CN120278680B - Urban and rural planning and mapping result generation method and system - Google Patents

Abstract

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

Description

Urban and rural planning and mapping result generation method and system

Technical Field

The application relates to the technical field of urban and rural planning and mapping, in particular to a method and a system for generating urban and rural planning and mapping achievements.

Background

In the urban and rural planning field, the balance relation between ecological protection and urban development needs to be comprehensively considered, and an intelligent mapping method capable of accurately identifying potential conflict between ecological sensitive areas and construction development is needed in the prior art so as to realize scientific decision of a planning scheme.

At present, an urban and rural planning auxiliary system based on a remote sensing image and geographic information system is available, the system adopts a convolutional neural network to extract earth surface coverage characteristics, combines space statistical analysis to generate a construction suitability evaluation chart, and divides ecological protection and construction development areas by setting a fixed threshold value.

The scheme is insufficient in describing the dynamic interaction relation between the ecological elements and the construction activities, and complex conflict conditions in actual planning are difficult to reflect only by means of static thresholds, so that deviation exists between a generated evaluation result and the field conditions, and the implementation effect of the planning scheme is affected.

Disclosure of Invention

The application provides a method and a system for generating urban and rural planning and mapping achievements, which are used for solving the problem of poor collaborative optimization effect of ecological protection and construction development in urban and rural planning in the prior art.

In a first aspect, the application provides a method for generating urban and rural planning and mapping achievements, which comprises the following steps:

Acquiring ecological resource monitoring data, multiband spectrum remote sensing data and current urban and rural road network space distribution data in a target area, wherein the ecological resource monitoring data comprises vegetation coverage type distribution information and water boundary coordinates, and the multiband spectrum remote sensing data comprises surface reflectivity time sequence change characteristics;

dynamically coupling the vegetation coverage type distribution information with the time sequence change characteristics of the earth surface reflectivity to generate multi-mode environment perception data;

dynamically matching the multi-mode environment sensing data with road network density gradient distribution information for performing antagonistic learning driving to generate mapping intermediate data, wherein the road network density gradient distribution information is obtained by performing space kernel density analysis on the current urban and rural road network space distribution data;

Analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of the preset density constraint and the preset ecological bearing threshold, and extracting topological constraint information fusing the road network permeation path and the ecological sensitive area boundary conflict degree from the analysis result;

And carrying out iterative spatial superposition on the topology constraint information and the multi-mode environment perception data to generate urban and rural planning and mapping achievements.

Optionally, the analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of the preset density constraint and the preset ecological bearing threshold value, and extracting topology constraint information of the degree of collision between the penetration path of the fusion road network and the boundary of the ecological sensitive area from the analysis result, includes:

Establishing a dynamic weight relation between a human activity intensity quantization value of each space unit in the road network density gradient distribution information and the ecological tolerance of the corresponding space unit in the ecological resource monitoring data;

generating a spatial proximity relation network according to the dynamic weight relation and the mapping intermediate data;

Defining the propagation range of the human activity intensity quantized value through preset density constraint, and defining the attenuation gradient of the ecological tolerance through a preset ecological bearing threshold value to form a grouping boundary dynamic adjustment rule;

Based on the dynamic adjustment rule of the grouping boundary, carrying out topological structure reorganization on the spatial adjacent relation network;

and extracting topology constraint information from the recombined spatial adjacent relation network.

Optionally, the performing topology structure reorganization on the spatial proximity relation network based on the dynamic adjustment rule of the packet boundary includes:

Identifying a conflict ratio between human activity intensity quantized values of each network node in the spatial proximity relation network and the ecological tolerance;

Marking a network node corresponding to the conflict proportion exceeding a preset first threshold as an over-development node according to the numerical range of the preset density constraint in the grouping boundary dynamic adjustment rule, and disconnecting the connection relationship between the over-development node and the adjacent node;

marking the network nodes with the conflict proportion lower than a preset second threshold value as ecological protection nodes according to the attenuation gradient of the ecological bearing threshold value in the grouping boundary dynamic adjustment rule, and enhancing the connection strength between the ecological protection nodes and the adjacent ecological protection nodes;

In the spatial proximity relation network, a new connection relation is established between nodes which are not disconnected, wherein the nodes which are not disconnected comprise common nodes and ecological protection nodes;

and generating a recombined spatial adjacent relation network based on the over-development node, the ecological protection node and the new connection relation.

Optionally, in the spatial proximity relation network, a new connection relation is established between nodes which are not disconnected, including:

determining a set of nodes to be planned in the spatial proximity relation network that are not marked as over-developed nodes and are not marked as bioprotective nodes;

Determining candidate connection paths according to the node set to be planned;

Verifying the candidate connection paths based on a preset continuity constraint condition;

and carrying out topological integration on the candidate connection paths meeting the continuity constraint condition and the connection relations reserved in the spatial adjacent relation network to form a new connection relation.

Optionally, the generating a spatial proximity relationship network according to the dynamic weight relationship and the mapping intermediate data includes:

Extracting space turning points of vegetation coverage migration tracks, boundary intersection points of water distribution coupling characteristics and topology nodes of the extending direction of a road network from the mapping intermediate data, taking the space turning points, the boundary intersection points and the topology nodes as basic network nodes, and establishing a connection model between the basic network nodes;

generating a connection weight value combination between adjacent basic network nodes based on the dynamic weight relation;

and combining the connection model with the connection weight value combination to establish a spatial proximity relation network.

Optionally, the dynamically coupling the vegetation coverage type distribution information with the surface reflectivity time sequence variation feature generates multi-mode environment perception data, including:

Establishing a mapping relation between various vegetation boundaries in the vegetation coverage type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity time sequence change characteristics;

Identifying an intersection area of the vegetation coverage type stable area and the reflectivity abrupt change area according to the mapping relation;

coupling the intersection region to generate an environmental characteristic mark;

and combining the environmental characteristic marks with the corresponding space position coordinates to form multi-mode environmental perception data.

Optionally, the performing iterative spatial superposition on the topology constraint information and the multi-mode environment awareness data to generate urban and rural planning and mapping results includes:

marking potential conflict areas in the multi-mode environment sensing data according to curvature change characteristics of the road network penetration paths in the topology constraint information;

based on the multi-modal environment awareness data, performing spatial weight distribution on the potential conflict area;

Performing iterative superposition processing on the space weight distribution result until a preset iteration stopping condition is met;

and carrying out space fusion on the superposition result and the multi-mode environment perception data to generate urban and rural planning and mapping results.

In a second aspect, the application provides an urban and rural planning and mapping result generation system, comprising:

The system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring ecological resource monitoring data, multiband spectrum remote sensing data and current urban and rural road network space distribution data in a target area, the ecological resource monitoring data comprises vegetation coverage type distribution information and water boundary coordinates, and the multiband spectrum remote sensing data comprises surface reflectivity time sequence change characteristics;

the coupling module is used for dynamically coupling the vegetation coverage type distribution information and the surface reflectivity time sequence change characteristics to generate multi-mode environment perception data;

The matching module is used for dynamically matching the multi-mode environment sensing data with road network density gradient distribution information for performing antagonistic learning driving to generate mapping intermediate data, and the road network density gradient distribution information is obtained by performing space kernel density analysis on the current urban and rural road network space distribution data;

The extraction module is used for analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of the preset density constraint and the preset ecological bearing threshold value, and extracting topological constraint information of the degree of collision between the road network penetration path and the ecological sensitive area boundary from an analysis result;

And the generation module is used for carrying out iterative spatial superposition on the topological constraint information and the multi-mode environment perception data so as to generate urban and rural planning and mapping achievements.

In a third aspect, the application provides a computing device comprising a processor and a memory, the memory having stored therein a computer program, the processor being arranged to run the computer program to perform a town and country planning and mapping outcome generation method as described in any of the first aspects.

In a fourth aspect, the present application provides a computer storage medium having stored thereon computer program instructions which when executed by a processor implement a method of urban and rural planning and mapping outcome generation according to any of the first aspects.

The application provides an urban and rural planning and mapping result generation method, which comprises the steps of obtaining ecological resource monitoring data, multiband spectrum remote sensing data and current urban and rural road network space distribution data in a target area, wherein the ecological resource monitoring data comprise vegetation coverage type distribution information and water body boundary coordinates, the multiband spectrum remote sensing data comprise earth surface reflectivity time sequence change characteristics, dynamically coupling the vegetation coverage type distribution information and the earth surface reflectivity time sequence change characteristics to generate multimode environment perception data, carrying out dynamic matching of resistance learning driving on the multimode environment perception data and road network density gradient distribution information to generate mapping intermediate data, the road network density gradient distribution information is obtained by carrying out space nuclear density analysis on the current urban and rural road network space distribution data, analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of preset density constraint and a preset ecological bearing threshold, extracting topological constraint information of fusion road network permeation path and ecological sensitive area boundary degree from analysis results, and carrying out iteration and urban planning and rural planning result perception.

The technical scheme provided by the application has the following beneficial effects:

According to the application, through integrating ecological resources, spectrum remote sensing and road network data, comprehensive and multidimensional basic data support is provided for subsequent analysis, and the data integrity of planning decisions is ensured. The depth correlation analysis of vegetation coverage characteristics and the change of the earth surface reflectivity is realized, and the capturing capability of the dynamic evolution rule of the ecological environment is enhanced. The ecological environment characteristics and the road network density distribution are effectively fused, and intermediate data capable of reflecting the natural background and the human activity influence simultaneously is generated. The conflict area of construction development and ecological protection is accurately identified through double threshold constraint, and accurate conflict positioning is provided for a planning scheme. And finally generating a planning result comprehensively considering ecological protection and construction requirements, and realizing scientific optimization of a planning scheme.

Furthermore, the application also builds a dynamic weight relation between human activity intensity and ecological tolerance, and then applies double constraint rules to reorganize network topology after building a space adjacent network, and finally extracts topology constraint information reflecting actual planning conflicts.

In addition, the scheme realizes dynamic balance evaluation of human activity influence and ecological bearing capacity, and the generated topological constraint information can accurately describe the interactive relation between construction development and ecological protection, so that a quantitative basis is provided for planning decisions.

These and other aspects of the application will be more readily apparent from the following description of the embodiments.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for generating urban and rural planning and mapping achievements, which is provided by an embodiment of the application;

fig. 2 is a schematic structural diagram of an urban and rural planning and mapping result generation system according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a computing device according to an embodiment of the present application.

Detailed Description

In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.

In some of the flows described in the specification and claims of the present application and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

The existing technical scheme based on remote sensing images and geographic information systems has obvious limitations in the urban and rural planning and mapping field, namely, the dynamic interaction process of human activities and ecological environments is difficult to accurately describe by adopting a static threshold dividing ecological protection and construction areas. Especially when facing the complex conflict of road network expansion and ecological sensitive area protection, the rigid dividing method can not reflect the gradual transition characteristics in space, so that the generated planning scheme has the problems of excessive stiffness of ecological protection or uncontrolled construction and development in actual implementation, and the coordinated development of cities and villages is severely restricted.

Aiming at the technical bottleneck, the application provides a urban and rural planning and mapping result generation method, and an environment sensing network with spatial semantics is constructed through dynamic coupling analysis of ecological resource data, spectrum remote sensing characteristics and road network density. The method creatively introduces a dynamic weight mechanism of human activity intensity and ecological tolerance, utilizes antagonism learning to realize self-adaptive balance of construction development pressure and ecological bearing capacity, and generates planning results of fusion conflict gradient through iterative spatial superposition. Compared with a static threshold method in the prior art, the scheme can accurately identify the transition region of ecological-construction interaction, provide an elastic space for reasonable development while maintaining the integrity of an ecological sensitive region, fundamentally solve the problem of insufficient description of complex space conflict by the traditional method, and promote the scientificity and the practicability of a planning scheme.

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

Fig. 1 is a flowchart of a method for generating urban and rural planning and mapping achievements, which is provided by an embodiment of the present application, as shown in fig. 1, and includes:

Step 101, acquiring ecological resource monitoring data, multiband spectrum remote sensing data and current urban and rural road network space distribution data in a target area, wherein the ecological resource monitoring data comprises vegetation coverage type distribution information and water boundary coordinates, and the multiband spectrum remote sensing data comprises surface reflectivity time sequence change characteristics.

In step 101, the target area refers to a specific geographical range to be mapped and mapped by urban and rural planning, and the target area includes a urban and rural transition zone, an ecologically sensitive area and a spatial range of a planned construction land. The ecological resource monitoring data comprises geographic information data of vegetation type spatial distribution and water boundary positions. The multiband spectrum remote sensing data represent remote sensing image data for recording the change of the earth surface reflectivity along with the time. The road network spatial distribution data represents urban and rural road vector line data. The vegetation coverage type distribution information refers to spatial distribution data of different vegetation types, which are acquired through remote sensing interpretation or field investigation, and comprises vegetation type codes, coverage density and other attributes, and is used for analyzing regional ecological background conditions. The water boundary coordinates represent a set of continuous geographic coordinate points describing the water space outlines of rivers, lakes and the like, and are used for defining an ecological protection range and analyzing the ecological influence of the water. The time sequence change characteristic of the earth surface reflectivity represents a dynamic index reflecting the change of the earth surface to the electromagnetic wave reflection capability in different periods, is obtained through multi-time phase remote sensing image calculation and is used for monitoring vegetation growth state and environment change.

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

For example, taking a certain urban and rural junction as an example, forest resource investigation data of a forestry department is obtained as vegetation data, river and lake demarcation results of a water conservancy department are taken as water body data, an environmental satellite multispectral image is taken as reflectivity data, and road network data of a natural resource bureau are uniformly converted into a CGCS2000 coordinate system.

Step 102, dynamically coupling the vegetation coverage type distribution information with the time sequence change characteristics of the earth surface reflectivity to generate multi-mode environment perception data.

In step 102, the dynamic coupling represents modeling the spatial-temporal correlation of vegetation type and reflectivity changes. The multi-modal environmental awareness data represents a spatial dataset fusing the vegetation stability and reflectivity anomaly characteristics.

In the embodiment of the application, a space-time matching algorithm is adopted to carry out superposition analysis on a vegetation type distribution map and a reflectivity change sequence, identify an abnormal region with stable vegetation types and abrupt reflectivity, calculate the vegetation anti-interference coefficient of each space unit and generate an environment perception data layer containing an ecological stability evaluation result.

For example, it is found that the woodland type of a certain area is unchanged for three consecutive quarters, but the reflectivity fluctuation exceeds the average level, and an ecologically vulnerable area which is judged to be affected by potential development is given a lower anti-interference coefficient and marked.

Step 103, dynamically matching the multi-mode environment sensing data with road network density gradient distribution information to generate mapping intermediate data, wherein the road network density gradient distribution information is obtained by carrying out space nuclear density analysis on the current urban and rural road network space distribution data.

In step 103, the road network density gradient represents a spatially distributed field reflecting the intensity of human activity. Antagonism learning represents a generation-discrimination mechanism that balances ecological protection and construction requirements. The mapping intermediate data represents transitional data products generated through antagonism learning, and simultaneously contains spatial distribution information of ecological environment suitability evaluation and road network optimization suggestions, so that a basis is provided for subsequent conflict analysis.

In the embodiment of the application, a road network is converted into a density field through nuclear density analysis, a generator simulation construction development mode is constructed, compatibility of a development scheme and an ecological environment is evaluated by a discriminator, and intermediate data which meets requirements of both parties is generated through multiple countermeasure training.

For example, when a new city planning scheme is generated, the countermeasure model automatically adjusts road network density, avoids an ecological fragile area with an anti-interference coefficient lower than a threshold value, and forms an optimized layout.

And 104, analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of the preset density constraint and the preset ecological bearing threshold, and extracting topological constraint information fusing the road network permeation path and the ecological sensitive area boundary conflict degree from the analysis result.

In step 104, the density constraint represents a spatial threshold that controls the road network expansion strength. The ecological load threshold represents the minimum tolerance for maintaining the ecosystem stable. The road network penetration path represents the potential expansion direction identified by analyzing the spatial variation trend of the road density gradient, reflects the erosion risk of human activities on the ecological area, and is derived from the spatial diffusion simulation of the nuclear density analysis. The ecology sensitive area boundary represents a core protection area boundary line defined based on ecology resource data and water body data, and is determined through vegetation coverage continuity and water body connectivity analysis. And (5) quantifying an index of the road network on the compression degree of the ecological boundary, and calculating according to the ratio of the road density to the ecological tolerance. The topological constraint information represents structured data describing spatial conflict characteristics of roads and ecology sensitive areas, and the structured data comprises conflict positions, types, intensities and other factors.

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

For example, an area road density exceeds a threshold but is located near an ecological red line, the system marks a highly conflicting area, and a detour proposed path is generated.

And 105, carrying out iterative spatial superposition on the topological constraint information and the multi-mode environment perception data to generate urban and rural planning and mapping achievements.

In step 105, the iterative superposition represents a spatial coordination process that gradually optimizes the conflict area. The urban and rural planning and mapping result represents a finally generated planning and guiding drawing piece, and comprises elements such as an ecological restoration priority partition, a construction development suitability level, an infrastructure layout optimization suggestion, an ecological-construction buffer zone scheme and the like.

In the embodiment of the application, the topology constraint information and the multi-mode data are subjected to multi-round space operation, a serious conflict area is processed in the first round, a transition area is optimized in the second round, and finally a planning result comprising ecological restoration priority and construction suitability is generated.

For example, a first round delimits a construction forbidden area around a certain wetland, and a second round sets a low-density development zone in a buffer area, so that a hierarchical management and control scheme is finally formed.

According to the method, through multi-source data fusion and a dynamic balance mechanism, the precise coordination of the space between ecological protection and construction development is realized, the generated planning result can effectively protect an ecological sensitive area, scientific basis is provided for reasonable development, and the implementation effect and ecological sustainability of urban and rural planning are improved.

In order to further improve coordination of ecological protection and development and construction in urban and rural planning, in some embodiments, step 104 includes analyzing the mapping intermediate data and the road network density gradient distribution information based on a synergistic effect of a preset density constraint and a preset ecological bearing threshold, and extracting topology constraint information of a degree of collision between a fused road network penetration path and an ecological sensitive area boundary from an analysis result, wherein the topology constraint information comprises:

step 201, establishing a dynamic weight relation between a human activity intensity quantization value of each space unit in the road network density gradient distribution information and ecological tolerance of the corresponding space unit in the ecological resource monitoring data.

In step 201, the quantized value of human activity intensity is derived from the quantized result obtained by performing spatial kernel density analysis calculation on the road network density gradient distribution information, and reflects the road network density of each spatial unit. The ecological tolerance is a quantization index obtained by carrying out space superposition analysis on vegetation coverage type distribution information and water boundary coordinates in the ecological resource monitoring data and combining protection grade coefficients of different ecological elements. The dynamic weight relation refers to a relation that a road network density gradient value (human activity intensity quantization value) and an ecological tolerance are dynamically weighted according to a preset proportion, and by way of example, the road density of a certain space unit is 0.6/km2 (human activity intensity quantization value), the ecological tolerance is 0.4, and a dynamic weight = road density x 0.7+ ecological tolerance x 0.3 is set to obtain the unit comprehensive weight value of 0.54.

In the embodiment of the application, the road density data is normalized to obtain the human activity intensity quantized value, the ecological tolerance is calculated according to ecological factors such as vegetation coverage, water body distance and the like, and then the weighted proportion of the vegetation coverage and the water body distance is set according to the planning emphasis to form a spatially differentiated weight distribution diagram.

And 202, generating a spatial proximity relation network according to the dynamic weight relation and the mapping intermediate data.

In step 202, the spatial proximity relation network is a graph structure formed by grid cells as nodes and spatial adjacency relations among the cells as edges, and the weights of the edges reflect the interaction strength among adjacent cells.

In the embodiment of the application, the dynamic weight relation generated in the previous step is taken as a basis, and the connection relation between network nodes is constructed by combining the spatial association characteristics in the mapping intermediate data. Specifically, units with adjacent spaces and weight differences within a set range are connected, and the connection strength is calculated according to the ecological-development coordination degree of the units.

Step 203, limiting the propagation range of the human activity intensity quantized value through preset density constraint, and limiting the attenuation gradient of the ecological tolerance through a preset ecological bearing threshold value to form a grouping boundary dynamic adjustment rule.

In step 203, the propagation range of the quantized human activity intensity value refers to the spatial radiation boundary of the road network on human activity, the attenuation degree of the road effect along with the distance is calculated through nuclear density analysis, and when the density value is reduced to a certain peak value, the propagation boundary is determined to reflect the spatial diffusion limit of development pressure. The range is generated by a spatial interpolation algorithm based on road class, traffic flow, etc. The attenuation gradient of the ecological tolerance characterizes the decreasing change rate of the ecological protection effect along with the increase of the distance from the core area, and is determined by analyzing the space distribution rules of ecological elements such as vegetation continuity, water connectivity and the like. And setting tolerance decreasing curves of the core area, the buffer area and the transition area according to the ecological sensitivity evaluation result, and reflecting the spatial variation characteristics of the self-regulating capacity of the ecological system. The grouping boundary dynamic adjustment rules include density constraints that control the radiation range of the road effect and ecological attenuation conditions that define the diffusion gradient of the ecological effect.

In the embodiment of the application, the basic rule of space grouping is formed by setting the maximum distance threshold value of road density propagation and the minimum maintenance value of ecological tolerance. When the density of a certain unit road exceeds a threshold value, the influence range is limited, and when the ecological tolerance is lower than a maintenance value, the protection range is attenuated outwards.

And 204, carrying out topological structure reorganization on the spatial adjacent relation network based on the dynamic regulation rule of the packet boundary.

In step 204, topology reorganization refers to a process of optimizing and reorganizing the network connection relationship according to the adjustment rule.

In the embodiment of the application, the direct connection between the areas with high development strength is firstly disconnected to obstruct excessive development, then the connection between the areas with high ecological value is enhanced to form a protection network, and finally the connection weight of the transition area is adjusted to conform to the gradient change rule.

And 205, extracting topology constraint information from the recombined spatial adjacent relation network.

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

In the embodiment of the application, the reinforced ecological corridor connection in the network is extracted as a protection red line, and the weakened development radiation path is used as a construction limiting area to form a space constraint condition for guiding planning.

The following is a specific example:

in the planning implementation process of a certain urban and rural junction, based on the basic data acquired and processed by the weight 1, firstly, calculating a human activity intensity quantization value for a 500-meter grid unit, and adopting a nuclear density formula WhereinTaking 300 meters, d is the distance from the grid center to the road. Meanwhile, the ecological tolerance of each unit is calculated according to the formula ecological tolerance = 0.6 x vegetation coverage +0.4 x (1-distance from water/1000 meters). And establishing a dynamic weight relation between the two types of data according to a weight of 7:3, and generating an initial spatial adjacent relation network, wherein the connection is established when the distance between adjacent grids is smaller than 800 meters. The development influence range is limited to be not more than 800 meters of buffer zone in the core ecological area by setting the road density propagation threshold value of 0.35/km2, and meanwhile, the ecological tolerance attenuation gradient is set to be reduced by 5% every 100 meters, so that the grading protection rule is formed. When the network is recombined, 26 units with the density exceeding 0.4/km2 and positioned in the range of 300 meters of the ecological red line are disconnected, and the connection strength between 58 units with the tolerance higher than 0.7 is enhanced by 1.5 times. And finally, the extracted topological constraint information shows that 3 planned road directions are required to be adjusted in the expansion area of the New castle in the northeast part, the east side area of the wetland marked as high conflict is avoided, and a low-density construction area is planned in a buffer zone 500-800 m away from the wetland, so that an optimization scheme with decreasing development intensity gradient is formed. Wherein the curvature of the new road path is controlled within 0.1 radian/meter, the conflict proportion of each node is maintained within a reasonable interval of 0.6-0.9, the ecological functional integrity of the wetland is ensured, and the necessary infrastructure construction requirement is met.

In the embodiment of the application, the method realizes the accurate coordination of the space of ecological protection and construction development through quantitative analysis and dynamic adjustment, and the generated constraint conditions not only ensure the integrity of an ecological system, but also provide an elastic space for reasonable development, thereby improving the scientificity and the feasibility of a planning scheme.

In order to further improve the ecological adaptability of the spatial network in urban and rural planning, in some embodiments, step 204, performing topology recombination on the spatial proximity relation network based on the dynamic adjustment rule of the packet boundary includes:

Step 301, identifying conflict proportions between human activity intensity quantized values of all network nodes in the spatial proximity relation network and the ecological tolerance.

In step 301, a network node is formed by mapping intersections of vegetation migration trajectories, water distribution coupling features and road network extension directions in intermediate data. The conflict ratio refers to the ratio of the human activity intensity quantized value of the network node to the ecological tolerance, and reflects the contradiction degree of development and protection.

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

And 302, marking the network node corresponding to the conflict proportion exceeding the preset first threshold as an over-development node according to the numerical range of the preset density constraint in the dynamic adjustment rule of the group boundary, and disconnecting the connection relationship between the over-development node and the adjacent node.

In step 302, the first threshold is a critical value for determining whether the network node belongs to an over-development state, and is marked as an over-development node when the conflict ratio (human activity intensity quantization value/ecological tolerance) exceeds the threshold, and is generally set to a value between 1.2 and 1.5, and is determined by analyzing the critical relation between the development intensity and the ecological damage in the area history planning data, and is determined as over-development when the conflict ratio (human activity intensity/ecological tolerance) exceeds the threshold. The specific value needs to be adjusted by combining the regional ecological sensitivity evaluation result, the ecological sensitive region can adopt a lower threshold (such as 1.2), and the general region can adopt a higher threshold (such as 1.5). The over development node is a node with a conflict ratio exceeding the upper limit of development density, and represents an over development area. Disconnecting the connection between the over-developed nodes refers to disconnecting the connection between the over-developed node and all other neighboring nodes (including other over-developed nodes and non-over-developed nodes) marked as over-developed nodes.

In the embodiment of the application, the nodes with serious conflicts are screened out according to the preset development density constraint threshold, and the connection between the nodes and the peripheral nodes is cut off to limit development spreading.

And 303, marking the network nodes with the conflict proportion lower than the corresponding network nodes with the preset second threshold value as ecological protection nodes according to the attenuation gradient of the ecological bearing threshold value in the dynamic adjustment rule of the group boundary, and enhancing the connection strength between the ecological protection nodes and the adjacent ecological protection nodes.

In step 303, the second threshold is a critical value for determining whether the network node belongs to the ecological protection state, and is marked as the ecological protection node when the conflict ratio is lower than the threshold, and is generally set to 0.5-0.7, and is determined by referring to the core protection area management and control requirement in the ecological protection red line standard, and is determined as the ecological node needing important protection when the conflict ratio is lower than the threshold. For the primary protection area, 0.5 can be adopted, for the secondary protection area, 0.6 can be adopted, and for the regional ecological corridor, 0.7 can be adopted. The ecological protection nodes are nodes with conflict proportion lower than the lower limit of ecological protection, and represent core ecological protection areas. The technical support for enhancing the connection strength is achieved by multiplying the connection weight value between the ecological protection nodes by a preset amplification factor (e.g. 1.5 times), which is derived from the protection priority calculated by the ecological bearing threshold. The connection strength refers to a quantitative index of interaction among network nodes, the tightness degree of the space connection is represented, and the larger the value is, the more important the connection is. The connectivity and stability of the ecological network can be enhanced by enhancing the connection strength between the ecological protection nodes.

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

And 304, establishing a new connection relation between nodes which are not disconnected in the spatial adjacent relation network, wherein the nodes which are not disconnected comprise common nodes and ecological protection nodes.

In step 304, the nodes which are not disconnected include three connection relations between the common nodes, between the common nodes and the ecological protection nodes, and between the ecological protection nodes. The new connection relationship refers to the optimized connection established between the common node and the ecological protection node. Common nodes refer to intermediate state network nodes in the spatial proximity relationship network that are not marked as either over-developed nodes or as physiological protection nodes.

In the embodiment of the application, on the premise of meeting ecological constraints, the connection path of the transition area is re-planned, so that the overall connectivity of the network is ensured.

And 305, generating a recombined spatial adjacent relation network based on the over-development node, the ecological protection node and the new connection relation.

In the embodiment of the application, the processed various nodes and connection relations are integrated to form a final spatial network structure.

The following is a specific example:

In the planning implementation case of a certain urban and rural junction, based on a spatial proximity relation network established in advance, firstly, calculating conflict proportion of each network node, and adopting a formula conflict proportion=human activity intensity quantized value/ecological tolerance, wherein the human activity intensity quantized value passes through a kernel density formula The calculation result shows that the method comprises the steps of,The ecological tolerance is obtained by taking 300 meters according to the ratio of 0.6Xvegetation coverage to 0.4X1-distance from water body/1000 meters. 42 nodes with conflict ratios exceeding 1.3 are marked as over-developed nodes, the threshold value of 1.3 is determined by analyzing the critical relationship of development intensity and ecological damage in regional ten-year planning data, and then all the nodes are disconnected from adjacent nodes. Meanwhile, 55 nodes with conflict ratio lower than 0.6 are marked as ecological protection nodes, the threshold value of 0.6 is set by referring to the control requirement of a secondary protection area in the ecological protection red line standard, and the connection strength between the nodes is improved to 1.5 times of the original value. And establishing a new connection relation between the rest common nodes and the ecological protection nodes according to the condition that the space distance is less than 800 meters and the ecological tolerance difference is not more than 0.25, and adding 28 connection paths in total. The finally generated recombined network shows that the original planning of 3 road paths crossing the wetland buffer zone is readjusted, the curvature of the new path is controlled within 0.08 radian/meter, the conflict ratio of the connected nodes is kept between 0.65 and 0.85, the connection strength of the newly added two ecological galleries reaches 1.2, and the scattered ecological patches are effectively connected in series.

In the embodiment of the application, the method realizes the accurate regulation and control of development intensity and ecological protection by dynamically adjusting the network structure, and provides a scientific space optimization scheme for urban and rural planning.

To further improve the spatial connectivity of the transition region in urban and rural planning, in some embodiments, step 304 includes establishing a new connection relationship between nodes that are not disconnected in the spatial proximity relationship network, including:

Step 401, determining a set of nodes to be planned in the spatial proximity relation network that are not marked as over-developed nodes and are not marked as ecological protection nodes.

In step 401, the node set to be planned refers to intermediate state nodes in the network that are not classified as either over-development nodes or ecological protection nodes, representing a buffer transition region between development and protection.

In the embodiment of the application, nodes with the ratio of human activity intensity to ecological tolerance in the middle interval are screened out by traversing network nodes, and the set formed by the nodes is the area to be planned, and has development potential but needs reasonable guidance.

And step 402, determining candidate connection paths according to the node set to be planned.

In step 402, the candidate connection paths refer to development contact paths possibly established between nodes to be planned, and space connectivity and ecological compatibility need to be considered at the same time. And screening out a candidate path set which meets the following conditions that the starting point of the candidate connecting path is positioned at a node on the advancing side of the extending direction of the road network, the end point of the candidate connecting path is positioned at a node with the ecological tolerance attenuation gradient smaller than the average value of the area, and the conflict proportion of the candidate connecting path nodes is between the preset first threshold value and the second threshold value.

In the embodiment of the application, all possible connection schemes including potential paths such as road extension and ecological corridor are initially generated based on the spatial distribution characteristics of the nodes to be planned, and serve as the basis of subsequent screening.

And step 403, verifying the candidate connection paths based on a preset continuity constraint condition.

In step 403, the continuity constraint condition includes 1) geometric constraint requires that the curvature of the connection path does not exceed 45 degrees, 2) ecological constraint requires that the path span different ecological types of zones requiring at least 200 meters of buffer, and 3) topological constraint requires that each node to be planned retain at least 2 connections. And the verification step is that at least one candidate connection path is reserved between adjacent nodes along the extending direction of the road network, and the number of the candidate connection paths between the nodes crossing the ecological tolerance attenuation gradient mutation zone does not exceed a preset upper limit.

In the embodiment of the application, double verification is carried out on each candidate path, namely whether the path trend accords with the existing road network extending trend is checked, whether the ecological sensitivity of the area where the path passes is within an allowable range is evaluated, and only the path meeting two requirements at the same time is reserved.

And 404, performing topological integration on the candidate connection paths meeting the continuity constraint condition and the connection relations reserved in the spatial adjacent relation network to form a new connection relation.

In step 404, topology integration refers to a process of organically fusing a new path that is eligible with an original network.

In the embodiment of the application, the candidate paths passing through verification are gradually added into the existing network according to the priority order of the candidate paths, the connection weight of the related nodes is adjusted, and the introduction of the new paths is ensured not to damage the structural stability of the original ecological protection network. In the planning of a certain urban and rural junction, a candidate road connection path (such as a newly added branch road) meeting the road extension continuity (at least keeping 2 parallel connection) is integrated with the existing reserved ecological gallery connection relationship (such as a river-crossing bridge), so as to form a final road planning scheme for guaranteeing the connectivity of a traffic network and maintaining the ecological barrier function, wherein the joint point distance between the newly added branch road and the reserved bridge is not more than 200 meters, so as to ensure the network continuity. When the candidate connection path includes nodes A-B-C (all belong to nodes to be planned) and the continuity constraint condition is met, combining the path with the original connection reserved in the network (for example, nodes C-D, D are ecological protection nodes) to form a new target connection relationship including A-B-C-D, wherein A-B-C is a new construction path, and C-D is the reserved original connection.

The following is a specific example:

In the planning and optimizing case of a certain urban and rural junction, based on the spatial adjacent relation network with the reorganized weight 3, 71 nodes to be planned which are not marked as over-development nodes or ecological protection nodes are screened out from 168 network nodes, and the conflict proportion of the nodes is in the middle interval of 0.6-1.3. And 5 candidate connection paths are initially generated according to the spatial distribution of the nodes to be planned, wherein the path lengths are controlled within 800 meters. And meanwhile, verifying the path curvature change, and ensuring that the steering angle difference of any three continuous nodes is less than 15 degrees. After verification, the 3 paths meet the requirements, and comprise two trunk extension lines connecting new and old urban areas and an ecological landscape corridor. When the qualified path is integrated with the reserved original connection, a connection strength adjustment formula is adopted, wherein the adjusted strength=the original strength multiplied by 0.6+ path cooperative schedule multiplied by 0.4, and finally 24 newly added optimized connections are formed.

In the embodiment of the application, the method provides a reasonable space expansion scheme for urban and rural development and realizes win-win of construction requirements and ecological protection on the premise of ensuring ecological safety by scientifically screening and verifying potential development paths.

To further improve the accuracy of the spatial network construction in urban and rural planning, in some embodiments, step 202, generating a spatial proximity relationship network according to the dynamic weight relationship and the mapping intermediate data includes:

Step 501, extracting a space turning point of a vegetation coverage migration track, a boundary intersection point of a water body distribution coupling characteristic and a topological node of a road network extending direction from the mapping intermediate data, and establishing a connection model between the foundation network nodes.

In step 501, the basic network node refers to the key spatial feature points extracted from the mapping intermediate data, including turning points with obvious vegetation coverage changes, intersection points of multiple water boundaries, and feature points with road trend changes, and these nodes together form the basic skeleton of the network. The vegetation coverage migration track is a space motion path obtained by analyzing time sequence change characteristics of vegetation coverage type distribution information, and reflects the evolution trend of vegetation types in space. The space turning points of the vegetation coverage migration track refer to boundary change positions appearing in the vegetation type space-time evolution process, reflect transition zones or ecological staggered areas among different vegetation communities, and are usually obtained by analyzing superposition change identification of a multi-period vegetation type graph. The water body distribution coupling characteristic is a composite characteristic obtained by carrying out spatial correlation analysis on water body boundary coordinates, and reflects the spatial interaction relation between the water body and other ecological elements. The boundary intersection points of the water distribution coupling features refer to key points of intersection or turning of a plurality of water boundary lines, and represent structural nodes of a water system network and reflect the water morphology change features and connectivity. The topological node of the extending direction of the road network refers to a characteristic position where the trend of the road is obviously changed, and the characteristic position comprises a road intersection, a bifurcation point or a turning point and is used for describing the space expansion trend of the road network. A connection model refers to a set of spatial adjacencies between base network nodes defining potential interaction paths between nodes, typically established based on spatial distance thresholds or functional associations. The existence of the connection model depends on the fact that connection is established forcedly among nodes which are continuously distributed on a vegetation coverage migration track, connection is established among nodes at two sides of a water body distribution coupling characteristic boundary only when a connection weight value exceeds a preset threshold value, and unidirectional connection is established among nodes in the extending direction of a road network according to the gradient change direction of a human activity intensity quantification value.

In the embodiment of the application, the space-time variation trend of vegetation coverage is firstly analyzed, the position of vegetation type mutation is identified as a turning point, the intersection of the boundary of the water body and the turning position are detected as intersection points, and the intersection of the road and the direction variation point are extracted as topological nodes. And then establishing a preliminary connection model between nodes within a distance threshold according to a space proximity principle.

Step 502, generating a connection weight value combination between adjacent base network nodes based on the dynamic weight relation.

In step 502, adjacent base network nodes refer to pairs of base network nodes that satisfy a predetermined proximity condition (e.g., a spacing of less than 500 meters) in spatial location and that have potential interactions. The combination of the connection weight values refers to a quantized representation of the interaction strength between adjacent nodes, reflecting the balance state of human activities and ecological protection.

In the embodiment of the application, the connection weight of each pair of adjacent nodes is calculated according to the dynamic weight relation, and the human activity intensity difference and the ecological tolerance coordination degree between the nodes are considered to generate a weight matrix reflecting the space interaction characteristics. The method comprises the steps of calculating a connection weight value between every two adjacent basic network nodes based on a proportional relation between human activity intensity quantized values and ecological tolerance in the dynamic weight relation, increasing a first correction coefficient by the connection weight value when the human activity intensity quantized values of the two adjacent basic network nodes are higher than a median value in a region, and reducing a second correction coefficient by the connection weight value when the ecological tolerance of the two adjacent basic network nodes is lower than the median value in the region, so as to generate a connection weight value combination.

Step 503, combining the connection model and the connection weight value combination to establish a spatial proximity relation network.

In the embodiment of the application, the basic node connection model and the weight matrix are fused, the connection relation is weighted, and finally the composite network capable of simultaneously expressing the spatial proximity and the functional relevance is constructed.

The following is a specific example:

In a planning case of a certain urban and rural junction, based on mapping intermediate data and dynamic weight relation generated by the weight 2, key feature points are firstly extracted from the intermediate data, namely vegetation coverage migration turning points of 8 forests to grasslands are identified, water body boundary intersection points formed by river intersection of 5 forests are identified, and 7 main road intersections are used as topology nodes. The 20 basic nodes build an initial connection model according to the condition that the space distance is less than 800 meters, and 35 connection edges are formed in a conformal mode. Then calculating the connection weight among the nodes according to the dynamic weight relation, and adopting a weight calculation formula, wherein the weight = human activity intensity average value multiplied by 0.7+ ecological tolerance average value multiplied by 0.3, and the human activity intensity passes through a kernel density formula The calculation is performed such that,The ecological tolerance is obtained by taking 300 meters according to the ratio of 0.6Xvegetation coverage to 0.4X1-distance from water body/1000 meters. In particular, the connection weight of the two water body junction points at the west side of the wetland and the adjacent road nodes is calculated to be 0.65 and lower than the planning threshold value of 0.7, so that the connection is reserved but marked as connection to be observed, and the connection weight among 3 road nodes in the newcastle development area reaches 0.82 and exceeds the development upper limit of 0.8, so that the weight is reduced to 0.75. The finally constructed spatial adjacent relation network comprises 20 nodes and 32 effective connections, wherein the average weight of the connections along the wetland buffer zone is controlled between 0.6 and 0.7, thus ensuring the ecological protection requirement, maintaining necessary traffic connectivity and providing an accurate spatial relation model for the subsequent planning decision.

In the embodiment of the application, the method constructs a space network model reflecting the actual planning requirement by accurately identifying the interaction between the key space feature points and the quantization nodes, and provides a reliable space analysis basis for urban and rural coordinated development.

In order to further improve the comprehensive analysis capability of the ecological environment data, in some embodiments, step 102, the dynamically coupling the vegetation coverage type distribution information and the surface reflectivity time sequence variation feature to generate multi-mode environment sensing data includes:

And 601, establishing a mapping relation between various vegetation boundaries in the vegetation coverage type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity time sequence change characteristic.

In step 601, the mapping relationship refers to a space-time correspondence relationship between vegetation type boundaries and reflectivity change features, and reflects the relevance between vegetation growth states and environmental changes.

In the embodiment of the application, through space superposition analysis and time sequence comparison, a corresponding table of different vegetation type areas and reflectivity change characteristics is established, and typical reflectivity change modes of various vegetation are identified.

And 602, identifying an intersection area of the vegetation coverage type stable area and the reflectivity abrupt change area according to the mapping relation.

In step 602, a vegetation cover type stable region refers to a region where the vegetation type does not change for 3 consecutive observation periods. The reflectance abrupt change region refers to a region in which the reflectance difference value between adjacent spatial units exceeds the region average fluctuation value by more than 2 times. Intersection region refers to the spatial range where vegetation types remain stable but reflectivity fluctuates abnormally, suggesting a potential ecological environment change.

In the embodiment of the application, comparing the vegetation type distribution diagram with the multi-time opposite reflectivity change diagram, and screening out the area with unchanged vegetation type but reflectivity fluctuation exceeding the normal range as the key analysis area.

And 603, performing coupling processing on the intersection area to generate an environment characteristic mark.

In step 603, the environmental signature is a classification identification of the intersection area ecological environmental status, containing change type and degree information. The coupling treatment process comprises the steps of generating an environment characteristic mark containing vegetation type anti-interference coefficients when a vegetation coverage type stable region is overlapped with a reflectivity abrupt change region, and generating an environment characteristic mark containing vegetation type dominant factors when the vegetation coverage type stable region is overlapped with a reflectivity non-abrupt change region.

In the embodiment of the application, the intersection area is marked as a type such as a potential degradation area, an artificial interference area and the like according to the change direction and the change amplitude of the reflectivity, and corresponding ecological sensitivity scores are given.

And step 604, combining the environmental characteristic marks with corresponding space position coordinates to form multi-mode environmental perception data.

In step 604, the corresponding spatial position coordinates refer to geographic coordinate positions that spatially exactly match the intersection region of the vegetation cover type stabilizing region and the reflectivity mutation region. The space position coordinates are derived from longitude and latitude or plane coordinate information of the ecological resource monitoring data and the multiband spectrum remote sensing data. The multimodal context awareness data is a structured dataset integrating spatial locations and ecological environmental features.

In the embodiment of the application, various environmental characteristic marks are associated with geographic coordinates, and a comprehensive data layer comprising spatial positions, vegetation types, reflectivity change characteristics and ecological scores is constructed. In the specific embodiment, an environment characteristic mark marked as 'anti-interference coefficient 0.8' is bound with coordinates (116.404 DEG E, 39.915 DEG N) to form a multi-mode environment perception data record containing space position and ecological characteristics.

The following is a specific example:

In a planning case of a certain urban and rural junction, a mapping relation between a vegetation boundary and reflectivity change is firstly established based on forest resource investigation data acquired in the weight 1 and an environmental satellite multispectral image. Using the formula reflectance fluctuation amplitude = Calculating the change degree of the reflectivity of each pixel, whereinIs of three years of synchronizationAverage value. Analysis shows that a certain region on the east side of the wetland is kept in a woodland type for three years continuously, but the fluctuation range of the reflectivity reaches 0.25, and exceeds the average level of the periphery by 0.18 to about 1.4 times, and the intersection region with stable vegetation coverage but abrupt reflectivity is judged. The area is subjected to coupling treatment, and according to the reflectance reduction trend and the distance from the road, the calculated score of the ecological sensitivity=0.7×reflectance reduction +0.3×road influence coefficient is calculated to be 0.78 according to the formula, and the area is marked as a high-intensity artificial interference area. And combining the marking result with the space position under the CGCS2000 coordinate system, displaying the generated multi-mode environment perception data, wherein the overlap ratio of the interference area and the planned road reaches 65%, automatically bringing the area into a forbidden construction area by the system, and east-shifting the original planned road by 120 meters.

In the embodiment of the application, the method realizes the accurate identification of the ecological environment change by fusing the vegetation static distribution and the dynamic reflection characteristics, and provides more comprehensive environment background information for planning decisions.

In order to further improve scientificity and operability of the urban and rural planning result, in some embodiments, step 105, performing iterative spatial superposition on the topology constraint information and the multi-mode environment awareness data to generate the urban and rural planning and mapping result includes:

And 701, marking a potential conflict area in the multi-mode environment sensing data according to curvature change characteristics of the road network penetration path in the topology constraint information.

In step 701, the curvature change characteristic of the road network penetration path refers to the degree of spatial turning in the extending direction of the road and the change rule thereof, and is obtained by calculating the curvature value of each node of the road center line, and reflects the intrusion mode of the road into the ecological space and the potential conflict position. The characteristics are derived from geometric analysis of the center line of the road, and a curvature formula is adoptedCalculation of whereinAs the direction angle variation of the adjacent road segments,For the road length, when the curvature of a certain point exceeds a set threshold (such as 0.1 radian/meter), the road is judged to be a turning point, which indicates that the road can cut or interfere with the ecological area. The potential conflict area refers to an area where the road network expansion direction intersects with the ecologically sensitive area and is at development risk, and is determined by analyzing the road path curvature and the ecologically sensitive area. Wherein the first order conflict zone is marked when the permeate path curvature exceeds 45 degrees and the second order conflict zone is marked when the permeate path curvature is between 15 and 45 degrees.

In the embodiment of the application, firstly, a section with larger curvature change in a road penetration path is identified, then, spatial superposition is carried out on the section and an ecologically sensitive area in multi-mode data, and a high risk area overlapped with the section and the ecologically sensitive area is screened out.

Step 702, spatial weight distribution is performed on the potential conflict area based on the multi-mode environment sensing data.

In step 702, spatial weight assignment refers to a process of ranking importance of conflict areas according to ecological sensitivity and development requirements. Specifically, in the first-stage conflict area, the area with vegetation anti-interference coefficient lower than 0.5 or water body impedance intensity higher than 0.7 is assigned the highest weight, in the second-stage conflict area, the area with vegetation anti-interference coefficient between 0.5 and 0.8 and water body impedance intensity between 0.3 and 0.7 is assigned the medium weight

In the embodiment of the application, the coordination priority of each conflict area is calculated by combining the ecological score in the environment-aware data and the development intensity in the topological constraint, and the higher the priority is, the more important processing is needed.

And 703, performing iterative superposition processing on the space weight distribution result until a preset iteration stopping condition is met.

In step 703, the iterative overlap-add process refers to a process of gradually optimizing the conflict area solution through multiple spatial analyses. The iterative superposition operation is carried out, namely, the highest weight conflict area is subjected to spatial intersection with an area with the terrain gradient larger than 25 degrees in the multi-mode environment perception data, the secondary superposition is carried out, namely, the medium weight conflict area is subjected to spatial union with the biological dominant factor area in the 500-meter buffer zone of the primary superposition result, and finally, the superposition is carried out, namely, the previous two superposition results are combined, and isolated areas deviating from the extending direction of the road network by more than 30 degrees are removed. The preset iteration stopping condition comprises 1) reaching 5 maximum iteration times, 2) enabling the area change rate of the conflict area of the adjacent two superposition results to be smaller than 5% (the calculation formula: . Specific examples are that when the area of the conflict area after the third superposition is 105km2 and the fourth superposition is 102km2, the calculated area change rate is 2.85% (|102-105|/105=0.0285), and is less than a 5% threshold, namely, the stop condition is satisfied.

In the embodiment of the application, the conflict area with the highest priority is processed for the first round, the trend of the road is adjusted or a protection buffer zone is set, the medium priority area is processed for the second round, and the residual conflict is reevaluated after each round of processing until the convergence condition is met.

And step 704, performing space fusion on the superposition result and the multi-mode environment perception data to generate urban and rural planning and mapping achievements.

In the embodiment of the application, the optimized conflict solution and the environment perception data are fused to generate a comprehensive planning map containing ecological protection requirements, construction control indexes and infrastructure layout.

The following is a specific example:

curvature characteristic of the collateral penetration path adopts a curvature calculation formula Key turning points with curvature exceeding 0.12 radian/meter at 3 positions are identified, and the key turning points are overlapped with ecological fragile areas with anti-interference coefficients lower than 0.6 marked in the multi-mode data and marked as first-level potential conflict areas. And carrying out space weight distribution according to the formula conflict weight = 0.6 multiplied by road importance coefficient +0.4 multiplied by ecological sensitivity, wherein the road importance coefficient is calculated through traffic flow and road network connectivity, and the ecological sensitivity is taken from the environment perception data. The first round of superposition treatment is aimed at the region with weight exceeding 0.8, the main road crossing the east side of the wetland is shifted by 150 m to reduce the curvature to 0.08 radian/m, the second round of treatment is aimed at the region with weight of 0.6-0.8, and a 30m wide ecological buffer zone is arranged on the west side of the wetland. After two iterations, the weight of all conflict areas is reduced to below 0.6, and the stopping condition is met. Finally, the generated urban and rural planning and mapping result shows that the adjusted road network perfectly avoids the core ecological area, a grading management and control system of 200 m absolute protection rings and 300 m limited construction areas is formed around the wetland, wherein the curvature of a newly built road is strictly controlled within 0.1 radian/m, and the ecological score of each node is kept above 0.7, so that the organic unification of ecological protection and urban development is realized.

In the embodiment of the application, the method realizes the fine coordination of ecological protection and city development through iterative optimization and spatial fusion, and the generated planning result has definite control boundary and implementation requirement, thereby improving the feasibility and ecological benefit of the planning scheme.

Fig. 2 is a schematic structural diagram of an urban and rural planning and mapping result generation system according to an embodiment of the present application, as shown in fig. 2, the system includes:

The acquiring module 21 is configured to acquire ecological resource monitoring data in a target area, multiband spectrum remote sensing data, and current urban and rural road network spatial distribution data, where the ecological resource monitoring data includes vegetation coverage type distribution information and water boundary coordinates, and the multiband spectrum remote sensing data includes surface reflectivity time sequence change features.

The coupling module 22 is configured to dynamically couple the vegetation coverage type distribution information and the surface reflectivity time sequence variation feature to generate multi-mode environmental perception data.

The matching module 23 is configured to dynamically match the multimodal environment awareness data with road network density gradient distribution information, which is obtained by performing spatial nuclear density analysis on the current urban and rural road network spatial distribution data, to perform antagonistic learning driving, so as to generate mapping intermediate data.

The extracting module 24 is configured to analyze the mapping intermediate data and the road network density gradient distribution information based on a synergistic effect of a preset density constraint and a preset ecological bearing threshold, and extract topology constraint information of a degree of collision between a penetration path of the road network and a boundary of the ecology sensitive area from an analysis result.

And the generating module 25 is configured to perform iterative spatial superposition on the topology constraint information and the multi-mode environment awareness data to generate urban and rural planning and mapping results.

The urban and rural planning and mapping result generation system shown in fig. 2 may execute the urban and rural planning and mapping result generation method shown in the embodiment shown in fig. 1, and its implementation principle and technical effects are not repeated. The specific manner in which the various modules and units perform operations in the urban and rural planning and mapping outcome generation system in the foregoing embodiments has been described in detail in the embodiments related to the method, and will not be described in detail herein.

In one possible design, an urban and rural planning and mapping outcome generation system of the embodiment of fig. 2 may be implemented as a computing device, as shown in fig. 3, which may include a storage component 31 and a processing component 32;

the storage component 31 stores one or more computer instructions for execution by the processing component 32.

The processing component 32 is configured to generate the urban and rural planning and mapping result according to the embodiment shown in fig. 1.

Wherein the processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps of the methods described above. Of course, the processing component may also be implemented as one or more Application-specific integrated circuits (ASICs), digital signal processors (DIGITAL SIGNAL processes, DSPs), digital signal processing devices (DIGITAL SIGNAL Process devices, DSPDs), programmable logic devices (Programmable Logic Device, PLDs), field programmable gate arrays (Field Programmable GATE ARRAY, FPGA), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above method.

The storage component 31 is configured to store various types of data to support operations at the terminal. The Memory component may be implemented by any type or combination of volatile or nonvolatile Memory devices such as Static Random-Access Memory (SRAM), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ ONLY MEMORY, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read Only Memory (ROM), magnetic Memory, flash Memory, magnetic or optical disk.

Of course, the computing device may necessarily include other components as well, such as input/output interfaces, display components, communication components, and the like.

The input/output interface provides an interface between the processing component and a peripheral interface module, which may be an output device, an input device, etc.

The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, and the like.

The computing device may be a physical device or an elastic computing host provided by the cloud computing platform, and at this time, the computing device may be a cloud server, and the processing component, the storage component, and the like may be a base server resource rented or purchased from the cloud computing platform.

The embodiment of the application also provides a computer storage medium which stores a computer program, and the computer program can realize the urban and rural planning and mapping result generation method of the embodiment shown in the figure 1 when being executed by a computer.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same, and although the present application has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present application.

Claims (8)

1. The urban and rural planning and mapping result generation method is characterized by comprising the following steps of:

Acquiring ecological resource monitoring data, multiband spectrum remote sensing data and current urban and rural road network space distribution data in a target area, wherein the ecological resource monitoring data comprises vegetation coverage type distribution information and water boundary coordinates, and the multiband spectrum remote sensing data comprises surface reflectivity time sequence change characteristics;

dynamically coupling the vegetation coverage type distribution information with the time sequence change characteristics of the earth surface reflectivity to generate multi-mode environment perception data;

dynamically matching the multi-mode environment sensing data with road network density gradient distribution information for performing antagonistic learning driving to generate mapping intermediate data, wherein the road network density gradient distribution information is obtained by performing space kernel density analysis on the current urban and rural road network space distribution data;

Analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of the preset density constraint and the preset ecological bearing threshold, and extracting topological constraint information fusing the road network permeation path and the ecological sensitive area boundary conflict degree from the analysis result;

Performing iterative spatial superposition on the topology constraint information and the multi-mode environment perception data to generate urban and rural planning and mapping achievements;

the dynamic coupling is performed on the vegetation coverage type distribution information and the surface reflectivity time sequence change characteristic, and the generation of the multi-mode environment perception data comprises the following steps:

Establishing a mapping relation between various vegetation boundaries in the vegetation coverage type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity time sequence change characteristics;

Identifying an intersection area of the vegetation coverage type stable area and the reflectivity abrupt change area according to the mapping relation;

coupling the intersection region to generate an environmental characteristic mark;

Combining the environmental characteristic marks with corresponding space position coordinates to form multi-mode environmental perception data;

The analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergic action of the preset density constraint and the preset ecological bearing threshold value, and extracting topological constraint information of the degree of conflict between the road network penetration path and the ecological sensitive area boundary from the analysis result comprises the following steps:

Establishing a dynamic weight relation between a human activity intensity quantization value of each space unit in the road network density gradient distribution information and the ecological tolerance of the corresponding space unit in the ecological resource monitoring data;

generating a spatial proximity relation network according to the dynamic weight relation and the mapping intermediate data;

Defining the propagation range of the human activity intensity quantized value through preset density constraint, and defining the attenuation gradient of the ecological tolerance through a preset ecological bearing threshold value to form a grouping boundary dynamic adjustment rule;

Based on the dynamic adjustment rule of the grouping boundary, carrying out topological structure reorganization on the spatial adjacent relation network;

and extracting topology constraint information from the recombined spatial adjacent relation network.

2. The method of claim 1, wherein the performing topology reorganization on the spatial proximity relationship network based on the packet boundary dynamic adjustment rule comprises:

Identifying a conflict ratio between human activity intensity quantized values of each network node in the spatial proximity relation network and the ecological tolerance;

Marking a network node corresponding to the conflict proportion exceeding a preset first threshold as an over-development node according to the numerical range of the preset density constraint in the grouping boundary dynamic adjustment rule, and disconnecting the connection relationship between the over-development node and the adjacent node;

marking the network nodes with the conflict proportion lower than a preset second threshold value as ecological protection nodes according to the attenuation gradient of the ecological bearing threshold value in the grouping boundary dynamic adjustment rule, and enhancing the connection strength between the ecological protection nodes and the adjacent ecological protection nodes;

In the spatial proximity relation network, a new connection relation is established between nodes which are not disconnected, wherein the nodes which are not disconnected comprise common nodes and ecological protection nodes;

and generating a recombined spatial adjacent relation network based on the over-development node, the ecological protection node and the new connection relation.

3. The method according to claim 2, wherein said establishing a new connection relationship between nodes that are not disconnected in said spatial proximity relationship network comprises:

determining a set of nodes to be planned in the spatial proximity relation network that are not marked as over-developed nodes and are not marked as bioprotective nodes;

Determining candidate connection paths according to the node set to be planned;

Verifying the candidate connection paths based on a preset continuity constraint condition;

and carrying out topological integration on the candidate connection paths meeting the continuity constraint condition and the connection relations reserved in the spatial adjacent relation network to form a new connection relation.

4. The method of claim 1, wherein the generating a spatial proximity relationship network from the dynamic weight relationships and the survey intermediate data comprises:

Extracting space turning points of vegetation coverage migration tracks, boundary intersection points of water distribution coupling characteristics and topology nodes of the extending direction of a road network from the mapping intermediate data, taking the space turning points, the boundary intersection points and the topology nodes as basic network nodes, and establishing a connection model between the basic network nodes;

generating a connection weight value combination between adjacent basic network nodes based on the dynamic weight relation;

and combining the connection model with the connection weight value combination to establish a spatial proximity relation network.

5. The method of claim 1, wherein the iteratively spatially superimposing the topology constraint information with the multi-modal context awareness data to generate urban and rural planning and mapping outcomes comprises:

marking potential conflict areas in the multi-mode environment sensing data according to curvature change characteristics of the road network penetration paths in the topology constraint information;

based on the multi-modal environment awareness data, performing spatial weight distribution on the potential conflict area;

Performing iterative superposition processing on the space weight distribution result until a preset iteration stopping condition is met;

and carrying out space fusion on the superposition result and the multi-mode environment perception data to generate urban and rural planning and mapping results.

6. An urban and rural planning and mapping result generation system, which is characterized by comprising:

The system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring ecological resource monitoring data, multiband spectrum remote sensing data and current urban and rural road network space distribution data in a target area, the ecological resource monitoring data comprises vegetation coverage type distribution information and water boundary coordinates, and the multiband spectrum remote sensing data comprises surface reflectivity time sequence change characteristics;

the coupling module is used for dynamically coupling the vegetation coverage type distribution information and the surface reflectivity time sequence change characteristics to generate multi-mode environment perception data;

The matching module is used for dynamically matching the multi-mode environment sensing data with road network density gradient distribution information for performing antagonistic learning driving to generate mapping intermediate data, and the road network density gradient distribution information is obtained by performing space kernel density analysis on the current urban and rural road network space distribution data;

The extraction module is used for analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergistic effect of the preset density constraint and the preset ecological bearing threshold value, and extracting topological constraint information of the degree of collision between the road network penetration path and the ecological sensitive area boundary from an analysis result;

The generation module is used for carrying out iterative spatial superposition on the topological constraint information and the multi-mode environment perception data so as to generate urban and rural planning and mapping achievements;

the dynamic coupling is performed on the vegetation coverage type distribution information and the surface reflectivity time sequence change characteristic, and the generation of the multi-mode environment perception data comprises the following steps:

Establishing a mapping relation between various vegetation boundaries in the vegetation coverage type distribution information and the reflectivity fluctuation amplitude in the surface reflectivity time sequence change characteristics;

Identifying an intersection area of the vegetation coverage type stable area and the reflectivity abrupt change area according to the mapping relation;

coupling the intersection region to generate an environmental characteristic mark;

Combining the environmental characteristic marks with corresponding space position coordinates to form multi-mode environmental perception data;

The analyzing the mapping intermediate data and the road network density gradient distribution information based on the synergic action of the preset density constraint and the preset ecological bearing threshold value, and extracting topological constraint information of the degree of conflict between the road network penetration path and the ecological sensitive area boundary from the analysis result comprises the following steps:

Establishing a dynamic weight relation between a human activity intensity quantization value of each space unit in the road network density gradient distribution information and the ecological tolerance of the corresponding space unit in the ecological resource monitoring data;

generating a spatial proximity relation network according to the dynamic weight relation and the mapping intermediate data;

Defining the propagation range of the human activity intensity quantized value through preset density constraint, and defining the attenuation gradient of the ecological tolerance through a preset ecological bearing threshold value to form a grouping boundary dynamic adjustment rule;

Based on the dynamic adjustment rule of the grouping boundary, carrying out topological structure reorganization on the spatial adjacent relation network;

and extracting topology constraint information from the recombined spatial adjacent relation network.

7. The computing device is characterized by comprising a processing component and a storage component, wherein the storage component stores one or more computer instructions, and the one or more computer instructions are used for being invoked and executed by the processing component to realize the urban and rural planning and mapping result generation method according to any one of claims 1-5.

8. A computer storage medium, wherein a computer program is stored, and when the computer program is executed by a computer, the method for generating urban and rural planning and mapping results according to any one of claims 1 to 5 is realized.