Method and system for generating urban and rural planning surveying and mapping results

Abstract

The present application provides a method and system for generating urban and rural planning surveying and mapping results, wherein the method includes: obtaining ecological resource monitoring data, multi-band spectral remote sensing data and current urban and rural road network data of the target area, wherein the ecological data includes vegetation coverage type and water body boundary information, and the spectral data includes surface reflectivity time series characteristics. Multimodal environmental perception data is generated by dynamically coupling vegetation distribution and reflectivity changes, and then adversarial learning matching is performed with road network density gradient information to obtain surveying and mapping intermediate data. Based on the preset density constraints and ecological carrying capacity thresholds, the intermediate data is collaboratively analyzed to extract topological constraint information that integrates the conflict between road infiltration paths and ecological boundaries. Urban and rural planning surveying and mapping results are generated through iterative spatial overlay processing. The present application improves the collaborative optimization effect of ecological protection and construction and development in urban and rural planning.

Description

Technical Field

[0001] The present application relates to the technical field of urban and rural planning and surveying, and in particular to a method and system for generating urban and rural planning and surveying results. Background Art

[0002] In the field of urban and rural planning, it is necessary to comprehensively consider the balance between ecological protection and urban development. Existing technologies urgently need intelligent surveying and mapping methods that can accurately identify potential conflicts between ecologically sensitive areas and construction and development, so as to achieve scientific decision-making in planning schemes.

[0003] Currently, there is an urban and rural planning assistance system based on remote sensing images and geographic information systems. The system uses convolutional neural networks to extract surface cover features, combines spatial statistical analysis to generate construction suitability evaluation maps, and divides ecological protection and construction and development areas by setting fixed thresholds.

[0004] This plan does not adequately depict the dynamic interaction between ecological factors and construction activities. Relying solely on static thresholds makes it difficult to reflect the complex conflicts in actual planning, resulting in deviations between the generated evaluation results and the actual situation, affecting the implementation effect of the planning scheme. Summary of the Invention

[0005] The present application provides a method and system for generating urban and rural planning surveying and mapping results, which is used to solve the problem of poor coordinated optimization effect of ecological protection and construction and development in urban and rural planning in the existing technology.

[0006] In a first aspect, the present application provides a method for generating urban and rural planning surveying and mapping results, comprising:

[0007] Obtain ecological resource monitoring data, multi-band spectral remote sensing data, and spatial distribution data of the existing urban and rural road network within the target area. The ecological resource monitoring data includes vegetation cover type distribution information and water body boundary coordinates, and the multi-band spectral remote sensing data includes temporal variation characteristics of surface reflectance.

[0008] Dynamically coupling the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data;

[0009] Dynamically matching the multimodal environmental perception data with road network density gradient distribution information driven by adversarial learning to generate surveying and mapping intermediate data, wherein the road network density gradient distribution information is obtained by performing spatial kernel density analysis on the existing urban and rural road network spatial distribution data;

[0010] Based on the synergy of the preset density constraint and the preset ecological carrying capacity threshold, the surveying and mapping intermediate data and the road network density gradient distribution information are analyzed, and topological constraint information that integrates the road network infiltration path and the degree of conflict between the boundaries of ecologically sensitive areas is extracted from the analysis results;

[0011] The topological constraint information is iteratively spatially superimposed with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results.

[0012] Optionally, based on the synergistic effect of the preset density constraint and the preset ecological carrying capacity threshold, the surveying and mapping intermediate data and the road network density gradient distribution information are analyzed, and topological constraint information integrating the road network infiltration path and the degree of conflict between the boundaries of ecologically sensitive areas is extracted from the analysis results, including:

[0013] Establishing a dynamic weight relationship between the quantified value of human activity intensity in each spatial unit in the road network density gradient distribution information and the ecological tolerance of the corresponding spatial unit in the ecological resource monitoring data;

[0014] generating a spatial proximity relationship network based on the dynamic weight relationship and the surveying and mapping intermediate data;

[0015] By presetting density constraints, the propagation range of the quantitative value of human activity intensity is limited. At the same time, the attenuation gradient of the ecological tolerance is limited by presetting ecological carrying thresholds, thereby forming a dynamic adjustment rule for grouping boundaries.

[0016] Reorganizing the topological structure of the spatial proximity relationship network based on the dynamic adjustment rule of the group boundary;

[0017] Topological constraint information is extracted from the reorganized spatial proximity relationship network.

[0018] Optionally, the reorganizing the topology structure of the spatial proximity relationship network based on the group boundary dynamic adjustment rule includes:

[0019] Identifying the conflict ratio between the quantified value of the human activity intensity of each network node in the spatial proximity relationship network and the ecological tolerance;

[0020] According to the numerical range of the preset density constraint in the group boundary dynamic adjustment rule, the network node corresponding to the conflict ratio exceeding the preset first threshold is marked as an over-developed node, and the connection relationship between the over-developed node and the adjacent node is disconnected;

[0021] According to the attenuation gradient of the ecological carrying threshold in the group boundary dynamic adjustment rule, the network node corresponding to the conflict ratio lower than the preset second threshold is marked as an ecological protection node, and the connection strength between the ecological protection node and the adjacent ecological protection nodes is strengthened;

[0022] In the spatial proximity relationship network, a new connection relationship is established between the undisconnected nodes, wherein the undisconnected nodes include ordinary nodes and ecological protection nodes;

[0023] Based on the overdevelopment nodes, the ecological protection nodes and the new connection relationships, a reorganized spatial proximity relationship network is generated.

[0024] Optionally, establishing a new connection relationship between undisconnected nodes in the spatial proximity relationship network includes:

[0025] Determining a set of nodes to be planned in the spatial proximity relationship network that are not marked as overdeveloped nodes and not marked as ecological protection nodes;

[0026] Determining candidate connection paths based on the set of nodes to be planned;

[0027] Verifying the candidate connection path based on a preset continuity constraint condition;

[0028] The candidate connection paths that meet the continuity constraint condition are topologically integrated with the connection relationships retained in the spatial proximity relationship network to form a new connection relationship.

[0029] Optionally, generating a spatial proximity relationship network based on the dynamic weight relationship and the surveying and mapping intermediate data includes:

[0030] Extracting spatial turning points of vegetation cover migration trajectories, boundary intersection points of water body distribution coupling characteristics, and topological nodes of road network extension directions from the surveying and mapping intermediate data as basic network nodes, and establishing a connection model between the basic network nodes;

[0031] Based on the dynamic weight relationship, generating a combination of connection weight values ​​between adjacent basic network nodes;

[0032] The connection model is combined with the connection weight value to establish a spatial proximity relationship network.

[0033] Optionally, dynamically coupling the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data includes:

[0034] Establishing a mapping relationship between the boundaries of various types of vegetation in the vegetation cover type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity temporal variation characteristics;

[0035] According to the mapping relationship, identifying the intersection area of ​​the vegetation cover type stable area and the reflectance mutation area;

[0036] performing coupling processing on the intersection area to generate an environmental feature marker;

[0037] The environmental feature markers are combined with the corresponding spatial position coordinates to form multimodal environmental perception data.

[0038] Optionally, the iterative spatial superposition of the topological constraint information and the multimodal environmental perception data to generate urban and rural planning surveying and mapping results includes:

[0039] marking potential conflict areas in the multimodal environmental perception data based on curvature change characteristics of the road network penetration path in the topology constraint information;

[0040] Based on the multimodal environmental perception data, assigning spatial weights to the potential conflict areas;

[0041] Iteratively superimpose the spatial weight allocation results until the preset iteration stop condition is met;

[0042] The overlay results are spatially fused with the multimodal environmental perception data to generate urban and rural planning and surveying results.

[0043] In a second aspect, the present application provides a system for generating urban and rural planning surveying and mapping results, including:

[0044] An acquisition module is used to obtain ecological resource monitoring data, multi-band spectral remote sensing data, and spatial distribution data of the existing urban and rural road network within the target area. The ecological resource monitoring data includes vegetation cover type distribution information and water body boundary coordinates, and the multi-band spectral remote sensing data includes temporal variation characteristics of surface reflectance;

[0045] A coupling module, configured to dynamically couple the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data;

[0046] a matching module for dynamically matching the multimodal environmental perception data with road network density gradient distribution information driven by adversarial learning to generate surveying and mapping intermediate data, wherein the road network density gradient distribution information is obtained by performing spatial kernel density analysis on the existing urban and rural road network spatial distribution data;

[0047] an extraction module for analyzing the surveying and mapping intermediate data and the road network density gradient distribution information based on the synergy of a preset density constraint and a preset ecological carrying capacity threshold, and extracting topological constraint information integrating the road network infiltration path and the degree of conflict between the boundaries of ecologically sensitive areas from the analysis results;

[0048] A generation module is used to iteratively spatially superimpose the topological constraint information with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results.

[0049] In a third aspect, the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a method for generating urban and rural planning and surveying results as described in any one of the first aspects.

[0050] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a method for generating urban and rural planning and surveying results as described in any one of the first aspects.

[0051] In the present application, a method for generating urban and rural planning surveying and mapping results is provided, which includes: obtaining ecological resource monitoring data, multi-band spectral remote sensing data, and current urban and rural road network spatial distribution data within a target area, the ecological resource monitoring data including vegetation cover type distribution information and water body boundary coordinates, and the multi-band spectral remote sensing data including surface reflectivity time-series variation characteristics; dynamically coupling the vegetation cover type distribution information with the surface reflectivity time-series variation characteristics to generate multimodal environmental perception data; dynamically matching the multimodal environmental perception data with road network density gradient distribution information driven by adversarial learning to generate surveying and mapping intermediate data, the road network density gradient distribution information being obtained by performing spatial kernel density analysis on the current urban and rural road network spatial distribution data; analyzing the surveying and mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of a preset density constraint and a preset ecological carrying capacity threshold, and extracting topological constraint information that integrates the road network infiltration path and the degree of conflict between the boundary of the ecologically sensitive area from the analysis results; and iteratively spatially superimposing the topological constraint information with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results.

[0052] The technical solution provided by this application has the following beneficial effects:

[0053] This application integrates ecological resources, spectral remote sensing, and road network data to provide comprehensive, multi-dimensional basic data support for subsequent analysis, ensuring data integrity for planning decisions. It achieves in-depth correlation analysis between vegetation cover characteristics and changes in surface reflectivity, enhancing the ability to capture the dynamic evolution of the ecological environment. It effectively integrates ecological and environmental characteristics with road network density distribution to generate intermediate data that can simultaneously reflect the natural background and the impact of human activities. It accurately identifies conflict areas between construction and development and ecological protection through dual threshold constraints, providing precise conflict positioning for planning schemes. It ultimately generates planning results that comprehensively consider ecological protection and construction needs, achieving scientific optimization of planning schemes.

[0054] Furthermore, this application also establishes a dynamic weight relationship between human activity intensity and ecological tolerance, constructs a spatial proximity network, and then applies dual constraint rules to reorganize the network topology, and finally extracts topological constraint information that reflects actual planning conflicts.

[0055] In addition, the scheme realizes a dynamic balance assessment of the impact of human activities and ecological carrying capacity. The generated topological constraint information can accurately depict the interactive relationship between construction and development and ecological protection, providing a quantitative basis for planning decisions.

[0056] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0058] Figure 1 A flowchart of a method for generating urban and rural planning surveying and mapping results provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of the structure of a system for generating urban and rural planning and surveying results provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0062] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0063] In the field of urban and rural planning and surveying, existing technical solutions based on remote sensing imagery and geographic information systems (GIS) have significant limitations. Their use of static thresholds to demarcate ecological protection and development zones makes it difficult to accurately depict the dynamic interactions between human activities and the ecological environment. This rigid demarcation method, particularly when faced with the complex conflict between road network expansion and the protection of ecologically sensitive areas, fails to reflect the gradual transition characteristics of space. Consequently, the resulting planning schemes often exhibit overly rigid ecological protection or uncontrolled construction and development in actual implementation, severely hindering coordinated urban and rural development.

[0064] In response to this technical bottleneck, this application proposes a method for generating urban and rural planning surveying and mapping results. Through dynamic coupling analysis of ecological resource data, spectral remote sensing characteristics and road network density, an environmental perception network with spatial semantics is constructed. This method innovatively introduces a dynamic weighting mechanism for human activity intensity and ecological tolerance, uses adversarial learning to achieve an adaptive balance between construction and development pressure and ecological carrying capacity, and generates planning results that integrate conflict gradients through iterative spatial superposition. Compared with the static threshold method of the existing technology, this scheme can accurately identify the transition area of ​​ecological-construction interaction, while maintaining the integrity of ecologically sensitive areas, providing flexible space for reasonable development, fundamentally solving the problem of insufficient characterization of complex spatial conflicts by traditional methods, and improving the scientific nature and feasibility of planning schemes.

[0065] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0066] Figure 1 A flowchart of a method for generating urban and rural planning surveying and mapping results provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method includes:

[0067] Step 101: Obtain ecological resource monitoring data, multi-band spectral remote sensing data, and current urban and rural road network spatial distribution data within the target area, wherein the ecological resource monitoring data includes vegetation cover type distribution information and water body boundary coordinates, and the multi-band spectral remote sensing data includes surface reflectance temporal variation characteristics.

[0068] In step 101, the target area refers to the specific geographic scope for urban and rural planning surveying and mapping. The target area includes the urban-rural transition zone, ecologically sensitive areas, and the spatial scope of planned construction land. Ecological resource monitoring data includes geographic information data on the spatial distribution of vegetation types and the location of water body boundaries. Multi-band spectral remote sensing data represents remote sensing image data that records the changes in surface reflectivity over time. Road network spatial distribution data represents urban and rural road vector line data. Vegetation cover type distribution information refers to the spatial distribution data of different vegetation types obtained through remote sensing interpretation or field surveys. It includes attributes such as vegetation type code and cover density and is used to analyze regional ecological background conditions. Water body boundary coordinates represent a set of continuous geographic coordinate points that describe the spatial contours of water bodies such as rivers and lakes. They are used to delineate ecological protection areas and analyze the ecological impact of water bodies. The temporal variation characteristics of surface reflectivity represent dynamic indicators reflecting the changes in the surface's ability to reflect electromagnetic waves over different periods of time. They are calculated using multi-temporal remote sensing images and are used to monitor vegetation growth status and environmental changes.

[0069] In an embodiment of the present application, ecological monitoring data, remote sensing image data and road network data of the target area are synchronously retrieved through a geographic information system platform, and the data are subjected to coordinate system conversion and spatial resolution uniform processing to form a standardized input data set.

[0070] For example, taking a certain urban-rural fringe area as an example, the forest resource survey data from the forestry department is obtained as vegetation data, the river and lake demarcation results from the water conservancy department are obtained as water body data, the environmental satellite multispectral imagery is used as reflectance data, and the road network data from the Natural Resources Bureau are obtained. All data are uniformly converted into the CGCS2000 coordinate system.

[0071] Step 102: Dynamically couple the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data.

[0072] In step 102, dynamic coupling refers to establishing a spatiotemporal correlation model between vegetation types and reflectivity changes. Multimodal environmental perception data refers to a spatial dataset that integrates vegetation stability and reflectivity anomaly characteristics.

[0073] In an embodiment of the present application, a spatiotemporal matching algorithm is used to overlay and analyze the vegetation type distribution map and the reflectivity change sequence, identify abnormal areas where the vegetation type is stable but the reflectivity changes suddenly, calculate the vegetation anti-interference coefficient of each spatial unit, and generate an environmental perception data layer containing the ecological stability evaluation results.

[0074] For example, if it is found that the forest type in a certain area has not changed for three consecutive quarters, but the reflectivity fluctuations exceed the average level, it will be determined to be an ecologically fragile area affected by potential development, and it will be assigned a lower anti-interference coefficient and marked.

[0075] Step 103: Dynamically match the multimodal environmental perception data with the road network density gradient distribution information driven by adversarial learning to generate surveying and mapping intermediate data. The road network density gradient distribution information is obtained by performing spatial kernel density analysis on the existing urban and rural road network spatial distribution data.

[0076] In step 103, the road network density gradient represents the spatial distribution field reflecting the intensity of human activities. Adversarial learning represents a generative and discriminative mechanism for balancing ecological protection and development needs. Intermediate surveying and mapping data represents a transitional data product generated through adversarial learning. It also includes spatial distribution information for ecological and environmental suitability assessments and road network optimization recommendations, providing a foundation for subsequent conflict analysis.

[0077] In an embodiment of the present application, the road network is converted into a density field through kernel density analysis, a generator is constructed to simulate the construction and development model, and a discriminator evaluates the compatibility of the development plan with the ecological environment. After multiple adversarial trainings, intermediate data that takes into account the needs of both parties is generated.

[0078] For example, when generating a new city planning scheme, the adversarial model automatically adjusts the road network density, avoids ecologically fragile areas where the anti-interference coefficient is lower than the threshold, and forms an optimized layout.

[0079] Step 104: Based on the synergistic effect of the preset density constraint and the preset ecological carrying capacity threshold, the surveying and mapping intermediate data and the road network density gradient distribution information are analyzed, and topological constraint information that integrates the road network infiltration path and the degree of conflict between the boundaries of the ecologically sensitive areas is extracted from the analysis results.

[0080] In step 104, the density constraint represents the spatial threshold that controls the expansion intensity of the road network. The ecological carrying capacity threshold represents the minimum tolerance limit for maintaining the stability of the ecosystem. The road network infiltration path represents the potential expansion direction identified by analyzing the spatial variation trend of the road density gradient, reflecting the erosion risk of human activities on the ecological region, and is derived from the spatial diffusion simulation of kernel density analysis. The boundary of the ecologically sensitive area represents the boundary of the core protection area based on ecological resource data and water body data, and is determined by analyzing the continuity of vegetation cover and water body connectivity. Degree of conflict: an indicator that quantifies the degree of oppression of the road network on the ecological boundary, calculated based on the ratio of road density to ecological tolerance. Topological constraint information represents structured data that describes the spatial conflict characteristics between roads and ecologically sensitive areas, including elements such as conflict location, type and intensity.

[0081] In an embodiment of the present application, a dynamic weight matrix of road density and ecological tolerance is established, a spatial clustering algorithm is applied to identify conflict areas, and the topological conflict characteristics of road infiltration paths and ecological boundaries are extracted.

[0082] For example, if the road density in a certain area exceeds the threshold but is located near the ecological red line, the system will mark it as a high-conflict area and generate a recommended detour route.

[0083] Step 105: Iteratively spatially superimpose the topological constraint information and the multimodal environmental perception data to generate urban and rural planning surveying and mapping results.

[0084] In step 105, iterative overlay represents the process of gradually optimizing the spatial coordination of conflicting areas. The urban and rural planning surveying and mapping results represent the final planning guidance map, which includes elements such as ecological restoration priority zoning, construction and development suitability classification, infrastructure layout optimization recommendations, and ecological-construction buffer zone plans.

[0085] In an embodiment of the present application, topological constraint information and multimodal data are subjected to multiple rounds of spatial operations. The first round processes severe conflict areas, and the second round optimizes transition areas, ultimately generating a planning result that includes ecological restoration priorities and construction suitability.

[0086] For example, in the first round, a prohibited construction zone was designated around a wetland, and in the second round, a low-density development zone was set up in the buffer zone, ultimately forming a hierarchical management and control plan.

[0087] This method achieves precise spatial coordination between ecological protection and construction and development through multi-source data fusion and dynamic balance mechanism. The generated planning results can not only effectively protect ecologically sensitive areas, but also provide a scientific basis for rational development, thereby improving the implementation effect and ecological sustainability of urban and rural planning.

[0088] To further improve the coordination between ecological protection and development and construction in urban and rural planning, in some embodiments, step 104: analyzing the surveying and mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of the preset density constraint and the preset ecological carrying capacity threshold, and extracting topological constraint information that integrates the road network penetration path and the degree of conflict between the boundaries of ecologically sensitive areas from the analysis results, includes:

[0089] Step 201: Establishing a dynamic weight relationship between the quantitative value of human activity intensity of each spatial unit in the road network density gradient distribution information and the ecological tolerance of the corresponding spatial unit in the ecological resource monitoring data.

[0090] In step 201, the quantified value of human activity intensity is derived from the numerical results of spatial kernel density analysis of the road network density gradient distribution information, reflecting the road network density of each spatial unit. Ecological tolerance is a quantitative indicator calculated by spatially overlaying the vegetation cover type distribution information and water body boundary coordinates in the ecological resource monitoring data, combined with the protection level coefficients of different ecological elements. The dynamic weight relationship is a relationship between the road network density gradient value (the quantified value of human activity intensity) and the ecological tolerance, dynamically weighted according to a preset ratio. For example, for a spatial unit with a road density of 0.6 / km² (the quantified value of human activity intensity) and an ecological tolerance of 0.4, the dynamic weight is set to = road density × 0.7 + ecological tolerance × 0.3, resulting in a comprehensive weight of 0.54 for the unit.

[0091] In an embodiment of the present application, the road density data is first normalized to obtain a quantitative value of human activity intensity. At the same time, the ecological tolerance is calculated based on ecological factors such as vegetation coverage and water body distance. Then, the weighted ratio of the two is set according to the planning focus to form a spatially differentiated weight distribution map.

[0092] Step 202: Generate a spatial proximity relationship network based on the dynamic weight relationship and the surveying and mapping intermediate data.

[0093] In step 202 , the spatial proximity relationship network is a graph structure consisting of grid units as nodes and spatial adjacency relationships between units as edges, and the weights of the edges reflect the interaction strength between adjacent units.

[0094] In this embodiment, the dynamic weight relationships generated in the previous step are used as a foundation, combined with the spatial correlation characteristics of the surveying and mapping intermediate data, to construct a connection relationship between network nodes. Specifically, spatially adjacent units with weight differences within a set range are connected, and the connection strength is calculated based on the degree of ecological and development coordination between the two.

[0095] Step 203: By presetting density constraints, the propagation range of the quantitative value of the human activity intensity is limited. At the same time, the attenuation gradient of the ecological tolerance is limited by presetting an ecological carrying threshold, thereby forming a dynamic adjustment rule for grouping boundaries.

[0096] In step 203, the propagation range of the quantified value of human activity intensity refers to the spatial radiation boundary of the road network's impact on human activities. Kernel density analysis is used to calculate the attenuation of road impact with distance. The propagation boundary is determined when the density value drops to a certain percentage of the peak value, reflecting the spatial diffusion limit of development pressure. This range is generated using a spatial interpolation algorithm based on factors such as road grade and traffic flow. The attenuation gradient of ecological tolerance represents the rate of change at which the ecological protection effect decreases with increasing distance from the core area. It is determined by analyzing the spatial distribution patterns of ecological factors such as vegetation continuity and water connectivity. Based on the results of the ecological sensitivity assessment, tolerance decay curves are set for the core area, buffer zone, and transition zone to reflect the spatial variation in the self-regulating capacity of the ecosystem. The dynamic adjustment rules for grouping boundaries include density constraints and ecological attenuation conditions. The former controls the radiation range of road impact, while the latter limits the diffusion gradient of the ecological effect.

[0097] In this embodiment, a basic principle for spatial grouping is established by setting a maximum distance threshold for road density propagation and a minimum ecological tolerance value. When the road density of a unit exceeds the threshold, its impact range is restricted; when the ecological tolerance falls below the maintenance value, its protection range is attenuated outward.

[0098] Step 204: reorganize the topology of the spatial proximity relationship network based on the group boundary dynamic adjustment rule.

[0099] In step 204, topology reorganization refers to the process of optimizing and reconstructing the network connection relationship according to the adjustment rules.

[0100] In the embodiment of the present application, the direct connection between high-development intensity areas is first disconnected to block over-development, then the connection between high-ecological value areas is enhanced to form a protection network, and finally the connection weight of the transition area is adjusted to conform to the gradient change law.

[0101] Step 205: extracting topological constraint information from the reorganized spatial proximity relationship network.

[0102] In step 205 , the reorganized spatial proximity relationship network is the analysis result.

[0103] In the embodiment of the present application, the strengthened ecological corridor connections in the network are extracted as protection red lines, and the weakened development radiation paths are extracted as restricted construction areas, forming spatial constraints to guide planning.

[0104] Here's a specific example:

[0105] In the process of implementing the planning of a certain urban-rural junction, based on the basic data that has been obtained and processed by Weight 1, the quantitative value of human activity intensity is first calculated for the 500-meter grid unit, and the kernel density formula is used. ,in Take 300 meters, d is the distance from the grid center to the road. At the same time, the ecological tolerance of each unit is calculated according to the formula ecological tolerance = 0.6 × vegetation coverage + 0.4 × (1-distance to water body / 1000 meters). The two types of data are used to establish a dynamic weight relationship with a weight of 7:3 to generate an initial spatial proximity relationship network, in which adjacent grids are connected if the distance is less than 800 meters. By setting a road density propagation threshold of 0.35 / km², the development impact range is limited to no more than an 800-meter buffer zone in the core ecological area, and the ecological tolerance attenuation gradient is set to decrease by 5% every 100 meters, forming a hierarchical protection rule. When reorganizing the network, the 26 units with a density exceeding 0.4 / km² and located within 300 meters of the ecological red line are disconnected, and the connection strength between the 58 units with a tolerance higher than 0.7 is enhanced by 1.5 times. The final extracted topological constraint information indicated that the routes of three planned roads in the northeastern new urban expansion area needed to be adjusted to avoid the area east of the wetland marked as highly conflicting. Low-density development areas were planned within a 500-800-meter buffer zone from the wetland, resulting in an optimized plan with a decreasing development intensity gradient. The curvature of the new roads was controlled within 0.1 radians per meter, and the conflict ratio at each node remained within a reasonable range of 0.6-0.9, ensuring the integrity of the wetland's ecological functions while meeting the necessary infrastructure construction needs.

[0106] In the embodiment of the present application, the method achieves precise spatial coordination between ecological protection and construction and development through quantitative analysis and dynamic adjustment. The generated constraints not only ensure the integrity of the ecosystem, but also provide flexible space for reasonable development, thereby improving the scientific nature and feasibility of the planning scheme.

[0107] In order to further improve the ecological adaptability of spatial networks in urban and rural planning, in some embodiments, step 204: reorganizing the topological structure of the spatial proximity relationship network based on the group boundary dynamic adjustment rule includes:

[0108] Step 301: Identify the conflict ratio between the quantitative value of the human activity intensity of each network node in the spatial proximity relationship network and the ecological tolerance.

[0109] In step 301, network nodes are formed by the intersection of vegetation migration trajectories, water distribution coupling characteristics, and road network extension directions in the intermediate mapping data. The conflict ratio is the ratio of the quantified human activity intensity value to the ecological tolerance at the network node, reflecting the degree of conflict between development and protection.

[0110] In an embodiment of the present application, the ratio of the human activity intensity to the ecological tolerance of each node is first calculated to obtain a quantitative conflict index for subsequent node classification.

[0111] Step 302: Based on the numerical range of the preset density constraint in the group boundary dynamic adjustment rule, the network node corresponding to the conflict ratio exceeding the preset first threshold is marked as an over-developed node, and the connection relationship between the over-developed node and the adjacent node is disconnected.

[0112] In step 302, the first threshold value refers to the critical value used to determine whether a network node is in an overdeveloped state. When the conflict ratio (quantified value of human activity intensity / ecological tolerance) exceeds the threshold, it is marked as an overdeveloped node. It is usually set to a value between 1.2 and 1.5. It is determined by analyzing the critical relationship between development intensity and ecological damage in regional historical planning data. When the conflict ratio (human activity intensity / ecological tolerance) exceeds the threshold, it is determined to be overdeveloped. The specific value needs to be adjusted in combination with the results of the regional ecological sensitivity assessment. Ecologically sensitive areas can use a lower threshold (such as 1.2), and general areas can use a higher threshold (such as 1.5). An overdeveloped node refers to a node whose conflict ratio exceeds the upper limit of development density, representing an overdeveloped area. Disconnecting the connection between the overdeveloped nodes means disconnecting the connection between the node marked as overdeveloped and all other adjacent nodes (including other overdeveloped nodes and non-overdeveloped nodes).

[0113] In an embodiment of the present application, based on a preset development density constraint threshold, nodes with serious conflicts are screened out and their connections with surrounding nodes are cut off to limit the spread of development.

[0114] Step 303: According to the attenuation gradient of the ecological carrying threshold in the group boundary dynamic adjustment rule, the network node corresponding to the conflict ratio lower than the preset second threshold is marked as an ecological protection node, and the connection strength between the ecological protection node and the adjacent ecological protection nodes is enhanced.

[0115] In step 303, the second threshold is used to determine whether a network node is in an ecological protection state. When the conflict ratio falls below this threshold, the node is marked as an ecological protection node. This threshold is generally set between 0.5 and 0.7, based on the core protection zone management requirements in the ecological protection redline standard. When the conflict ratio falls below this threshold, the node is identified as a key ecological protection node. A value of 0.5 is used for first-level protection zones, 0.6 for second-level protection zones, and 0.7 for regional ecological corridors. An ecological protection node is one whose conflict ratio falls below the ecological protection lower limit and represents a core ecological protection zone. The technical support for enhancing connectivity strength is achieved by multiplying the connection weights between ecological protection nodes by a preset amplification factor (e.g., 1.5). This factor is derived from the protection priority calculated from the ecological carrying capacity threshold. Connectivity strength is a quantitative indicator of the interactions between network nodes, representing the closeness of spatial connections; larger values ​​indicate more important connections. Enhancing connectivity strength between ecological protection nodes can enhance the connectivity and stability of the ecological network.

[0116] In an embodiment of the present application, the connection between ecologically sensitive nodes is strengthened, and an ecological protection network is formed by increasing the connection weight.

[0117] Step 304: In the spatial proximity relationship network, a new connection relationship is established between the undisconnected nodes, where the undisconnected nodes include common nodes and ecological protection nodes.

[0118] In step 304, the connections between nodes that remain undisconnected include three types of connections: between ordinary nodes, between ordinary nodes and ecological protection nodes, and between ecological protection nodes and ecological protection nodes. New connections refer to optimized connections established between ordinary nodes and ecological protection nodes. Ordinary nodes refer to intermediate network nodes in the spatial proximity network that are neither marked as overdeveloped nodes nor marked as ecological protection nodes.

[0119] In the embodiment of the present application, under the premise of complying with ecological constraints, the connection path of the transition area is replanned to ensure the overall connectivity of the network.

[0120] Step 305: Generate a reorganized spatial proximity relationship network based on the overdevelopment nodes, the ecological protection nodes, and the new connection relationships.

[0121] In the embodiment of the present application, the processed nodes and connection relationships are integrated to form a final spatial network structure.

[0122] Here's a specific example:

[0123] In a case study of urban-rural fringe planning implementation, based on the spatial proximity relationship network established in the early stage, the conflict ratio of each network node was first calculated using the formula conflict ratio = human activity intensity quantified value / ecological tolerance, where the human activity intensity quantified value was calculated using the kernel density formula. Calculated, Taking 300 meters as the threshold, the ecological tolerance is calculated as 0.6 × vegetation cover + 0.4 × 1 - distance to water / 1000 meters. Forty-two nodes with a conflict ratio exceeding 1.3 were marked as overdeveloped nodes. This threshold of 1.3 was determined by analyzing the critical relationship between development intensity and ecological damage in regional ten-year planning data. All connections between these nodes and adjacent nodes were subsequently disconnected. Furthermore, 55 nodes with a conflict ratio below 0.6 were marked as ecological protection nodes. This threshold of 0.6 was set based on the management requirements for secondary protected areas in the ecological protection red line standard. The connection strength between these nodes was increased to 1.5 times the original value. New connections were established between the remaining ordinary nodes and ecological protection nodes, with a spatial distance of less than 800 meters and an ecological tolerance difference of no more than 0.25. A total of 28 new connection paths were added. The final reorganized network showed that the three road paths originally planned to cross the wetland buffer zone were readjusted, the new path curvature was controlled within 0.08 radians / meter, and the conflict ratio of connected nodes was maintained between 0.65-0.85. Among them, the connection strength of the two newly added ecological corridors reached 1.2, effectively connecting the scattered ecological patches.

[0124] In the embodiment of the present application, the method achieves precise regulation of development intensity and ecological protection by dynamically adjusting the network structure, providing a scientific spatial optimization solution for urban and rural planning.

[0125] To further improve the spatial connectivity of transition areas in urban and rural planning, in some embodiments, step 304: establishing new connection relationships between undisconnected nodes in the spatial proximity relationship network includes:

[0126] Step 401: Determine a set of nodes to be planned in the spatial proximity relationship network that are not marked as overdeveloped nodes and not marked as ecological protection nodes.

[0127] In step 401, the set of nodes to be planned refers to intermediate nodes in the network that are neither classified as over-developed nodes nor as ecological protection nodes, representing a buffer transition area between development and protection.

[0128] In an embodiment of the present application, by traversing the network nodes, nodes whose ratio of human activity intensity to ecological tolerance is in the middle range are screened out. The set of these nodes is the area to be planned, which has development potential but requires reasonable guidance.

[0129] Step 402: Determine candidate connection paths based on the set of nodes to be planned.

[0130] In step 402, candidate connection paths are potential development pathways between nodes to be planned, taking both spatial connectivity and ecological compatibility into consideration. A set of candidate paths is selected that meets the following conditions: the starting point of the candidate connection path is located at a node on the forward side of the road network extension direction; the endpoint of the candidate connection path is located at a node with an ecological tolerance attenuation gradient less than the regional average; and the conflict ratio of the nodes along the candidate connection path is between the first and second preset thresholds.

[0131] In the embodiment of the present application, based on the spatial distribution characteristics of the nodes to be planned, all possible connection schemes are preliminarily generated, including potential paths such as road extensions and ecological corridors, as the basis for subsequent screening.

[0132] Step 403: Verify the candidate connection path based on the preset continuity constraint condition.

[0133] In step 403, the continuity constraints include: 1) a geometric constraint requiring that the curvature of the connecting path not exceed 45 degrees; 2) an ecological constraint requiring that paths cross different ecological zones to maintain a buffer zone of at least 200 meters; and 3) a topological constraint requiring that at least two connections be retained for each planned node. Verification steps: At least one candidate connecting path is retained between adjacent nodes along the road network extension direction; and the number of candidate connecting paths between nodes crossing the sudden change zone of the ecological tolerance attenuation gradient does not exceed a preset upper limit.

[0134] In the embodiment of the present application, each candidate path is double-verified: checking whether the path direction conforms to the extension trend of the existing road network, and evaluating whether the ecological sensitivity of the area through which the path passes is within the allowable range. Only paths that meet both requirements are retained.

[0135] Step 404: topologically integrate the candidate connection paths that satisfy the continuity constraint condition with the connection relationships retained in the spatial proximity relationship network to form a new connection relationship.

[0136] In step 404, topology integration refers to the process of organically integrating the qualified new path with the original network.

[0137] In an embodiment of the present application, verified candidate paths are gradually added to the existing network in order of priority, and the connection weights of relevant nodes are adjusted to ensure that the introduction of new paths does not undermine the structural stability of the original ecological protection network. For example, in the planning of a certain urban-rural junction, candidate road connection paths (such as planned new branches) that meet road extension continuity (maintaining at least two parallel connections) are integrated with existing preserved ecological corridor connections (such as cross-river bridges) to form a final road planning scheme that both ensures transportation network connectivity and maintains ecological barrier functions. The distance between the connection points of the new branches and the preserved bridges is no more than 200 meters to ensure network continuity. When a candidate connection path contains nodes ABC (both of which are to be planned nodes) and meets the continuity constraint, the path is merged with the existing connection retained in the network (such as node CD, where D is an ecological protection node) to form a new target connection relationship containing ABCD, where ABC is the newly constructed path and CD is the retained original connection.

[0138] Here is a specific example:

[0139] In a case study on optimizing urban-rural fringe planning, based on a reorganized spatial proximity network with weights 3, 71 nodes to be planned were first selected from 168 network nodes that were not marked as overdeveloped or ecologically protected. The conflict ratios for these nodes were all between 0.6 and 1.3. Based on the spatial distribution of the planned nodes, five candidate connecting paths were initially generated, all with a length of less than 800 meters. The ecological compatibility of the paths was assessed using the formula: compatibility = ∑(node ​​ecological tolerance) / number of nodes, with a requirement of no less than 0.65. Path curvature was also verified to ensure that the difference in turning angle between any three consecutive nodes was less than 15 degrees. Three paths met the requirements: two arterial road extensions connecting the old and new urban areas and one ecological landscape corridor. When integrating the qualified paths with the retained original connections, the connection strength adjustment formula was applied: adjusted strength = original strength × 0.6 + path compatibility × 0.4, ultimately resulting in 24 newly optimized connections.

[0140] In the embodiments of the present application, the method provides a reasonable space expansion plan for urban and rural development by scientifically screening and verifying potential development paths while ensuring ecological security, thus achieving a win-win situation for construction needs and ecological protection.

[0141] To further improve the accuracy of spatial network construction in urban and rural planning, in some embodiments, step 202: generating a spatial proximity relationship network based on the dynamic weight relationship and the surveying and mapping intermediate data includes:

[0142] Step 501: Extract the spatial turning points of vegetation cover migration trajectories, the boundary intersection points of water body distribution coupling characteristics, and the topological nodes of the road network extension direction from the surveying and mapping intermediate data, all of which are used as basic network nodes, and establish a connection model between the basic network nodes.

[0143] In step 501, the basic network nodes refer to the key spatial feature points extracted from the surveying and mapping intermediate data, including turning points where vegetation cover changes significantly, intersection points of multiple water body boundaries, and feature points where road directions change. These nodes together constitute the basic skeleton of the network. The vegetation cover migration trajectory is the spatial movement path obtained by analyzing the temporal change characteristics of the vegetation cover type distribution information, reflecting the spatial evolution trend of the vegetation type. The spatial turning point of the vegetation cover migration trajectory refers to the boundary change position that occurs during the spatiotemporal evolution of vegetation types, reflecting the transition zone or ecological intersection between different vegetation communities, and is usually identified by analyzing the superimposed changes of multiple vegetation type maps. The water body distribution coupling feature is a composite feature obtained by performing spatial correlation analysis on the water body boundary coordinates, reflecting the spatial interaction relationship between water bodies and other ecological elements. The boundary intersection point of the water body distribution coupling feature refers to the key point where multiple water body boundary lines intersect or turn, representing the structural node of the water system network, reflecting the morphological change characteristics and connectivity of the water body. The topological nodes in the extension direction of the road network refer to characteristic locations where the direction of the road changes significantly, including road intersections, bifurcations or turning points, and are used to describe the spatial expansion trend of the road network. The connection model refers to the set of spatial adjacency relationships between the nodes of the basic network, which defines the potential interaction paths between nodes and is usually established based on spatial distance thresholds or functional relevance. The existence of the connection model depends on: forcing connections between nodes that are continuously distributed on the vegetation cover migration trajectory; establishing connections between nodes on both sides of the boundary of the water body distribution coupling feature only when the connection weight value exceeds the preset threshold; and establishing unidirectional connections between nodes in the extension direction of the road network according to the gradient change direction of the quantified value of human activity intensity.

[0144] In this example, we first analyze the spatiotemporal trends of vegetation cover, identifying locations where vegetation types suddenly change as turning points. We then detect intersections and turning points at water boundaries as junctions. We then extract road intersections and direction change points as topological nodes. Then, based on the principle of spatial proximity, we establish a preliminary connectivity model between nodes within a distance threshold.

[0145] Step 502: Based on the dynamic weight relationship, generate a combination of connection weight values ​​between adjacent basic network nodes.

[0146] In step 502, adjacent basic network nodes are pairs of basic network nodes that meet preset proximity requirements (e.g., a distance less than 500 meters) and have potential for interaction. The combination of connection weights quantifies the strength of interaction between adjacent nodes, reflecting the balance between human activities and ecological protection.

[0147] In an embodiment of the present application, the connection weights of each pair of adjacent nodes are calculated based on a dynamic weight relationship, taking into account the differences in human activity intensity and the degree of ecological tolerance between the nodes, to generate a weight matrix reflecting the spatial interaction characteristics. The specific process is as follows: based on the proportional relationship between the quantified human activity intensity and ecological tolerance in the dynamic weight relationship, the connection weight value between each two adjacent basic network nodes is calculated; when the quantified human activity intensity values ​​of the two adjacent basic network nodes are both higher than the median value of the region, the connection weight value is increased by a first correction coefficient; when the ecological tolerance of the two adjacent basic network nodes is both lower than the median value of the region, the connection weight value is reduced by a second correction coefficient, thereby generating a combination of connection weight values.

[0148] Step 503: Combine the connection model and the connection weight value to establish a spatial proximity relationship network.

[0149] In an embodiment of the present application, the basic node connection model is integrated with the weight matrix, the connection relationship is weighted, and finally a composite network is constructed that can simultaneously express spatial proximity and functional correlation.

[0150] Here's a specific example:

[0151] In a certain urban-rural fringe planning case, based on the surveying and mapping intermediate data and dynamic weight relationship generated by Quan2, key feature points are first extracted from the intermediate data: 8 vegetation cover migration turning points where woodland turns into grassland, 5 water boundary intersections formed by the confluence of rivers, and 7 major road intersections are identified as topological nodes. These 20 basic nodes establish an initial connection model based on the condition that the spatial distance is less than 800 meters, forming a total of 35 connection edges. Then, the connection weights between nodes are calculated based on the dynamic weight relationship, and the weight calculation formula is adopted: Weight = average human activity intensity × 0.7 + average ecological tolerance × 0.3, where the human activity intensity is calculated by the kernel density formula calculate, Taking 300 meters as the distance, the ecological tolerance was calculated as 0.6 × vegetation cover + 0.4 × 1 - distance to the water body / 1000 meters. In particular, the connection weight between the two water body intersections on the west side of the wetland and the adjacent road nodes was calculated to be 0.65, below the planning threshold of 0.7, so it was retained but marked as a connection under observation. The connection weight between the three road nodes in the new urban development zone reached 0.82, exceeding the development upper limit of 0.8, so its weight was lowered to 0.75. The final spatial proximity relationship network constructed contains 20 nodes and 32 valid connections. The average connection weight along the wetland buffer zone is controlled between 0.6 and 0.7, ensuring both ecological protection requirements and necessary transportation connectivity, providing a precise spatial relationship model for subsequent planning decisions.

[0152] In the embodiment of the present application, the method constructs a spatial network model that reflects actual planning needs by accurately identifying key spatial feature points and quantifying the interactions between nodes, providing a reliable spatial analysis basis for coordinated urban and rural development.

[0153] To further improve the comprehensive analysis capability of ecological and environmental data, in some embodiments, step 102: dynamically coupling the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data includes:

[0154] Step 601: Establishing a mapping relationship between the boundaries of various types of vegetation in the vegetation cover type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity temporal variation characteristics.

[0155] In step 601, the mapping relationship refers to the spatiotemporal correspondence between vegetation type boundaries and reflectivity change characteristics, reflecting the correlation between vegetation growth status and environmental changes.

[0156] In the embodiment of the present application, through spatial overlay analysis and time series comparison, a correspondence table between different vegetation type areas and their reflectivity change characteristics is established to identify the typical reflectivity change patterns of various types of vegetation.

[0157] Step 602: Identify the intersection area of ​​the vegetation cover type stable area and the reflectivity mutation area according to the mapping relationship.

[0158] In step 602, a stable vegetation cover type area refers to an area where the vegetation type remains unchanged for three consecutive observation periods. A reflectance mutation area refers to an area where the reflectance difference between adjacent spatial units exceeds twice the regional average fluctuation value. An intersection area refers to a spatial range where the vegetation type remains stable but the reflectance fluctuates abnormally, indicating potential ecological and environmental changes.

[0159] In the embodiment of the present application, the vegetation type distribution map and the multi-temporal reflectance change map are compared to screen out areas where the vegetation type remains unchanged but the reflectance fluctuation exceeds the normal range as key analysis areas.

[0160] Step 603: performing coupling processing on the intersection area to generate an environmental feature marker.

[0161] In step 603, the environmental signature is a classification identifier for the ecological and environmental status of the intersection area, including information on the type and degree of change. The coupling process: When a stable vegetation cover type area overlaps with a reflectivity mutation area, an environmental signature is generated containing the vegetation type's anti-interference coefficient; when a stable vegetation cover type area overlaps with a reflectivity non-mutation area, an environmental signature is generated containing the vegetation type's dominant factor.

[0162] In the embodiment of the present application, the intersection area is marked as a "potential degradation area", "human interference area" or other types according to the direction and amplitude of the reflectivity change, and is assigned a corresponding ecological sensitivity score.

[0163] Step 604: Combine the environmental feature tags with the corresponding spatial position coordinates to form multimodal environmental perception data.

[0164] In step 604, the corresponding spatial location coordinates refer to the geographic coordinates that spatially perfectly match the intersection of the stable vegetation cover type area and the reflectance mutation area. Spatial location coordinates are derived from the latitude and longitude or plane coordinate information inherent in ecological resource monitoring data and multi-band spectral remote sensing data. Multimodal environmental perception data is a structured dataset that integrates spatial location and ecological environmental characteristics.

[0165] In this embodiment, various environmental feature tags are associated with geographic coordinates to construct a comprehensive data layer that includes spatial location, vegetation type, reflectivity variation characteristics, and ecological score. Specifically, an environmental feature tag labeled "anti-interference coefficient 0.8" is associated with the coordinates (116.404°E, 39.915°N), forming a multimodal environmental perception data record that includes both spatial location and ecological characteristics.

[0166] Here's a specific example:

[0167] In a case study of urban-rural fringe planning, based on the forest resource survey data and environmental satellite multispectral images obtained by Quan1, the mapping relationship between vegetation boundaries and reflectivity changes was first established. The formula reflectivity fluctuation amplitude = Calculate the degree of change of each pixel reflectivity, where For the same period of three years The analysis revealed that an area east of the wetland had remained woodland for three consecutive years, but its reflectance fluctuated by 0.25, approximately 1.4 times the surrounding average of 0.18. This region was identified as an intersection of stable vegetation cover and abrupt changes in reflectance. This area was coupled with a score of 0.78 calculated based on the decreasing reflectance trend and distance from the road, using the formula: ecological sensitivity = 0.7 × reflectance decrease + 0.3 × road impact coefficient. This area was labeled a "high-intensity human interference area." Combining this labeling with the spatial location in the CGCS2000 coordinate system, the resulting multimodal environmental perception data revealed a 65% overlap between the interference area and the planned road. The system automatically added the area to the prohibited construction zone and relocated the original planned road 120 meters east.

[0168] In the embodiment of the present application, the method achieves accurate identification of ecological environment changes by integrating the static distribution and dynamic reflection characteristics of vegetation, providing more comprehensive environmental background information for planning decisions.

[0169] To further improve the scientific nature and operability of urban and rural planning results, in some embodiments, step 105: iteratively spatially superimposing the topological constraint information with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results includes:

[0170] Step 701: Mark potential conflict areas in the multimodal environmental perception data based on curvature change characteristics of the road network penetration path in the topology constraint information.

[0171] In step 701, the curvature variation characteristics of the road network infiltration path refer to the spatial turning degree and its variation law of the road extension direction. It is obtained by calculating the curvature value of each node of the road centerline, reflecting the way the road invades the ecological space and the potential conflict location. This characteristic is derived from the geometric analysis of the road centerline, using the curvature formula Calculate, where is the change in direction angle between adjacent road sections, =The length of a road segment. When the curvature at a certain point exceeds a set threshold (e.g., 0.1 radians / meter), it is identified as a turning point, indicating that the road there may cut into or interfere with the ecological zone. Potential conflict areas are areas where the road network expansion direction intersects with ecologically sensitive areas and poses development risks. These areas are determined by analyzing road path curvature and ecological sensitivity. When the infiltration path curvature exceeds 45 degrees, it is marked as a Level 1 conflict area; when the infiltration path curvature is between 15 and 45 degrees, it is marked as a Level 2 conflict area.

[0172] In an embodiment of the present application, sections with large curvature changes in the road infiltration path are first identified, and then spatially superimposed with the ecologically sensitive areas in the multimodal data to screen out high-risk areas where the two overlap.

[0173] Step 702: Based on the multimodal environmental perception data, perform spatial weight allocation on the potential conflict area.

[0174] In step 702, spatial weight allocation refers to the process of grading the importance of conflicting areas based on ecological sensitivity and development needs. Specifically, in the first-level conflict area, areas with a vegetation anti-interference coefficient lower than 0.5 or a water body impedance intensity higher than 0.7 are assigned the highest weight; in the second-level conflict area, areas with a vegetation anti-interference coefficient between 0.5 and 0.8 and a water body impedance intensity between 0.3 and 0.7 are assigned a medium weight.

[0175] In an embodiment of the present application, the coordination priority of each conflicting area is calculated by combining the ecological score in the environmental perception data and the development intensity in the topological constraint. The higher the priority, the more important it needs to be handled.

[0176] Step 703: Iteratively superimpose the spatial weight allocation results until a preset stopping condition is met.

[0177] In step 703, iterative overlay processing refers to the process of gradually optimizing the conflict area solution through multiple spatial analyses. Perform iterative overlay operations: First overlay: spatially intersect the highest weight conflict area with the area with a terrain slope greater than 25 degrees in the multimodal environmental perception data; Second overlay: spatially union the medium weight conflict area with the ecological dominant factor area within the 500-meter buffer zone of the first overlay result; Final overlay: merge the first two overlay results and eliminate isolated areas that deviate from the road network extension direction by more than 30 degrees. The preset conditions for stopping iteration include: 1) reaching the maximum number of 5 iterations; 2) the rate of change of the conflict area between two adjacent overlay results is less than 5% (calculation formula: For example, when the conflict area is 105 km² after the third overlay and 102 km² after the fourth overlay, the calculated area change rate is 2.85% (|102 - 105| / 105 = 0.0285), which is less than the 5% threshold, thus meeting the stopping condition.

[0178] In an embodiment of the present application, the first round processes the conflict area with the highest priority, adjusts the road direction or sets a protective buffer zone; the second round processes the medium priority area; and after each round of processing, the remaining conflicts are re-evaluated until the convergence conditions are met.

[0179] Step 704: spatially fuse the overlay result with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results.

[0180] In an embodiment of the present application, the optimized conflict resolution method is integrated with environmental perception data to generate a comprehensive planning map that includes ecological protection requirements, construction control indicators and infrastructure layout.

[0181] Here's a specific example:

[0182] The curvature characteristics of the network permeation path are calculated using the curvature calculation formula Three key turning points with curvatures exceeding 0.12 radians per meter were identified. These points overlapped with ecologically vulnerable areas marked in the multimodal data with anti-interference coefficients below 0.6, and were therefore designated as Level 1 potential conflict areas. Spatial weights were assigned according to the formula: conflict weight = 0.6 × road importance coefficient + 0.4 × ecological sensitivity, where the road importance coefficient is calculated based on traffic flow and road network connectivity, and the ecological sensitivity is derived from environmental perception data. In the first round of overlay processing, for areas with weights exceeding 0.8, the main road passing through the eastern side of the wetland was moved 150 meters south, reducing the curvature to 0.08 radians per meter. In the second round, for areas with weights between 0.6 and 0.8, a 30-meter-wide ecological buffer zone was established on the western side of the wetland. After two rounds of iteration, the weights of all conflict areas dropped below 0.6, meeting the stopping criteria. The final urban and rural planning and surveying results showed that the adjusted road network perfectly avoided the core ecological zone, and a hierarchical management and control system of a 200-meter absolute protection circle and a 300-meter restricted construction zone was formed around the wetland. The curvature of the newly built roads was strictly controlled within 0.1 radians / meter, and the ecological score of each node was maintained above 0.7, realizing the organic unity of ecological protection and urban development.

[0183] In the embodiment of the present application, the method achieves fine coordination between ecological protection and urban development through iterative optimization and spatial integration. The generated planning results have clear control boundaries and implementation requirements, which improves the feasibility and ecological benefits of the planning scheme.

[0184] Figure 2 This is a schematic diagram of a system for generating urban and rural planning and surveying results provided in an embodiment of the present application. Figure 2 As shown, the system includes:

[0185] Acquisition module 21 is used to obtain ecological resource monitoring data, multi-band spectral remote sensing data and current urban and rural road network spatial distribution data in the target area. The ecological resource monitoring data includes vegetation cover type distribution information and water body boundary coordinates, and the multi-band spectral remote sensing data includes surface reflectivity time series change characteristics.

[0186] The coupling module 22 is used to dynamically couple the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data.

[0187] The matching module 23 is used to dynamically match the multimodal environmental perception data with the road network density gradient distribution information driven by adversarial learning to generate surveying and mapping intermediate data. The road network density gradient distribution information is obtained by performing spatial kernel density analysis on the spatial distribution data of the current urban and rural road network.

[0188] The extraction module 24 is used to analyze the surveying and mapping intermediate data and the road network density gradient distribution information based on the synergy of the preset density constraint and the preset ecological carrying threshold, and extract the topological constraint information that integrates the road network infiltration path and the degree of conflict between the boundary of the ecologically sensitive area from the analysis results.

[0189] The generation module 25 is used to iteratively spatially superimpose the topological constraint information with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results.

[0190] Figure 2 The urban and rural planning surveying and mapping results generation system can execute Figure 1 The implementation principle and technical effects of the method for generating urban and rural planning and surveying results described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the system for generating urban and rural planning and surveying results in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated on here.

[0191] In one possible design, Figure 2 The urban and rural planning surveying and mapping results generation system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0192] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0193] The processing component 32 is as follows Figure 1 The embodiment provides a method for generating urban and rural planning surveying and mapping results.

[0194] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0195] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0196] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0197] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0198] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0199] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0200] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A method for generating urban and rural planning surveying and mapping results in the illustrated embodiment.

[0201] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0202] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0203] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for generating urban and rural planning surveying and mapping results, characterized in that: include: Obtain ecological resource monitoring data, multi-band spectral remote sensing data, and spatial distribution data of the existing urban and rural road network within the target area. The ecological resource monitoring data includes vegetation cover type distribution information and water body boundary coordinates, and the multi-band spectral remote sensing data includes temporal variation characteristics of surface reflectance. Dynamically coupling the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data; Dynamically matching the multimodal environmental perception data with road network density gradient distribution information driven by adversarial learning to generate surveying and mapping intermediate data, wherein the road network density gradient distribution information is obtained by performing spatial kernel density analysis on the existing urban and rural road network spatial distribution data; Based on the synergy of the preset density constraint and the preset ecological carrying capacity threshold, the surveying and mapping intermediate data and the road network density gradient distribution information are analyzed, and topological constraint information that integrates the road network infiltration path and the degree of conflict between the boundaries of ecologically sensitive areas is extracted from the analysis results; Iteratively spatially superimposing the topological constraint information with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results; The dynamically coupling the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data includes: Establishing a mapping relationship between the boundaries of various types of vegetation in the vegetation cover type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity temporal variation characteristics; According to the mapping relationship, identifying the intersection area of ​​the vegetation cover type stable area and the reflectance mutation area; performing coupling processing on the intersection area to generate an environmental feature marker; Combining the environmental feature markers with corresponding spatial position coordinates to form multimodal environmental perception data; Based on the synergy of the preset density constraint and the preset ecological carrying capacity threshold, the surveying and mapping intermediate data and the road network density gradient distribution information are analyzed, and topological constraint information that integrates the road network infiltration path and the degree of conflict between the boundaries of ecologically sensitive areas is extracted from the analysis results, including: Establishing a dynamic weight relationship between the quantified value of human activity intensity in each spatial unit in the road network density gradient distribution information and the ecological tolerance of the corresponding spatial unit in the ecological resource monitoring data; generating a spatial proximity relationship network based on the dynamic weight relationship and the surveying and mapping intermediate data; By presetting density constraints, the propagation range of the quantitative value of human activity intensity is limited. At the same time, the attenuation gradient of the ecological tolerance is limited by presetting ecological carrying thresholds, thereby forming a dynamic adjustment rule for grouping boundaries. Reorganizing the topological structure of the spatial proximity relationship network based on the dynamic adjustment rule of the group boundary; Topological constraint information is extracted from the reorganized spatial proximity relationship network.

2. The method according to claim 1, characterized in that The topological structure reorganization of the spatial proximity relationship network based on the group boundary dynamic adjustment rule includes: Identifying the conflict ratio between the quantified value of the human activity intensity of each network node in the spatial proximity relationship network and the ecological tolerance; According to the numerical range of the preset density constraint in the group boundary dynamic adjustment rule, the network node corresponding to the conflict ratio exceeding the preset first threshold is marked as an over-developed node, and the connection relationship between the over-developed node and the adjacent node is disconnected; According to the attenuation gradient of the ecological carrying threshold in the group boundary dynamic adjustment rule, the network node corresponding to the conflict ratio lower than the preset second threshold is marked as an ecological protection node, and the connection strength between the ecological protection node and the adjacent ecological protection nodes is strengthened; In the spatial proximity relationship network, a new connection relationship is established between the undisconnected nodes, wherein the undisconnected nodes include ordinary nodes and ecological protection nodes; Based on the overdevelopment nodes, the ecological protection nodes and the new connection relationships, a reorganized spatial proximity relationship network is generated.

3. The method according to claim 2, characterized in that The establishing of a new connection relationship between the undisconnected nodes in the spatial proximity relationship network includes: Determining a set of nodes to be planned in the spatial proximity relationship network that are not marked as overdeveloped nodes and not marked as ecological protection nodes; Determining candidate connection paths based on the set of nodes to be planned; Verifying the candidate connection path based on a preset continuity constraint condition; The candidate connection paths that meet the continuity constraint condition are topologically integrated with the connection relationships retained in the spatial proximity relationship network to form a new connection relationship.

4. The method according to claim 1, wherein Generating a spatial proximity relationship network based on the dynamic weight relationship and the surveying and mapping intermediate data includes: Extracting spatial turning points of vegetation cover migration trajectories, boundary intersection points of water body distribution coupling characteristics, and topological nodes of road network extension directions from the surveying and mapping intermediate data as basic network nodes, and establishing a connection model between the basic network nodes; Based on the dynamic weight relationship, generating a combination of connection weight values ​​between adjacent basic network nodes; The connection model is combined with the connection weight value to establish a spatial proximity relationship network.

5. The method according to claim 1, wherein The iterative spatial superposition of the topological constraint information and the multimodal environmental perception data to generate urban and rural planning surveying and mapping results includes: marking potential conflict areas in the multimodal environmental perception data based on curvature change characteristics of the road network penetration path in the topology constraint information; Based on the multimodal environmental perception data, assigning spatial weights to the potential conflict areas; Iteratively superimpose the spatial weight allocation results until the preset iteration stop condition is met; The overlay results are spatially fused with the multimodal environmental perception data to generate urban and rural planning and surveying results.

6. A system for generating urban and rural planning surveying and mapping results, characterized in that: include: An acquisition module is used to obtain ecological resource monitoring data, multi-band spectral remote sensing data, and spatial distribution data of the existing urban and rural road network within the target area. The ecological resource monitoring data includes vegetation cover type distribution information and water body boundary coordinates, and the multi-band spectral remote sensing data includes temporal variation characteristics of surface reflectance; A coupling module, configured to dynamically couple the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data; a matching module for dynamically matching the multimodal environmental perception data with road network density gradient distribution information driven by adversarial learning to generate surveying and mapping intermediate data, wherein the road network density gradient distribution information is obtained by performing spatial kernel density analysis on the existing urban and rural road network spatial distribution data; an extraction module for analyzing the surveying and mapping intermediate data and the road network density gradient distribution information based on the synergy of a preset density constraint and a preset ecological carrying capacity threshold, and extracting topological constraint information integrating the road network infiltration path and the degree of conflict between the boundaries of ecologically sensitive areas from the analysis results; A generation module, configured to iteratively spatially superimpose the topological constraint information with the multimodal environmental perception data to generate urban and rural planning surveying and mapping results; The dynamically coupling the vegetation cover type distribution information with the temporal variation characteristics of the surface reflectivity to generate multimodal environmental perception data includes: Establishing a mapping relationship between the boundaries of various types of vegetation in the vegetation cover type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity temporal variation characteristics; According to the mapping relationship, identifying the intersection area of ​​the vegetation cover type stable area and the reflectance mutation area; performing coupling processing on the intersection area to generate an environmental feature marker; Combining the environmental feature markers with corresponding spatial position coordinates to form multimodal environmental perception data; Based on the synergy of the preset density constraint and the preset ecological carrying capacity threshold, the surveying and mapping intermediate data and the road network density gradient distribution information are analyzed, and topological constraint information that integrates the road network infiltration path and the degree of conflict between the boundaries of ecologically sensitive areas is extracted from the analysis results, including: Establishing a dynamic weight relationship between the quantified value of human activity intensity in each spatial unit in the road network density gradient distribution information and the ecological tolerance of the corresponding spatial unit in the ecological resource monitoring data; generating a spatial proximity relationship network based on the dynamic weight relationship and the surveying and mapping intermediate data; By presetting density constraints, the propagation range of the quantitative value of human activity intensity is limited. At the same time, the attenuation gradient of the ecological tolerance is limited by presetting ecological carrying thresholds, thereby forming a dynamic adjustment rule for grouping boundaries. Reorganizing the topological structure of the spatial proximity relationship network based on the dynamic adjustment rule of the group boundary; Topological constraint information is extracted from the reorganized spatial proximity relationship network.

7. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for generating urban and rural planning surveying and mapping results as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for generating urban and rural planning surveying and mapping results as described in any one of claims 1 to 5 is implemented.

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