Land space planning decision-making method and system based on multi-dimensional influence evaluation

Abstract

The invention relates to the technical field of computer-aided planning, and discloses a territorial space planning decision-making method and system based on multi-dimensional influence assessment, and the method sequentially comprises the steps: multi-source data fusion and problem diagnosis, multi-dimensional influence quantitative assessment, scheme collaborative optimization, and scheme comprehensive screening and decision-making. The system corresponds to the method. According to the method, through multi-source data fusion processing, the data reliability is improved, and a solid foundation is provided for subsequent problem identification; according to a quantitative index and a GIS spatial analysis technology, key problem region identification is more targeted, and the defect of generalization adjustment of a traditional qualitative method is avoided; through traffic, environment and municipal dimension parallel evaluation, the efficiency bottleneck of serial evaluation is avoided, and the evaluation period is shortened; a Pareto candidate scheme set generated through a multi-objective evolutionary algorithm is combined with a comprehensive screening mechanism to ensure that a final scheme is balanced between performance requirements and constraint conditions, and the feasibility of planning landing is enhanced.

Description

Technical Field

[0001] This application relates to the field of computer-aided planning technology, specifically a land spatial planning decision-making method and system based on multi-dimensional impact assessment. Background Technology

[0002] As a crucial technical means for coordinating regional development, the scientific rigor and accuracy of territorial spatial planning directly impact the rational utilization and sustainable development of land resources. Currently, territorial spatial planning faces challenges such as a large volume of data, complex system relationships, and difficulties in balancing multiple objectives.

[0003] In practice, planning decisions often rely on the experience and judgment of planners, who formulate planning schemes through the analysis and assessment of various spatial elements. However, due to the complexity of the national land spatial system, manual analysis struggles to fully grasp the inherent connections and potential conflicts among various elements, resulting in an insufficiently systematic and comprehensive problem diagnosis. Furthermore, during the scheme evaluation stage, only a few explicit indicators are often considered, making it difficult to make a multi-dimensional and quantitative comprehensive prediction of the implementation effect of the planning scheme.

[0004] While existing computer-aided planning tools can provide support in specific stages, such as land use analysis or traffic impact assessment, the lack of effective coordination between these stages creates information silos. This fragmented technological support makes the planning decision-making process lack systematicity and hinders the formation of a complete technological loop from problem diagnosis to solution generation.

[0005] Therefore, a more scientific technical solution for land spatial planning is urgently needed. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for land spatial planning decision-making based on multi-dimensional impact assessment, so as to solve the technical problems mentioned in the background.

[0007] To achieve the above objectives, this application discloses the following technical solutions: Firstly, this application discloses a land spatial planning decision-making method based on multi-dimensional impact assessment, including the following steps: Includes the following steps: Multi-source data fusion and problem diagnosis: Obtain the current status data of the land space of the planning area and the constraints of higher-level planning; based on the current status data, identify key problem areas with deficiencies in land use, transportation, environment or infrastructure through GIS spatial analysis; based on the key problem areas and the constraints of higher-level planning, generate at least one preliminary land space planning scheme, which includes land use layout, road network and facility configuration; Multi-dimensional impact quantitative assessment: For the preliminary land space planning scheme, quantitative impact simulation assessments of traffic, environment and municipal dimensions are performed in parallel. Among them, the traffic dimension assessment outputs the road network load level, the environmental dimension assessment outputs the impact range and intensity of the new pollution sources in the plan, and the municipal dimension assessment outputs the new load of the municipal system after the implementation of the plan. Cooperative optimization of schemes: The results of quantitative impact simulation assessment are quantified into optimization objectives and constraints, a multi-objective optimization model is established, and the preliminary land spatial planning scheme is iteratively optimized through the multi-objective evolutionary algorithm built into the model to generate a Pareto candidate scheme set that weighs multiple objectives. Comprehensive screening and decision-making: Based on the job-housing balance performance and infrastructure load performance of each candidate scheme in the Pareto candidate scheme set, the comprehensive performance score of each candidate scheme is quantitatively calculated, and the final recommended land spatial planning scheme is determined from the Pareto candidate scheme set according to the score ranking and constraints.

[0008] Optionally, the key issue area is obtained through the following methods: Based on the GIS platform, the current spatial data layer representing each indicator is overlaid and analyzed with the corresponding planning target or standard threshold layer to calculate the spatial distribution maps of planning compliance index, road network density deficit value, ecological pollution conflict intensity and public service coverage rate. Based on the spatial distribution map, areas whose planning compliance index (used to assess land use conflict) and public service coverage rate (used to assess infrastructure services) are below the corresponding lower threshold are identified as key problem areas; areas whose road network density deficit (used to assess traffic connectivity) and ecological pollution conflict intensity (used to assess environmental carrying capacity) are above the corresponding upper threshold are identified as key problem areas.

[0009] Optionally, the calculation of the road network density deficit and the identification of key problem areas include: Dynamic population heat maps are generated based on mobile signaling data; Based on urban transportation planning standards, a dynamic expected road network density calculation model is established, with the dynamic population heat map as its input. The existing road network density layer obtained by GIS network analysis based on the existing road centerline data is spatially overlaid with the dynamic expected road network density layer obtained by the dynamic expected road network density calculation model, and the road network density deficit value is calculated for each grid. Continuous grid areas where the road network density deficit value is greater than a preset sensitivity threshold are identified as key traffic connectivity problem areas. The calculation of the intensity of the ecological pollution conflict and the identification of key problem areas include: Historical and real-time observation data from environmental monitoring stations within and around the planning area are obtained, and pollutant concentration distribution maps are generated using spatial interpolation algorithms. The pollutant concentration distribution map is overlaid with the ecological protection red line distribution map; For areas located within the ecological protection red line after superposition, the ratio of their pollutant concentration value to the regional environmental background concentration value is quantified as the ecological pollution conflict intensity. Areas with an ecological pollution conflict intensity greater than 1 are identified as key areas for environmental carrying capacity issues.

[0010] Optionally, the calculation of the public service coverage rate and the identification of key problem areas include: Based on real-time traffic data, the isochronous circle is calculated using GIS network analysis, with the location of the public service facility as the starting point and the specified travel time as the threshold. The geographical area covered by the isochronous circle is defined as the real-time service range of the facility. Generate a residential population distribution density map based on mobile phone signaling data or nighttime light data; The real-time service range of each public service facility is merged into a comprehensive service range layer, and then overlaid with the residential population distribution density map. Residential areas located outside the comprehensive service area layer and with a population density higher than the population threshold are identified as critical infrastructure service problem areas.

[0011] Optionally, based on the key issue areas and higher-level planning constraints, at least one preliminary land spatial planning scheme is generated, including: The land use layout determined by the overall national spatial plan serves as the spatial base, and the identified key problem areas are mapped onto the spatial base. For each identified key problem area, a predefined correction strategy library is invoked to automatically adjust the spatial base. Establish arbitration rules for conflicting plans to adjudicate spatial layout conflicts arising from the implementation of different modification strategies. The arbitration rules shall give the mandatory content of the overall national spatial plan the highest priority. Based on the arbitration results, at least one preliminary national spatial planning scheme shall be generated.

[0012] Optionally, the correction strategy library includes the following strategies: The correction strategy for key problem areas corresponding to the planning compliance index is to adjust the planned land use of plots located in prohibited construction zones and planned for construction purposes to the corresponding non-construction land. The correction strategy for key traffic connectivity issues is to generate planned routes for new connecting branch roads within the key issue area based on road design specifications. The corrective strategy for key environmental carrying capacity issues is to replace industrial land parcels with high intensity of ecological pollution conflicts within the key issue areas with land use within the urban development boundary. The revised strategy for key infrastructure service problem areas is to designate land for public service facilities within a pre-set service radius around the key problem area.

[0013] Optionally, the establishment of the multi-objective optimization model includes the following steps: Determine the optimization objectives and constraints: The planning compliance index, road network density deficit, ecological pollution conflict intensity, public service coverage, road network load level, the impact intensity of newly added pollution sources in the plan, and the new load of the municipal system are quantified as inputs to the model. The optimization objectives are set as follows: maximize the average planning compliance across the entire region, minimize the road network density deficit and road network load level, minimize the ecological pollution conflict intensity and the impact intensity of newly added pollution sources in the plan, and maximize the public service coverage and minimize the new load of the municipal system. The constraints are set as follows: the amount of cultivated land at the end of the planning period shall not be lower than the higher-level planning indicators, and all municipal loads shall not exceed the facility capacity limit. Encoding decision variables: The preliminary land spatial planning scheme to be optimized is encoded as a chromosome, and each gene locus in the chromosome represents the land use attribute of a plot, including land use nature and development intensity. Optimization algorithm setting: The non-dominated sorting genetic algorithm with elitist strategy is selected as the multi-objective optimization algorithm; Integration model: The optimization objective, constraints, and coding scheme are integrated with the multi-objective optimization algorithm to obtain the multi-objective optimization model.

[0014] Optionally, the overall performance score is calculated in the following way: For each candidate scheme in the Pareto candidate scheme set, the ratio of the average theoretical commuting distance to the commuting distance threshold in each candidate scheme is calculated, and the difference between 1 and the ratio is calculated as the job-housing balance performance of the candidate scheme. The infrastructure load performance of each candidate scheme is calculated by normalizing the load level of the road network and the new load of the municipal system. The overall performance score of each candidate scheme is calculated by weighting and summing the work-life balance performance and the infrastructure load performance.

[0015] Optionally, the final recommended land spatial planning scheme is determined in the following ways: From the Pareto candidate solution set, all solutions whose infrastructure load performance is lower than the preset safety threshold are selected to form a subset of qualified solutions; From the subset of qualified schemes, the candidate scheme with the highest comprehensive performance score is selected as the final recommended land spatial planning scheme.

[0016] Secondly, this application discloses a land spatial planning decision-making system based on multi-dimensional impact assessment, which applies the land spatial planning decision-making method based on multi-dimensional impact assessment as described above. The system includes: The problem diagnosis module is configured to: acquire the current status data of the land space of the planning area and the constraints of the higher-level planning; based on the current status data, identify key problem areas with deficiencies in land use, transportation, environment or infrastructure through GIS spatial analysis; and generate at least one preliminary land space planning scheme based on the key problem areas and the constraints of the higher-level planning, the preliminary land space planning scheme including land use layout, road network and facility configuration. The impact assessment module is configured to perform quantitative impact simulation assessments of the traffic, environmental, and municipal dimensions in parallel on the preliminary land spatial planning scheme. Specifically, the traffic dimension assessment outputs the road network load level, the environmental dimension assessment outputs the impact range and intensity of the new pollution sources in the plan, and the municipal dimension assessment outputs the new load of the municipal system after the implementation of the plan. The collaborative optimization module is configured to: quantify the results of quantitative impact simulation assessment into optimization objectives and constraints, establish a multi-objective optimization model, and iteratively optimize the preliminary land spatial planning scheme through the multi-objective evolutionary algorithm built into the model to generate a Pareto candidate scheme set that weighs multiple objectives; The comprehensive screening module is configured to: quantitatively calculate the comprehensive performance score of each candidate scheme based on the job-housing balance performance and infrastructure load performance of each candidate scheme in the Pareto candidate scheme set, and determine the final recommended land spatial planning scheme from the Pareto candidate scheme set according to the score ranking and constraints.

[0017] Beneficial Effects: The land spatial planning decision-making method and system based on multi-dimensional impact assessment proposed in this application systematically and automatically identifies key problem areas in the land space through multi-source data fusion and problem diagnosis, and generates targeted preliminary planning schemes accordingly. This overcomes the shortcomings of traditional methods that rely on human experience, resulting in incomplete diagnosis and inaccurate understanding of the root causes of problems. Through parallel quantitative impact simulation assessment of multiple dimensions, it accurately predicts the complex impacts that may occur in different dimensions after the implementation of the scheme, avoiding the risk that traditional assessment methods, which focus on a single dimension, may solve old problems but create new conflicts. By establishing a multi-objective optimization model to iteratively optimize the scheme, it automatically generates a set of candidate schemes that achieve equilibrium among multiple conflicting objectives, improving the efficiency and scientific nature of scheme generation and overcoming the limitations of manual trial and error and single-objective optimization. Based on the comprehensive screening and decision-making of candidate schemes, it improves the scientific nature, accuracy, and reliability of land spatial planning. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the land spatial planning decision-making method based on multi-dimensional impact assessment provided in this application embodiment. Detailed Implementation

[0020] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0021] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0022] This embodiment provides a first aspect such as Figure 1The method for land spatial planning decision-making based on multi-dimensional impact assessment, as shown, includes, in sequence: multi-source data fusion and problem diagnosis, multi-dimensional impact quantitative assessment, collaborative optimization of alternative plans, and comprehensive selection and decision-making of alternative plans. Details are as follows.

[0023] Multi-source data fusion and problem diagnosis are prerequisites for land and space planning decisions. The core is to identify planning shortcomings through data integration and generate preliminary land and space planning schemes based on constraints. Specifically, this includes: obtaining current land and space data of the planning area and constraints from higher-level plans; identifying key problem areas with deficiencies in land use, transportation, environment, or infrastructure through GIS spatial analysis based on current data; and generating at least one preliminary land and space planning scheme based on key problem areas and constraints from higher-level plans. The preliminary land and space planning scheme includes land use layout, road network, and facility configuration.

[0024] It should be noted that the current status quo data of the national land space refers to the basic spatial data characterizing land use, infrastructure, and environmental quality in the planning area, including land type distribution maps (such as industrial land, agricultural land, green space, etc.), current road centerline data (including road grade, right-of-way width, and number of lanes), environmental monitoring point data (such as PM2.5 and COD monitoring values ​​for air and water), and vector data of public service facilities (such as coordinates of educational, medical, and cultural facilities). Higher-level planning constraints refer to the mandatory control requirements of higher-level national land space planning on the planning area, including the scope of urban development boundaries, the area of ​​ecological protection red lines, arable land retention indicators, and the upper limit of infrastructure carrying capacity (such as road network capacity and sewage treatment scale).

[0025] In one practical application, the acquisition of current land space data can be: ① Land cover data: acquired through interpretation of 0.5m resolution remote sensing images with an interpretation accuracy of ≥95%, and processed using existing object-oriented classification algorithms (such as eCognition); ② Road data: Road cross-sectional parameters were collected using mobile measurement technology (MMS). The right-of-way width of main roads is 36-40m, secondary roads are 26-30m, and branch roads are 15-26m. ③ Environmental data: One IoT sensor is deployed every 2km to collect PM2.5 data for 72 consecutive hours (accuracy ±2μg / m³). 3 COD (accuracy ±5mg / L) data; ④ Facility data: Obtain the location (latitude and longitude error ≤10m) and scale (such as school land area, hospital bed capacity) of public service facilities through the government data platform.

[0026] Secondly, the data fusion processing adopts a three-step method: spatial benchmark unification, outlier removal, and weight assignment. ① All data is converted to the GCS2000 coordinate system using existing coordinate transformation tools (such as ArcGIS projection transformation module); ② The environmental monitoring data were clustered using the DBSCAN clustering algorithm (neighborhood radius ε=0.2, minimum number of samples MinPts=6) to identify and remove outliers, and replaced with the moving average of three adjacent timestamps; ③ Calculate the weights of each data source using the Analytic Hierarchy Process (AHP), the formula is as follows: ,in, The weight of the i-th type of data (such as land use data, environmental data), The system assigns scores (1-10 points) to experts for the j-th indicator (such as interpretation accuracy and timeliness of land use data) of the i-th data type, where m is the number of data types (e.g., 4 types) and n is the number of indicators for each data type (e.g., land use data indicators include interpretation accuracy and timeliness). The system ultimately forms a standardized fusion dataset.

[0027] Optional, key issue areas can be obtained through the following methods: Based on the GIS platform, the current spatial data layer representing each indicator is overlaid and analyzed with the corresponding planning target or standard threshold layer to calculate the spatial distribution maps of planning compliance index, road network density deficit value, ecological pollution conflict intensity and public service coverage rate. Based on the spatial distribution map, areas whose planning compliance index (used to assess land use conflict) and public service coverage (used to assess infrastructure services) values ​​are below the corresponding lower threshold are identified as key problem areas; areas whose road network density deficit (used to assess traffic connectivity) and ecological pollution conflict intensity (used to assess environmental carrying capacity) values ​​are above the corresponding upper threshold are identified as key problem areas.

[0028] It should be noted that the following terms apply: Current spatial data layer: refers to a GIS layer carrying a single indicator, such as a land use type layer (vector) or a population density layer (raster, 100m×100m resolution); Planning target or standard threshold layer: refers to a control requirement layer, such as a planned land use type layer (vector) or an environmental quality standard threshold; Lower / upper limit thresholds: a lower limit of 0.8 for the planning compliance index, a lower limit of 85% for public service coverage, and an upper limit of 0.8 km / km for the road network density deficit. 2 The upper limit for the intensity of ecological pollution conflict is 1.

[0029] In this embodiment, the planning compliance index map is calculated grid-by-grid using a 100m×100m grid, obtained through any existing technology, such as comparing the current land use with the planned land use map layer; a value of 1 is assigned if they match, and 0 if they do not. The road network density deficit map calculates the current density through GIS network analysis, combines it with population density to generate the expected density, and the difference is the deficit value. The ecological pollution conflict intensity map uses Kriging interpolation to generate pollutant concentration distribution, which is then overlaid with the ecological red line to calculate the intensity (concentration / background concentration). The public service coverage map generates the facility service area through isochronous circle analysis, which is then overlaid with the residential population distribution to calculate the coverage rate.

[0030] Based on the exemplary lower / upper threshold settings given above, the determination of key problem areas is as follows: ① Continuous grid areas with a planning compliance index < 0.8 are key land use problem areas; ② Road network density deficit > 0.8 km / km 2 ① Continuous grid areas are key areas for traffic connectivity issues; ② Areas with ecological pollution conflict intensity > 1 are key areas for environmental carrying capacity issues; ③ Areas with public service coverage < 85% and population density > 1500 people / km² are key areas for environmental carrying capacity issues. 2 The residential areas are key areas for infrastructure services.

[0031] Further optional steps include calculating the road network density deficit and identifying key problem areas, including: Dynamic population heat maps are generated based on mobile signaling data; Based on urban transportation planning standards, a dynamic expected road network density calculation model is established, with the input being a dynamic population heat map. The existing road network density layer obtained by GIS network analysis based on the existing road centerline data is spatially overlaid with the dynamic expected road network density layer obtained by the dynamic expected road network density calculation model, and the road network density deficit value is calculated for each grid. Continuous grid areas with a road network density deficit exceeding a preset sensitivity threshold are identified as key traffic connectivity problem areas.

[0032] It should be noted that the dynamic population heat map is generated based on mobile phone signaling data (50m×50m resolution, 1-hour time slice) and reflects the spatiotemporal distribution of the population; the dynamic expected road network density model considers the dual influence of population density and land use type, which is different from the traditional static model.

[0033] The expression for the dynamic expected road network density model is as follows: In the formula, Desired road network density (km / km) 2 ), Average population density (people / km)2 ), This is a correction factor for industrial land use, with a value of 1.2. This is a correction factor for residential land use, with a value of 0.9. This is a correction factor for public service land use, with a value of 1.1. , and This represents the percentage of applications (totaling 1).

[0034] Secondly, the existing road network density is analyzed using GIS line density analysis, and the formula is: in, The length of the road within the grid (km). The weights for road classification are as follows: main roads 1.0, secondary roads 0.9, and local roads 0.8. Grid area (km) 2 ).

[0035] Therefore, the deficit value is calculated as follows: ,when Furthermore, areas with a continuous grid number of at least 4 are identified as critical traffic connectivity problem areas.

[0036] Further optional steps include calculating the intensity of ecological pollution conflict and identifying key problem areas, including: Historical and real-time observation data from environmental monitoring stations within and around the planning area are obtained, and pollutant concentration distribution maps are generated using spatial interpolation algorithms. Overlay the pollutant concentration distribution map with the ecological protection red line distribution map; For areas located within the ecological protection red line after superposition, the ratio of their pollutant concentration values ​​to the regional environmental background concentration values ​​is quantified as the intensity of ecological pollution conflict, using the following formula: ,in, The intensity of ecological pollution conflict, The ecological sensitivity coefficient is 1.2 for the core red line area and 1.0 for the general red line area. Extract the concentration raster data within the red lines from the overlaid vector layer. The regional environmental background concentration can be obtained by selecting a clean area (such as forest land or cultivated land) without pollution sources within 5km outside the ecological protection red line and calculating the average concentration of that area. Areas with an ecological pollution conflict intensity greater than 1 are identified as key areas for environmental carrying capacity issues.

[0037] It should be noted that mobile signaling data refers to user location and behavior data recorded by mobile communication operators; spatial interpolation algorithm refers to a mathematical method for generating continuous spatial distribution based on data from limited monitoring points. This embodiment uses Kriging interpolation (suitable for spatial correlation fitting of environmental monitoring data); preset sensitivity threshold refers to the critical value of road network density deficit set according to the traffic demand characteristics of the planning area. The threshold is higher in industrial-dominated areas than in agricultural-dominated areas, reflecting the differentiated needs of the region.

[0038] Further optional steps include calculating public service coverage and identifying key issue areas, including: Based on real-time traffic data, the isotime circle is calculated using GIS network analysis, with the location of public service facilities (such as educational or medical facilities) as the starting point and a specified travel time as the threshold. The geographical area covered by the isotime circle is defined as the real-time service range of the facility. Generate a residential population distribution density map based on mobile phone signaling data or nighttime light data; The real-time service range of each public service facility is merged into a comprehensive service range layer, which is then overlaid with the residential population distribution density map. Residential areas located outside the integrated service coverage layer and with a population density higher than the population threshold are identified as key infrastructure service problem areas.

[0039] It should be noted that: real-time traffic data refers to dynamic road speed data collected by floating car GPS and road monitoring equipment; GIS network analysis method refers to the technology of calculating spatial accessibility based on road network topology, which constructs a "node-edge" network model, considers the dynamic changes in road speed, and outputs the spatial coverage range under different time thresholds; isochronous circle refers to the geographical range that can be reached within a specified travel time centered on public service facilities, and is an intuitive indicator of facility service accessibility; nighttime light data refers to nighttime surface light radiation value data obtained by satellite remote sensing (such as NPP-VIIRS data), which can indirectly reflect the intensity of residential population concentration and is used to assist in generating population distribution density maps; population threshold refers to the critical population density value for determining whether a residential area needs public service coverage.

[0040] Further, optionally, based on the key issue areas and higher-level planning constraints, at least one preliminary land spatial planning scheme is generated, including: The land use layout determined by the overall national spatial plan serves as the spatial base, and the identified key problem areas are mapped onto the spatial base. For each identified key problem area, a predefined correction strategy library is invoked to automatically adjust the spatial base. Establish arbitration rules for conflicting plans to adjudicate spatial layout conflicts arising from the implementation of different modification strategies. The arbitration rules shall give the mandatory content of the overall national spatial plan the highest priority. Based on the arbitration results, at least one preliminary national spatial planning scheme shall be generated.

[0041] It should be noted that: spatial base refers to the rigid land use layout framework determined by the overall national land use plan, which is the benchmark framework for scheme revision; revision strategy library refers to the set of standardized adjustment rules preset for different types of key issue areas; spatial layout conflict refers to the spatial overlap contradictions that arise when implementing different revision strategies; mandatory content refers to the control requirements that cannot be violated in the overall national land use plan, including the area of ​​ecological protection red lines, the number of permanent basic farmland, the scope of urban development boundaries, major infrastructure corridors, etc., which are the highest basis for conflict arbitration.

[0042] It is feasible to include the following strategies in the correction strategy library: The correction strategy for the key problem areas corresponding to the planning compliance index (i.e., the aforementioned key land use problem areas) is to adjust the planned land use nature of plots located in prohibited construction zones and planned for construction land to the corresponding non-construction land. The correction strategy for key traffic connectivity issues is to generate planned routes for new connecting branch roads within the key issue area based on road design specifications. The corrective strategy for key environmental carrying capacity issues is as follows: industrial land plots with high intensity of ecological pollution conflict in these key issue areas will be replaced within the urban development boundary. The industrial land will be determined based on the current status survey data of national land space. The revised strategy for key infrastructure service problem areas is to designate land for public service facilities within a pre-set service radius around the key problem area.

[0043] It should be noted that: Prohibited construction zones refer to areas where large-scale construction activities are prohibited in the national land space plan, including core areas of ecological protection red lines, permanent basic farmland, and areas with extremely high geological disaster risks; the planned land use type for these plots cannot be construction land. High intensity of ecological pollution conflict refers to areas with an ecological pollution conflict intensity greater than 1.2, indicating a serious conflict between industrial land and ecological protection in the area. Land replacement refers to a spatial adjustment method within the urban development boundary, where industrial land with high intensity of ecological pollution conflict is converted to other compatible land use types (such as green space or commercial land), while an equivalent amount of industrial land is allocated to other areas within the boundary. Preset service radius refers to a reasonable service distance set according to the level of public service facilities.

[0044] Multi-dimensional quantitative impact assessment is the core of feasibility verification of the plan. By performing quantitative assessments of three dimensions—transportation, environment, and municipal services—in parallel, specific impact indicators are output, providing data support for subsequent plan optimization. Specifically, this includes performing quantitative impact simulation assessments of the transportation, environment, and municipal dimensions in parallel on the preliminary land spatial planning plan. Specifically, the transportation dimension assessment outputs the road network load level, the environment dimension assessment outputs the impact range and intensity of the new pollution sources in the plan, and the municipal dimension assessment outputs the new load of the municipal system after the implementation of the plan.

[0045] It should be noted that: Road network load level: refers to the ratio of traffic volume to design capacity (V / C) of each road segment in the planned road network. It is a core indicator for measuring traffic operation efficiency, with a value range of 0-1. The closer the value is to 1, the higher the road network load. Impact range and intensity of planned new pollution sources: impact range refers to the geographical area of ​​the area where pollutants exceed the standard, and impact intensity refers to the multiple of exceedance or the maximum concentration value. New load of municipal system: refers to the new load that municipal facilities such as electricity, water supply, and sewage will need to bear after the implementation of the plan.

[0046] In one practical application, multi-threading technology is used to perform traffic, environmental, and municipal assessments simultaneously, avoiding the efficiency losses associated with sequential assessments. The specific process is as follows: ① Traffic dimension assessment: Traffic demand forecasting: Based on the land use layout and development intensity of the preliminary land spatial planning scheme (e.g., industrial land plot ratio of 3.5, residential land plot ratio of 2.0), the trip generation and attraction of each traffic analysis zone are calculated using the existing category trip generation rate method. Traffic flow assignment: Using existing technologies such as stochastic user equilibrium (SUE) models (e.g., VISSIM, TransCAD software built-in models), input road network topology data (road grade, number of lanes, design speed) and travel demand data, simulate the route selection behavior of travelers, and output the ratio of traffic volume to design capacity (V / C) of each road segment, i.e., the road network load level. The assessment results output includes: a road network load heat map (red indicates congested road sections with V / C > 0.75), a public transportation accessibility analysis report (such as the percentage of the employed population covered by public transportation within 30 minutes), and a clear indication of the location and degree of congestion of road sections.

[0047] ② Environmental Dimension Assessment: Pollution source identification and parameter determination: Identify newly added pollution sources (such as industrial land and sewage treatment facilities) in the preliminary land space planning scheme, and determine the source strength of pollutants (such as PM2.5 and VOCs emissions) according to industry type (such as electronics and machinery). Pollutant diffusion simulation: Using existing atmospheric diffusion models (such as AERMOD, CALPUFF) or water diffusion models (such as EFDC, SWMM), inputting pollution source parameters, meteorological data (average wind speed, prevailing wind direction, atmospheric stability), and topographic data (elevation, slope), to simulate the spatial diffusion process of pollutants. Determining the scope and intensity of impact: The simulated pollutant concentration distribution is compared with the secondary standards in the ambient air quality standard and the surface water environmental quality standard to identify the areas where the concentration exceeds the standard as the scope of impact, and the ratio of the maximum concentration in the area exceeding the standard to the standard value is calculated as the intensity of impact. Assessment results output: Generate pollutant concentration contour maps (marking areas exceeding standards), impact analysis reports for ecologically sensitive areas (such as water sources and forest land), and identify the locations of high-risk areas.

[0048] ③ Municipal Dimension Assessment: Municipal load forecasting: The unit index method in existing technology is used to determine the unit load index of various types of land use with reference to industry standards; based on the area of ​​various types of land use in the preliminary land space planning scheme, the total municipal load after the implementation of the plan is calculated, and then the current load is subtracted to obtain the new load of the municipal system; Facility capacity verification: Collect the design capacity and current load data of existing municipal facilities (substations, waterworks, sewage treatment plants), and use existing capacity verification methods to compare the new load with the remaining facility capacity (design capacity - current load) to identify capacity gaps; Assessment results output: Generate a municipal load increment table (statistically categorized by electricity, water supply, and sewage), a municipal facility capacity gap analysis diagram, and identify facility nodes that need to be expanded or newly built (such as substations and sewage pipe networks).

[0049] Coordinated optimization of schemes is a key step in balancing multiple objectives. By establishing a multi-objective optimization model, integrating multiple objectives such as development, protection, and service, and using evolutionary algorithms to search for the optimal set of schemes, the following steps are taken: quantifying the results of quantitative impact simulation assessment into optimization objectives and constraints, establishing a multi-objective optimization model, and iteratively optimizing the preliminary land spatial planning schemes through the multi-objective evolutionary algorithm built into the model to generate a Pareto candidate scheme set that weighs multiple objectives.

[0050] It should be noted that the Pareto candidate solution set refers to the set of solutions that cannot be absolutely optimal among multiple optimization objectives. Any improvement in one objective of any solution in the set will lead to the deterioration of other objectives, providing multiple scenarios for subsequent decision-making.

[0051] Optionally, the establishment of a multi-objective optimization model includes the following steps: Optimization objectives and constraints are defined as follows: The planning compliance index, road network density deficit, ecological pollution conflict intensity, public service coverage, road network load level, impact intensity of newly added pollution sources in the plan, and new load of the municipal system are quantified as inputs to the model. The optimization objectives are set as follows: maximize the average planning compliance across the entire region, minimize the road network density deficit and road network load level, minimize the ecological pollution conflict intensity and impact intensity of newly added pollution sources in the plan, and maximize the public service coverage and minimize the new load of the municipal system. The constraints are set as follows: the amount of cultivated land at the end of the planning period shall not be lower than the higher-level planning indicators, and all municipal loads shall not exceed the facility capacity limit. Encoding decision variables: The preliminary land spatial planning scheme to be optimized is encoded as a chromosome. Each gene locus in the chromosome represents the land use attribute of a plot, which includes land use nature and development intensity. Optimization algorithm setting: The non-dominated sorting genetic algorithm with elitist strategy is selected as the multi-objective optimization algorithm; Ensemble model: The optimization objective, constraints, and coding scheme are integrated with the multi-objective optimization algorithm to obtain a multi-objective optimization model.

[0052] It should be noted that the impact intensity of newly added pollution sources in the plan refers to the proportion of area exceeding pollutant standards caused by newly added pollution sources (such as industrial projects) after the implementation of the preliminary planning scheme. The value ranges from 0 to 1, with smaller values ​​indicating a lighter environmental impact. Decision variable encoding refers to converting the spatial elements (land type, plot ratio) of the planning scheme into a numerical sequence (chromosome) that the algorithm can recognize. Each gene position corresponds to the attribute parameter of a land plot (e.g., land type is encoded with 0-9, and plot ratio is represented by floating-point numbers). Non-dominated sorting genetic algorithm with elitist strategy (NSGA-Ⅱ): a multi-objective optimization algorithm that efficiently searches for the Pareto optimal solution through fast non-dominated sorting, crowding calculation, and elitist retention strategy. It is suitable for solving multi-objective trade-off problems in land spatial planning. Facility capacity limit: refers to the maximum carrying capacity of existing and planned municipal facilities (such as substations and sewage treatment plants).

[0053] Comprehensive screening and decision-making of schemes are crucial to determining the final recommended scheme. By quantifying the job-housing balance performance and infrastructure load performance of the schemes, a comprehensive performance score is calculated. Combined with safety threshold screening, the final scheme is ensured to be both optimal and feasible. Specifically, this includes: quantifying the job-housing balance performance and infrastructure load performance of each candidate scheme in the Pareto candidate scheme set, calculating the comprehensive performance score of each candidate scheme, and determining the final recommended land spatial planning scheme from the Pareto candidate scheme set according to the score ranking and constraints.

[0054] It should be noted that: Job-housing balance performance: reflects the degree of spatial matching between employment positions and residential population within the planning area. The higher the performance, the shorter the commuting distance and the higher the commuting efficiency. The value range is 0-1. Infrastructure load performance: reflects the operational load of transportation and municipal facilities after the implementation of the planning scheme. The lower the performance, the safer the facilities (no risk of overload). The value range is 0-1. Preset safety threshold: the upper limit of load performance set based on facility operation safety standards. Schemes that exceed the threshold have the risk of facility overload and must be excluded.

[0055] Optionally, the overall performance score is calculated in the following way: For each candidate scheme in the Pareto candidate scheme set, calculate the ratio of the average theoretical commuting distance to the commuting distance threshold in each candidate scheme, and calculate the difference between 1 and the ratio as the job-housing balance performance of the candidate scheme. The infrastructure load performance of each candidate scheme is calculated by normalizing the load level of the road network and the new load of the municipal system. The overall performance score of each candidate scheme is calculated by weighting and summing the work-life balance performance and the infrastructure load performance.

[0056] Further optional, ultimately recommended land spatial planning schemes are determined through the following methods: From the Pareto candidate solution set, all solutions whose infrastructure load performance is lower than the preset safety threshold are selected to form a subset of qualified solutions; From the subset of compliant schemes, the candidate scheme with the highest comprehensive performance score is selected as the final recommended land spatial planning scheme.

[0057] This embodiment provides a land spatial planning decision-making system based on multi-dimensional impact assessment in a second aspect. Applying the land spatial planning decision-making method based on multi-dimensional impact assessment as described above, the system includes: The problem diagnosis module is configured to: acquire the current land space data and higher-level planning constraints of the planning area; based on the current data, identify key problem areas with deficiencies in land use, transportation, environment, or infrastructure through GIS spatial analysis; and generate at least one preliminary land space planning scheme based on the key problem areas and higher-level planning constraints. The preliminary land space planning scheme includes land use layout, road network, and facility configuration. The impact assessment module is configured to perform quantitative impact simulation assessments of the traffic, environmental, and municipal dimensions in parallel on the preliminary land spatial planning scheme. Specifically, the traffic dimension assessment outputs the road network load level, the environmental dimension assessment outputs the impact range and intensity of the new pollution sources in the plan, and the municipal dimension assessment outputs the new load of the municipal system after the implementation of the plan. The collaborative optimization module is configured to: quantify the results of quantitative impact simulation assessment into optimization objectives and constraints, establish a multi-objective optimization model, and iteratively optimize the preliminary land spatial planning scheme through the multi-objective evolutionary algorithm built into the model to generate a Pareto candidate scheme set that weighs multiple objectives; The comprehensive screening module is configured to: quantitatively calculate the comprehensive performance score of each candidate scheme based on the job-housing balance performance and infrastructure load performance of each candidate scheme in the Pareto candidate scheme set, and determine the final recommended land spatial planning scheme from the Pareto candidate scheme set according to the score ranking and constraints.

[0058] It should be noted that the land spatial planning decision-making system based on multi-dimensional impact assessment in this embodiment corresponds to the aforementioned land spatial planning decision-making method based on multi-dimensional impact assessment. Therefore, the parts of the land spatial planning decision-making system based on multi-dimensional impact assessment in this embodiment that are not described in detail (including but not limited to specific technical means and technical effects) can be referred to the description in the aforementioned land spatial planning decision-making method based on multi-dimensional impact assessment, and will not be repeated here.

[0059] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0060] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A land spatial planning decision-making method based on multi-dimensional impact assessment, characterized in that, Includes the following steps: Multi-source data fusion and problem diagnosis: Obtain the current status data of the land space of the planning area and the constraints of higher-level planning; based on the current status data, identify key problem areas with deficiencies in land use, transportation, environment or infrastructure through GIS spatial analysis; based on the key problem areas and the constraints of higher-level planning, generate at least one preliminary land space planning scheme, which includes land use layout, road network and facility configuration; Multi-dimensional impact quantitative assessment: For the preliminary land space planning scheme, quantitative impact simulation assessments of traffic, environment and municipal dimensions are performed in parallel. Among them, the traffic dimension assessment outputs the road network load level, the environmental dimension assessment outputs the impact range and intensity of the new pollution sources in the plan, and the municipal dimension assessment outputs the new load of the municipal system after the implementation of the plan. Cooperative optimization of schemes: The results of quantitative impact simulation assessment are quantified into optimization objectives and constraints, a multi-objective optimization model is established, and the preliminary land spatial planning scheme is iteratively optimized through the multi-objective evolutionary algorithm built into the model to generate a Pareto candidate scheme set that weighs multiple objectives. Comprehensive screening and decision-making: Based on the job-housing balance performance and infrastructure load performance of each candidate scheme in the Pareto candidate scheme set, the comprehensive performance score of each candidate scheme is quantitatively calculated, and the final recommended land spatial planning scheme is determined from the Pareto candidate scheme set according to the score ranking and constraints.

2. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 1, characterized in that, The key issue area was obtained through the following methods: Based on the GIS platform, the current spatial data layer representing each indicator is overlaid and analyzed with the corresponding planning target or standard threshold layer to calculate the spatial distribution maps of planning compliance index, road network density deficit value, ecological pollution conflict intensity and public service coverage rate. Based on the spatial distribution map, areas whose planning compliance index (used to assess land use conflict) and public service coverage rate (used to assess infrastructure services) are below the corresponding lower threshold are identified as key problem areas; areas whose road network density deficit (used to assess traffic connectivity) and ecological pollution conflict intensity (used to assess environmental carrying capacity) are above the corresponding upper threshold are identified as key problem areas.

3. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 2, characterized in that, The calculation of the road network density deficit and the identification of key problem areas include: Dynamic population heat maps are generated based on mobile signaling data; Based on urban transportation planning standards, a dynamic expected road network density calculation model is established, with the dynamic population heat map as its input. The existing road network density layer obtained by GIS network analysis based on the existing road centerline data is spatially overlaid with the dynamic expected road network density layer obtained by the dynamic expected road network density calculation model, and the road network density deficit value is calculated for each grid. Continuous grid areas where the road network density deficit value is greater than a preset sensitivity threshold are identified as key traffic connectivity problem areas. The calculation of the intensity of the ecological pollution conflict and the identification of key problem areas include: Historical and real-time observation data from environmental monitoring stations within and around the planning area are obtained, and pollutant concentration distribution maps are generated using spatial interpolation algorithms. The pollutant concentration distribution map is overlaid with the ecological protection red line distribution map; For areas located within the ecological protection red line after superposition, the ratio of their pollutant concentration value to the regional environmental background concentration value is quantified as the ecological pollution conflict intensity. Areas with an ecological pollution conflict intensity greater than 1 are identified as key areas for environmental carrying capacity issues.

4. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 2, characterized in that, The calculation of public service coverage and the identification of key problem areas include: Based on real-time traffic data, the isochronous circle is calculated using GIS network analysis, with the location of the public service facility as the starting point and the specified travel time as the threshold. The geographical area covered by the isochronous circle is defined as the real-time service range of the facility. Generate a residential population distribution density map based on mobile phone signaling data or nighttime light data; The real-time service range of each public service facility is merged into a comprehensive service range layer, and then overlaid with the residential population distribution density map. Residential areas located outside the comprehensive service area layer and with a population density higher than the population threshold are identified as critical infrastructure service problem areas.

5. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 1, characterized in that, Based on the aforementioned key issue areas and higher-level planning constraints, at least one preliminary land spatial planning scheme is generated, including: The land use layout determined by the overall national spatial plan serves as the spatial base, and the identified key problem areas are mapped onto the spatial base. For each identified key problem area, a predefined correction strategy library is invoked to automatically adjust the spatial base. Establish arbitration rules for conflicting plans to adjudicate spatial layout conflicts arising from the implementation of different modification strategies. The arbitration rules shall give the mandatory content of the overall national spatial plan the highest priority. Based on the arbitration results, at least one preliminary national spatial planning scheme shall be generated.

6. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 5, characterized in that, The correction strategy library includes the following strategies: The correction strategy for key problem areas corresponding to the planning compliance index is to adjust the planned land use of plots located in prohibited construction zones and planned for construction purposes to the corresponding non-construction land. The correction strategy for key traffic connectivity issues is to generate planned routes for new connecting branch roads within the key issue area based on road design specifications. The corrective strategy for key environmental carrying capacity issues is to replace industrial land parcels with high intensity of ecological pollution conflicts within the key issue areas with land use within the urban development boundary. The revised strategy for key infrastructure service problem areas is to designate land for public service facilities within a pre-set service radius around the key problem area.

7. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 1, characterized in that, The establishment of the multi-objective optimization model includes the following steps: Determine the optimization objectives and constraints: The planning compliance index, road network density deficit, ecological pollution conflict intensity, public service coverage, road network load level, the impact intensity of newly added pollution sources in the plan, and the new load of the municipal system are quantified as inputs to the model. The optimization objectives are set as follows: maximize the average planning compliance across the entire region, minimize the road network density deficit and road network load level, minimize the ecological pollution conflict intensity and the impact intensity of newly added pollution sources in the plan, and maximize the public service coverage and minimize the new load of the municipal system. The constraints are set as follows: the amount of cultivated land at the end of the planning period shall not be lower than the higher-level planning indicators, and all municipal loads shall not exceed the facility capacity limit. Encoding decision variables: The preliminary land spatial planning scheme to be optimized is encoded as a chromosome, and each gene locus in the chromosome represents the land use attribute of a plot, including land use nature and development intensity. Optimization algorithm setting: The non-dominated sorting genetic algorithm with elitist strategy is selected as the multi-objective optimization algorithm; Integration model: The optimization objective, constraints, and coding scheme are integrated with the multi-objective optimization algorithm to obtain the multi-objective optimization model.

8. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 1, characterized in that, The overall performance score is calculated in the following way: For each candidate scheme in the Pareto candidate scheme set, the ratio of the average theoretical commuting distance to the commuting distance threshold in each candidate scheme is calculated, and the difference between 1 and the ratio is calculated as the job-housing balance performance of the candidate scheme. The infrastructure load performance of each candidate scheme is calculated by normalizing the load level of the road network and the new load of the municipal system. The overall performance score of each candidate scheme is calculated by weighting and summing the work-life balance performance and the infrastructure load performance.

9. The land spatial planning decision-making method based on multi-dimensional impact assessment according to claim 8, characterized in that, The final recommended land spatial planning scheme was determined in the following manner: From the Pareto candidate solution set, all solutions whose infrastructure load performance is lower than the preset safety threshold are selected to form a subset of qualified solutions; From the subset of qualified schemes, the candidate scheme with the highest comprehensive performance score is selected as the final recommended land spatial planning scheme.

10. A land spatial planning decision-making system based on multi-dimensional impact assessment, employing the land spatial planning decision-making method based on multi-dimensional impact assessment as described in any one of claims 1-9, characterized in that, The system includes: The problem diagnosis module is configured to: acquire the current status data of the land space of the planning area and the constraints of the higher-level planning; based on the current status data, identify key problem areas with deficiencies in land use, transportation, environment or infrastructure through GIS spatial analysis; and generate at least one preliminary land space planning scheme based on the key problem areas and the constraints of the higher-level planning, the preliminary land space planning scheme including land use layout, road network and facility configuration. The impact assessment module is configured to perform quantitative impact simulation assessments of the traffic, environmental, and municipal dimensions in parallel on the preliminary land spatial planning scheme. Specifically, the traffic dimension assessment outputs the road network load level, the environmental dimension assessment outputs the impact range and intensity of the new pollution sources in the plan, and the municipal dimension assessment outputs the new load of the municipal system after the implementation of the plan. The collaborative optimization module is configured to: quantify the results of quantitative impact simulation assessment into optimization objectives and constraints, establish a multi-objective optimization model, and iteratively optimize the preliminary land spatial planning scheme through the multi-objective evolutionary algorithm built into the model to generate a Pareto candidate scheme set that weighs multiple objectives; The comprehensive screening module is configured to: quantitatively calculate the comprehensive performance score of each candidate scheme based on the job-housing balance performance and infrastructure load performance of each candidate scheme in the Pareto candidate scheme set, and determine the final recommended land spatial planning scheme from the Pareto candidate scheme set according to the score ranking and constraints.

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