Intelligent industrial space planning layout method

Abstract

The invention relates to the technical field of industrial space planning, and particularly discloses an intelligent industrial space planning layout method, which comprises the following steps: collecting multi-dimensional data and preprocessing to form a standardized data set; constructing an evaluation model to output an industrial suitability score and a space bearing capacity grade; constructing a planning model by taking multi-objective maximization / minimization as a core, and solving to obtain a Pareto optimal solution set; an optimal scheme is screened through multi-scene simulation verification, and iterative optimization is carried out if the optimal scheme does not reach the standard; operation data is dynamically monitored after landing, and scheme adjustment is triggered when deviation exceeds the limit. According to the method, multi-source data are fused, subjective judgment is converted into quantitative analysis, collaborative optimization of economic benefits, social benefits and ecological benefits is achieved, planning scientificity and adaptability are guaranteed, dynamic optimization of the whole life cycle is achieved, and high-quality development of the industrial space is promoted.

Description

Technical Field

[0001] This invention relates to the field of industrial space planning technology, specifically to an intelligent industrial space planning and layout method. Background Technology

[0002] Current industrial spatial planning and layout largely rely on the experience and judgment of planners, combining static data such as population, transportation, and land use for design. This approach suffers from the following technical shortcomings: First, the data dimensions are limited. Traditional planning focuses primarily on basic indicators such as land use and floor area ratio, neglecting core influencing factors such as industrial suitability, resource supply and demand balance, and ecological carrying capacity, leading to a disconnect between planning schemes and actual industrial development needs. Second, the decision-making process is subjective, lacking quantitative analysis models, making it difficult to guarantee the rationality and scientific nature of the planning layout. This can easily result in problems such as insufficient industrial agglomeration, redundant or insufficient supporting facilities, and conflicting logistics flow lines. Third, the dynamic adjustment capability is weak. Existing planning schemes are mostly static results, unable to respond in real time to external factors such as industrial upgrading, policy adjustments, and market changes, resulting in low industrial space utilization and insufficient sustainable development capabilities. Fourth, the coordination of multiple objectives is poor, failing to effectively balance economic, social, and ecological benefits, and easily leading to problems such as environmental pollution, resource waste, and lagging public service facilities.

[0003] To address the aforementioned technical issues, there is currently no intelligent industrial space planning and layout method that can achieve multi-dimensional data fusion, quantitative modeling and decision-making, dynamic real-time optimization, and multi-objective collaborative balance. There is an urgent need to propose an innovative technical solution to make up for the shortcomings of existing technologies. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide an intelligent industrial space planning and layout method, so as to realize the digitalization, intelligence, precision and dynamism of industrial space planning, and improve the scientificity, adaptability and sustainability of planning schemes.

[0005] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: an intelligent industrial space planning and layout method, comprising the following steps:

[0006] S1. Multi-dimensional data collection and preprocessing: Establish an industrial spatial planning data collection system to collect basic geographic data, industrial development data, resource and environmental data, public service data, and policy constraint data of the planning area; preprocess the collected data, including data cleaning, deduplication, standardization transformation, and missing value imputation, to form a standardized dataset;

[0007] S2. Industry adaptability and spatial carrying capacity assessment: Based on the preprocessed standardized dataset, an industry adaptability assessment model and a spatial carrying capacity assessment model are constructed, and the industry adaptability score and spatial carrying capacity level are output respectively.

[0008] The industry suitability assessment model takes the leading industry type as its core and calculates the industry suitability score by weighting the degree of matching of the industrial chain, the degree of matching of resource demand, and the degree of policy fit. The calculation formula is as follows: ;

[0009] in, Score the industry suitability. , , These are the weighting coefficients for industrial chain matching degree, resource demand matching degree, and policy fit degree, respectively. , For the degree of matching of the industrial chain, For resource demand matching degree, For policy fit;

[0010] The spatial carrying capacity assessment model determines the spatial carrying capacity level by calculating the comprehensive value of land use intensity, resource supply and demand balance index, and environmental carrying capacity index;

[0011] S3. Construction and solution of multi-objective programming model: With the objectives of maximizing industrial agglomeration benefits, maximizing resource utilization efficiency, minimizing ecological and environmental impact, and optimizing public service facilities, a multi-objective industrial spatial planning model is constructed.

[0012] The constraints of the multi-objective industrial spatial planning model include land use constraints, policy constraints, resource and environmental constraints, and traffic flow constraints. An improved non-dominated sorting genetic algorithm is used to solve the multi-objective industrial spatial planning model and output a Pareto optimal solution set. Each solution corresponds to a set of initial industrial spatial planning schemes.

[0013] S4. Simulation Verification and Optimization of Planning Schemes: Build an industrial space planning simulation platform, import the initial schemes from the Pareto optimal solution set into the simulation platform, and conduct multi-scenario simulations; based on the simulation results, construct a comprehensive evaluation index system for the schemes, score and rank each initial scheme from four dimensions: scientificity, adaptability, sustainability, and operability, and select the optimal planning scheme; if the comprehensive score of the optimal planning scheme does not reach the preset threshold, adjust the weight coefficients and constraints of the multi-objective planning model, return to step S3 to solve again, until the optimal planning scheme that meets the requirements is obtained;

[0014] S5. Dynamic Monitoring and Real-time Adjustment: After the optimal planning scheme is implemented, a dynamic monitoring system is established to collect industrial space operation data in real time. The real-time monitoring data is compared and analyzed with the expected planning data. When the deviation value exceeds the preset deviation threshold, the scheme adjustment mechanism is triggered. Based on the real-time data, the standardized dataset is updated, and steps S2-S4 are repeated to generate scheme adjustment suggestions, thereby realizing the dynamic optimization of the planning scheme.

[0015] Furthermore, in step S1, the basic geographic data includes topography, current land use, distribution of transportation networks, and distribution of water systems; the industrial development data includes the type of leading industries, information on upstream and downstream enterprises in the industrial chain, production capacity, labor demand, and logistics demand; the resource and environmental data includes water resource reserves, energy supply capacity, environmental carrying capacity threshold, and the scope of ecological protection zones; the public service data includes the distribution and capacity of education, medical care, and commercial supporting facilities, as well as public transportation capacity; and the policy constraint data includes land use planning, industrial access policies, and environmental emission standards.

[0016] Furthermore, in step S1, the data collection methods specifically include: online capture of publicly available data from natural resources, industry supervision, and public service departments; offline organization of professional teams to conduct on-site verification of the planning area and supplement and improve the data; and deployment of sensors at key nodes in the planning area for real-time data collection.

[0017] Furthermore, in step S2, the space bearing capacity level is divided into three levels: high, medium, and low. The level classification criteria are as follows: a comprehensive value ≥ 80 is a high level, a comprehensive value ≤ 80 is a medium level, and a comprehensive value < 50 is a low level.

[0018] Furthermore, in step S2, the industrial chain matching degree is obtained by calculating the product of the spatial distance and industrial correlation between upstream and downstream enterprises; the resource demand matching degree is obtained by calculating the ratio of industrial resource demand to regional resource supply; and the policy fit degree is obtained by quantitatively scoring against industrial access policies and land use policies.

[0019] Further, in step S3, the objective function of the multi-objective industrial spatial planning model includes:

[0020] Objective function for maximizing the benefits of industrial agglomeration: ;in, For the number of industry types, To determine the number of land parcels, 0-1 variables Indicates the first Industry layout in the first land parcel, Indicates no layout. For the first Industry category in Agglomeration benefit coefficient of the land parcel;

[0021] Objective function for maximizing resource utilization efficiency: ;in, For the first Industry category in Resource utilization of the land parcel For the first Total resources of the land parcel;

[0022] Objective function for minimizing ecological and environmental impact: ;in, For the first Industry category in Ecological and environmental impact coefficient of the land parcel;

[0023] The objective function for optimizing public service facilities is: ,in, For the number of types of public service facilities, For 0-1 variables, Indicates the first The layout of public service facilities in the first land parcel, Indicates no layout. For the first The layout of public service facilities in the first Service suitability coefficient of the land parcel.

[0024] Furthermore, in step S4, the scenarios simulated in the multi-scenario simulation include industrial operation scenarios, traffic flow scenarios, resource consumption scenarios, and ecological environment evolution scenarios.

[0025] Furthermore, in step S4, the weights of the comprehensive evaluation index system are determined using the analytic hierarchy process (AHP). The dimensions of the comprehensive evaluation index system include: scientific indicators, adaptability indicators, sustainability indicators, and operability indicators. The scientific indicators include industrial agglomeration and resource utilization efficiency. The adaptability indicators include industrial adaptability and public service support adaptability. The sustainability indicators include the degree of ecological and environmental impact and resource carrying capacity. The operability indicators include construction costs and implementation difficulty.

[0026] Furthermore, in step S4, the preset threshold is ≥90 points; in step 5, the preset deviation threshold is ≤10%.

[0027] Furthermore, in step S5, the real-time collected industrial space operation data includes enterprise production data, traffic flow data, resource consumption data, environmental quality data, and public service usage data.

[0028] The advantages of this invention compared to the prior art are:

[0029] This invention breaks through the limitations of traditional planning relying on single data support. By integrating five categories of multi-dimensional data, it provides a comprehensive and accurate data foundation for planning decisions, effectively improving the adaptability and scientific nature of planning schemes.

[0030] This invention transforms subjective experience-based judgments into quantitative analysis by constructing an industry adaptability assessment model, a spatial carrying capacity assessment model, and a multi-objective planning model, thereby avoiding the subjectivity and blindness of planning decisions and ensuring the rationality of planning layout.

[0031] This invention, by establishing a dynamic monitoring system and scheme adjustment mechanism, can respond in real time to changes in the external environment and deviations in the operation process, realize dynamic optimization of the planning scheme throughout its entire life cycle, and enhance the sustainable development capability of industrial space.

[0032] This invention constructs a multi-objective planning model based on four core objectives, achieving synergistic optimization of economic, social, and ecological benefits, solving the problem of multi-objective conflicts in traditional planning, and promoting high-quality development of industrial space. Attached Figure Description

[0033] Figure 1 This is a flowchart of an intelligent industrial space planning and layout method according to the present invention. Detailed Implementation

[0034] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0035] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0036] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0037] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0038] The following detailed description of an intelligent industrial space planning and layout method of the present invention, with reference to the accompanying drawings, provides further insight.

[0039] Combined with appendix Figure 1 This invention will be described in detail below.

[0040] This invention provides an intelligent industrial space planning and layout method, comprising the following steps:

[0041] S1. Multi-dimensional Data Collection and Preprocessing: Establish an industrial spatial planning data collection system, collecting five major categories of data: basic geographic data, industrial development data, resource and environmental data, public service data, and policy constraint data for the planning area. Preprocess the collected data, including data cleaning, deduplication, standardization transformation, and missing value imputation, to form a standardized dataset. Basic geographic data includes topography, land use status, transportation network distribution, and water system distribution. Industrial development data includes leading industry types, upstream and downstream enterprise information, production capacity, labor demand, and logistics demand. Resource and environmental data includes water resource reserves, energy supply capacity, environmental carrying capacity thresholds, and ecological protection zone boundaries. Public service data includes the distribution and capacity of education, medical, and commercial facilities, and public transportation capacity. Policy constraint data includes land use planning, industrial access policies, and environmental emission standards.

[0042] S2. Industry Adaptability and Spatial Carrying Capacity Assessment: Based on the preprocessed standardized dataset, an industry adaptability assessment model and a spatial carrying capacity assessment model are constructed, outputting industry adaptability scores and spatial carrying capacity levels, respectively. The industry adaptability assessment model takes the dominant industry type as its core and obtains the industry adaptability score by calculating the weighted sum of the industrial chain matching degree, resource demand matching degree, and policy fit degree. The calculation formula is as follows: ;in, Score the industry suitability. , , These are the weighting coefficients for industrial chain matching degree, resource demand matching degree, and policy fit degree, respectively. , For the degree of matching of the industrial chain, For resource demand matching degree, To assess policy alignment, the spatial carrying capacity assessment model determines the spatial carrying capacity level by calculating the comprehensive value of land use intensity, resource supply and demand balance index, and environmental carrying capacity index. The carrying capacity level is divided into three levels: high, medium, and low. When the comprehensive value is ≥80, it is a high level; when the comprehensive value is ≤50 and <80, it is a medium level; and when the comprehensive value is <50, it is a low level.

[0043] S3. Construction and Solution of Multi-Objective Programming Model: A multi-objective industrial spatial planning model is constructed with the objectives of maximizing industrial agglomeration benefits, maximizing resource utilization efficiency, minimizing ecological and environmental impact, and optimizing public service facilities. The model's constraints include land use constraints, policy constraints, resource and environmental constraints, and traffic flow constraints. An improved non-dominated sorting genetic algorithm (NSGA-Ⅲ) is used to solve the multi-objective programming model, outputting a Pareto optimal solution set. Each solution corresponds to an initial industrial spatial planning scheme. The objective function of the multi-objective programming model is as follows:

[0044] Objective function for maximizing the benefits of industrial agglomeration: ;in, For the number of industry types, To determine the number of land parcels, 0-1 variables Indicates the first Industry layout in the first land parcel, Indicates no layout. For the first Industry category in Agglomeration benefit coefficient of the land parcel;

[0045] Objective function for maximizing resource utilization efficiency: ;in, For the first Industry category in Resource utilization of the land parcel For the first Total resources of the land parcel;

[0046] Objective function for minimizing ecological and environmental impact: ;in, For the first Industry category in Ecological and environmental impact coefficient of the land parcel;

[0047] The objective function for optimizing public service facilities is: ,in, For the number of types of public service facilities, For 0-1 variables, Indicates the first The layout of public service facilities in the first land parcel, Indicates no layout. For the first The layout of public service facilities in the first Service suitability coefficient of the land parcel.

[0048] S4. Simulation Verification and Optimization of Planning Schemes: An industrial space planning simulation platform is established. Initial schemes from the Pareto optimal solution set are imported into the platform for multi-scenario simulation, including industrial operation, traffic flow, resource consumption, and ecological environment evolution scenarios. Based on the simulation results, a comprehensive evaluation index system is constructed. Each initial scheme is scored and ranked from four dimensions: scientific validity, adaptability, sustainability, and operability, to select the optimal planning scheme. If the comprehensive score of the optimal planning scheme does not reach the preset threshold (preset threshold ≥ 90 points), the weight coefficients and constraints of the multi-objective programming model are adjusted, and the process returns to step S3 to re-solve until a satisfactory optimal planning scheme is obtained.

[0049] S5. Dynamic Monitoring and Real-time Adjustment: After the optimal planning scheme is implemented, a dynamic monitoring system is established to collect real-time industrial space operation data, including enterprise production data, traffic flow data, resource consumption data, environmental quality data, and public service usage data. The real-time monitoring data is compared and analyzed with the expected planning data. When the deviation value exceeds the preset deviation threshold (preset deviation threshold ≤ 10%), the scheme adjustment mechanism is triggered. Based on the real-time data, the standardized dataset is updated, and steps S2-S4 are repeated to generate scheme adjustment suggestions, thereby realizing the dynamic optimization of the planning scheme.

[0050] In one embodiment of this application, the data collection in step S1 adopts a fusion collection method of "online capture + offline verification + real-time sensor collection": online capture of publicly available data from departments such as natural resources, industry supervision, and public services; offline organization of professional teams to conduct on-site verification of the planning area and supplement and improve the data; and deployment of sensors at key nodes in the planning area, including traffic flow sensors, resource consumption sensors, and environmental quality sensors, to achieve real-time data collection.

[0051] In one embodiment of this application, the supply chain matching degree in step S2 The resource demand matching degree is obtained by calculating the product of the spatial distance and industrial linkage between upstream and downstream enterprises. The policy fit is obtained by calculating the ratio of industrial resource demand to regional resource supply. The score is obtained by comparing the results with industry access policies and land use policies.

[0052] In one embodiment of this application, the improvement strategy of the improved non-dominated sorting genetic algorithm (NSGA-Ⅲ) in step S3 includes: adopting adaptive crossover and mutation probabilities, dynamically adjusting the crossover and mutation probabilities according to the number of generations of population evolution; introducing a crowding factor and an elite retention strategy to improve the diversity of the solution set and the convergence speed.

[0053] In one embodiment of this application, the weights of the comprehensive evaluation index system for the scheme in step S4 are determined by the analytic hierarchy process. The scientific indicators include industrial agglomeration and resource utilization efficiency, the adaptability indicators include industrial adaptability and public service support adaptability, the sustainability indicators include the degree of ecological and environmental impact and resource carrying capacity, and the operability indicators include construction cost and implementation difficulty.

[0054] The specific implementation process of the intelligent industrial space planning and layout method of the present invention is as follows:

[0055] This embodiment takes a provincial-level industrial park as the planning object. The park has a planned area of ​​10 square kilometers and its existing leading industry is high-end equipment manufacturing. It needs to carry out intelligent industrial spatial planning and layout. The specific implementation steps are as follows:

[0056] Step S1: Multi-dimensional data collection and preprocessing

[0057] Establish a data collection system to collect the following five categories of data:

[0058] Basic geographic data: Topographic and landform data and land use status data are captured online from the natural resources department, and the transportation network (including the distribution of main roads, secondary roads and branch roads) and water system distribution (location of rivers and lakes) are verified offline to form a basic geographic dataset;

[0059] Industry development data: Online data collection of enterprise registration and production capacity data from the park management committee; offline surveys of upstream and downstream enterprises in the high-end equipment manufacturing industry chain (including core component manufacturers, complete machine assembly companies, and logistics support companies) to determine their employment needs (expected to create 5,000 new jobs) and logistics needs (expected daily freight volume of 2,000 tons) to form an industry development dataset.

[0060] Resource and environmental data: Collect water resource storage data from the local water conservancy department (annual average water resources of 120 million cubic meters), power supply data from the energy department (annual average power supply of 500 million kilowatt-hours), environmental carrying capacity thresholds from the environmental protection department (SO2 emission limit of 1,000 tons / year), and the scope of the ecological protection zone (1 square kilometer of ecological green space in the northern part of the park) to form a resource and environmental dataset.

[0061] Public service data: Collect school distribution data from the education sector (1 primary school and 1 middle school), hospital distribution data from the medical sector (1 community hospital), commercial facilities data (15 supermarkets and restaurants), and public transportation capacity data (3 bus routes with an average daily capacity of 8,000 passengers) to form a public service dataset;

[0062] Policy constraint data: Capture local land use planning (industrial land accounts for no less than 60%), industry access policies (prohibiting high-pollution and high-energy-consuming industries from entering), and environmental emission standards (GB3095-2012 air quality standards) to form a policy constraint dataset.

[0063] The above data is preprocessed as follows: duplicate data is removed, abnormal data is cleaned, data of different formats is standardized and converted, and missing data is filled in using the mean imputation method, finally forming a standardized dataset.

[0064] Step S2: Industry Adaptability and Spatial Carrying Capacity Assessment

[0065] Construct an industry adaptability assessment model and set weight coefficients. :

[0066] Calculate the matching degree of the industrial chain The spatial distance between core high-end equipment manufacturing enterprises and their upstream and downstream component suppliers is within 3 kilometers, with an industrial correlation of 0.9. Therefore... ;

[0067] Computational resource demand matching degree The high-end equipment manufacturing industry in the park has an average annual water demand of 0.3 billion cubic meters, accounting for 25% of the available water resources; and an average annual electricity demand of 300 million kilowatt-hours, accounting for 60% of the electricity supply. ;

[0068] Calculate policy fit The high-end equipment manufacturing industry meets industry access policies and environmental standards, with a quantitative score of 0.95. .

[0069] According to the formula =0.4×0.9+0.3×0.75+0.3×0.95=0.87, resulting in an industry suitability score of 87 out of 100, indicating good suitability.

[0070] A spatial carrying capacity assessment model was constructed, and a comprehensive value was calculated: land use intensity 65% ​​(industrial land accounts for 62%), resource supply and demand balance index 80%, environmental carrying capacity index 90%, comprehensive value = (65+80+90) / 3 = 78.33, spatial carrying capacity level is medium.

[0071] Step S3: Construction and Solution of Multi-Objective Programming Model

[0072] With the goals of maximizing the benefits of industrial agglomeration, maximizing resource utilization efficiency, minimizing ecological and environmental impact, and optimizing public service facilities, a multi-objective planning model is constructed. The constraints include: industrial land accounting for no less than 60%, prohibiting the establishment of industrial enterprises in ecological protection zones, a road network density of no less than 5 kilometers per square kilometer, and SO2 emissions not exceeding 1,000 tons per year.

[0073] An improved non-dominated sorting genetic algorithm (NSGA-III) was used to solve the model, with a population size of 100, 50 generations, an adaptive crossover probability range of 0.6-0.9, and a mutation probability range of 0.01-0.05. A crowding factor and an elite retention strategy were introduced. The Pareto optimal solution set was obtained, containing 10 initial industrial spatial planning schemes, each corresponding to different industrial layouts, public service facility layouts, and traffic flow designs.

[0074] Step S4: Simulation verification and optimization of the planning scheme

[0075] An industrial space planning simulation platform was established, and 10 initial plans were imported into the platform for multi-scenario simulation.

[0076] Industrial operation scenario: Simulating the production efficiency and supply chain collaboration efficiency of enterprises under each scheme, the results show that scheme 6 has the highest industrial agglomeration degree (agglomeration coefficient 0.92).

[0077] Traffic flow scenario: Simulation of traffic flow and congestion index under each scheme. The results show that scheme 6 has the lowest traffic congestion index (0.2).

[0078] Resource consumption scenario: Simulation of water and electricity consumption under each scheme. The results show that scheme 6 has the highest resource utilization efficiency (utilization efficiency coefficient 0.85).

[0079] Ecological and environmental evolution scenarios: Simulation of SO2 emissions and ecological green space coverage under each scheme. The results show that scheme 6 has the least ecological and environmental impact (impact coefficient 0.15).

[0080] A comprehensive evaluation index system for the proposed scheme was constructed, and the weights of each index were determined using the analytic hierarchy process (AHP): scientific validity (0.3), suitability (0.25), sustainability (0.25), and operability (0.2). Scheme 6 was comprehensively scored: scientific validity 92, suitability 90, sustainability 93, and operability 88. The comprehensive score was calculated as 92 × 0.3 + 90 × 0.25 + 93 × 0.25 + 88 × 0.2 = 91, reaching the preset threshold (90 points). Therefore, Scheme 6 was determined to be the optimal planning scheme.

[0081] The core content of the optimal planning scheme is as follows: the southern part of the park will be a high-end equipment manufacturing core industrial zone (covering an area of ​​4 square kilometers), the eastern part will be a parts and components supporting industrial zone (covering an area of ​​2.5 square kilometers), the western part will be a logistics and warehousing zone (covering an area of ​​1.5 square kilometers), the northern part will retain ecological green space (covering an area of ​​1 square kilometer), and the central part will be a public service supporting zone (covering an area of ​​1 square kilometer, including 1 new kindergarten, 1 community health service center, and 2 bus lines).

[0082] Step S5: Dynamic monitoring and real-time adjustment

[0083] After the optimal planning scheme is implemented, a dynamic monitoring system will be established: sensors will be deployed at the main roads of the park, enterprise factory areas, and public service facility nodes to collect traffic flow data, enterprise production energy consumption data, environmental quality data (SO2 concentration), and public service usage data (bus route passenger flow) in real time; enterprise operation data and policy adjustment information will be captured online.

[0084] A deviation threshold of 10% is set, and real-time monitoring data is compared and analyzed with the planned expected data every month. If the monitoring in a certain month shows that the actual value of the average daily freight volume of the logistics and warehousing area is 15% higher than the planned expected value (exceeding the deviation threshold), the scheme adjustment mechanism is triggered: the standardized dataset is updated (supplementing real-time logistics demand data), the industry adaptability and spatial carrying capacity are reassessed (adjusting the resource demand matching degree calculation), the multi-objective planning model is optimized (adjusting the land use constraints of the logistics and warehousing area), and after solving, a scheme adjustment suggestion is generated (expanding the land area of ​​the logistics and warehousing area to 1.8 square kilometers), thereby realizing the dynamic optimization of the planning scheme.

[0085] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An intelligent industrial spatial planning and layout method, characterized in that, Includes the following steps: S1. Multi-dimensional data collection and preprocessing: Establish an industrial spatial planning data collection system to collect basic geographic data, industrial development data, resource and environmental data, public service data, and policy constraint data of the planning area; preprocess the collected data, including data cleaning, deduplication, standardization transformation, and missing value imputation, to form a standardized dataset; S2. Industry adaptability and spatial carrying capacity assessment: Based on the preprocessed standardized dataset, an industry adaptability assessment model and a spatial carrying capacity assessment model are constructed, and the industry adaptability score and spatial carrying capacity level are output respectively. The industry suitability assessment model takes the leading industry type as its core and calculates the industry suitability score by weighting the degree of matching of the industrial chain, the degree of matching of resource demand, and the degree of policy fit. The calculation formula is as follows: ; in, Score the industry suitability. , , These are the weighting coefficients for industrial chain matching degree, resource demand matching degree, and policy fit degree, respectively. , For the degree of matching of the industrial chain, For resource demand matching degree, For policy fit; The spatial carrying capacity assessment model determines the spatial carrying capacity level by calculating the comprehensive value of land use intensity, resource supply and demand balance index, and environmental carrying capacity index; S3. Construction and Solution of Multi-Objective Programming Model: A multi-objective industrial spatial planning model is constructed with the objectives of maximizing industrial agglomeration benefits, maximizing resource utilization efficiency, minimizing ecological and environmental impact, and optimizing public service facilities. The constraints of the multi-objective industrial spatial planning model include land use constraints, policy constraints, resource and environmental constraints, and transportation route constraints. An improved non-dominated sorting genetic algorithm is used to solve the multi-objective industrial spatial planning model, and outputs a Pareto optimal solution set, with each solution corresponding to a set of initial industrial spatial planning schemes; S4. Simulation Verification and Optimization of Planning Schemes: Build an industrial space planning simulation platform, import the initial schemes from the Pareto optimal solution set into the simulation platform, and conduct multi-scenario simulations; based on the simulation results, construct a comprehensive evaluation index system for the schemes, score and rank each initial scheme from four dimensions: scientificity, adaptability, sustainability, and operability, and select the optimal planning scheme; if the comprehensive score of the optimal planning scheme does not reach the preset threshold, adjust the weight coefficients and constraints of the multi-objective planning model, return to step S3 to solve again, until the optimal planning scheme that meets the requirements is obtained; S5. Dynamic Monitoring and Real-time Adjustment: After the optimal planning scheme is implemented, a dynamic monitoring system is established to collect industrial space operation data in real time. The real-time monitoring data is compared and analyzed with the expected planning data. When the deviation value exceeds the preset deviation threshold, the scheme adjustment mechanism is triggered. Based on the real-time data, the standardized dataset is updated, and steps S2-S4 are repeated to generate scheme adjustment suggestions, thereby realizing the dynamic optimization of the planning scheme.

2. The intelligent industrial spatial planning and layout method according to claim 1, characterized in that: In step S1, the basic geographic data includes topography, land use status, transportation network distribution, and water system distribution; the industrial development data includes leading industry types, information on upstream and downstream enterprises in the industrial chain, production capacity, labor demand, and logistics demand; the resource and environmental data includes water resource reserves, energy supply capacity, environmental carrying capacity threshold, and ecological protection zone scope; the public service data includes the distribution and capacity of education, medical care, and commercial supporting facilities, as well as public transportation capacity; and the policy constraint data includes land use planning, industrial access policies, and environmental emission standards.

3. The intelligent industrial spatial planning and layout method according to claim 2, characterized in that: In step S1, the data collection methods specifically include: online capture of publicly available data from natural resources, industry supervision, and public service departments; offline organization of professional teams to conduct on-site verification of the planning area and supplement and improve the data; and deployment of sensors at key nodes in the planning area for real-time data collection.

4. The intelligent industrial spatial planning and layout method according to claim 3, characterized in that: In step S2, the space bearing capacity level is divided into three levels: high, medium and low. The level classification criteria are as follows: when the comprehensive value is ≥80, it is a high level; when the comprehensive value is ≤50 and <80, it is a medium level; and when the comprehensive value is <50, it is a low level.

5. The intelligent industrial spatial planning and layout method according to claim 4, characterized in that: In step S2, the industrial chain matching degree is obtained by calculating the product of the spatial distance and industrial correlation between upstream and downstream enterprises; the resource demand matching degree is obtained by calculating the ratio of industrial resource demand to regional resource supply; and the policy fit degree is obtained by quantitatively scoring by comparing with industrial access policies and land use policies.

6. The intelligent industrial spatial planning and layout method according to claim 5, characterized in that: In step S3, the objective function of the multi-objective industrial spatial planning model includes: Objective function for maximizing the benefits of industrial agglomeration: ;in, For the number of industry types, To determine the number of land parcels, 0-1 variables Indicates the first Industry layout in the first land parcel, Indicates no layout. For the first Industry category in Agglomeration benefit coefficient of the land parcel; Objective function for maximizing resource utilization efficiency: ;in, For the first Industry category in Resource utilization of the land parcel For the first Total resources of the land parcel; Objective function for minimizing ecological and environmental impact: ;in, For the first Industry category in Ecological and environmental impact coefficient of the land parcel; The objective function for optimizing public service facilities is: ,in, For the number of types of public service facilities, For 0-1 variables, Indicates the first The layout of public service facilities in the first land parcel, Indicates no layout. For the first The layout of public service facilities in the first Service suitability coefficient of the land parcel.

7. The intelligent industrial spatial planning and layout method according to claim 6, characterized in that: In step S4, the scenarios simulated in the multi-scenario simulation include industrial operation scenarios, traffic flow scenarios, resource consumption scenarios, and ecological environment evolution scenarios.

8. The intelligent industrial spatial planning and layout method according to claim 7, characterized in that: In step S4, the weights of the comprehensive evaluation index system are determined using the analytic hierarchy process (AHP). The dimensions of the comprehensive evaluation index system include: scientific indicators, adaptability indicators, sustainability indicators, and operability indicators. The scientific indicators include industrial agglomeration and resource utilization efficiency. The adaptability indicators include industrial adaptability and public service support adaptability. The sustainability indicators include the degree of ecological and environmental impact and resource carrying capacity. The operability indicators include construction costs and implementation difficulty.

9. The intelligent industrial spatial planning and layout method according to claim 8, characterized in that: In step S4, the preset threshold is ≥90 points; in step 5, the preset deviation threshold is ≤10%.

10. The intelligent industrial spatial planning and layout method according to claim 9, characterized in that: In step S5, the real-time collected industrial space operation data includes enterprise production data, traffic flow data, resource consumption data, environmental quality data, and public service usage data.

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