A production-teaching integration method and system based on double-chain dynamic coupling

Abstract

The application discloses a kind of production and teaching integration methods based on double-chain dynamic coupling, comprising, by NLP technology, the multi-source heterogeneous data of industry chain and education chain are collected, semantic alignment and structured processing, based on business mapping relationship, ability-knowledge graph is constructed;Based on the graph computation industry chain demand heat map and education chain professional agility index;The semantic correlation strength between the nodes of demand on the knowledge graph on the industry side and the nodes of supply on the education side is calculated;Combine potential value with demand heat map and professional agility, compare semantic correlation strength and heat index with pre-set similarity threshold, heat threshold respectively, and comprehensive matching degree analysis is carried out by combining potential value, identify key capability gap and high potential fusion point, convert into smart contract automatically through blockchain credible network, realize production and teaching integration closed loop for whole process on-chain notarization and audit;The application solves the problems of "response lag", "matching extensive" and "poor sustainability".

Description

Technical Field

[0001] This invention relates to the field of vocational education and industry collaborative development technology, and in particular to a method and system for industry-education integration based on dual-chain dynamic coupling. Background Technology

[0002] As an important strategy to promote the coordinated development of education and industry, the integration of industry and education has fallen far short of expectations in terms of its implementation path and effectiveness in the existing technological fields, and generally suffers from the following three deep-seated defects and systemic bottlenecks: Currently, the field of industry-education integration faces three core bottlenecks: First, the lack of operational and systematic methods in macroeconomic policy and theoretical research has led to a disconnect between top-level design and grassroots practice. Existing policy frameworks mostly remain at the level of directional guidance, lacking standardized technical paths to transform macroeconomic strategies into executable, assessable, and scalable ones. This makes the implementation process reliant on individual experience and ad-hoc coordination, making it difficult to form a stable, replicable, and scalable operational model.

[0003] Second, existing information platforms only achieve a superficial connection between supply and demand information, failing to drive deep and dynamic coupling. Mainstream platforms are limited to "information bulletin boards," exhibiting two major limitations: semantic gaps and static responses. Industry and education terminology and standards are inconsistent, supporting only shallow keyword matching with low accuracy. Furthermore, they lack dynamic perception of industry trends and quantitative analysis of educational resources, hindering the education system's forward-looking and agile iteration in response to industry needs, resulting in severe lag in response.

[0004] Third, the reliance on trust between universities and enterprises in the process of collaboration results in low transparency and difficulty in traceability, making the collaboration fragile and unsustainable. Traditional cooperation models depend on personal relationships, requiring high communication costs in areas such as mutual trust in qualifications, process supervision, and achievement recognition. Key data, such as student training processes, enterprise resource investment, and the use of policy subsidies, lack objective, real-time, and tamper-proof recording mechanisms, making it difficult to quantify and trace achievements. Cooperation is highly susceptible to interruption due to personnel changes or conflicts of interest.

[0005] Therefore, these shortcomings have long caused industry-education integration to face core bottlenecks such as slow response, extensive matching, and poor sustainability. This has led to problems such as difficulty in dynamic matching of supply and demand, unsustainable collaboration, and low overall efficiency due to the lack of a real-time, accurate, and reliable integration mechanism between the industry chain and the education chain. Summary of the Invention

[0006] This invention provides a method and system for industry-education integration based on dual-chain dynamic coupling to overcome the above-mentioned technical problems.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: A method for industry-education integration based on dual-chain dynamic coupling includes: S1: Introduce NLP technology to collect multi-source heterogeneous data from the industry chain side and the education chain side, and use NLP technology to perform semantic alignment and structuring processing on the multi-source heterogeneous data to obtain structured data; S2: Based on the business mapping relationship between industry demand on the industry chain side and educational content on the education chain side, a capability-knowledge graph connecting industry chain nodes and education chain nodes is constructed based on structured data; based on the capability-knowledge graph, a dynamically updated industry chain demand heat map and education chain professional agility index are calculated through data processing and statistical algorithms. S3: On the aforementioned capability-knowledge graph, calculate the semantic association strength between industry-side demand nodes and education-side supply nodes through semantic vector similarity; S4: Introduce collaborative filtering and association rule mining algorithms to calculate potential values ​​based on the industry chain demand heat map and the professional agility of the education chain. Compare the semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map with preset similarity thresholds and heat thresholds respectively to obtain comparison results. Combine the potential values ​​to conduct comprehensive matching degree analysis and identify key capability gaps and high-potential integration points. S5: Output the key capability gaps and the high-potential integration points as the analysis results; S6: Based on blockchain technology, a trusted collaborative network is built, which transforms the analysis results output by S5 into smart contracts for execution, and performs on-chain evidence storage and auditing of the execution process and results, thus achieving industry-education integration.

[0008] Furthermore, the semantic association strength between industry-side demand nodes and education-side supply nodes is calculated using semantic vector similarity, including: The formula for calculating the semantic association strength between industry-side demand nodes and education-side supply nodes is as follows:

[0009] in, For the demand nodes on the industry side, To provide nodes for the education side, For weight parameters, This is the semantic vector of the corresponding node. The shortest path length for the connection points on the capability-knowledge graph.

[0010] Furthermore, collaborative filtering and association rule mining algorithms are introduced to calculate potential values ​​based on the aforementioned industry chain demand heatmap and the professional agility of the education chain, including: The formula for calculating high-potential fusion points is as follows:

[0011] in, This represents the potential value of the education-side node. Representing educational nodes With industry demand cluster The probability of them occurring simultaneously This indicates the growth rate of demand cluster popularity. This represents the professional agility index of the institution to which the education-side node belongs.

[0012] Furthermore, the semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heatmap are compared with preset similarity thresholds and heat thresholds, respectively, to obtain comparison results. Combined with potential values, a comprehensive matching degree analysis is performed to identify key capability gaps and high-potential integration points, including: The semantic association strength is compared with a preset similarity threshold to obtain a first comparison result; the heat index of each industry-side demand node in the industry chain demand heat map is compared with a preset heat threshold to obtain a second comparison result. By combining the potential value, the first comparison result, and the second comparison result, a comprehensive matching analysis is conducted to identify key capability gaps and high-potential integration points, as detailed below: For any industry demand node, if its corresponding first comparison result is that the semantic association strength between it and all related education-side supply nodes is lower than the similarity threshold, and its corresponding second comparison result is that its popularity index is higher than the popularity threshold, then the node is determined to be a key capability gap. The potential values ​​are sorted, and the educational supply nodes with potential values ​​higher than the preset potential threshold are identified as high-potential integration points.

[0013] Furthermore, the key capability gaps and high-potential convergence points are output as analysis results, including: The identified key capability gaps and high-potential integration points are mapped onto a visualized industry chain demand heatmap; Based on the mapped heat map, structured decision-making information is generated, including suggestions for adjusting professional settings, early warnings for curriculum content updates, recommendations for school-enterprise cooperation projects, and talent supply and demand forecast reports.

[0014] Furthermore, a trusted collaborative network is built based on blockchain technology to transform the analysis results output by S5 into smart contracts for execution, and to perform on-chain notarization and auditing of the execution process and results, including: The steps for digitally storing talent capabilities are as follows: students' practical training results and project experiences are generated into digital summaries and stored on the blockchain to form an unalterable digital twin resume; Smart contract-driven execution steps: Encode the recommended solution for the university-enterprise cooperation project into a smart contract, which will be automatically triggered and executed when preset conditions are met; On-chain auditing steps: The process of using and evaluating the benefits of government incentive policies and corporate resource investment is put on the blockchain to achieve transparent and traceable management and auditing.

[0015] Based on the same inventive concept, a dual-chain dynamic coupling-based industry-education integration system is also proposed, which applies a dual-chain dynamic coupling-based industry-education integration method, including: a data perception and fusion module, a dual-chain dynamic coupling and intelligent decision-making module, and a trustworthy collaboration and execution feedback module. The data perception and fusion module is used to collect multi-source heterogeneous data from the industry chain side and the education chain side, perform semantic alignment and structured processing on the multi-source heterogeneous data through NLP technology, store and maintain a capability-knowledge graph built based on business mapping relationships, and calculate dynamically updated industry chain demand heat map and education chain professional agility index based on the capability-knowledge graph through data processing and statistical algorithms. The dual-chain dynamic coupling and intelligent decision-making module is used to calculate the semantic association strength between industry-side and education-side nodes on the capability-knowledge graph, calculate the potential value based on the industry chain demand heat map and the professional agility of the education chain, compare the semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map with preset similarity thresholds and heat thresholds respectively, obtain the comparison results, and perform comprehensive matching degree analysis in combination with the potential value to identify key capability gaps and high-potential integration points, and output the analysis results of the key capability gaps and the high-potential integration points. The Trusted Collaboration and Execution Feedback Module is used to drive the execution of analysis results through smart contracts in the blockchain trusted collaboration network, provide on-chain evidence storage and auditing functions, and feed back the results.

[0016] Beneficial effects: This invention provides a method for industry-education integration based on dual-chain dynamic coupling, which has the following advantages: 1. This invention solves the problem of "delayed response," realizing a shift from passive response to proactive adaptation and improving response agility: By constructing a capability-knowledge graph, an industry chain demand heatmap, and professional agility within the education chain, it can perceive and quantify the evolutionary trends of industry technology demands (through the heatmap) and the adaptability of the education system (through agility) in real time and continuously. This transforms the education chain from a static system that passively responds to demands to one that proactively identifies high-potential integration points based on quantified heatmap trends and its own agility indicators, thereby actively planning professional adjustments, curriculum updates, and collaborative projects. This fundamentally shortens the response cycle of education to industry demands, achieving dynamic and forward-looking coupling.

[0017] 2. This invention solves the problem of "coarse matching" and realizes the transformation from keyword matching to deep semantic coupling, thereby improving the accuracy of matching: Through the calculation of "semantic association strength", it can deeply understand the inherent logical connection between "industry skills" and "educational knowledge"; the comprehensive matching degree analysis makes the supply and demand matching upgrade from a crude job-professional comparison to a precise and deep semantic matching based on ability units and knowledge points, which significantly improves the accuracy and effectiveness of matching, thereby driving the deep coupling between educational content and industry needs.

[0018] 3. This invention solves the problem of "poor sustainability," realizing a shift from relying on human governance to relying on technology governance, and ensuring the reliability and traceability of the collaborative process: A trusted collaborative network built on blockchain technology transforms the output analysis results into automatically executable "smart contracts," automating resource matching and collaborative processes. Simultaneously, on-chain notarization throughout the entire process establishes a transparent and tamper-proof trust mechanism. This fundamentally reduces reliance on personal relationships and resolves the trust gaps and collaborative vulnerabilities caused by opaque processes and difficulty in tracing results. It provides a technological foundation for the institutionalized and sustainable operation of cross-organizational industry-education collaboration, thereby enhancing overall collaborative efficiency. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a method for industry-education integration based on dual-chain dynamic coupling provided by the present invention; Figure 2 A schematic diagram of the dual-chain dynamic coupling principle provided by the present invention; Figure 3 A schematic diagram of the capability-knowledge graph structure provided by this invention; Figure 4 This invention provides a schematic diagram of the industrial chain demand heat map data. Figure 5 The present invention provides a system block diagram of an industry-education integration system based on dual-chain dynamic coupling. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0022] This embodiment provides a method for industry-education integration based on dual-chain dynamic coupling, such as... Figure 1 As shown, it includes: S1: Introduce NLP technology to collect multi-source heterogeneous data from the industry chain side and the education chain side, and use NLP technology to perform semantic alignment and structuring processing on the multi-source heterogeneous data to obtain structured data; S2: Based on the business mapping relationship between industry demand on the industry chain side and educational content on the education chain side, a capability-knowledge graph connecting industry chain nodes and education chain nodes is constructed based on structured data; based on the capability-knowledge graph, a dynamically updated industry chain demand heat map and education chain professional agility index are calculated through data processing and statistical algorithms. S3: On the aforementioned capability-knowledge graph, calculate the semantic association strength between industry-side demand nodes and education-side supply nodes through semantic vector similarity; S4: Introduce collaborative filtering and association rule mining algorithms to calculate potential values ​​based on the industry chain demand heat map and the professional agility of the education chain. Compare the semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map with preset similarity thresholds and heat thresholds respectively to obtain comparison results. Combine the potential values ​​to conduct comprehensive matching degree analysis and identify key capability gaps and high-potential integration points. S5: Output the key capability gaps and the high-potential integration points as the analysis results; S6: Based on blockchain technology, a trusted collaborative network is built, which transforms the analysis results output by S5 into smart contracts for execution, and performs on-chain evidence storage and auditing of the execution process and results, thus achieving industry-education integration.

[0023] Specifically, the dual-chain structure of the industrial chain and the education chain is as follows: Figure 2As shown, the demand side of the industrial chain focuses on the "5+4+3+1" industrial system (hardcore manufacturing, digital empowerment, new quality services, etc.), representing the core development direction of regional industries and serving as the "demand side" for talent. The supply side of the education chain is represented by a cluster of vocational colleges consisting of "10 high-level colleges + 20 characteristic professional groups," serving as the "supply side" for talent. The industrial chain and education chain break down the "data silos" and "semantic gap" between them through a dual-chain integration system. Through technologies (such as NLP and knowledge graphs), the data on both sides is "perceived, aligned, and structured," providing a unified data foundation for subsequent analysis. In the spiral progression section, the core requirements of the industry for talent are clearly defined: "Addressing Shortages": Industrial upgrading forces dynamic adjustments to talent capabilities (such as shortages in new technology positions). "Digital Craftsman": Emphasizes the popularization of digital skills and cross-border integration capabilities (such as the combined capabilities of "intelligent manufacturing + data analysis").

[0024] This will provide a demand-driven framework for the subsequent operation of the "double helix" model, ensuring that the integration of industry and education does not deviate from the actual needs of the industry. The main spiral is government-school-enterprise (organizational collaboration layer), with participating entities being the government, schools, and enterprises. The operational logic is "policy guidance × resource investment × shared responsibility." Specifically, the government introduces industry-education integration policies (such as subsidies and assessment mechanisms) to guide resource flow; schools invest educational resources (curriculum, teachers, training bases); and enterprises invest industrial resources (technology, positions, projects). Ultimately, this completes "energy exchange" (the two-way flow of talent, technology, and capital).

[0025] The practice spiral is teaching-training-innovation (talent cultivation level), and its implementation path is "theoretical teaching + on-the-job training + technology feedback". Among them, theoretical teaching provides schools with basic knowledge; on-the-job training provides enterprises / training bases with practical skills; technology feedback is students / teachers feeding back the teaching with the technologies optimized in practice (such as integrating the latest enterprise algorithms into the curriculum); and finally, it achieves "quality improvement" (such as improving the quality of talent and improving industrial efficiency).

[0026] The results of the "double helix" operation will be scaled up and standardized for export, forming a replicable industry-education integration ecosystem. For example, through a district-level industry-education consortium (a regional "resource-sharing platform"), government, school, and enterprise resources can be integrated to achieve centralized allocation of talent, technology, and capital. Furthermore, through an international standards export platform, local industry-education integration experiences can be transformed into international standards and disseminated externally. Ultimately, this achieves output and feedback optimization. When the collaboration enters the physical operation stage, the practice spiral is for students to go from theoretical teaching to on-the-job training in enterprises, and then feed back the problems found in practice to the teaching and technology research and development links of the school, forming a closed loop of "teaching-practice-innovation" to improve capabilities; the main spiral is for the government to fulfill its policies through on-chain audits, schools to accurately invest educational resources, and enterprises to invest technology and project resources, and the three parties to exchange energy through talent flow, technology flow, and capital flow.

[0027] The two spirals interact: the main spiral provides resources and institutional guarantees for the practice spiral; the high-quality talents and technological solutions produced by the practice spiral, in turn, nourish and consolidate the cooperative relationship of the main spiral, and promote the spiral-like rise of system value.

[0028] All key interactions, outcomes, and policy implementation data are stored on the blockchain, forming an immutable audit trail. Based on this reliable data, the system dynamically evaluates the effectiveness of this integration and generates a feedback report. The core function of this report is to optimize the rules and parameters of the double helix matching algorithm, forming a "feedback loop" that allows for more intelligent and precise operation in the next cycle.

[0029] In a specific embodiment, NLP technology is introduced to collect multi-source heterogeneous data from both the industry chain and education chain sides. The NLP technology is then used to perform semantic alignment and structured processing on this multi-source heterogeneous data to obtain structured data. Collect heterogeneous data from multiple sources, including the industry chain (such as enterprise technology needs, job skill maps, and project challenges) and the education chain (such as major settings, curriculum outlines, practical training projects, and student ability data). The data sources on the industry chain side include enterprise recruitment websites, industry technology communities, and industry reports released by the government; the data sources on the education chain side include the academic affairs management system of colleges and universities, high-quality course platforms, and practical training management platforms. The specific data sources can be added according to the actual platform or needs. This embodiment does not impose any specific limitations. The collected data is processed using NLP technology. This process includes: First, word segmentation and part-of-speech tagging are performed to break down sentences into basic units. Second, Named Entity Recognition (NER) models (such as BERT-based pre-trained models) are used to identify "skill entities" (e.g., "PyTorch"), "course entities" (e.g., "deep learning"), and "project entities" in the text. Then, a relation extraction model is used to extract the relationships between entities; for example, from "job requirement: proficiency in Spring Boot framework," the relation triple <job, required, Spring Boot framework> is extracted. Finally, to address inconsistencies in terminology (e.g., companies use "big data development," while schools use "data science and big data technology"), the system uses a word vector model to calculate semantic similarity between terms, mapping terms with similarity higher than a threshold to a unified, standardized concept, thus achieving semantic alignment. Through this step, unstructured text is transformed into machine-readable, standardized structured data (e.g., a JSON-formatted entity-relation list), providing input for subsequent graph construction.

[0030] In this solution, a unified capability-knowledge graph is constructed using NLP, achieving deep semantic alignment between industry skills and educational knowledge, which goes beyond simple keyword matching.

[0031] In a specific embodiment, based on the business mapping relationship between industry demand on the industry chain side and educational content on the education chain side, a capability-knowledge graph connecting industry chain nodes and education chain nodes is constructed based on structured data; the scheme for obtaining dynamically updated industry chain demand heatmaps and education chain professional agility indicators based on the capability-knowledge graph through data processing and statistical algorithms is as follows: Based on the business mapping relationship between industry demand in the industry chain and educational content in the education chain, a capability-knowledge graph connecting nodes in the industry chain and nodes in the education chain is constructed based on structured data, as detailed below: The aforementioned capability-knowledge graph is a unified semantic mapping model connecting the industry chain and the education chain. In this solution, based on structured data and with the "capability-knowledge" mapping relationship as the core (such as "industry skills → educational knowledge" and "capability item → knowledge point"), a relationship graph between industry chain nodes and education chain nodes is constructed, specifically as follows: Figure 3 As shown; In this solution, the business mapping relationship is confirmed and divided based on business research and experts. The construction of knowledge graphs is a commonly used technical means in the field of artificial intelligence. Therefore, this solution does not elaborate on the specific mapping relationship and graph construction process. Those skilled in the art can use it according to the actual situation. Based on the aforementioned capability-knowledge graph, dynamic updates of the industry chain demand heatmap and the professional agility index of the education chain are calculated through data processing and statistical algorithms, including: Supply Chain Demand Heat Map: Regularly (e.g., weekly / monthly), calculate the heat index of industry-side nodes (e.g., the frequency of mention of this skill in company recruitment, the growth rate of this demand in industry reports), and update the heat value through data processing and statistical algorithms (e.g., weighted moving average, heat propagation model) to generate a visual heat map (e.g., using color depth to represent demand intensity); the completed supply chain demand heat map looks like... Figure 4 As shown; the industrial chain demand heat map is a multi-dimensional dynamic data set with a specific structure. This industrial chain demand heat map transforms fuzzy and scattered industrial demand information into a standardized, quantifiable, and spatiotemporally traceable technical object, which is a prerequisite for achieving accurate insight and dynamic matching of industry-education supply and demand. Education Chain Professional Agility: This involves aggregating historical behavioral data for educational nodes and their affiliated institutions, such as course content update cycles, the speed of opening new specialized programs, and the number and response time of collaborative projects with emerging industries. A comprehensive evaluation model, such as a linear weighted model, is used: Professional Agility = Course update frequency score + School-enterprise cooperation response speed+ The flexibility of adjusting teaching resources is used to calculate a professional agility index that reflects the educational entity's ability to adapt to change. This index is a dynamic value used to quantify the effectiveness of the education system in responding to changes in industry demands, and it is recalculated periodically as educational activities are updated. The professional agility index of the education chain transforms the abstract concept of the education system's adaptability into a measurable, analyzable, and interventionist system feedback signal, serving as a key closed-loop control variable for achieving dynamic and precise coupling between industry and education.

[0032] In a specific embodiment, the scheme for calculating the semantic association strength between industry-side demand nodes and education-side supply nodes on the capability-knowledge graph using semantic vector similarity is as follows: The formula for calculating the semantic association strength between industry-side demand nodes and education-side supply nodes is as follows:

[0033] in, For the demand nodes on the industry side, To provide nodes for the education side, The weight parameter adjusts the proportion of text similarity to graph structure similarity. , This is the semantic vector of the corresponding node. The shortest path length for the connection points on the capability-knowledge graph.

[0034] In a specific embodiment, a collaborative filtering and association rule mining algorithm is introduced to calculate potential values ​​based on the industry chain demand heatmap and the professional agility of the education chain. The semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heatmap are compared with preset similarity thresholds and heat thresholds, respectively, to obtain the comparison results. Combined with the potential values, a comprehensive matching degree analysis is performed to identify key capability gaps and high-potential integration points. The formula for calculating high-potential fusion points is as follows:

[0035] in, This represents the potential value of the education-side node. Representing educational nodes With industry demand cluster The probability of them occurring simultaneously This indicates the growth rate of demand cluster popularity. This represents the professional agility index of the institution to which the education-side node belongs; The semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map are compared with preset similarity thresholds and heat thresholds, respectively. The comparison results are then combined with the potential value for comprehensive matching analysis to identify key capability gaps and high-potential integration points, including: The semantic association strength is compared with a preset similarity threshold to obtain a first comparison result; the heat index of each industry-side demand node in the industry chain demand heat map is compared with a preset heat threshold to obtain a second comparison result. By combining the potential value, the first comparison result, and the second comparison result, a comprehensive matching analysis is conducted to identify key capability gaps and high-potential integration points, as detailed below: For any industry demand node, if its corresponding first comparison result is that the semantic association strength between it and all related education-side supply nodes is lower than the similarity threshold, and its corresponding second comparison result is that its popularity index is higher than the popularity threshold, then the node is determined to be a key capability gap. The potential values ​​are sorted, and the educational supply nodes with potential values ​​higher than the preset potential threshold are identified as high-potential integration points. Comprehensive matching analysis quantifies the overall matching degree between a specific industry demand cluster and a specific education supply cluster, representing whether education supply effectively covers industry demand, whether supply and demand are partially matched (with gaps requiring optimization), and whether supply and demand are severely mismatched (requiring systemic adjustment). This solution accurately identifies key capability gaps and high-potential integration points by setting similarity and popularity thresholds. The similarity threshold is a critical value for judging whether education supply "effectively covers" a certain industry demand node. When the probability of an education node and an industry demand cluster appearing simultaneously is greater than the similarity threshold, the demand node is considered to be effectively covered; otherwise, a gap exists in the demand node. The popularity threshold is a critical value for screening "important demand nodes," used to select the most urgent and valuable optimization directions from numerous gaps.

[0036] In a specific embodiment, the approach of outputting the key capability gap and the high-potential convergence point as the analysis result is as follows: The identified key capability gaps and high-potential integration points are mapped onto a visualized industry chain demand heatmap; Based on the mapped heatmap, structured decision-making information is generated, including suggestions for adjusting professional settings, early warnings for updating course content, recommendations for school-enterprise cooperation projects, and talent supply and demand forecast reports. In this solution, those skilled in the art or education experts can manually generate the final text-based content such as "adjustment suggestions" and "update early warnings" based on the mapped heatmap.

[0037] Structured decision information is a set of structured, executable instructions or triggering conditions that are generated. This output is transformed into operational instructions that can directly drive the operation of the "subject spiral" and the "practice spiral," and is a key link in realizing the transformation from intelligent analysis to actual action.

[0038] In this solution, the analysis results are mapped into a heat map of industry chain demand, which is then transformed into structured decision outputs, including suggestions for adjusting professional settings, early warning of course content updates, a precise recommendation list of school-enterprise cooperation projects, and talent supply and demand forecast reports. These outputs serve as the direct basis for the automatic triggering or manual intervention of smart contracts. This solution transforms the unstructured industry-education matching problem into a computable, optimizable, and iterative technical decision-making process based on a unified semantic model, achieving a fundamental shift from static, experience-based cooperation to dynamic, algorithmic coupling.

[0039] In a specific embodiment, a trusted collaborative network is built based on blockchain technology. The analysis results output by S5 are transformed into smart contracts for execution, and the execution process and results are stored and audited on-chain. The solution for achieving industry-education integration is as follows: The steps for digitally storing talent capabilities are as follows: students' practical training results and project experiences are generated into digital summaries and stored on the blockchain to form an unalterable digital twin resume; Smart contract-driven execution steps: Encode the recommended solution for the university-enterprise cooperation project into a smart contract, which will be automatically triggered and executed when preset conditions are met; On-chain auditing steps: The process of using and evaluating the benefits of government incentive policies and corporate resource investment is put on the blockchain to achieve transparent and traceable management and auditing.

[0040] The aforementioned blockchain-based trusted collaborative network is the underlying technological infrastructure that ensures the credibility, transparency, and automatic execution of the entire process of industry-education integration. This blockchain-based trusted collaborative network establishes a trust and collaboration mechanism that does not rely on the reputation of the main body but is guaranteed by technology through technical means. It breaks through the vulnerability of cooperation caused by the lack of trust and ensures the sustainability of the collaborative process. It fundamentally solves the problems of data silos and inefficient collaboration caused by the lack of trust and the lack of transparency in the process in traditional industry-education integration.

[0041] This embodiment also provides a system for industry-education integration based on dual-chain dynamic coupling, such as... Figure 5 As shown, it includes: a data perception and fusion module, a dual-chain dynamic coupling and intelligent decision-making module, and a trusted collaboration and execution feedback module; The data perception and fusion module (data perception and fusion layer) is used to collect multi-source heterogeneous data from the industry chain side and the education chain side, perform semantic alignment and structured processing on the multi-source heterogeneous data through NLP technology, store and maintain a capability-knowledge graph built based on business mapping relationships, and calculate dynamically updated industry chain demand heat map and education chain professional agility index based on the capability-knowledge graph through data processing and statistical algorithms. The dual-chain dynamic coupling and intelligent decision-making module (dual-chain dynamic coupling and intelligent decision-making layer) is used to calculate the semantic association strength between industry-side and education-side nodes on the capability-knowledge graph, calculate the potential value based on the industry chain demand heat map and the professional agility of the education chain, compare the semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map with preset similarity thresholds and heat thresholds respectively, obtain the comparison results, and perform comprehensive matching degree analysis in combination with the potential value to identify key capability gaps and high-potential integration points, and output the analysis results of the key capability gaps and the high-potential integration points. The Trusted Collaboration and Execution Feedback Module (Trusted Collaboration and Execution Feedback Layer) is used to drive the execution analysis results through smart contracts in the blockchain trusted collaboration network, provide on-chain evidence storage and auditing functions, and feed back the results.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for industry-education integration based on dual-chain dynamic coupling, characterized in that, include: S1: Introduce NLP technology to collect multi-source heterogeneous data from the industry chain side and the education chain side, and use NLP technology to perform semantic alignment and structuring processing on the multi-source heterogeneous data to obtain structured data; S2: Based on the business mapping relationship between industry demand on the industry chain side and educational content on the education chain side, a capability-knowledge graph connecting industry chain nodes and education chain nodes is constructed based on structured data; based on the capability-knowledge graph, a dynamically updated industry chain demand heat map and education chain professional agility index are calculated through data processing and statistical algorithms. S3: On the aforementioned capability-knowledge graph, calculate the semantic association strength between industry-side demand nodes and education-side supply nodes through semantic vector similarity; S4: Introduce collaborative filtering and association rule mining algorithms to calculate potential values ​​based on the industry chain demand heat map and the professional agility of the education chain. Compare the semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map with preset similarity thresholds and heat thresholds respectively to obtain comparison results. Combine the potential values ​​to conduct comprehensive matching degree analysis and identify key capability gaps and high-potential integration points. S5: Output the key capability gaps and high-potential integration points as analysis results; S6: Based on blockchain technology, a trusted collaborative network is built, which transforms the analysis results output by S5 into smart contracts for execution, and performs on-chain evidence storage and auditing of the execution process and results, thus achieving industry-education integration.

2. The industry-education integration method based on dual-chain dynamic coupling according to claim 1, characterized in that, The semantic similarity is used to calculate the semantic association strength between industry-side demand nodes and education-side supply nodes, including: The formula for calculating the semantic association strength between industry-side demand nodes and education-side supply nodes is as follows: in, For the demand nodes on the industry side, To provide nodes for the education side, For weight parameters, This is the semantic vector of the corresponding node. The shortest path length for the connection points on the capability-knowledge graph.

3. The industry-education integration method based on dual-chain dynamic coupling according to claim 1, characterized in that, The algorithm incorporates collaborative filtering and association rule mining to calculate potential values ​​based on the industry chain demand heatmap and the professional agility of the education chain, including: The formula for calculating high-potential fusion points is as follows: in, This represents the potential value of the education-side node. Representing educational nodes With industry demand cluster The probability of them occurring simultaneously This indicates the growth rate of demand cluster popularity. This represents the professional agility index of the institution to which the education-side node belongs.

4. The industry-education integration method based on dual-chain dynamic coupling according to claim 1, characterized in that, The semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map are compared with preset similarity thresholds and heat thresholds, respectively. The comparison results are then combined with the potential value for comprehensive matching analysis to identify key capability gaps and high-potential integration points, including: The semantic association strength is compared with a preset similarity threshold to obtain a first comparison result; the heat index of each industry-side demand node in the industry chain demand heat map is compared with a preset heat threshold to obtain a second comparison result. By combining the potential value, the first comparison result, and the second comparison result, a comprehensive matching analysis is conducted to identify key capability gaps and high-potential integration points, as detailed below: For any industry demand node, if its corresponding first comparison result is that the semantic association strength between it and all related education-side supply nodes is lower than the similarity threshold, and its corresponding second comparison result is that its popularity index is higher than the popularity threshold, then the node is determined to be a key capability gap. The potential values ​​are sorted, and the educational supply nodes with potential values ​​higher than the preset potential threshold are identified as high-potential integration points.

5. The industry-education integration method based on dual-chain dynamic coupling according to claim 1, characterized in that, The analysis results are output as the key capability gaps and the high-potential convergence points, including: The identified key capability gaps and high-potential integration points are mapped onto a visualized industry chain demand heatmap; Based on the mapped heat map, structured decision-making information is generated, including suggestions for adjusting professional settings, early warnings for curriculum content updates, recommendations for school-enterprise cooperation projects, and talent supply and demand forecast reports.

6. The industry-education integration method based on dual-chain dynamic coupling according to claim 1, characterized in that, A trusted collaborative network is built based on blockchain technology, transforming the analysis results output by S5 into smart contracts for execution, and performing on-chain notarization and auditing of the execution process and results, including: The steps for digitally storing talent capabilities are as follows: students' practical training results and project experiences are generated into digital summaries and stored on the blockchain to form an unalterable digital twin resume; Smart contract-driven execution steps: Encode the recommended solution for the university-enterprise cooperation project into a smart contract, which will be automatically triggered and executed when preset conditions are met; On-chain auditing steps: The process of using and evaluating the benefits of government incentive policies and corporate resource investment is put on the blockchain to achieve transparent and traceable management and auditing.

7. A system for industry-education integration based on dual-chain dynamic coupling, employing the industry-education integration method based on dual-chain dynamic coupling as described in claim 1, characterized in that, include: Data perception and fusion module, dual-chain dynamic coupling and intelligent decision-making module, and trusted collaboration and execution feedback module; The data perception and fusion module is used to collect multi-source heterogeneous data from the industry chain side and the education chain side, perform semantic alignment and structured processing on the multi-source heterogeneous data through NLP technology, store and maintain a capability-knowledge graph built based on business mapping relationships, and calculate dynamically updated industry chain demand heat map and education chain professional agility index based on the capability-knowledge graph through data processing and statistical algorithms. The dual-chain dynamic coupling and intelligent decision-making module is used to calculate the semantic association strength between industry-side and education-side nodes on the capability-knowledge graph, calculate the potential value based on the industry chain demand heat map and the professional agility of the education chain, compare the semantic association strength and the heat index of the industry-side demand nodes in the industry chain demand heat map with preset similarity thresholds and heat thresholds respectively, obtain the comparison results, and perform comprehensive matching degree analysis in combination with the potential value to identify key capability gaps and high-potential integration points, and output the analysis results of the key capability gaps and the high-potential integration points. The Trusted Collaboration and Execution Feedback Module is used to drive the execution of analysis results through smart contracts in the blockchain trusted collaboration network, provide on-chain evidence storage and auditing functions, and feed back the results.

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