Urban and rural planning surveying and mapping result generation method and system

Abstract

The invention provides an urban and rural planning surveying and mapping result generation method and system, and the method comprises the steps: obtaining ecological resource monitoring data, multiband spectrum remote sensing data and current urban and rural road network data of a target region, the spectral data comprises surface reflectance time sequence characteristics. Multi-modal environment sensing data is generated through dynamic coupling processing of vegetation distribution and reflectivity change, and then the multi-modal environment sensing data and road network density gradient information are subjected to adversarial learning matching to obtain surveying and mapping intermediate data. And based on a preset density constraint and an ecological bearing threshold value, cooperatively analyzing the intermediate data, and extracting topological constraint information fusing the road permeation path and the ecological boundary conflict. And generating an urban and rural planning surveying and mapping result through iterative spatial superposition processing. The collaborative optimization effect of ecological protection and construction development in urban and rural planning is improved.

Description

Technical Field

[0001] This application relates to the technical field of urban and rural planning surveying and mapping, and particularly relates to a method and system for generating urban and rural planning surveying and mapping 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. The existing technology urgently needs an intelligent surveying and mapping method that can accurately identify potential conflicts between ecological sensitive areas and construction and development, so as to achieve scientific decision-making for planning schemes.

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

[0004] This solution inadequately depicts the dynamic interaction relationship between ecological elements and construction activities. Relying solely on static thresholds is difficult to reflect the complex conflict situations in actual planning, resulting in deviations between the generated evaluation results and the actual field situations, and affecting the implementation effect of the planning scheme. Summary of the Invention

[0005] This application provides a method and system for generating urban and rural planning surveying and mapping results to solve the problem of poor collaborative optimization effect between ecological protection and construction and development in urban and rural planning in the existing technology.

[0006] In a first aspect, this application provides a method for generating urban and rural planning surveying and mapping results, including: Obtain ecological resource monitoring data, multi-band spectral remote sensing data, and the spatial distribution data of the current 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 the temporal variation characteristics of surface reflectance; Dynamically couple the vegetation cover type distribution information and the temporal variation characteristics of surface reflectance to generate multi-modal environmental perception data; Perform dynamic matching driven by adversarial learning on the multi-modal environmental perception data and the road network density gradient distribution information. 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; Based on the synergistic effect of a preset density constraint and a preset ecological carrying capacity threshold, analyze the surveying and mapping intermediate data and the road network density gradient distribution information, and extract topological constraint information that combines the conflict degree between the road network penetration path and the boundary of the ecological sensitive area from the analysis results; Iteratively spatially superimpose the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning surveying and mapping results.

[0007] Optionally, based on the synergistic effect of the preset density constraint and the preset ecological carrying capacity threshold, analyze the surveying and mapping intermediate data and the road network density gradient distribution information, and extract topological constraint information that integrates the conflict degree between the road network penetration path and the ecological sensitive area boundary from the analysis results, including: Establish a dynamic weight relationship between the human activity intensity quantization value 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; Generate a spatial proximity relationship network according to the dynamic weight relationship and the surveying and mapping intermediate data; Through the preset density constraint, limit the propagation range of the human activity intensity quantization value, and at the same time, through the preset ecological carrying capacity threshold, limit the attenuation gradient of the ecological tolerance to form a dynamic adjustment rule for the grouping boundary; Based on the dynamic adjustment rule for the grouping boundary, reorganize the topological structure of the spatial proximity relationship network; Extract topological constraint information from the reorganized spatial proximity relationship network.

[0008] Optionally, the reorganizing the topological structure of the spatial proximity relationship network based on the dynamic adjustment rule for the grouping boundary includes: Identify the conflict ratio between the human activity intensity quantization value and the ecological tolerance of each network node in the spatial proximity relationship network; According to the numerical range of the preset density constraint in the dynamic adjustment rule for the grouping boundary, mark the network nodes corresponding to the conflict ratio exceeding the preset first threshold as over-developed nodes, and disconnect the connection relationship between the over-developed nodes and adjacent nodes; According to the attenuation gradient of the ecological carrying capacity threshold in the dynamic adjustment rule for the grouping boundary, mark the network nodes corresponding to the conflict ratio lower than the preset second threshold as ecological protection nodes, and enhance the connection strength between the ecological protection nodes and adjacent ecological protection nodes; In the spatial proximity relationship network, establish new connection relationships between the nodes that are not disconnected, and the nodes that are not disconnected include ordinary nodes and ecological protection nodes; Generate a reorganized spatial proximity relationship network based on the over-developed nodes, the ecological protection nodes and the new connection relationships.

[0009] Optionally, the establishing new connection relationships between the nodes that are not disconnected in the spatial proximity relationship network includes: Determine a set of nodes to be planned in the spatial proximity relationship network that are not marked as over-developed nodes and not marked as ecological protection nodes; Determine candidate connection paths according to the set of nodes to be planned; Verify the candidate connection paths based on preset continuity constraint conditions; Topologically integrate the candidate connection paths that meet the continuity constraint conditions with the remaining connection relationships in the spatial proximity relationship network to form new connection relationships.

[0010] Optionally, the generating the spatial proximity relationship network according to the dynamic weight relationship and the mapping intermediate data includes: Extract the spatial turning points of the vegetation cover migration trajectory, the boundary intersection points of the water body distribution coupling characteristics, and the topological nodes of the road network extension direction from the mapping intermediate data as basic network nodes, and establish a connection model between the basic network nodes; Generate a combination of connection weight values between adjacent basic network nodes based on the dynamic weight relationship; Combine the connection model and the combination of connection weight values to establish a spatial proximity relationship network.

[0011] Optionally, the dynamically coupling the vegetation cover type distribution information and the temporal variation characteristics of the surface reflectivity to generate multi-modal environmental perception data includes: Establish a mapping relationship between the boundaries of various types of vegetation in the vegetation cover type distribution information and the amplitude of reflectivity fluctuations in the temporal variation characteristics of the surface reflectivity; Identify the intersection area of the stable area of the vegetation cover type and the area of reflectivity mutation according to the mapping relationship; Perform coupling processing on the intersection area to generate environmental feature markers; Combine the environmental feature markers with the corresponding spatial position coordinates to form multi-modal environmental perception data.

[0012] Optionally, the iteratively spatially superimposing the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning mapping results includes: Mark potential conflict areas in the multi-modal environmental perception data according to the curvature change characteristics of the road network penetration path in the topological constraint information; Perform spatial weight assignment on the potential conflict areas based on the multi-modal environmental perception data; Perform iterative superposition processing on the spatial weight assignment results until the preset iteration stop condition is met; Spatially fuse the superposition result with the multi-modal environmental perception data to generate urban and rural planning mapping results.

[0013] In a second aspect, the present application provides an urban and rural planning surveying and mapping result generation system, including: An acquisition module, configured to acquire ecological resource monitoring data, multi-band spectral remote sensing data, and current urban and rural road network spatial distribution data within a target area, where the ecological resource monitoring data includes vegetation coverage 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 coverage type distribution information and the temporal variation characteristics of the surface reflectance to generate multi-modal environmental perception data; A matching module, configured to perform dynamically matching driven by adversarial learning on the multi-modal environmental perception data and the road network density gradient distribution information to generate surveying and mapping intermediate data, where the road network density gradient distribution information is obtained by performing spatial kernel density analysis on the current urban and rural road network spatial distribution data; An extraction module, configured to analyze 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 extract topological constraint information that integrates the conflict degree between the road network penetration path and the ecological sensitive area boundary from the analysis result; A generation module, configured to perform iterative spatial superposition on the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning surveying and mapping results.

[0014] In a third aspect, the present application provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the method for generating an urban and rural planning surveying and mapping result according to any one of the first aspect.

[0015] In a fourth aspect, the present application provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method for generating an urban and rural planning surveying and mapping result according to any one of the first aspect is implemented.

[0016] In this application, a method for generating urban and rural planning surveying and mapping results is provided. The method 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 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 and the temporal variation characteristics of surface reflectance to generate multi-modal environmental perception data; performing dynamically matching driven by adversarial learning on the multi-modal environmental perception data and the road network density gradient distribution information to generate intermediate surveying and mapping data. The road network density gradient distribution information is obtained by performing spatial kernel density analysis on the current urban and rural road network spatial distribution data; based on the synergistic effect of a preset density constraint and a preset ecological carrying capacity threshold, analyzing the intermediate surveying and mapping data and the road network density gradient distribution information, and extracting topological constraint information that integrates the conflict degree between the penetration path of the road network and the boundary of the ecological sensitive area from the analysis results; performing iterative spatial superposition on the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning surveying and mapping results.

[0017] The technical solution provided by this application has the following beneficial effects: By integrating ecological resources, spectral remote sensing, and road network data, this application provides comprehensive and multi-dimensional basic data support for subsequent analysis, ensuring the data integrity of planning decisions. It realizes in-depth correlation analysis of vegetation cover characteristics and surface reflectance changes, enhancing the ability to capture the dynamic evolution law of the ecological environment. It effectively integrates ecological environment characteristics and road network density distribution to generate intermediate data that can reflect both the natural background and the impact of human activities. By double-threshold constraints, it accurately identifies the conflict areas between construction and development and ecological protection, providing accurate conflict positioning for the planning scheme. Finally, it generates a planning result that comprehensively considers ecological protection and construction needs, realizing the scientific optimization of the planning scheme.

[0018] Furthermore, this application also establishes a dynamic weight relationship between the intensity of human activities and the ecological tolerance. After constructing a spatial proximity network, it applies double-constraint rules to reorganize the network topology, and finally extracts topological constraint information that reflects the actual planning conflicts.

[0019] Moreover, this solution realizes the dynamic balance evaluation of the impact of human activities and the ecological carrying capacity. The generated topological constraint information can accurately depict the interaction relationship between construction and development and ecological protection, providing a quantitative basis for planning decisions.

[0020] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of a method for generating urban and rural planning surveying and mapping results provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of a system for generating urban and rural planning surveying and mapping results provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. Detailed implementation manners

[0023] To enable those skilled in the art to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.

[0024] In some processes described in the specification, claims and the above accompanying drawings of the present application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. 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 such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0025] In the field of urban and rural planning surveying and mapping, the existing technical solutions based on remote sensing images and geographic information systems have obvious limitations: the way of using static thresholds to divide ecological protection and construction areas makes it difficult to accurately depict the dynamic interaction process between human activities and the ecological environment. Especially when facing the complex conflict between road network expansion and ecological sensitive area protection, this rigid division method cannot reflect the gradual transition characteristics in space, resulting in problems such as overly rigid ecological protection or out-of-control construction and development in the generated planning schemes during actual implementation, which seriously restricts the coordinated development of urban and rural areas.

[0026] To address this technical bottleneck, this application proposes a method for generating urban and rural planning surveying and mapping results. By dynamically coupling and analyzing ecological resource data, spectral remote sensing features, and road network density, an environmental perception network with spatial semantics is constructed. This method innovatively introduces a dynamic weight mechanism for human activity intensity and ecological tolerance, uses adversarial learning to achieve an adaptive balance between construction development pressure and ecological carrying capacity, and generates planning results that integrate conflict gradients through iterative spatial overlay. Compared with the static threshold method of the existing technology, this solution can accurately identify the transition areas of ecological-construction interaction, provide elastic space for reasonable development while maintaining the integrity of ecological sensitive areas, fundamentally solve the problem of insufficient characterization of complex spatial conflicts by traditional methods, and improve the scientificity and feasibility of the planning scheme.

[0027] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0028] Figure 1 The flowchart of a method for generating urban and rural planning surveying and mapping results provided by an embodiment of the present application is as Figure 1 shown, and this method includes: Step 101: Obtain ecological resource monitoring data, multi-band spectral remote sensing data, and the spatial distribution data of the current 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 the temporal variation characteristics of surface reflectance.

[0029] In step 101, the target area refers to the specific geographical range to be surveyed and mapped for urban and rural planning. The target area includes the spatial ranges of the urban-rural transition zone, ecological sensitive areas, and planned construction land. The ecological resource monitoring data includes geographical information data on the spatial distribution of vegetation types and the location of water body boundaries. The multi-band spectral remote sensing data represents remote sensing image data recording the change of surface reflectance over time. The spatial distribution data of the road network represents urban and rural road vector line data. The vegetation cover type distribution information refers to the spatial distribution data of different vegetation species obtained through remote sensing interpretation or field investigation, including attributes such as vegetation type codes and coverage density, and is used to analyze the regional ecological background conditions. The water body boundary coordinates represent a set of continuous geographical coordinate points describing the spatial outline of water bodies such as rivers and lakes, and are used to delimit the ecological protection range and analyze the ecological impact of water bodies. The temporal variation characteristics of surface reflectance represent dynamic indicators reflecting the change of the surface's ability to reflect electromagnetic waves at different times, calculated from multi-temporal remote sensing images, and are used to monitor the vegetation growth status and environmental changes.

[0030] In the embodiments 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 is subjected to coordinate system conversion and spatial resolution unification processing to form a standardized input data set.

[0031] For example, taking a certain urban-rural fringe area as an example, the forest resource survey data of the forestry department is obtained as vegetation data, the river and lake demarcation results of the water conservancy department are used as water body data, the multi-spectral images of environmental satellites are used as reflectance data, and the road network data of the natural resources bureau. All data is uniformly converted to the CGCS2000 coordinate system.

[0032] Step 102: Dynamically couple the vegetation cover type distribution information and the temporal and spatial variation characteristics of the surface reflectance to generate multi-modal environmental perception data.

[0033] In step 102, dynamic coupling means establishing a spatio-temporal correlation model between the vegetation type and the reflectance change. Multi-modal environmental perception data represents a spatial data set that integrates the vegetation stability and the abnormal characteristics of the reflectance.

[0034] In the embodiments of the present application, a spatio-temporal matching algorithm is used to superimpose and analyze the vegetation type distribution map and the reflectance change sequence, identify abnormal areas where the vegetation type is stable but the reflectance 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.

[0035] For example, it is found that the forest land type in a certain area has not changed for three consecutive quarters, but the reflectance fluctuation exceeds the average level, which is determined as an ecologically fragile area affected by potential development, and a lower anti-interference coefficient is assigned and marked.

[0036] Step 103: Perform dynamic matching of the multi-modal environmental perception data and the road network density gradient distribution information driven by adversarial learning to generate 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.

[0037] In step 103, the road network density gradient represents a spatial distribution field reflecting the intensity of human activities. Adversarial learning represents a generation-discrimination mechanism that balances ecological protection and construction needs. Mapping intermediate data represents a transitional data product generated through adversarial learning, which simultaneously contains the spatial distribution information of the ecological environment suitability evaluation and the road network optimization suggestions, providing a basis for subsequent conflict analysis.

[0038] In the embodiments 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 mode, a discriminator is used to evaluate the compatibility between the development plan and the ecological environment, and intermediate data that takes into account the needs of both parties is generated through multiple adversarial trainings.

[0039] For example, when generating a new urban planning scheme, the confrontation model automatically adjusts the road network density, avoids ecological fragile areas with anti-interference coefficients lower than the threshold, and forms an optimized layout.

[0040] Step 104: Based on the synergistic effect of the preset density constraint and the preset ecological carrying capacity threshold, analyze the surveyed intermediate data and the road network density gradient distribution information, and extract topological constraint information that integrates the conflict degree between the road network penetration path and the boundary of the ecological sensitive area from the analysis results.

[0041] In step 104, the density constraint represents the spatial threshold for controlling the expansion intensity of the road network. The ecological carrying capacity threshold represents the minimum tolerance for maintaining the stability of the ecosystem. The road network penetration path represents the potential expansion direction identified by analyzing the spatial change trend of the road density gradient, reflecting the erosion risk of human activities on the ecological area, and is derived from the spatial diffusion simulation of kernel density analysis. The boundary of the ecological sensitive area represents the boundary of the core protection area delimited based on ecological resource data and water body data, and is determined through the analysis of vegetation cover continuity and water body connectivity. Conflict degree: An index quantifying 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 the structured data describing the spatial conflict characteristics between the road and the ecological sensitive area, including elements such as conflict location, type, and intensity.

[0042] In the embodiment of the present application, a dynamic weight matrix of road density and ecological tolerance is established, and a spatial clustering algorithm is applied to identify the conflict area and extract the topological conflict characteristics between the road penetration path and the ecological boundary.

[0043] For example, if the road density in a certain area exceeds the threshold but is near the ecological red line, the system marks it as a highly conflict area and generates a detour suggestion path.

[0044] Step 105: Iteratively spatially superimpose the topological constraint information and the multi-modal environmental perception data to generate the urban-rural planning surveying and mapping results.

[0045] In step 105, iterative superimposition represents the spatial coordination process of gradually optimizing the conflict area. The urban-rural planning surveying and mapping results represent the finally generated planning guidance drawings, including elements such as the priority zoning of ecological restoration, the grading of construction development suitability, the optimization suggestions for infrastructure layout, and the ecological-construction buffer zone scheme.

[0046] In the embodiment of the present application, the topological constraint information and the multi-modal data are subjected to multiple rounds of spatial operations. The first round processes the severely conflict areas, the second round optimizes the transition areas, and finally generates the planning results including the priority of ecological restoration and the construction suitability.

[0047] For example, in the first round, a no-construction area is demarcated around a certain wetland. In the second round, a low-density development zone is set up in the buffer zone, and finally a hierarchical control plan is formed.

[0048] Through multi-source data fusion and a dynamic balance mechanism, this method realizes the precise spatial coordination between ecological protection and construction development. The generated planning results can not only effectively protect ecological sensitive areas but also provide a scientific basis for reasonable development, improving the implementation effect and ecological sustainability of urban and rural planning.

[0049] To further improve the coordination between ecological protection and development and construction in urban and rural planning, in some embodiments, step 104: Based on the synergistic effect of a preset density constraint and a preset ecological carrying capacity threshold, analyze the surveyed intermediate data and the road network density gradient distribution information, and extract topological constraint information that integrates the conflict degree between the road network penetration path and the ecological sensitive area boundary from the analysis results, including: Step 201: Establish a dynamic weight relationship between the human activity intensity quantification value 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.

[0050] In step 201, the human activity intensity quantification value is obtained from the numerical result calculated by performing spatial kernel density analysis on the road network density gradient distribution information, reflecting the density of the road network in each spatial unit. The ecological tolerance is a quantitative index calculated by performing spatial overlay analysis on the vegetation coverage type distribution information and the water body boundary coordinates in the ecological resource monitoring data and combining the protection level coefficients of different ecological elements. The dynamic weight relationship refers to the relationship of dynamically weighted calculation of the road network density gradient value (human activity intensity quantification value) and the ecological tolerance according to a preset ratio. Exemplarily, for a certain spatial unit, the road density is 0.6 / km² (human activity intensity quantification value), and the ecological tolerance is 0.4. Set the dynamic weight = road density × 0.7 + ecological tolerance × 0.3, and the comprehensive weight value of this unit is 0.54.

[0051] In the embodiments of the present application, first, the road density data is normalized to obtain the human activity intensity quantification value. At the same time, the ecological tolerance is calculated according to ecological factors such as vegetation coverage and water body distance, and then the weighted ratio of the two is set according to the planning focus to form a spatially differentiated weight distribution map.

[0052] Step 202: Generate a spatial proximity relationship network according to the dynamic weight relationship and the surveyed intermediate data.

[0053] In step 202, the spatial proximity relationship network is a graph structure composed of grid units as nodes and the spatial adjacency relationship between units as edges. The weight of the edge reflects the interaction intensity between adjacent units.

[0054] In the embodiment of the present application, based on the dynamic weight relationship generated in the previous step and combined with the spatial association characteristics in the mapping intermediate data, the connection relationship between network nodes is constructed. Specifically, units that are spatially adjacent and have a weight difference within a set range are connected, and the connection strength is calculated according to the ecological-development coordination degree between the two.

[0055] Step 203: Through a preset density constraint, limit the propagation range of the human activity intensity quantization value, and at the same time, through a preset ecological carrying capacity threshold, limit the attenuation gradient of the ecological tolerance, to form a dynamic adjustment rule for the grouping boundary.

[0056] In step 203, the propagation range of the human activity intensity quantization value refers to the spatial radiation boundary of the impact of the road network on human activities. The attenuation degree of the road impact with distance is calculated through kernel density analysis. When the density value drops to a certain proportion of the peak value, the propagation boundary is determined, reflecting the spatial diffusion limit of the development pressure. This range is generated through a spatial interpolation algorithm based on elements such as road grade and traffic flow. The attenuation gradient of the ecological tolerance characterizes the change rate at which the ecological protection effect decreases with the increase in distance from the core area, and is determined by analyzing the spatial distribution laws of ecological elements such as vegetation continuity and water body connectivity. Specifically, according to the ecological sensitivity evaluation results, tolerance decay curves for the core area, buffer area, and transition area are set to reflect the spatial variation characteristics of the self-regulation ability of the ecosystem. The dynamic adjustment rule for the grouping boundary includes density constraint conditions and ecological attenuation conditions. The former controls the radiation range of the road impact, and the latter limits the diffusion gradient of the ecological effect.

[0057] In the embodiment of the present application, by setting the maximum distance threshold for road density propagation and the minimum maintenance value of ecological tolerance, the basic criterion for spatial grouping is formed. When the road density of a certain unit exceeds the threshold, its influence range is restricted; when the ecological tolerance is lower than the maintenance value, its protection range decays outward.

[0058] Step 204: Based on the dynamic adjustment rule for the grouping boundary, perform topological structure reorganization on the spatial proximity relationship network.

[0059] In step 204, topological structure reorganization refers to the process of optimizing and reconstructing the network connection relationship according to the adjustment rule.

[0060] In the embodiment of the present application, first, the direct connections between high-development-intensity regions are disconnected to block overdevelopment, then the connections between high-ecological-value regions are enhanced to form a protection network, and finally, the connection weights in the transition region are adjusted to conform to the gradient change law.

[0061] Step 205: Extract topological constraint information from the reorganized spatial proximity relationship network.

[0062] In step 205, the reorganized spatial proximity relationship network is the analysis result.

[0063] In the embodiments of the present application, the strengthened ecological corridor connections in the network are extracted as the protection red line, and the weakened development radiation paths are used as restricted construction areas to form spatial constraint conditions for guiding the planning.

[0064] The following is a specific example: During the implementation process of the planning of a certain urban-rural fringe area, based on the basic data obtained and processed in accordance with the right 1, first, the quantification value of human activity intensity is calculated for the 500-meter grid unit, and the kernel density formula is used , where is taken as 300 meters, and 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 from water body / 1000 meters). A dynamic weight relationship is established between the two types of data with a weight of 7:3 to generate an initial spatial proximity relationship network, and a connection is established if the distance between adjacent grids is less than 800 meters. By setting the road density propagation threshold of 0.35 / km², the development influence range is limited not to exceed the 800-meter buffer zone of the core ecological area. At the same time, the ecological tolerance attenuation gradient is set to decrease by 5% per 100 meters to form a hierarchical protection rule. When reorganizing the network, the connections of 26 units with a density exceeding 0.4 / km² and within 300 meters of the ecological red line are disconnected, and the connection strength between 58 units with a tolerance higher than 0.7 is increased by 1.5 times. The finally extracted topological constraint information shows that in the new town expansion area in the northeast, 3 planned road alignments need to be adjusted to avoid the area east of the wetland marked as highly conflicting, and a low-density construction area is planned within the 500 - 800-meter buffer zone of the wetland to form an optimized plan with a decreasing gradient of development intensity. The curvature of the new road path is controlled within 0.1 radian / meter, and the conflict ratio of each node is maintained within a reasonable range of 0.6 - 0.9, which not only ensures the integrity of the wetland ecological function but also meets the necessary infrastructure construction needs.

[0065] In the embodiments of the present application, through quantitative analysis and dynamic adjustment, the method realizes the precise spatial coordination between ecological protection and construction development. The generated constraint conditions not only ensure the integrity of the ecosystem but also provide elastic space for reasonable development, improving the scientificity and feasibility of the planning scheme.

[0066] To further improve the ecological adaptability of the spatial network in urban and rural planning, in some embodiments, step 204: Based on the dynamic adjustment rule of the grouping boundary, reorganizing the topological structure of the spatial proximity relationship network includes: Step 301: Identify the conflict ratio between the quantification value of human activity intensity and the ecological tolerance of each network node in the spatial proximity relationship network.

[0067] In step 301, the network nodes are composed of the intersection points of the vegetation migration trajectory, the coupling characteristics of water body distribution and the extension direction of the road network in the mapping intermediate data. The conflict ratio is the ratio of the quantified value of the human activity intensity of the network node to the ecological tolerance, reflecting the degree of contradiction between development and protection.

[0068] In the embodiment of the present application, first, the ratio of the human activity intensity to the ecological tolerance of each node is calculated to obtain a quantified conflict index for subsequent node classification.

[0069] Step 302: According to the numerical range of the preset density constraint in the group boundary dynamic adjustment rule, mark the network nodes corresponding to the conflict ratio exceeding the preset first threshold as over-developed nodes, and disconnect the connection relationship between the over-developed nodes and adjacent nodes.

[0070] In step 302, the first threshold is the critical value for determining whether a network node belongs to the over-developed state. When the conflict ratio (quantified value of human activity intensity / ecological tolerance) exceeds this threshold, it is marked as an over-developed node, usually set as a value between 1.2 - 1.5, determined by analyzing the critical relationship between the development intensity and ecological damage in the regional historical planning data. When the conflict ratio (human activity intensity / ecological tolerance) exceeds this threshold, it is determined as over-developed. The specific value needs to be adjusted in combination with the regional ecological sensitivity assessment results. A lower threshold (such as 1.2) can be used in the ecologically sensitive area, and a higher threshold (such as 1.5) can be used in the general area. The over-developed node refers to the node whose conflict ratio exceeds the upper limit of the development density, representing the over-developed area. Disconnecting the connection relationship between the over-developed nodes means disconnecting the connection relationship between the nodes marked as over-developed and all other adjacent nodes (including other over-developed nodes and non-over-developed nodes).

[0071] In the embodiment of the present application, according to the preset development density constraint threshold, the nodes with serious conflicts are screened out, and their connections with surrounding nodes are cut off to limit the spread of development.

[0072] Step 303: According to the attenuation gradient of the ecological carrying capacity threshold in the group boundary dynamic adjustment rule, mark the network nodes corresponding to the conflict ratio lower than the preset second threshold as ecological protection nodes, and enhance the connection strength between the ecological protection nodes and adjacent ecological protection nodes.

[0073] In step 303, the second threshold refers to the critical value used to determine whether a network node belongs to the ecological protection state. When the conflict ratio is lower than this threshold, it is marked as an ecological protection node. Generally, it is set to 0.5 - 0.7, determined with reference to the control requirements of the core protection area in the ecological protection red line standard. When the conflict ratio is lower than this value, it is determined as an ecological node that needs to be key protected. For the first-level protection area, 0.5 can be adopted; for the second-level protection area, 0.6 can be adopted; and for the regional ecological corridor, 0.7 can be adopted. An ecological protection node refers to a node with a conflict ratio lower than the ecological protection lower limit, representing the core ecological protection area. The technical support for enhancing the connection strength is achieved by multiplying the connection weight value between ecological protection nodes by a preset amplification factor (such as 1.5 times). This coefficient is derived from the protection priority calculated based on the ecological carrying capacity threshold. The connection strength refers to the quantitative index of the interaction between network nodes, representing the tightness of the spatial connection. The larger the value, the more important the connection. Enhancing the connection strength between ecological protection nodes can strengthen the connectivity and stability of the ecological network.

[0074] In the embodiment of the present application, the connection between ecological sensitive nodes is strengthened to form an ecological protection network by increasing the connection weight.

[0075] Step 304: In the spatial proximity relationship network, establish new connection relationships between the nodes that have not been disconnected. The nodes that have not been disconnected include ordinary nodes and ecological protection nodes.

[0076] In step 304, the connections between the nodes that have not been disconnected include three types of connection relationships: between ordinary nodes and ordinary nodes, between ordinary nodes and ecological protection nodes, and between ecological protection nodes and ecological protection nodes. The new connection relationship refers to the optimized connection established between ordinary nodes and ecological protection nodes. An ordinary node refers to an intermediate state network node in the spatial proximity relationship network that has neither been marked as an over-developed node nor an ecological protection node.

[0077] In the embodiment of the present application, on the premise of meeting ecological constraints, re-plan the connection path of the transition area to ensure the overall connectivity of the network.

[0078] Step 305: Generate a reorganized spatial proximity relationship network based on the over-developed nodes, the ecological protection nodes, and the new connection relationships.

[0079] In the embodiment of the present application, integrate the processed various types of nodes and connection relationships to form the final spatial network structure.

[0080] The following is a specific example: In a case of the implementation of the urban-rural fringe planning, based on the spatial proximity relationship network established in the early stage, first calculate the conflict ratio of each network node, using the formula conflict ratio = quantification value of human activity intensity / ecological tolerance, where the quantification value of human activity intensity is calculated by the kernel density formula and obtained by taking 300 meters. The ecological tolerance is obtained by 0.6×vegetation coverage + 0.4×(1 - distance to water body / 1000 meters). Mark the 42 nodes with a conflict ratio exceeding 1.3 as over-developed nodes. The threshold 1.3 is determined by analyzing the critical relationship between the development intensity and ecological damage in the regional ten-year planning data. Subsequently, disconnect all the connections between these nodes and their adjacent nodes. At the same time, mark the 55 nodes with a conflict ratio lower than 0.6 as ecological protection nodes. The threshold 0.6 is set with reference to the control requirements of the secondary protection area in the ecological protection red line standard, and increase the connection strength between these nodes to 1.5 times the original value. Between the remaining ordinary nodes and ecological protection nodes, establish new connection relationships according to the conditions that the spatial distance is less than 800 meters and the difference in ecological tolerance does not exceed 0.25. A total of 28 connection paths are newly added. The final generated reorganized network shows that the 3 road paths that originally crossed the wetland buffer zone are readjusted. The curvature of the new paths is controlled within 0.08 radians / meter, and the conflict ratios of the connected nodes are all maintained between 0.65 - 0.85. Among them, the connection strength of the newly added two ecological corridors reaches 1.2, effectively connecting the scattered ecological patches

[0081] In the embodiment of the present application, this method realizes the precise regulation of the development intensity and ecological protection by dynamically adjusting the network structure, providing a scientific spatial optimization plan for urban-rural planning

[0082] In order to further improve the spatial connectivity of the transition area in urban-rural planning, in some embodiments, step 304: establishing new connection relationships between the un-disconnected nodes in the spatial proximity relationship network includes: Step 401: Determine the set of nodes to be planned in the spatial proximity relationship network that are not marked as over-developed nodes and not marked as ecological protection nodes

[0083] In step 401, the set of nodes to be planned refers to the intermediate state nodes in the network that are neither classified as over-developed nodes nor classified as ecological protection nodes, representing the buffer transition area between development and protection

[0084] In the embodiment of the present application, by traversing the network nodes, filter out the nodes whose ratio of human activity intensity to ecological tolerance is in the intermediate range. The set composed of these nodes is the area to be planned, which has development potential but requires reasonable guidance

[0085] Step 402: Determine candidate connection paths according to the set of nodes to be planned.

[0086] In step 402, the candidate connection path refers to the development connection channel that may be established between the nodes to be planned, and both spatial connectivity and ecological compatibility need to be considered. A set of candidate paths that meet the following conditions is selected: the starting point of the candidate connection path is located at the forward node in the extension direction of the road network; the end point of the candidate connection path is located at a node where the ecological tolerance attenuation gradient is less than the average value of the region; the conflict ratio of the nodes passed by the candidate connection path is between the preset first threshold and the second threshold.

[0087] 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 a basis for subsequent screening.

[0088] Step 403: verifying the candidate connection path based on the preset continuity constraint condition.

[0089] In step 403, the continuity constraints include: 1) the geometric constraint requires that the curvature of the connection path does not exceed 45 degrees; 2) the ecological constraint requires that the path must maintain at least a 200-meter buffer when crossing different ecological types; 3) the topological constraint requires that each node to be planned retain at least 2 connections. Verification steps: at least one candidate connection path is retained between adjacent nodes along the extension direction of the road network; the number of candidate connection paths between nodes crossing the ecological tolerance attenuation gradient mutation zone does not exceed the preset upper limit.

[0090] In the embodiment of the present application, each candidate path is doubly verified: checking whether the direction of the path is consistent with 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.

[0091] 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.

[0092] In step 404, topology integration refers to the process of organically integrating the qualified new path with the original network.

[0093] In the embodiments of the present application, the verified candidate paths are gradually added to the existing network according to their priority order, and the connection weights of relevant nodes are adjusted to ensure that the introduction of new paths will not damage the structural stability of the original ecological protection network. Exemplarily, in the planning of a certain urban-rural fringe area, the candidate road connection paths (such as the newly planned branch roads) that meet the continuity of road extension (at least maintaining 2 parallel connections) are integrated with the existing retained ecological corridor connection relationships (such as cross-river bridges) to form a final road planning scheme that not only ensures the connectivity of the transportation network but also maintains the ecological barrier function. The distance between the connection points of the newly added branch roads and the retained bridges does not exceed 200 meters to ensure network continuity. When the candidate connection path includes nodes A - B - C (all belonging to the nodes to be planned) and meets the continuity constraint conditions, this path is merged with the original connection retained in the network (such as node C - D, where D is an ecological protection node) to form a new target connection relationship including A - B - C - D, where A - B - C is the newly constructed path and C - D is the retained original connection.

[0094] The following is a specific example: In an optimization case of the planning of a certain urban-rural fringe area, based on the spatially adjacent relationship network that has been reorganized by weight 3, first, 71 nodes to be planned that are not marked as over-developed nodes or ecological protection nodes are screened out from 168 network nodes, and the conflict ratios of these nodes are all in the intermediate range of 0.6 - 1.3. According to the spatial distribution of the nodes to be planned, 5 candidate connection paths are initially generated, and the lengths of all paths are controlled within 800 meters. The path ecological coordination evaluation formula is used: coordination degree = ∑(node ecological tolerance) / number of nodes, and the calculation result is required to be not less than 0.65; at the same time, the path curvature change is verified to ensure that the steering angle difference of any three consecutive nodes is less than 15 degrees. After verification, 3 paths meet the requirements, including two arterial road extension lines connecting the new and old urban areas and an ecological landscape corridor. When integrating the qualified paths with the retained original connections, the connection strength adjustment formula is used: adjusted strength = original strength × 0.6 + path coordination degree × 0.4, and finally 24 new optimized connections are formed.

[0095] In the embodiments of the present application, this method provides a reasonable spatial expansion plan for urban and rural development while ensuring ecological safety by scientifically screening and verifying potential development paths, achieving a win-win situation between construction needs and ecological protection.

[0096] To further improve the accuracy of spatial network construction in urban and rural planning, in some embodiments, step 202: generating a spatially adjacent relationship network according to the dynamic weight relationship and the surveying and mapping intermediate data includes: Step 501: Extract the spatial turning points of the vegetation cover migration trajectory, the boundary intersection points of the water body distribution coupling characteristics, and the topological nodes of the road network extension direction from the surveyed intermediate data, all of which are used as basic network nodes, and establish a connection model between the basic network nodes.

[0097] In step 501, the basic network nodes refer to the key spatial feature points extracted from the surveyed intermediate data, including the turning points with obvious changes in vegetation cover, the intersection points of multiple water body boundaries, and the feature points with changes in road directions. These nodes together constitute the basic framework of the network. The vegetation cover migration trajectory is a spatial movement path obtained by analyzing the temporal change characteristics of the vegetation cover type distribution information, reflecting the spatial evolution trend of vegetation types. The spatial turning points of the vegetation cover migration trajectory refer to the boundary change positions that appear during the spatio-temporal evolution of vegetation types, reflecting the transition zone or ecological ecotone between different vegetation communities, and are usually identified by analyzing the superimposed changes of multi-period vegetation type maps. The water body distribution coupling characteristics are composite characteristics 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 points of the water body distribution coupling characteristics refer to the key points where multiple water body boundary lines intersect or turn, representing the structural nodes of the water system network and reflecting the morphological change characteristics and connectivity of water bodies. The topological nodes of the road network extension direction refer to the feature positions where the road directions change significantly, including road intersections, bifurcation points, 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 basic network nodes, defining the potential interaction paths between nodes, and is usually established based on a spatial distance threshold or functional relevance. The existence of the connection model depends on: forcibly establishing connections between nodes continuously distributed on the vegetation cover migration trajectory; establishing connections between nodes on both sides of the water body distribution coupling characteristic boundary only when the connection weight value exceeds a preset threshold; establishing one-way connections between nodes in the road network extension direction according to the gradient change direction of the quantified value of human activity intensity.

[0098] In the embodiment of the present application, first, analyze the spatio-temporal change trend of the vegetation cover, identify the positions where vegetation types mutate as turning points; detect the intersection and turning positions of the water body boundaries as intersection points; extract road intersections and direction change points as topological nodes. Then, according to the principle of spatial proximity, establish a preliminary connection model between nodes within the distance threshold range.

[0099] Step 502: Generate a combination of connection weight values between adjacent basic network nodes based on the dynamic weight relationship.

[0100] In step 502, adjacent basic network nodes refer to pairs of basic network nodes that satisfy a preset proximity condition (such as a spacing less than 500 meters) in terms of spatial position and have potential interactions. The combination of connection weight values refers to a quantitative representation of the interaction strength between adjacent nodes, reflecting the balance state between human activities and ecological protection.

[0101] In the embodiment of the present application, the connection weights of each pair of adjacent nodes are calculated according to the dynamic weight relationship, considering the difference in human activity intensity and the coordination degree of ecological tolerance between nodes, and a weight matrix reflecting spatial interaction characteristics is generated. The specific process is as follows: Based on the proportional relationship between the human activity intensity quantization value and the ecological tolerance in the dynamic weight relationship, the connection weight value between every two adjacent basic network nodes is calculated; when the human activity intensity quantization values of two adjacent basic network nodes are both higher than the median value of the region where they are located, the connection weight value is increased by a first correction coefficient, and when the ecological tolerances of two adjacent basic network nodes are both lower than the median value of the region where they are located, the connection weight value is decreased by a second correction coefficient to generate a combination of connection weight values.

[0102] Step 503: Combine the connection model with the combination of connection weight values to establish a spatial proximity relationship network.

[0103] In the embodiment of the present application, the basic node connection model and the weight matrix are fused, and the connection relationship is weighted, and finally a composite network that can simultaneously express spatial proximity and functional relevance is constructed.

[0104] The following is a specific example: In a certain urban-rural fringe planning case, based on the surveyed intermediate data and the dynamic weight relationship generated by Weight 2, key feature points are first extracted from the intermediate data: 8 vegetation cover migration turning points where forest land turns into grassland are identified, 5 water body boundary intersection points formed by river confluences, and 7 main road intersections are used as topological nodes. These 20 basic nodes establish an initial connection model according to the condition that the spatial distance is less than 800 meters, and a total of 35 connection edges are formed. Then, the connection weights between nodes are calculated according to the dynamic weight relationship, and the weight calculation formula is used: 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, Take 300 meters. The ecological tolerance is calculated as 0.6×vegetation coverage + 0.4×(1 - distance to water body / 1000 meters). In particular, the connection weight between the two water body confluence points on the west side of the wetland and the adjacent road nodes is calculated to be 0.65, which is lower than the planned threshold of 0.7. Therefore, it is retained but marked as a connection to be observed. The connection weight between the 3 road nodes in the new town development zone reaches 0.82, exceeding the development upper limit of 0.8. Therefore, its weight is adjusted down to 0.75. The finally constructed spatial proximity relationship network contains 20 nodes and 32 effective connections. The average weight of the connections along the wetland buffer zone is controlled between 0.6 and 0.7, which not only ensures the ecological protection requirements but also maintains the necessary traffic connectivity, providing an accurate spatial relationship model for subsequent planning decisions.

[0105] In the embodiment of the present application, this method constructs a spatial network model that reflects the actual planning requirements by accurately identifying key spatial feature points and quantifying the interactions between nodes, providing a reliable spatial analysis basis for the coordinated development of urban and rural areas.

[0106] To further improve the comprehensive analysis ability of ecological environment data, in some embodiments, step 102: The dynamic coupling of the vegetation cover type distribution information and the temporal change characteristics of the surface reflectance to generate multimodal environmental perception data includes: Step 601: Establish a mapping relationship between the boundaries of various vegetation types in the vegetation cover type distribution information and the amplitude of reflectance fluctuations in the temporal change characteristics of the surface reflectance.

[0107] In step 601, the mapping relationship refers to the spatio-temporal correspondence between the vegetation type boundaries and the reflectance change characteristics, reflecting the correlation between the vegetation growth state and environmental changes.

[0108] In the embodiment of the present application, through spatial overlay analysis and temporal comparison, a correspondence table between different vegetation type regions and their reflectance change characteristics is established to identify the typical reflectance change patterns of various vegetation types.

[0109] Step 602: According to the mapping relationship, identify the intersection area between the stable area of the vegetation cover type and the area of reflectance mutation.

[0110] In step 602, the stable area of the vegetation cover type refers to the area where the vegetation type has not changed within 3 consecutive observation periods. The area of reflectance mutation refers to the area where the difference in reflectance between adjacent spatial units exceeds 2 times the regional average fluctuation value. The intersection area refers to the spatial range where the vegetation type remains stable but the reflectance shows abnormal fluctuations, indicating potential ecological environment changes.

[0111] 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 has not changed but the reflectance fluctuation exceeds the normal range as key analysis areas.

[0112] Step 603: performing coupling processing on the intersection area to generate an environmental feature mark.

[0113] In step 603, the environmental characteristic mark is a classification mark of the ecological environment status of the intersection area, including the type and degree of change information. Coupling processing process: when the vegetation coverage type stable area overlaps with the reflectivity mutation area, an environmental characteristic mark containing the vegetation type anti-interference coefficient is generated; when the vegetation coverage type stable area overlaps with the reflectivity non-mutation area, an environmental characteristic mark containing the dominant factor of the vegetation type is generated.

[0114] 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.

[0115] Step 604: Combine the environmental feature markers with the corresponding spatial position coordinates to form multimodal environmental perception data.

[0116] In step 604, the corresponding spatial position coordinates refer to the geographic coordinates that completely match the intersection area of ​​the vegetation cover type stable area and the reflectance mutation area in space. The spatial position coordinates are derived from the longitude and latitude or plane coordinate information in the ecological resource monitoring data and multi-band spectral remote sensing data. Multimodal environmental perception data is a structured data set that integrates spatial position and ecological environment characteristics.

[0117] In the embodiment of the present application, various environmental feature tags are associated with geographic coordinates to construct a comprehensive data layer including spatial location, vegetation type, reflectivity change characteristics and ecological score. Specific embodiment: The environmental feature tag marked as "anti-interference coefficient 0.8" is bound to the coordinates (116.404°E, 39.915°N) to form a multimodal environmental perception data record including spatial location and ecological characteristics.

[0118] Here is a specific example: In a certain urban-rural junction planning case, based on the forest resource survey data and environmental satellite multispectral images obtained by Quan1, the mapping relationship between vegetation boundary and reflectivity change is first established. The formula reflectivity fluctuation amplitude = Calculate the reflectivity change of each pixel, where Three-year period Average value. Analysis found that a certain area on the east side of the wetland remained as a forest land type for three consecutive years, but the fluctuation range of the reflectivity reached 0.25, exceeding the surrounding average level of 0.18 by about 1.4 times. It was determined as the intersection area with stable vegetation cover but sudden change in reflectivity. Coupling processing was carried out on this area. According to the reflectivity decline trend and the distance from the road, the ecological sensitivity was calculated as 0.7×reflectivity decline + 0.3×road impact coefficient according to the formula, and the score was 0.78, marked as "high-intensity human interference area". Combining the marking result with the spatial position under the CGCS2000 coordinate system, the generated multi-modal environmental perception data showed that the coincidence degree of this interference area with the planned road reached 65%. The system automatically included this area in the prohibited construction area and shifted the original planned road eastward by 120 meters.

[0119] In the embodiment of the present application, this method realizes the accurate identification of ecological environment changes by fusing the static distribution of vegetation and the dynamic reflection characteristics, and provides more comprehensive environmental background information for planning decisions.

[0120] In order to further improve the scientificity and operability of urban and rural planning results, in some embodiments, step 105: The iterative spatial superposition of the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning surveying and mapping results includes: Step 701: Mark potential conflict areas in the multi-modal environmental perception data according to the curvature change characteristics of the road network penetration path in the topological constraint information.

[0121] In step 701, the curvature change characteristics of the road network penetration path refer to the spatial turning degree and its change law of the road extension direction, which are obtained by calculating the curvature values of each node of the road center line, and reflect the intrusion mode of the road into the ecological space and the potential conflict positions. This feature comes from the geometric analysis of the road center line and is calculated using the curvature formula where is the change amount of the direction angle of adjacent road sections, is the road section length. When the curvature of a certain point exceeds the set threshold (such as 0.1 radian / meter), it is determined as a turning point, indicating that the road may cause cutting or interference to the ecological area at this place. The potential conflict area refers to the area where the expansion direction of the road network intersects with the ecological sensitive area and there is a development risk, which is determined by analyzing the curvature of the road path and the ecological sensitivity. Among them: when the curvature of the penetration path exceeds 45 degrees, it is marked as a first-level conflict area; when the curvature of the penetration path is between 15 and 45 degrees, it is marked as a second-level conflict area.

[0122] In the embodiment of the present application, first, the sections with large curvature changes in the road penetration path are identified, and then they are spatially superimposed with the ecological sensitive areas in the multi-modal data to screen out the overlapping high-risk areas.

[0123] Step 702: Based on the multi-modal environment perception data, perform spatial weight assignment on the potential conflict areas.

[0124] In step 702, spatial weight assignment refers to the process of grading the importance of conflict areas according to ecological sensitivity and development needs. Specifically, in the first-level conflict areas, the areas where the vegetation anti-interference coefficient is lower than 0.5 or the water body impedance intensity is higher than 0.7 are assigned the highest weights; in the second-level conflict areas, the areas where the vegetation anti-interference coefficient is between 0.5 and 0.8 and the water body impedance intensity is between 0.3 and 0.7 are assigned medium weights. In the embodiment of the present application, by combining the ecological scores in the environment perception data and the development intensity in the topological constraints, the coordination priorities of each conflict area are calculated, and the higher the priority, the more key processing is required.

[0125] Step 703: Perform iterative superposition processing on the spatial weight assignment results until the preset iteration stop condition is met.

[0126] In step 703, iterative superposition processing refers to the process of gradually optimizing the conflict area solution through multiple spatial analyses. Perform iterative superposition operations: First superposition: Take the intersection of the conflict areas with the highest weights and the areas with a terrain slope greater than 25 degrees in the multi-modal environment perception data; Second superposition: Take the union of the conflict areas with medium weights and the areas of the ecological dominant factors within the 500-meter buffer zone of the first superposition result; Final superposition: Combine the results of the first two superpositions and eliminate the isolated areas that deviate from the road network extension direction by more than 30 degrees. The preset iteration stop conditions include: 1) reaching the maximum number of 5 iterations; 2) the change rate of the conflict area area between two adjacent superposition results is less than 5% (calculation formula: . Specific example: When the conflict area area after the third superposition is 105 km² and the fourth is 102 km², the calculated area change rate is 2.85% (|102 - 105| / 105 = 0.0285), which is less than the 5% threshold, that is, the stop condition is met.

[0127] In the embodiment of the present application, for the conflict areas with the highest processing priority in the first round, adjust the road alignment or set protection buffer zones; for the areas with medium priority in the second round; re-evaluate the remaining conflicts after each round of processing until the convergence condition is met.

[0128] Step 704: Perform spatial fusion on the superposition result and the multi-modal environment perception data to generate urban and rural planning surveying and mapping results.

[0129] In the embodiment of the present application, fuse the optimized conflict solution with the environment perception data to generate a comprehensive planning map including ecological protection requirements, construction control indicators, and infrastructure layout.

[0130] The following is a specific example: For the curvature characteristics of the network penetration path, the curvature calculation formula is used Three key turning points with a curvature exceeding 0.12 radians / meter are identified. These points overlap with the ecologically fragile areas marked in the multi-modal data with an anti-interference coefficient lower than 0.6 and are marked as first-level potential conflict areas. Spatial weight distribution is carried out according to the formula conflict weight = 0.6 × road importance coefficient + 0.4 × ecological sensitivity, where the road importance coefficient is calculated through traffic flow and road network connectivity, and the ecological sensitivity is obtained from environmental perception data. The first-round overlay processing targets areas with a weight exceeding 0.8, moving the main road crossing the east side of the wetland 150 meters southward to reduce the curvature to 0.08 radians / meter; the second-round processing targets areas with a weight of 0.6 - 0.8, setting up a 30-meter-wide ecological buffer zone on the west side of the wetland. After two rounds of iteration, the weights of all conflict areas are reduced to below 0.6, meeting the stop condition. The final urban and rural planning surveying and mapping results show that the adjusted road network perfectly avoids the core ecological area, forming a hierarchical control system of an absolute protection circle of 200 meters and a restricted construction area of 300 meters around the wetland. Among them, the curvature of the newly built roads is strictly controlled within 0.1 radians / meter, and the ecological scores of each node are maintained above 0.7, realizing the organic unity of ecological protection and urban development.

[0131] In the embodiment of the present application, through iterative optimization and spatial integration, the method realizes the fine coordination of ecological protection and urban development. The generated planning results have clear control boundaries and implementation requirements, improving the implementability and ecological benefits of the planning scheme.

[0132] Figure 2 It is a schematic structural diagram of a system for generating urban and rural planning surveying and mapping results provided by the embodiment of the present application, as Figure 2 shown, the system includes: An acquisition module 21, configured to acquire ecological resource monitoring data, multi-band spectral remote sensing data, and the spatial distribution data of the current 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 the temporal change characteristics of surface reflectivity.

[0133] A coupling module 22, configured to dynamically couple the vegetation cover type distribution information and the temporal change characteristics of the surface reflectivity to generate multi-modal environmental perception data.

[0134] A matching module 23, configured to perform dynamically matching driven by adversarial learning on the multi-modal environmental perception data and the road network density gradient distribution information 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.

[0135] An extraction module 24, configured to analyze the surveyed 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 threshold, and extract topological constraint information that integrates the conflict degree between the road network penetration path and the boundary of the ecological sensitive area from the analysis result.

[0136] A generation module 25, configured to perform iterative spatial superposition on the topological constraint information and the multi-modal environmental perception data to generate the urban and rural planning surveying and mapping results.

[0137] Figure 2 The described urban and rural planning surveying and mapping result generation system can execute Figure 1 The described method for generating urban and rural planning surveying and mapping results in the illustrated embodiment, the implementation principle and technical effects will not be elaborated further. For each module and unit in the above-mentioned urban and rural planning surveying and mapping result generation system, the specific manner of performing operations has been described in detail in the embodiments related to this method, and will not be elaborated here. In a possible design, Figure 2 The urban and rural planning surveying and mapping result generation system in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, the computing device may include a storage component 31 and a processing component 32; 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.

[0138] The processing component 32 above Figure 1 The described method for generating urban and rural planning surveying and mapping results in the illustrated embodiment.

[0139] Wherein, 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 by 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, and is used to execute the above method.

[0140] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage 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.

[0141] Of course, the computing device may necessarily further include other components, such as an input / output interface, a display component, a communication component, and the like.

[0142] The input / output interface provides an interface between the processing component and the peripheral interface module, and the peripheral interface module may be an output device, an input device, or the like.

[0143] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, and the like.

[0144] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0145] The embodiment of the present application further provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 method for generating urban and rural planning surveying and mapping results shown in the embodiment.

[0146] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0147] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0148] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution, in essence, or the part 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, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for generating urban and rural planning surveying and mapping results, characterized in that, Including: Obtain ecological resource monitoring data, multi-band spectral remote sensing data, and the spatial distribution data of the current 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 the temporal variation characteristics of surface reflectance; Dynamically couple the vegetation cover type distribution information and the temporal variation characteristics of surface reflectance to generate multi-modal environmental perception data; Perform dynamic matching driven by adversarial learning on the multi-modal environmental perception data and the road network density gradient distribution information to generate 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; Based on the synergistic effect of a preset density constraint and a preset ecological carrying capacity threshold, analyze the mapping intermediate data and the road network density gradient distribution information, and extract topological constraint information that integrates the conflict degree between the penetration path of the road network and the boundary of the ecological sensitive area from the analysis results; Perform iterative spatial superposition on the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning mapping results.

2. The method according to claim 1, wherein The step of, based on the synergistic effect of a preset density constraint and a preset ecological carrying capacity threshold, analyzing the mapping intermediate data and the road network density gradient distribution information, and extracting topological constraint information that integrates the conflict degree between the penetration path of the road network and the boundary of the ecological sensitive area from the analysis results includes: Establish a dynamic weight relationship between the human activity intensity quantization value 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; Generate a spatial proximity relationship network according to the dynamic weight relationship and the mapping intermediate data; Through the preset density constraint, limit the propagation range of the human activity intensity quantization value, and at the same time limit the attenuation gradient of the ecological tolerance through the preset ecological carrying capacity threshold to form a dynamic adjustment rule for the group boundary; Based on the dynamic adjustment rule for the group boundary, reorganize the topological structure of the spatial proximity relationship network; Extract topological constraint information from the reorganized spatial proximity relationship network.

3. The method according to claim 2, characterized in that, The step of, based on the dynamic adjustment rule for the group boundary, reorganizing the topological structure of the spatial proximity relationship network includes: Identify the conflict ratio between the human activity intensity quantization value and the ecological tolerance of each network node in the spatial proximity relationship network; According to the numerical range of the preset density constraint in the dynamic adjustment rule for the group boundary, mark the network nodes corresponding to the conflict ratio exceeding a preset first threshold as over-developed nodes, and disconnect the connection relationship between the over-developed nodes and adjacent nodes; According to the attenuation gradient of the ecological carrying capacity threshold in the dynamic adjustment rule for the group boundary, mark the network nodes corresponding to the conflict ratio lower than a preset second threshold as ecological protection nodes, and enhance the connection strength between the ecological protection nodes and adjacent ecological protection nodes; In the spatial proximity relationship network, new connection relationships are established between the nodes that are not disconnected, and the nodes that are not disconnected include ordinary nodes and ecological protection nodes; Based on the over-developed nodes, the ecological protection nodes, and the new connection relationships, a recombined spatial proximity relationship network is generated.

4. The method according to claim 3, characterized in that The establishing of new connection relationships between the nodes that are not disconnected 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 over-developed nodes and not marked as ecological protection nodes; Determining candidate connection paths according to the set of nodes to be planned; Verifying the candidate connection paths based on preset continuity constraint conditions; Topologically integrating the candidate connection paths that meet the continuity constraint conditions with the remaining connection relationships in the spatial proximity relationship network to form new connection relationships.

5. The method according to claim 2, wherein The generating of the spatial proximity relationship network according to the dynamic weight relationship and the mapping intermediate data includes: Extracting the spatial turning points of the vegetation cover migration trajectory, the boundary intersection points of the water body distribution coupling characteristics, and the topological nodes of the road network extension direction from the mapping intermediate data as basic network nodes, and establishing a connection model between the basic network nodes; Generating a combination of connection weight values between adjacent basic network nodes based on the dynamic weight relationship; Combining the connection model and the combination of connection weight values to establish a spatial proximity relationship network.

6. The method according to claim 1, wherein The dynamically coupling the vegetation cover type distribution information and the temporal variation characteristics of the surface reflectance to generate multi-modal 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 amplitude of reflectance fluctuations in the temporal variation characteristics of the surface reflectance; Identifying the intersection area of the stable area of the vegetation cover type and the area of reflectance mutation according to the mapping relationship; Performing coupling processing on the intersection area to generate environmental feature markers; Combining the environmental feature markers with the corresponding spatial position coordinates to form multi-modal environmental perception data.

7. The method according to claim 1, wherein The iteratively spatially superimposing the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning mapping results includes: Marking potential conflict areas in the multi-modal environmental perception data according to the curvature change characteristics of the road network penetration path in the topological constraint information; Performing spatial weight allocation on the potential conflict areas based on the multi-modal environmental perception data; Performing iterative superposition processing on the spatial weight allocation results until a preset iteration stop condition is met; Spatially fusing the superposition result with the multi-modal environmental perception data to generate urban and rural planning mapping results.

8. An urban and rural planning surveying and mapping result generation system, characterized in that, Including: An acquisition module for acquiring ecological resource monitoring data, multi-band spectral remote sensing data, and the spatial distribution data of the current urban and rural road network within a target area, where 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 the temporal variation characteristics of the surface reflectance; A coupling module, configured to dynamically couple the vegetation coverage type distribution information and the temporal variation characteristics of the surface reflectance to generate multi-modal environmental perception data; A matching module, configured to perform dynamically matching driven by adversarial learning on the multi-modal environmental perception data and the road network density gradient distribution information to generate mapping intermediate data, where the road network density gradient distribution information is obtained by performing spatial kernel density analysis on the current urban and rural road network spatial distribution data; An extraction module, configured to analyze the 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 extract topological constraint information that integrates the conflict degree between the road network penetration path and the boundary of the ecological sensitive area from the analysis results; A generation module, configured to perform iterative spatial superposition on the topological constraint information and the multi-modal environmental perception data to generate urban and rural planning mapping results.

9. 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 mapping results as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, There is a computer program stored, and when the computer program is executed by a computer, it implements a method for generating urban and rural planning mapping results as described in any one of claims 1 to 7.

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