimization objective equation is: function core variable weights (adjusted), industry development prediction correction coefficient (0.88), and factor adaptation priority ranking result (industry trend indicator first).

[0042] The optimization objective equation and the multidimensional matching matrix are collaboratively calculated to generate key matching gap values, including data on the fit gap between personal traits and innovation resources, data on the matching deviation between innovation resources and career paths, and data on the fit gap between personal traits and career paths. First, an optimization objective equation is constructed with the goal of "maximizing the fit and minimizing the factor deviation." Then, combined with a multidimensional matching matrix containing three dimensions—personal, innovation factors, and career paths—collaborative iterative calculations are used to accurately identify various fit gaps.

[0043] Based on the aforementioned fundamental data, the objective equation for optimization is constructed. ,in, (k is the industry development forecast correction coefficient). Quantification values ​​for individual factors, Quantify the values ​​of external factors (innovation resources, career paths), is the deviation penalty coefficient (set to 0.5), and n is the number of elements.

[0044] The multidimensional matching matrix is ​​constructed with the dimensions of "personal traits - innovation resources - career path". The row vectors represent personal trait elements (such as hard skills, soft skills, and career aspirations), and the column vectors represent innovation resource elements (funding, technology, and policies) and career path elements (job requirements, career development paths, and salary expectations). The matrix elements represent the matching degree (0-1) of the corresponding elements. A partial multidimensional matching matrix for a job seeker is shown below: The first step in the collaborative computing process is to substitute the quantified values ​​of the elements in the multidimensional matching matrix into the optimization objective equation to calculate the initial objective function value. The second step involves adjusting the quantization values ​​of low-matching elements in the matrix one by one based on the element matching priority, iteratively calculating the objective function value until F reaches its minimum. The third step is to compare the differences in element quantization values ​​between the initial matrix and the optimal matrix; this difference represents the matching gap. Example: Initial calculation = +0.5×(|0.85-0.9|+|0.92-0.88|+...)=0.12, after 3 iterations =0.03, at which point the difference between the various elements is the gap.

[0045] The data on the mismatch between personal traits and innovation resources are as follows: The mismatch between entrepreneurial financing experience and entrepreneurial capital is 0.9 - 0.82 = 0.08 (original matching degree 0.82, optimal matching degree 0.9); the mismatch between mid-term entrepreneurial vision and artificial intelligence technology resources is 0.86 - 0.79 = 0.07. The data on the mismatch between innovation resources and career paths are as follows: The mismatch between entrepreneurial capital and expected salary for AI startup positions is 0.85 - 0.77 = 0.08; the mismatch between artificial intelligence technology resources and skill requirements for AI startup positions is 0.92 - 0.85 = 0.07. The data on the fit between personal traits and career paths are as follows: The fit between Python programming skills and skill requirements for AI startup positions is 0.88 - 0.83 = 0.05; the fit between entrepreneurial financing experience and entrepreneurial career development path is 0.87 - 0.81 = 0.06.

[0046] S104 combines key matching gap values, extracts competency fit, resource accessibility, and path feasibility according to career development stages, classifies matching levels, and constructs a multi-dimensional matching feature matrix that includes personal characteristics, innovation elements, industry needs, and job standard information.

[0047] In one implementation, key matching gap values ​​are correlated and extracted with characteristics of career development stages to generate quantifiable indicators of competency fit, resource accessibility assessment parameters, and path feasibility analysis dimensions. Combining key matching gap values ​​(three types of gaps: personal traits-innovation resources, innovation resources-career paths, and personal traits-career paths) with the core characteristics of career development stages (start-up, growth, and maturity), three core assessment indicators / dimensions—competency fit, resource accessibility, and path feasibility—are extracted through correlation mapping and quantification transformation, clarifying the correspondence between each indicator and the gap / stage.

[0048] The characteristics of career development stages are defined as follows: Start-up stage (1-3 years): The core characteristics are "capability accumulation, resource exploration, and path trial and error," focusing on basic skill adaptation, acquisition of entry-level resources, and feasibility of short-term paths. Growth stage (3-5 years): The core characteristics are "capability enhancement, resource integration, and path focus," focusing on deepening professional skills, connecting with core resources, and mid-term development stability. Maturity stage (5 years and above): The core characteristics are "capability breakthrough, resource dominance, and path leadership," focusing on comprehensive capability leaps, control of high-quality resources, and long-term development leadership.

[0049] This parameter is generated based on the correlation between "personal traits - career path alignment gap" and stage-specific ability requirements, using a 0-10 score scale (higher scores indicate higher alignment). Example 1: For a job seeker in the start-up phase, the "alignment gap between Python programming skills and AI startup requirements is 0.05," corresponding to the stage's basic skill requirements, resulting in a "basic programming skills alignment score of 8.5." Example 2: For a job seeker in the growth phase, the "alignment gap between entrepreneurial financing experience and entrepreneurial development path is 0.06," corresponding to the stage's professional ability requirements, resulting in a "financing and operation ability alignment score of 7.2." Resource accessibility assessment parameters are generated based on the correlation between "personal traits - innovation resource matching gap" and stage-specific resource needs, including resource acquisition difficulty (levels 1-5, level 1 being the easiest) and resource matching timeliness (0-1, higher values ​​indicate more timely). Example 1: For job seekers in the startup phase, the "gap between mid-term entrepreneurial vision and AI technology resources is 0.07", the stage requirement is "entry-level technology resources", and the generated evaluation parameters are "difficulty of acquiring technology resources level 2, and timeliness of matching 0.8"; Example 2: For job seekers in the mature phase, the "gap between core management capabilities and high-end entrepreneurial funding resources is 0.12", the stage requirement is "large-scale financing resources", and the generated evaluation parameters are "difficulty of acquiring funding resources level 4, and timeliness of matching 0.6".

[0050] This analysis is generated based on the correlation between the "matching deviation between innovation resources and career paths" and the characteristics of different career paths, including the path achievement cycle (short-term / medium-term / long-term), path dependence conditions (resources / capabilities / policies), and path risk coefficient (0-1, with higher values ​​indicating greater risk). Example 1: For job seekers in the growth stage, the "matching deviation between artificial intelligence technology resources and the skill requirements of AI startup positions is 0.07," the career path is "in-depth cultivation of the professional field," and the generated analysis dimensions are "medium-term achievement cycle, dependence condition of deepening technology resources, and risk coefficient 0.3." Example 2: For job seekers in the start-up stage, the "matching deviation between startup funding and expected salary for AI startup positions is 0.08," the career path is "entry-level trial and error," and the generated analysis dimensions are "short-term achievement cycle, dependence condition of basic financial support, and risk coefficient 0.2."

[0051] The competency fit metric, resource accessibility assessment parameters, and path feasibility analysis dimensions are graded to generate high / medium / low fit level standards, level judgment thresholds, and level correlation rules. Using a percentage system conversion, threshold setting, and logical correlation analysis, competency fit, resource accessibility, and path feasibility are divided into high / medium / low levels, clearly defining the standards, judgment thresholds, and correlation rules for each level to ensure the objectivity and feasibility of the level classification. The competency fit metric (0-10 points converted to 0-100 points) is as follows: High fit level: 80-100 points, standard is "core competencies fully meet the stage's career requirements, no key gaps," judgment threshold ≥ 80 points. Medium fit level: 60-79 points, standard is "core competencies basically meet the stage's career requirements, with minor gaps," judgment threshold 60-79 points. Low fit level: 0-59 points, standard is "core competencies do not meet the stage's career requirements, with key gaps," judgment threshold < 60 points.

[0052] If an indicator falls under the "hard skills" category (such as programming or finance), the level will be adjusted down by one level (e.g., an 80-point hard skills indicator will be judged as a medium fit level). Soft skills indicators will remain at their original level. Example: A job seeker in the early stages with a "basic programming skills fit 8.5 points (converted to 85 points)" falls under the hard skills category and will be adjusted to a medium fit level; a job seeker in the growth stage with a "financing and operational capabilities fit 7.2 points (converted to 72 points)" falls under the soft skills category and will be judged as a medium fit level.

[0053] Resource accessibility assessment parameters (calculated based on acquisition difficulty and adaptation timeliness, 0-100 points): Resource accessibility score = (6 - acquisition difficulty) × 20 + adaptation timeliness × 20. Specifically: High adaptation level: 80-100 points, standard is "required resources are easily obtained and adapted promptly, with no resource bottlenecks", judgment threshold ≥ 80 points. Medium adaptation level: 60-79 points, standard is "required resources are obtainable but adaptation timeliness is average, with minor resource bottlenecks", judgment threshold 60-79 points. Low adaptation level: 0-59 points, standard is "required resources are difficult to obtain or adaptation is delayed, with serious resource bottlenecks", judgment threshold < 60 points.

[0054] If the resource type is "policy resource", the level will be adjusted upward by 1 level (e.g., a policy resource score of 75 points will be judged as a high fit level). Example: A job seeker in the start-up stage with "difficulty of acquiring technical resources level 2, fit timeliness 0.8" will have a score of (6-2)×20+0.8×20=96 points, which is judged as a high fit level; a job seeker in the mature stage with "difficulty of acquiring financial resources level 4, fit timeliness 0.6" will have a score of (6-4)×20+0.6×20=52 points, which is judged as a low fit level.

[0055] The path feasibility analysis dimension (calculated based on the overall achievement period, dependent conditions, and risk coefficient, 0-100 points) is calculated as follows: Path feasibility score = Period weight (short-term 0.4, medium-term 0.3, long-term 0.2) × 50 + Dependency condition satisfaction × 30 + (1 - Risk coefficient) × 20. Specifically: High fit level: 80-100 points, standard is "reasonable path period, easily satisfied dependent conditions, low risk", judgment threshold ≥ 80 points; Medium fit level: 60-79 points, standard is "moderate path period, basically satisfied dependent conditions, moderate risk", judgment threshold 60-79 points; Low fit level: 0-59 points, standard is "excessively long path period, difficult to satisfy dependent conditions, high risk", judgment threshold < 60 points.

[0056] If the path belongs to the "Entrepreneurship-oriented" category, the level is adjusted down by 1 level (e.g., an 80-point entrepreneurial path indicator is judged as a medium fit level). For job seekers in the growth stage, with "mid-stage achievement period (weight 0.3), dependency condition satisfaction 0.8, risk coefficient 0.3," the score is 0.3×50+0.8×30+(1-0.3)×20=65 points, and they are judged as medium fit level. For job seekers in the start-up stage, with "short-stage achievement period (weight 0.4), dependency condition satisfaction 0.9, risk coefficient 0.2," the score is 0.4×50+0.9×30+(1-0.2)×20=73 points, and they are judged as medium fit level. The final output of the level classification results generates high / medium / low fit level standards (including specific descriptions of each indicator), level judgment thresholds (including percentage score ranges), and level association rules (including adjustment rules for indicator types, path types, etc.), forming a complete level classification system.

[0057] Based on the matching level standards and judgment thresholds, personal characteristic data, innovation element supply data, industry demand dynamic data, and job standard specification data are integrated and processed to generate a multi-dimensional information association table. Using the matching level standards and judgment thresholds as a basis, personal characteristic data, innovation element supply data, industry demand dynamic data, and job standard specification data are integrated according to the logic of "stage-indicator-data-level" to generate a structured multi-dimensional information association table, clearly presenting the relationships and matching levels of various data types. The data integration logic is as follows: For the row dimension, it is divided according to career development stages (start-up, growth, maturity). For the column dimension, it includes core evaluation indicators (ability fit, resource accessibility, path feasibility), corresponding original data, matching level, and related data sources (individual / innovation element / industry / job). Various data types are associated with indicators, and the matching level is determined according to the level classification rules, clarifying the mapping relationship between data.

[0058] The multi-dimensional information association table undergoes structured reconstruction and feature mapping to generate a multi-dimensional matching feature matrix containing four dimensions of information: individual, element, industry, and job. The multi-dimensional information association table is then expanded in dimensions, features are extracted, and the structure is reorganized to construct a multi-dimensional matching feature matrix containing four dimensions of information: individual, element, industry, and job. This ensures that the matrix elements are quantified and the dimensions are clear, allowing direct use in subsequent model calculations. The logic of structured reconstruction and feature mapping is as follows: the matrix row vector represents the "individual characteristic dimension" (including sub-dimensions such as hard skills, soft skills, and development vision), and the column vectors represent the "innovation element dimension" (funding, technology, policy, etc.), "industry demand dimension" (market size, technology direction, etc.), and "job standard dimension" (job requirements, salary range, etc.). The original data and adaptation levels in the association table are converted into matrix element values. The original data is normalized (mapped to the 0-1 range), and the adaptation levels are converted to high=1, medium=0.5, and low=0. The matrix elements are filled according to the association logic of "person-factor", "person-industry", "person-position", "factor-industry", "factor-position", and "industry-position" to form a four-dimensional cross feature matrix.

[0059] S105 generates abnormal signals of capability shortcomings, resource mismatch, and path deviation by comparing real-time matching status with industry benchmark data based on a multi-dimensional matching feature matrix, and generates optimization potential coefficients based on the slope of the matching degree-development potential curve.

[0060] In one implementation, personal characteristic dimension data, innovation element adaptation data, industry demand matching data, and job standard fit data from the multidimensional matching feature matrix are extracted and classified to generate real-time matching status quantitative indicators, industry benchmark data reference thresholds, feature dimension association rules, and data comparison benchmark framework. Four core data categories are extracted from the multidimensional matching feature matrix, and through classification, quantification transformation, and rule definition, basic data for subsequent comparative analysis is generated, ensuring clear data dimensions and direct applicability for benchmarking analysis. For personal characteristic dimension data, sub-dimensional data such as hard skills (programming, patent applications), soft skills (communication, innovation), and development vision are extracted and quantified into values ​​ranging from 0 to 1. Example: A job seeker's "Python programming skill fit 0.85, communication ability normalized score 0.8, entrepreneurial vision fit 0.9".

[0061] For data on the matching of innovation elements, sub-dimension matching data such as funding, technology, and policy are extracted and categorized by resource type. Example: "Artificial intelligence technology resource matching degree 0.92, entrepreneurial policy subsidy matching degree 0.9, angel funding matching degree 0.7". For data on matching industry needs, data such as market growth rate, technology direction matching degree, and talent demand fit are extracted and categorized by core industry development indicators. Example: "Cross-border e-commerce industry market growth rate 0.82, technology direction matching degree 0.85, talent demand fit degree 0.76". For data on the matching of job standards, data such as job requirements, salary matching, and career development path matching are extracted and categorized by core job requirements. Example: "AI startup job requirement matching degree 0.88, salary matching degree 0.8, career development path matching degree 0.83".

[0062] The above data is integrated to generate metrics such as "Individual-Factor Fit Overall Value 0.86, Individual-Industry Matching Value 0.81, Individual-Job Fit Value 0.84". Based on industry-leading enterprise standards and requirements for high-quality startup projects, benchmark thresholds are set, such as "Hard Skill Fit Benchmark Threshold 0.9, Innovation Resource Fit Benchmark Threshold 0.85, Job Fit Benchmark Threshold 0.88". The correlation logic between various data types is clarified, such as "Programming skills and technical resource fit must simultaneously meet the standards (both ≥ 0.8)" and "Industry trend matching degree is positively correlated with job requirement fit". Comparison dimensions (Individual-Factor, Individual-Industry, Individual-Job) and comparison methods (absolute deviation, relative deviation) are determined to form a standardized comparison template.

[0063] Based on industry standards for career planning matching (such as career fit assessment standards and talent assessment standards), the basic data is standardized and calibrated to eliminate differences in dimensions, generating assessment information that can be directly used for deviation analysis. Real-time matching status quantitative indicators are standardized: Z-Score standardization is used to eliminate the influence of different dimensions. For example, the original "person-job fit value 0.84" is standardized to 0.72, and the "comprehensive value of innovation element fit 0.86" is standardized to 0.81.

[0064] Industry benchmark data reference threshold calibration: Adjust benchmark thresholds according to industry standards, such as calibrating the "technical resource matching benchmark threshold" from 0.85 to 0.88 (compliant with technical job matching standards). Feature dimension association rule compliance verification: Verify whether the rules comply with industry standards, such as adding a hard rule that "policy resource matching degree must be ≥0.7 (minimum requirement of industry standards)". Quantify the difference between real-time data and benchmark data, such as "hard skill matching degree difference 0.05 (0.85-0.9), innovation resource matching degree difference 0.03 (0.92-0.88)". Verify whether the benchmark data complies with industry standards, such as "job fit benchmark threshold of 0.88 passes compliance verification and can be used for deviation judgment".

[0065] Based on the assessment information of matching status differences and the verification information of benchmark data suitability, deviations between real-time matching status and industry benchmark data are marked and analyzed to generate capability deficiency identification results, resource mismatch judgment information, and path deviation location data. Data in personal characteristic dimensions that are below the benchmark threshold are marked to analyze core shortcomings. Example: "Python programming skill suitability 0.85 < benchmark threshold 0.9, judged as a hard skill deficiency; innovation ability 0.75 < benchmark threshold 0.8, judged as a soft skill deficiency." Items in innovation element suitability data that do not match industry and job requirements are marked. Example: "Angel funding suitability 0.7 < benchmark threshold 0.8, and lower than industry requirement matching 0.82, judged as funding resource mismatch; technical resource suitability 0.92 is higher than job requirement fit 0.88, indicating over-suitability of resources that has not been transformed into job advantages." Deviations between personal development paths and industry and job trends are analyzed. Example: "The individual's entrepreneurial vision focuses on cross-border e-commerce, while the industry technology direction matching degree is 0.85 < artificial intelligence field 0.92, and the job demand is concentrated in the AI ​​field, which is judged as a deviation between career path and industry hotspots."

[0066] The results of capability deficiency identification, resource mismatch judgment, and path deviation location data are integrated and slope calculated. Combined with the dynamic changes in the matching degree-development potential curve, an optimization potential coefficient is generated. The capability deficiency, resource mismatch, and path deviation data are integrated into a "comprehensive deviation value," for example: "Hard skill deficiency deviation 0.05, funding mismatch deviation 0.1, path deviation 0.07, comprehensive deviation value 0.07." A matching degree-development potential curve is constructed with time as the horizontal axis (short-term 1 year, medium-term 3 years, long-term 5 years) and matching degree as the vertical axis, for example: "Short-term matching degree 0.84, medium-term prediction 0.87, long-term prediction 0.91." The slope of the calculated curve reflects the growth rate of development potential, for example: "Short-term to medium-term slope 0.03 ((0.87-0.84) / 2), medium-term to long-term slope 0.02 ((0.91-0.87) / 2)." Combining the comprehensive deviation value and the curve slope, the optimization potential coefficient is calculated using the formula "Optimization potential coefficient = (1 - comprehensive deviation value) × (1 + average slope)". For example: (1 - 0.07) × (1 + 0.025) = 0.95, that is, the optimization potential coefficient is 0.95.

[0067] S106 integrates the optimization potential coefficient with personal development planning, groups career paths, uses deep neural network algorithms to screen key matching factors, and integrates personal traits, innovation resources, industry needs and job standard information to construct an adaptive matching optimization model.

[0068] In one implementation, the optimization potential coefficient and personal development plan are correlated and integrated based on career planning adaptation logic. This is achieved by matching potential levels with hierarchical planning goals, optimization directions corresponding to development stages, and improvement opportunities through implementation paths. Matching rules are clearly defined for core related nodes, generating a planning-potential fusion correlation result. Personal development plan goals are categorized into short-term (1-3 years), medium-term (3-5 years), and long-term (5 years and above), corresponding to high (≥0.8), medium (0.6-0.79), and low (<0.6) optimization potential coefficients. Example: A job seeker's short-term goal is "Mastering the application of large AI models" (potential coefficient 0.85, high potential), medium-term goal is "Establishing an AI application startup company" (potential coefficient 0.72, medium potential), and long-term goal is "Becoming a leading entrepreneur in the industry" (potential coefficient 0.65, medium potential). Optimization priorities are matched according to the start-up, growth, and maturity stages. Example: The initial stage (1-2 years) corresponds to the optimization direction of "strengthening hard skills", the growth stage (3-4 years) corresponds to the optimization direction of "improving resource integration capabilities", and the mature stage (5+ years) corresponds to the optimization direction of "breaking through the industry resource dominance capability".

[0069] Link the planned implementation path with potential enhancement space. Example: The path "Participate in AI large-scale model training → Join a startup incubation project → Independently launch a startup project" connects to "Hard skills enhancement space 0.15, resource acquisition space 0.2, project operation space 0.18". For example, "High-potential goals need to be paired with resource-intensive implementation paths," and "Growth-stage optimization directions need to have a matching degree of ≥0.8 with industry trends." The planning-potential integration result includes a "Goal-Potential-Direction-Path" association table, such as "Short-term goal (AI skills mastery) - High potential - Hard skills enhancement - Training + Practice path."

[0070] Based on the scientific principles of career development, the rationality of career path grouping is verified. The path fit dimension, group boundary thresholds, and stage transition logic are checked one by one to determine whether they conform to personal growth patterns and industry development trends. Unreasonable items are marked with adjustment directions, generating a career path grouping rationality verification result. The path fit dimension verification verifies whether the grouping covers the core fit points of personal characteristics, innovation resources, and industry needs. Example: For a certain "Artificial Intelligence Technology Entrepreneurship Path Group," the verification found that it did not include the "Policy Resource Fit" dimension; the adjustment direction was marked as "supplementing the entrepreneurial policy connection path."

[0071] Grouping boundary threshold verification confirms the reasonableness of grouping thresholds, avoiding those that are too broad or too narrow. Example: The original threshold for the technology entrepreneurship path was "hard skill fit ≥ 0.7". Verification revealed that the industry benchmark threshold was 0.75. The threshold was adjusted to 0.75, with the annotation "Raising hard skill entry standards to align with industry requirements". Stage transition logic verification verifies the smoothness of transitions between stages of the path, without gaps or conflicts. Example: The path "Junior Technical Position → Technical Backbone → Entrepreneurial Partner" lacks a "resource integration" transition stage from "Technical Backbone to Entrepreneurial Partner". The adjustment direction is marked as "Adding a 'Resource Matching Specialist' transitional position to strengthen resource accumulation". The career path grouping rationality verification results include a list of "Verification Dimension - Verification Results - Adjustment Direction", such as "Path Fit Dimension - Missing Policy Fit - Supplementing Policy Matching Path".

[0072] Significance verification of the association between key fitting factors and multidimensional information was employed. Screening weights were defined for personal trait indicators, innovation resource parameters, industry demand elements, and job standard conditions in the fused data. A weighted ranking method was used to determine the core influencing factors. By defining screening weights and weighted ranking, core factors significantly affecting the matching effect were screened from the fused data, ensuring the model focuses on key variables. Specifically, screening weights were assigned to personal trait indicators (hard skills 0.3, soft skills 0.25), innovation resource parameters (funding 0.2, technology 0.15), industry demand elements (technical direction 0.05), and job standard conditions (job requirements 0.05) with a total weight of 1.

[0073] Factor score is calculated as "Factor Score = Factor Fit × Screening Weight". Example: A job seeker's "Python hard skills fit: 0.85 × 0.3 = 0.255, Startup capital fit: 0.7 × 0.2 = 0.14, AI technology fit: 0.85 × 0.05 = 0.0425". The top 3 factors are selected based on their scores. Example: The ranking is "Hard skills (0.255) > Soft skills (0.21) > Startup capital (0.14)", with hard skills, soft skills, and startup capital as the core influencing factors.

[0074] By integrating the results of planning-potential fusion correlation, career path grouping rationality verification, and factor-information correlation significance verification, an adaptive matching optimization model is generated, which includes matching factor priority ranking, multi-dimensional information fusion weights, and dynamic adjustment rules. The results of the first three steps are then integrated to construct an adaptive model that includes matching factor priority, fusion weights, and dynamic adjustment rules, ensuring that the model can adjust its matching strategy according to dynamic changes.

[0075] Priorities are determined based on the scores of core influencing factors. Example: Priority 1 (Hard Skills), Priority 2 (Soft Skills), Priority 3 (Startup Funding), Priority 4 (Technological Resources), Priority 5 (Industry Trends). Integration weights are assigned by combining the planning-potential fusion results with the verification results. Example: Personal Trait Dimension Weight 0.5 (Hard Skills 0.3, Soft Skills 0.2), Innovation Resource Dimension Weight 0.3 (Funding 0.2, Technology 0.1), Industry-Job Dimension Weight 0.2.

[0076] Adjustment rules are set based on industry trends and individual development dynamics. Examples: "If the industry's technology direction iteration rate is >30%, then the technology resource matching weight increases by 0.1." "If an individual's hard skills improve by ≥0.1, then the startup funding matching weight increases by 0.05." The adaptive matching optimization model includes a "matching factor priority ranking table, a multi-dimensional information fusion weight table, and a dynamic adjustment rule list," which can automatically adjust the matching strategy according to individual development dynamics and changes in external resources.

[0077] S107, based on the adaptation strategy output of the adaptive matching optimization model, combines the correlation information between personal development dynamics and innovation and entrepreneurship resource supply to generate career planning guidance information.

[0078] In one implementation, the adaptive matching optimization model outputs targeted adaptation strategies based on adaptation factor priorities, multi-dimensional information fusion weights, and dynamic adjustment rules. This clarifies the optimal adaptation direction and core adjustment priorities for individuals, innovation elements, industries, and positions. Combining factor priorities (hard skills > soft skills > startup capital), the model outputs the core direction of "strengthening hard skills, supplementing startup capital resources, and simultaneously adapting to industry technology trends." Example: A job seeker's adaptation strategy is "Focus on improving hard skills in AI large-scale model application, connecting with angel investment resources in the artificial intelligence field, and keeping up with the dynamics of generative AI industry technology." Specific adjustment suggestions are given for each dimension based on fusion weights (personal traits 0.5, innovation resources 0.3, industry-position 0.2). Example: "Personal trait dimension: Improve Python programming skills to 0.9 or higher; Innovation resource dimension: Connect with at least 2 new AI investment and financing institutions; Industry-position dimension: Adapt to the large-scale model application requirements of the AI ​​product manager position." Based on the adjustment rules, it prompts, "If the industry's technology iteration rate exceeds 30%, the skills learning plan needs to be updated 3 months in advance."

[0079] Collect dynamic data on individual development (skills improvement progress, willingness to adjust plans, etc.) and real-time supply information on innovation and entrepreneurship resources (policy updates, fund releases, job vacancies, etc.) to establish a correlation mapping and ensure that guidance information is aligned with dynamic changes. Collect real-time data such as "AI large-scale model training progress 80%, entrepreneurial plan adjusted to 'first join an AI company to gain experience', communication skills improved by 0.1 in soft skills." Obtain supply information such as "a province's new AI startup subsidy policy (subsidy amount increased to 600,000 yuan), 3 angel investment institutions opening AI project financing channels, 2 leading companies posting AI product manager job vacancies." Establish a correlation between "skills improvement progress - job suitability" and "plan adjustment - resource supply type," for example: "AI large-scale model training is about to be completed (80% progress), which highly matches the current AI product manager job vacancy; the plan is adjusted to 'join an AI company to gain experience,' which can connect with the company's internal entrepreneurial incubation resources."

[0080] By integrating matching strategies and dynamic correlation analysis results, structured guidance information is generated, including short-term actions, medium-term plans, resource matching, and risk warnings, ensuring practicality and operability. Examples of generated guidance information are as follows: Short-term action plan (1-6 months): Focusing on skills implementation and resource matching, example: "1-2 months: Complete AI large-scale model training and obtain relevant certifications, apply for AI product manager positions at 3 leading companies; 3-6 months: Connect with 2 AI investment and financing institutions, apply for preliminary eligibility for provincial AI startup subsidies." Medium-term development plan (1-3 years): Clarifying the path for advancement and capability enhancement, example: "1-2 years: After joining the company, focus on the implementation of large-scale model products, accumulate project experience, and improve cross-departmental collaboration soft skills; 2-3 years: Participate in the company's internal startup incubation project, integrate technical and financial resources, and prepare for the early stages of independent entrepreneurship."

[0081] The resource matching list clearly defines the types and channels of resources that can be directly accessed, for example: "Policy resources: Application channel for AI enterprise subsidies in a certain province (attached link); Funding resources: Contact information for AI project contact persons at XX angel investment institution; Job resources: Referral channel for AI product managers at XX company." Based on dynamic adjustment rules, for example: "Risk 1: Rapid technological iteration in the AI ​​industry → Solution: Follow up on 3 industry technical reports monthly and participate in 1 skills advancement training session quarterly; Risk 2: Delay in accessing startup funding → Solution: Simultaneously apply for startup loans as backup funds."

[0082] In one implementation, such as Figure 2 As shown, this application also provides an intelligent career planning matching device that integrates elements of innovation and entrepreneurship, comprising: The data acquisition module 201 is used to acquire data on individual career aspirations, abilities, development visions, innovation and entrepreneurship resource supply, industry trends, and job requirements. It uses a feature filtering algorithm to remove invalid and redundant information and generate a standardized career-resource feature sequence. Processing module 202 is used to convert the standardized career-resource feature sequence into a supply-demand matching relationship network graph, then use a hierarchical clustering algorithm to subdivide career development scenarios and construct a dynamic supply-demand coupling model. Based on the dynamic supply-demand coupling model, it performs collaborative calculations on the adaptation relationship between personal traits, innovation resources, and career paths to obtain key matching gap values. Combining the key matching gap values, it extracts capability fit, resource accessibility, and path feasibility according to career development stages, classifies matching levels, and constructs a multi-dimensional matching feature matrix containing personal characteristics, innovation elements, industry needs, and job standard information. Based on the multi-dimensional matching feature matrix, it compares the real-time matching status with industry benchmark data to generate abnormal signals of capability shortcomings, resource mismatch, and path deviation, and generates an optimization potential coefficient based on the slope of the matching degree-development potential curve. It integrates the optimization potential coefficient with personal development planning, groups career paths, uses a deep neural network algorithm to screen key adaptation factors, and integrates personal traits, innovation resources, industry needs, and job standard information to construct an adaptive matching optimization model. Based on the adaptation strategy output of the adaptive matching optimization model, and combined with the correlation information between personal development dynamics and innovation and entrepreneurship resource supply, it generates career planning guidance information.

[0083] The computer-readable storage medium provided in the above embodiments of this application and the intelligent career planning matching method integrating innovation and entrepreneurship elements provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

Claims

1. A method for intelligent career planning and matching that integrates elements of innovation and entrepreneurship, characterized in that, include: Data on individual career aspirations, abilities, development visions, innovation and entrepreneurship resource supply, industry trends, and job requirements are obtained. Invalid and redundant information is removed using feature filtering algorithms to generate standardized career-resource feature sequences. After converting the standardized occupation-resource feature sequence into a supply-demand matching relationship network graph, a hierarchical clustering algorithm is used to subdivide occupational development scenarios and construct a dynamic supply-demand coupling model. Based on a dynamic supply and demand coupling model, the matching relationship between personal traits, innovative resources, and career paths is calculated collaboratively to obtain key matching gap values. By combining key matching gap values, we extract competency fit, resource accessibility, and path feasibility according to career development stages, classify matching levels, and construct a multi-dimensional matching feature matrix that includes personal characteristics, innovation elements, industry needs, and job standard information. Based on the comparison of real-time matching status and industry benchmark data using a multi-dimensional matching feature matrix, abnormal signals such as capability shortcomings, resource mismatch, and path deviation are generated, and optimization potential coefficients are generated based on the slope of the matching degree-development potential curve. The optimization potential coefficient is integrated with personal development plans, career paths are grouped, and a deep neural network algorithm is used to screen key matching factors. Personal traits, innovation resources, industry needs and job standard information are integrated to build an adaptive matching optimization model. Based on the adaptive matching optimization model, the output of the adaptation strategy, combined with the correlation information between personal development dynamics and the supply of innovation and entrepreneurship resources, generates career planning guidance information.

2. The method as described in claim 1, characterized in that, After converting standardized occupation-resource feature sequences into a supply-demand matching network graph, a hierarchical clustering algorithm is used to further subdivide occupational development scenarios and construct a dynamic supply-demand coupling model, including: The standardized occupation-resource characteristic sequence is transformed into a graph to generate a supply-demand matching network graph. The standardized occupation-resource characteristic sequence includes standardized information on individual career aspirations, ability traits, development vision, supply of innovation and entrepreneurship resources, industry trends, and job requirements. Model and analyze the supply and demand matching network graph to generate factor correlation analysis results, which include correlation data between personal traits and innovation resources, and between industry needs and job standards. Based on the results of the element association analysis, a hierarchical clustering algorithm is used to subdivide career development scenarios and generate subdivided career development scenario categories. These categories are then used to construct a dynamic supply and demand coupling model.

3. The method as described in claim 2, characterized in that, Based on a dynamic supply and demand coupling model, the matching fit is set as an element-scenario dependent function. The matching relationship between personal traits, innovative resources, and career paths is collaboratively calculated to obtain key matching gap values, including: The parameters of the dynamic supply and demand coupling model and the characteristic parameters of innovation and entrepreneurship elements are set with correlation functions to generate element-scenario dependent functional relationships with matching adaptability. The parameters of the dynamic supply and demand coupling model include the subdivision dimension of career development scenarios and the threshold of element correlation strength. The characteristic parameters of innovation and entrepreneurship elements include the type of innovation resource supply, the adaptability conditions of entrepreneurship policies, and the dynamic indicators of industry trends. The factor-scenario dependent function is coupled with industry development forecast data to generate the basic data for constructing the optimization objective equation. The basic data for constructing the optimization objective equation includes the weights of the core variables of the function, the correction coefficients of industry development forecasts, and the priority ranking results of factor adaptation. The optimization objective equation and the multidimensional matching matrix are calculated collaboratively to generate key matching gap values, including data on the matching gap between personal traits and innovation resources, data on the matching deviation between innovation resources and career paths, and data on the fit gap between personal traits and career paths.

4. The method as described in claim 1, characterized in that, Based on key matching gap values, we extract competency fit, resource accessibility, and path feasibility according to career development stages, classify matching levels, and construct a multi-dimensional matching feature matrix that includes personal characteristics, innovation elements, industry demands, and job standard information, including: The key matching gap values ​​are correlated and extracted with the characteristics of career development stages to generate quantifiable indicators of ability matching, resource accessibility assessment parameters, and path feasibility analysis dimensions. The capability fit metrics, resource accessibility assessment parameters, and path feasibility analysis dimensions are classified into levels to generate high / medium / low fit level standards, level judgment thresholds, and level association rules. Based on the adaptation level standards and judgment thresholds, personal characteristic data, innovation element supply data, industry demand dynamic data, and job standard specification data are integrated and processed to generate a multi-dimensional information association table. The multi-dimensional information association table is structurally reconstructed and feature-mapped to generate a multi-dimensional matching feature matrix containing four-dimensional information: individual, element, industry, and job.

5. The method as described in claim 1, characterized in that, Based on a multi-dimensional matching feature matrix, real-time matching status is compared with industry benchmark data to generate abnormal signals such as capability shortcomings, resource mismatches, and path deviations. Optimization potential coefficients are generated based on the slope of the matching degree-development potential curve, including: The data on personal characteristics, innovation elements, industry demand matching, and job standard fit in the multidimensional matching feature matrix are extracted and classified to generate real-time matching status quantitative indicators, industry benchmark data reference thresholds, feature dimension association rules, and data comparison benchmark framework. Based on industry standards for career planning matching, the quantitative indicators of real-time matching status, reference thresholds of industry benchmark data, association rules of feature dimensions, and data comparison benchmark framework are standardized to generate matching status difference assessment information and benchmark data fit verification information. Based on the matching status difference assessment information and benchmark data adaptability verification information, the deviation items between the real-time matching status and industry benchmark data are marked and analyzed to generate capability shortcoming identification results, resource mismatch judgment information, and path deviation location data. The results of capability deficiency identification, resource mismatch judgment information, and path deviation location data are integrated and slope calculated. Combined with the dynamic change law of the matching degree-development potential curve, an optimization potential coefficient is generated.

6. The method as described in claim 5, characterized in that, The optimization potential coefficient is integrated with personal development plans, career paths are grouped, and a deep neural network algorithm is used to screen key matching factors. This model integrates personal traits, innovation resources, industry demands, and job standards to construct an adaptive matching optimization model, including: Based on the career planning adaptation logic, the optimization potential coefficient is linked and integrated with personal development plan. By matching the potential level with the planning goal in a hierarchical manner, the optimization direction corresponding to the development stage, and the improvement space connected by the implementation path, the matching rules for the core related nodes are clarified, and the planning-potential integration and association results are generated. Based on the scientific principles of career development, the rationality of career path grouping is verified. The path adaptation dimension, group boundary threshold, and stage connection logic dimension are checked one by one to determine whether they conform to personal growth patterns and industry development trends. Unreasonable items are marked with adjustment directions, and career path grouping rationality verification results are generated. Significance verification of the association between key fitting factors and multidimensional information was adopted. The weights of personal trait indicators, innovation resource parameters, industry demand elements, and job standard conditions in the fused data were defined and screened. The core influencing factors were determined by weighted ranking method. By integrating the planning-potential fusion correlation results, the career path grouping rationality verification results, and the factor-information correlation significance verification results, an adaptive matching optimization model is generated, which includes the priority ranking of matching factors, multi-dimensional information fusion weights, and dynamic adjustment rules.

7. An intelligent career planning matching device integrating elements of innovation and entrepreneurship, characterized in that, The device includes: The data acquisition module is used to obtain data on individual career aspirations, abilities, development visions, innovation and entrepreneurship resource supply, industry trends, and job requirements. It uses a feature filtering algorithm to remove invalid and redundant information and generate a standardized career-resource feature sequence. The processing module transforms standardized career-resource feature sequences into a supply-demand matching network graph. It then uses a hierarchical clustering algorithm to subdivide career development scenarios and construct a dynamic supply-demand coupling model. Based on this model, it collaboratively calculates the fit between personal traits, innovative resources, and career paths to obtain key matching gap values. Combining these gap values, it extracts capability fit, resource accessibility, and path feasibility according to career development stages, classifying matching levels and constructing a multi-dimensional matching feature matrix containing personal characteristics, innovative elements, industry demands, and job standards. Based on this multi-dimensional feature matrix, it compares real-time matching status with industry benchmark data to generate abnormal signals indicating capability shortcomings, resource mismatches, and path deviations. It also generates optimization potential coefficients based on the slope of the matching degree-development potential curve. The optimization potential coefficients are then integrated with personal development plans. Career paths are grouped, and a deep neural network algorithm is used to screen key adaptation factors. This integrates personal traits, innovative resources, industry demands, and job standards to construct an adaptive matching optimization model. Finally, based on the adaptation strategy output of the adaptive matching optimization model and combined with the correlation information between personal development dynamics and the supply of innovation and entrepreneurship resources, it generates career planning guidance information.

8. An electronic device, characterized in that, include: First processor; and memory for storing executable instructions of the first processor; The first processor is configured to execute the intelligent career planning and matching method integrating innovation and entrepreneurship elements as described in any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the second processor, it implements the intelligent career planning and matching method that integrates innovation and entrepreneurship elements as described in any one of claims 1 to 6.

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