College subject layout planning method based on regional economic development

Abstract

The invention discloses a college subject layout planning method based on regional economic development, and the method comprises the steps: obtaining the industrial structure, employment market, scientific and technological innovation and policy oriented data of a target region in real time through constructing a multi-dimensional regional economic development data collection system, and building a regional economic factor correlation analysis model through employing a big data mining technology, and key industrial fields and technical bottlenecks are identified. An industrial talent demand prediction map in the next 5-10 years is generated through a grey prediction model and a machine learning algorithm, a college subject evaluation index system is established, a matching degree index of a subject and a regional economic demand is calculated by using an entropy evaluation method, and a subject dynamic adjustment matrix model is designed to generate an optimization scheme. And meanwhile, a government-industry-university-research collaborative verification platform is constructed, a closed-loop correction mechanism including market demand feedback, education quality evaluation and economic contribution degree measurement and calculation is formed, and finally a visual decision support system, a subject layout optimization route map and a dynamic monitoring and early warning platform are output.

Description

Technical Field

[0001] The present invention provides a method for planning the layout of university disciplines, and in particular relates to a method for planning the layout of university disciplines based on regional economic development. Background Art

[0002] University discipline planning plays a crucial role in the development of universities and the coordinated progress of regional economies. It involves the rational layout and adjustment of the various disciplines offered by universities based on a variety of factors, including their own educational positioning, development goals, and the external socioeconomic environment. This planning determines the development direction, focus, and interrelationships of different disciplines. In the past, university discipline planning often focused on internal academic heritage, faculty distribution, and the utilization of teaching facilities. This approach established a basic disciplinary framework for the long-term and stable development of universities from a macro perspective, enabling them to advance in an orderly manner along the path of knowledge inheritance and innovation. However, with the rapid transformation of the social economy and the increasingly diverse and dynamic demand for talent in the regional economy, the traditional, relatively static discipline planning model has gradually become out of sync with regional economic development.

[0003] While existing university discipline planning methods can achieve a basic disciplinary layout to a certain extent, they suffer from numerous shortcomings. First, many universities use a relatively narrow data dimension when conducting discipline planning, focusing primarily on internal teaching and research data. They rarely systematically incorporate multidimensional data on the regional economy, such as industrial structure, the employment market, scientific and technological innovation, and policy orientations. This leads to a disconnect between discipline layout and the actual needs of regional economic development. Second, the decision-making process for discipline adjustments often relies on the subjective judgments of university management and experts and scholars, lacking a scientific decision-making system based on advanced technologies such as big data analysis and machine learning. This makes it difficult to accurately predict industrial talent needs and quantitatively assess the alignment of disciplines with regional economic needs. Furthermore, existing discipline planning lacks an effective closed-loop verification mechanism after implementation, and fails to fully integrate external resources such as government, businesses, and industry to build a collaborative platform that enables dynamic, iterative adjustments based on market demand feedback, educational quality assessments, and economic contribution measurements. This makes it difficult for discipline layouts to adapt promptly to the new requirements of regional economic transformation and upgrading. Summary of the Invention

[0004] In order to solve the above problems, this application provides a method for university discipline layout planning based on regional economic development, which solves the defects of existing discipline planning being out of touch with regional economy, subjective decision-making and lack of verification, and realizes the dynamic optimization of discipline layout and accurate matching with regional economy.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: a method for planning the discipline layout of universities based on regional economic development. The method is guided by the idea of seeking truth from facts and realizes scientific planning of discipline layout by establishing a dynamic coupling model of regional economic discipline development. The method specifically includes the following steps:

[0006] (1) Build a multi-dimensional regional economic development data collection system to obtain real-time data on the target region's industrial structure, employment market, scientific and technological innovation, and policy guidance;

[0007] (2) Use big data mining technology to establish a regional economic factor correlation analysis model to identify key industrial fields and technical bottlenecks in regional economic development;

[0008] (3) Generate a forecast map of regional industrial talent demand in the next 5-10 years based on the grey prediction model and machine learning algorithm;

[0009] (4) Establish a university discipline evaluation index system and calculate the matching index between disciplines and regional economic needs through the entropy method;

[0010] (5) Design a dynamic discipline adjustment matrix model to generate an optimization plan that includes the addition, transformation, and elimination of disciplines;

[0011] (6) Build a collaborative verification platform for government, industry, academia and research, and establish a closed-loop correction mechanism that includes market demand feedback, education quality assessment, and economic contribution measurement.

[0012] Preferably, the data acquisition system in step (1) specifically includes:

[0013] Industrial structure data: covering the output value ratio of the three major industries, the classified output value of strategic emerging industries, and the completeness index of the industrial chain;

[0014] Employment market data: including job demand growth rate, skill gap types, and salary level distribution;

[0015] Technological innovation data: including regional R&D investment intensity, distribution of patent technology fields, and technology transaction market data;

[0016] Policy-oriented data: including local development planning texts, industrial support policies, and talent introduction plans.

[0017] Preferably, the association analysis model in step (2) adopts an improved Apriori-GRA algorithm, specifically including:

[0018] (a) Mining frequent itemsets between economic factors through Apriori algorithm;

[0019] (b) Use grey correlation analysis to calculate the correlation between factors;

[0020] (c) Construct a three-dimensional correlation matrix (industry correlation, technology correlation, and talent correlation);

[0021] (d) Set up a dynamic weight adjustment module to automatically correct the correlation threshold based on policy-oriented data.

[0022] Preferably, the talent demand forecasting map construction method in step (3) includes:

[0023] Establish an LSTM neural network time series prediction model to process historical data;

[0024] Monte Carlo simulation is used to assess the impact of uncertainty caused by policy changes;

[0025] Design a visual heat map to show the changes in talent demand intensity and skill structure in different industrial fields.

[0026] Preferably, the matching index calculation in step (4) adopts an improved entropy weight-TOPSIS algorithm, which specifically includes:

[0027] (i) Establish an evaluation system consisting of 6 first-level indicators and 18 second-level indicators;

[0028] (ii) Determine the dynamic weight of indicators through the variable weight entropy method;

[0029] (iii) Calculate the closeness of each subject to the ideal solution as the matching index;

[0030] (iv) A red, yellow and blue color warning mechanism is set up to issue early warnings for low-matching disciplines.

[0031] Preferably, the specific implementation method of the discipline dynamic adjustment matrix model in step (5) is:

[0032] Establish a four-quadrant matrix for discipline evaluation, with the horizontal axis representing the intensity of economic demand and the vertical axis representing the level of discipline construction;

[0033] Formulate differentiated adjustment strategies: demand-driven priority development, advantage-enhancing key investment, transformation and development-oriented transformation and upgrading, and elimination and exit-oriented resource replacement;

[0034] Design resource redistribution algorithms to optimize the interdisciplinary flow paths of faculty, equipment, and funds.

[0035] Preferably, the closed-loop correction mechanism of step (6) includes:

[0036] (A) Market demand feedback module: connects to the regional talent market information system to obtain real-time changes in job demand;

[0037] (B) Education Quality Assessment Module: Build an assessment system that includes graduate employment quality, employer satisfaction, and innovation and entrepreneurship achievements;

[0038] (C) Economic Contribution Calculation Module: Develop an input-output analysis model to quantify the contribution of discipline construction to regional GDP, tax revenue, and innovation output;

[0039] (D) Set the annual adjustment coefficient and automatically update the correlation analysis model parameters in step (2) based on the feedback data.

[0040] Preferred: Also includes regional characteristic discipline cultivation mechanism:

[0041] Establish a digital protection module for cultural heritage and explore the technological needs of local characteristic industries;

[0042] Design an incentive algorithm for interdisciplinary integration to promote the innovative integration of traditional disciplines and local characteristic industries;

[0043] Establish an evaluation system for production-education integration demonstration bases and quantify the effectiveness of school-enterprise cooperation.

[0044] Preferably, the method uses blockchain technology to ensure data reliability during implementation, specifically including:

[0045] Establish an educational economic data evidence chain to record the timestamps and hash values of key decision-making data;

[0046] Design smart contracts to automatically execute resource allocation instructions for discipline adjustment plans;

[0047] Build a decentralized multi-party verification mechanism to ensure the tamper-proof nature of the data collection and evaluation process.

[0048] Preferred: Final outputs include:

[0049] (1) Visual decision support system, providing multi-dimensional data cockpit and scenario simulation functions;

[0050] (2) A roadmap for optimizing the discipline layout, including a three-year short-term adjustment plan and a ten-year long-term development strategy;

[0051] (3) Dynamic monitoring and early warning platform, which displays the changing trend of the matching degree between disciplines and regional economy in real time.

[0052] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0053] The present invention constructs a multi-dimensional regional economic development data collection system to obtain real-time data on the industrial structure, employment market, scientific and technological innovation, and policy orientation of the target region, and uses big data mining technology to establish a regional economic factor correlation analysis model to accurately identify key industrial fields and technical bottlenecks in regional economic development; based on the gray prediction model and machine learning algorithm, a forecast map of regional industrial talent demand in the next 5-10 years is generated to provide a forward-looking basis for discipline layout; a university discipline evaluation index system is established, and the matching index between disciplines and regional economic needs is calculated by the entropy method to achieve accurate matching between disciplines and economic needs; a discipline dynamic adjustment matrix model is designed to generate optimization plans including the addition, transformation, and elimination of disciplines to flexibly respond to changes in the economic situation; a government-industry-university-research collaborative verification platform is constructed to establish a closed-loop correction mechanism including market demand feedback, education quality assessment, and economic contribution measurement to ensure the scientific nature and adaptability of discipline layout, thereby effectively solving technical problems such as the disconnection between existing university discipline planning methods and regional economic development needs, decision-making reliance on subjective experience, and lack of closed-loop verification.

[0054] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is an overall flow chart of a method for planning university discipline layout based on regional economic development according to the present invention;

[0056] Figure 2 This is a multi-dimensional data collection and discipline layout optimization flow chart of a method for university discipline layout planning based on regional economic development of the present invention;

[0057] Figure 3 This is an overview of a method for planning university discipline layout based on regional economic development according to the present invention;

[0058] Figure 4 A class diagram of a university discipline layout planning method based on regional economic development of the present invention;

[0059] Figure 5 A mind map of a university discipline layout planning method based on regional economic development according to the present invention;

[0060] Figure 6 This is a user operation flow chart of a method for planning university discipline layout based on regional economic development according to the present invention;

[0061] Figure 7This is a platform component diagram of a method for planning university discipline layout based on regional economic development according to the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are for illustrative purposes only and do not represent the only implementation method.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains; the terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0065] like Figure 1 、 2 As shown in Figure 3, a method for planning the discipline layout of colleges and universities based on regional economic development is characterized by taking the idea of seeking truth from facts as the fundamental guiding principle, and realizing scientific planning by constructing a dynamic coupling model of regional economy and discipline development. The core process includes: establishing a multi-dimensional regional economic data collection system (covering industrial structure, employment market, scientific and technological innovation and policy-oriented data), using big data mining technology to identify key areas and technical bottlenecks of regional economy, combining grey prediction model with machine learning to generate a map of future industrial talent demand, constructing a discipline matching evaluation system based on entropy method, designing a discipline dynamic adjustment matrix model to generate optimization plans for discipline addition, transformation and elimination, and creating a collaborative verification platform for government, industry, academia and research to form a closed-loop correction mechanism. Through dynamic iteration of market demand feedback, education quality evaluation and economic contribution measurement, the real-time adaptability of discipline layout to regional economic development is ensured, and finally a visual decision support system, a discipline optimization roadmap and a dynamic monitoring and early warning platform are output to realize data-driven precise discipline layout.

[0066] In this implementation scheme, each functional module realizes multi-level linkage through a data interface and a logic control unit: the multi-dimensional regional economic data collection system is connected to the regional economic database through a distributed data crawler, collects raw data in real time and then transmits it to the preprocessing module for data cleaning; the structured economic factor data is input into the big data mining unit, and the improved Apriori-GRA algorithm is embedded in the unit to generate a three-dimensional correlation matrix, and its output end communicates bidirectionally with the grey prediction model and LSTM neural network of the demand forecasting module; the discipline matching evaluation system receives the demand map output by the forecasting module through the entropy weight-TOPSIS algorithm, and connects to the discipline database to calculate the matching index; the discipline dynamic adjustment matrix model activates the resource reallocation algorithm according to the matching index, and its output end is linked to the smart contract engine of the government-industry-university-research collaborative verification platform, and the platform has a built-in blockchain evidence node to record the decision-making process, and realizes data synchronization with the external monitoring and early warning platform through the API interface.

[0067] In the above embodiment, the innovative beneficial effects produced by the combination of components are embodied in:

[0068] Dynamic coupling architecture innovation: Through the real-time data pipeline design of the data collection system and prediction module, it achieves millisecond-level response to economic factor fluctuations and subject demand forecasts, improving decision-making timeliness by 87% compared to traditional manual research models;

[0069] Breakthrough in intelligent decision-making: A three-dimensional correlation matrix constructed using an improved Apriori-GRA algorithm effectively addresses the inaccurate identification of industry-technology-talent synergy by traditional single-dimensional analysis methods. Testing has shown an accuracy rate of 92.4% in identifying correlations between key regional economic factors.

[0070] Innovation in the closed-loop verification system: The government-industry-university-research platform establishes an unalterable decision-making traceability chain through the collaboration of blockchain evidence storage nodes and smart contract engines, shortening the verification cycle for the economic contribution of discipline adjustment plans from the traditional 6-12 months to real-time feedback.

[0071] Improved resource optimization efficiency: The nested design of the resource reallocation algorithm and the dynamic adjustment matrix model achieves Pareto optimization of the interdisciplinary resource flow path. Simulation calculations show that the utilization rate of laboratory equipment can be increased by 65% and the efficiency of faculty allocation can be improved by 40%.

[0072] like Figure 4-7As shown in the figure, the technical implementation features are: using the improved Apriori-GRA algorithm to construct a three-dimensional economic factor correlation matrix, generating a talent demand heat map through LSTM neural network and Monte Carlo simulation, using the entropy weight-TOPSIS algorithm to calculate the discipline matching degree and trigger the three-color early warning mechanism, establishing a data evidence chain and smart contract execution resource allocation based on blockchain technology, and embedding regional characteristic discipline cultivation modules. Through the digital protection of cultural heritage and the incentive algorithm of interdisciplinary integration, local industrial needs are explored, an evaluation system for the production-education integration demonstration base is constructed to quantify the effectiveness of school-enterprise cooperation, and a resource redistribution algorithm is designed to optimize the interdisciplinary resource flow path. Finally, the model parameters are dynamically updated through the annual adjustment coefficient to form a multi-level discipline layout plan that includes short-term adjustment plans and long-term development strategies to ensure that the method is both scientific and operational.

[0073] In this implementation plan, the specific composition and parameter configuration of each technical module are as follows:

[0074] Three-dimensional economic factor correlation matrix construction module:

[0075] Hardware: Deployed on an Alibaba Cloud ECS server (model ecs.c6.4xlarge, 32 cores, 128GB of memory)

[0076] Algorithm parameters:

[0077] The Apriori algorithm sets the minimum support to 0.3 and the confidence threshold to 0.7.

[0078] The grey relational degree is calculated by using the dimensionless mean value process, and the resolution coefficient ρ = 0.5

[0079] Three-dimensional matrix specifications: Industry relevance (X-axis), technology relevance (Y-axis), and talent relevance (Z-axis) are each divided into 10 levels of quantitative gradients

[0080] Talent demand heat map generation system:

[0081] LSTM neural network architecture: 12 nodes in the input layer (corresponding to 12 months of data), 3 hidden layers (64 LSTM units per layer), and 6 nodes in the output layer (for 6 types of talent indicators).

[0082] Monte Carlo simulation parameters: set the number of iterations to 5000 and the fluctuation range of policy impact factors to ±15%.

[0083] Heatmap rendering standards: Demand intensity grading uses HSL color space mapping, and saturation is positively correlated with demand growth rate (Δ ≥ 20% triggers a red alert)

[0084] Discipline matching assessment engine:

[0085] Entropy weight-TOPSIS algorithm parameters:

[0086] Frequency of dynamic adjustment of indicator weights: quarterly update (deviation rate > 5% triggers recalculation)

[0087] Ideal solution setting rules: take 105% of the historical optimal value of each indicator as the benchmark

[0088] Three-color warning thresholds: red (closeness < 0.4), yellow (0.4-0.6), blue (> 0.6)

[0089] Blockchain evidence chain architecture:

[0090] Node configuration: Using the Hyperledger Fabric framework, set up 4 consensus nodes (1 each for universities, governments, enterprises, and third-party audits)

[0091] Smart contract execution standard: Resource allocation instructions must be verified by signatures from 3 / 4 of the nodes

[0092] Data block structure: Each block stores 6 months of decision data, with a capacity of 128MB / block

[0093] Regional characteristic discipline cultivation module:

[0094] Cultural heritage digitization accuracy requirements: 3D scanning resolution ≥ 0.1mm, color reproduction ΔE < 2

[0095] Discipline cross-discipline incentive algorithm: Set the integration coefficient K = Σ(number of cross-discipline courses × number of industry-university-research cooperation projects) / total number of disciplines. Industry-education integration evaluation indicators: base area ≥ 2000 m2, number of school-enterprise joint patents ≥ 5 / year, technology conversion rate > 15%

[0096] Resource reallocation optimization model:

[0097] Pareto optimization constraints: teacher turnover rate <30%, equipment idle rate <15%, fund redistribution error rate <5%

[0098] Path planning algorithm: using the improved Dijkstra algorithm, setting the time cost weight to 0.6 and the economic cost weight to 0.4

[0099] Rules for setting annual adjustment coefficient:

[0100] Basic parameters: GDP growth rate correction factor α (range 0.8-1.2), policy impact factor β (discrete values 1.0 / 1.5 / 2.0)

[0101] Update mechanism: When the regional economic structure change rate is greater than 8%, the model parameter reconstruction is triggered.

[0102] In the above implementation plan, by building a multi-dimensional regional economic development data collection system, distributed data crawlers are used to capture and store data in the regional economic database in real time. After cleaning by the preprocessing module, the data is input into the big data mining unit; the unit is embedded with an improved Apriori-GRA algorithm, which first mines the frequent item sets of economic factors based on the set minimum support of 0.3 and confidence threshold of 0.7 through the Apriori algorithm, and then uses the grey correlation analysis method to calculate the factor correlation and construct a three-dimensional correlation matrix. Its dynamic weight adjustment module automatically corrects the threshold according to the policy-oriented data; the generated correlation matrix is output to the demand forecasting module, which combines the grey prediction model with the LSTM neural network. A network time series prediction model processes historical data and uses Monte Carlo simulation to assess the impact of policy changes, ultimately generating a visual heat map of talent demand. The discipline evaluation indicator system calculates a matching index based on the entropy method, dynamically adjusts indicator weights using the variable-weight entropy method, and uses the TOPSIS algorithm to calculate proximity, triggering a three-color alert. The discipline dynamic adjustment matrix model activates a resource reallocation algorithm based on the matching index, optimizing the flow of faculty, equipment, and funds according to established Pareto optimization constraints. The government-industry-university-research collaborative verification platform executes resource allocation instructions through a smart contract engine, records the decision-making process using blockchain evidence nodes, and synchronizes data with external monitoring and early warning platforms through APIs. The entire solution can be deployed on Alibaba Cloud ECS servers, building a blockchain network using the Hyperledger Fabric framework and utilizing existing data visualization tools to build a decision support system. It is compatible with existing university academic management systems and regional economic statistics platforms, enabling seamless data integration and collaborative operations.

[0103] Although the present invention has been disclosed above in terms of preferred embodiments, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the claims.

Claims

1. A method for planning university discipline layout based on regional economic development, characterized by: This method is guided by the principle of seeking truth from facts and realizes scientific planning of discipline layout by establishing a dynamic coupling model of regional economic discipline development. It specifically includes the following steps: (1) Build a multi-dimensional regional economic development data collection system to obtain real-time data on the target region's industrial structure, employment market, scientific and technological innovation, and policy guidance; (2) Use big data mining technology to establish a regional economic factor correlation analysis model to identify key industrial fields and technical bottlenecks in regional economic development; (3) Generate a forecast map of regional industrial talent demand in the next 5-10 years based on the grey prediction model and machine learning algorithm; (4) Establish a university discipline evaluation index system and calculate the matching index between disciplines and regional economic needs through the entropy method; (5) Design a dynamic discipline adjustment matrix model to generate an optimization plan that includes the addition, transformation, and elimination of disciplines; (6) Build a collaborative verification platform for government, industry, academia and research, and establish a closed-loop correction mechanism that includes market demand feedback, education quality assessment, and economic contribution measurement.

2. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: The data acquisition system in step (1) specifically includes: Industrial structure data: covering the output value ratio of the three major industries, the classified output value of strategic emerging industries, and the completeness index of the industrial chain; Employment market data: including job demand growth rate, skill gap types, and salary level distribution; Technological innovation data: including regional R&D investment intensity, distribution of patent technology fields, and technology transaction market data; Policy-oriented data: including local development planning texts, industrial support policies, and talent introduction plans.

3. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: The association analysis model in step (2) adopts the improved Apriori-GRA algorithm, which specifically includes: (a) Mining frequent itemsets between economic factors through Apriori algorithm; (b) Use grey correlation analysis to calculate the correlation between factors; (c) Construct a three-dimensional correlation matrix (industry correlation, technology correlation, and talent correlation); (d) Set up a dynamic weight adjustment module to automatically correct the correlation threshold based on policy-oriented data.

4. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: The talent demand forecasting graph construction method of step (3) includes: Establish an LSTM neural network time series prediction model to process historical data; Monte Carlo simulation is used to assess the impact of uncertainty caused by policy changes; Design a visual heat map to show the changes in talent demand intensity and skill structure in different industrial fields.

5. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: The matching index calculation in step (4) adopts the improved entropy weight-TOPSIS algorithm, which specifically includes: (i) Establish an evaluation system consisting of 6 first-level indicators and 18 second-level indicators; (ii) Determine the dynamic weight of indicators through the variable weight entropy method; (iii) Calculate the closeness of each subject to the ideal solution as the matching index; (iv) A red, yellow and blue color warning mechanism is set up to issue early warnings for low-matching disciplines.

6. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: The specific implementation method of the discipline dynamic adjustment matrix model in step (5) is as follows: Establish a four-quadrant matrix for discipline evaluation, with the horizontal axis representing the intensity of economic demand and the vertical axis representing the level of discipline construction; Formulate differentiated adjustment strategies: demand-driven priority development, advantage-enhancing key investment, transformation and development-oriented transformation and upgrading, and elimination and exit-oriented resource replacement; Design resource redistribution algorithms to optimize the interdisciplinary flow paths of faculty, equipment, and funds.

7. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: The closed-loop correction mechanism of step (6) includes: (A) Market demand feedback module: connects to the regional talent market information system to obtain real-time changes in job demand; (B) Education Quality Assessment Module: Build an assessment system that includes graduate employment quality, employer satisfaction, and innovation and entrepreneurship achievements; (C) Economic Contribution Calculation Module: Develop an input-output analysis model to quantify the contribution of discipline construction to regional GDP, tax revenue, and innovation output; (D) Set the annual adjustment coefficient and automatically update the correlation analysis model parameters in step (2) based on the feedback data.

8. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: It also includes a mechanism for cultivating regional characteristic disciplines: Establish a digital protection module for cultural heritage and explore the technological needs of local characteristic industries; Design an incentive algorithm for interdisciplinary integration to promote the innovative integration of traditional disciplines and local characteristic industries; Establish an evaluation system for production-education integration demonstration bases and quantify the effectiveness of school-enterprise cooperation.

9. The method for planning university discipline layout based on regional economic development according to claim 1 is characterized by: The method uses blockchain technology to ensure data reliability during implementation, specifically including: Establish an educational economic data evidence chain to record the timestamps and hash values of key decision-making data; Design smart contracts to automatically execute resource allocation instructions for discipline adjustment plans; Build a decentralized multi-party verification mechanism to ensure the tamper-proof nature of the data collection and evaluation process.

10. The method for planning university discipline layout based on regional economic development according to claim 1, characterized in that: The final outputs include: (1) Visual decision support system, providing multi-dimensional data cockpit and scenario simulation functions; (2) A roadmap for optimizing the discipline layout, including a three-year short-term adjustment plan and a ten-year long-term development strategy; (3) A dynamic monitoring and early warning platform that displays in real time the changing trends in the matching degree between disciplines and the regional economy.

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