Layout generation method and system for urban ruin boundary transformation and storage medium

Abstract

The invention discloses a layout generation method and system for urban ruin boundary transformation and a storage medium, and relates to the field of urban planning and building design, and the method comprises the steps: obtaining the three-dimensional scanning data of a ruin region, extracting space control points, and recording the space coordinates of each space control point; with the space control points as the center, the sight line access range of each space control point is calculated based on the three-dimensional scanning data and the space coordinates; converting the sight line access range into a space potential energy value, and constructing a regional potential energy network; analyzing the regional potential energy network, generating a natural flow path in the region, determining a preset activity channel based on the natural flow path, and generating a space separation line; function partitions are divided according to the jump position of the potential energy value, and an initial space layout is generated; and adjusting the initial spatial layout based on the urban planning control index and the surrounding road network to obtain a boundary transformation layout scheme of the paleoruins region. By implementing the method, the layout generation efficiency of paleo-ruin boundary transformation can be improved.

Description

Technical Field

[0001] The present application relates to the fields of urban planning and architectural design, and in particular to a layout generation method, system and storage medium for the reconstruction of the boundaries of ancient urban ruins. Background Art

[0002] With the acceleration of urbanization and the growing awareness of historical and cultural preservation, the transformation and protection of urban historical and cultural boundaries are receiving increasing attention. As a crucial component of a city's historical and cultural heritage, these boundaries not only carry a wealth of historical information and cultural value but also play a significant role in the evolution of urban spatial patterns. How to preserve the historical and cultural characteristics of these ancient sites while achieving the rational utilization and functional enhancement of these spaces has become a key issue in current urban planning.

[0003] Currently, relevant technologies use 3D laser scanning combined with drone aerial photography to obtain spatial data of the ancient city's boundaries. A 3D model is constructed using a BIM platform, and spatial analysis is performed using a GIS system. During the planning and design phase, designers develop a design based on the 3D model and use computer-aided design software to complete the spatial layout. During the construction phase, a real-time monitoring system tracks progress and quality, and a basic data management platform is established to record construction information.

[0004] However, the massive amount of data obtained by relevant technologies still requires a lot of manual processing and screening, and the spatial layout design mainly relies on the experience and judgment of designers. The overall efficiency of the layout work of the ancient ruins boundary transformation is low. Summary of the Invention

[0005] The present application provides a layout generation method, system and storage medium for the reconstruction of the boundaries of urban ancient ruins, which are used to improve the layout generation efficiency of the reconstruction of the boundaries of urban ancient ruins.

[0006] In the first aspect, the present application provides a layout generation method for the transformation of the boundaries of urban ancient ruins, which is applied to the urban planning system. The method includes: obtaining three-dimensional scanning data of the ancient ruins area, extracting building corners, interface intersections and terrain feature points as spatial control points, and recording the spatial coordinates of each spatial control point; with the spatial control point as the center, calculating the line of sight range of each spatial control point based on the three-dimensional scanning data and spatial coordinates; converting the line of sight range into a spatial potential energy value, and constructing a regional potential energy network; parsing the regional potential energy network, generating a natural flow path within the area from high to low according to the spatial potential energy value, determining a preset activity channel based on the natural flow path, and generating a spatial separation line corresponding to the preset activity channel; dividing functional zones according to the jump position of the potential energy value, and generating an initial spatial layout including spatial separation lines and functional zones; the jump position is a position where the potential energy difference is greater than the maximum allowable difference within the preset distance range; adjusting the initial spatial layout based on urban planning control indicators and the surrounding road network to obtain a boundary transformation layout plan for the ancient ruins area.

[0007] In the above embodiment, the urban planning system obtains three-dimensional scanning data and extracts key control points, combines line of sight range analysis and potential network construction, realizes the automatic generation of natural flow paths and the scientific division of functional zones, and transforms qualitative spatial experience into quantitative data indicators, making the generation process of the ancient ruins boundary transformation plan more systematic and improving the efficiency and accuracy of planning and design.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the line of sight range of each spatial control point based on three-dimensional scanning data and spatial coordinates with the spatial control point as the center specifically includes: generating a fan-shaped scanning grid with each spatial control point as the center point to obtain a horizontal ray array; performing spatial collision simulation along the ray array to extract the three-dimensional coordinates of the ray array at each occlusion point; generating a line of sight boundary polygon based on the three-dimensional coordinates, and converting the line of sight boundary polygon into horizontal projection area data; vectorizing the projection area data to generate a line of sight range.

[0009] In the above embodiment, the urban planning system achieves accurate calculation of spatial line of sight accessibility through fan-shaped scanning grid and ray array analysis, can capture the occlusion effect of the spatial environment, provide accurate basic data for potential energy calculation, and ensure the reliability of the line of sight analysis results.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of converting the line of sight range into a spatial potential energy value and constructing a regional potential energy network specifically includes: substituting the line of sight range into a distance attenuation function to generate a potential energy distribution field; performing a superposition operation on the potential energy distribution field of each spatial control point to determine the regional potential energy distribution; grid sampling the regional potential energy distribution to generate a potential energy data matrix; and constructing a regional potential energy network containing multiple grid cells based on the potential energy data matrix.

[0011] In the above embodiment, the urban planning system establishes a complete conversion mechanism from the visual range to the potential energy network. Through the distance attenuation function and grid sampling, the spatial characteristics are quantified into computable numerical indicators, making the spatial analysis more scientific and making the path generation and functional zoning more reliable.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of substituting the line of sight range into the distance attenuation function to generate a potential energy distribution field specifically includes: substituting the boundary coordinate sequence of the line of sight range into the grid division function to generate a calculation grid unit matrix; reading the spatial distance value from each grid unit to the spatial control point in the calculation grid unit matrix to generate distance matrix data; calculating the potential energy attenuation coefficient of each grid unit based on the distance matrix data, generating an attenuation function curve, and converting the generated potential energy distribution field based on the attenuation function curve.

[0013] In the above embodiment, the urban planning system establishes an accurate potential energy attenuation model through grid division and distance matrix calculation. The model can accurately reflect the decreasing law of spatial influence and provide a scientific basis for the construction of the potential energy distribution field.

[0014] In combination with some embodiments of the first aspect, in some embodiments, before obtaining three-dimensional scanning data of the ancient ruins area, extracting building corners, interface intersections and terrain feature points as spatial control points, and recording the spatial coordinates of each spatial control point, the method also includes: reading aerial data and historical map data of the ancient ruins area; performing geometric correction on the aerial data to generate an orthophoto; performing coordinate conversion on the historical map data to generate a geographic information layer; and integrating and generating a spatial database based on the orthophoto and geographic information layers.

[0015] In the above embodiment, the urban planning system establishes a complete spatial database by integrating aerial photography data and historical map information, providing comprehensive data support for the current status analysis of the ancient ruins area and ensuring the historical continuity of the renovation plan.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of converting the line of sight range into spatial potential energy values and constructing a regional potential energy network, the method also includes: substituting the three-dimensional coordinates of the topographic measurement points into the interpolation function to generate a surface elevation grid; performing Boolean operations on the point data of the underground pipelines to generate a pipeline density grid; converting the geological structure data into a bearing capacity coefficient matrix; and generating a constraint condition data set based on the surface elevation grid, the pipeline density grid and the bearing capacity coefficient matrix.

[0017] In the above embodiment, the urban planning system establishes a complete constraint system by comprehensively considering the terrain, pipelines and geological conditions, ensuring the feasibility of the transformation plan and avoiding conflicts between the plan and actual engineering conditions.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the step of adjusting the initial spatial layout based on urban planning control indicators and surrounding road networks to obtain a boundary transformation layout plan for the ancient ruins area specifically includes: performing topological operations on the boundary geometry data of multiple grid units in the initial spatial layout to obtain connection relationship data of boundary line segments; generating a three-dimensional grid model based on the connection relationship data and building volume parameters, and determining the building density distribution data of the three-dimensional grid model; substituting the spatial coordinates of the natural flow path into the flow prediction function to calculate the traffic capacity parameters; calculating the green space layout data based on the green space coverage rate threshold and constraint condition data set; integrating and generating the boundary transformation layout plan for the ancient ruins area based on the building density distribution data, traffic capacity parameters and green space layout data.

[0019] In the above embodiment, the urban planning system optimizes and adjusts the renovation plan through integrated analysis of building density, traffic capacity, and green space layout, ensuring that the plan meets the requirements of various planning indicators and improving the feasibility of the plan.

[0020] In a second aspect, an embodiment of the present application provides an urban planning system, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code comprising computer instructions, the one or more processors calling the computer instructions to enable the urban planning system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when run on an urban planning system, enables the urban planning system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on an urban planning system, the urban planning system executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understood that the urban planning system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting a method of extracting spatial control points based on 3D scanning data and constructing a potential energy network, the visual range is converted into a quantitative spatial potential energy value. Natural flow paths and functional zoning are generated based on the potential energy value. This allows the designer's spatial experience and judgment to be converted into calculable data indicators. This effectively solves the problem of existing technologies relying on manual experience and judgment and lacking objective quantitative standards. It also realizes the automation and standardization of the generation of layout plans for the reconstruction of the ancient ruins boundary, improving the efficiency of planning and design.

[0025] 2. By converting the visual range into a potential energy distribution field through a distance attenuation function and constructing a regional potential energy network through grid sampling, the method can accurately quantify the decreasing law of spatial influence and the interaction relationship between spatial elements, effectively solving the problem that the spatial analysis method in the existing technology is too simple and cannot reflect complex spatial relationships, and thus realizes the precise description and analysis of the spatial characteristics of the ancient ruins boundary.

[0026] 3. By adopting a scheme integration method based on building density distribution, traffic capacity parameters and green space layout data, and optimizing and adjusting it in combination with urban planning control indicators, it is possible to achieve a comprehensive balance of multiple planning elements, effectively solving the problems of difficulty in coordinating and unifying various indicators and poor feasibility of schemes in existing technologies, thereby improving the scientific nature and feasibility of the ancient ruins boundary reconstruction scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a layout generation method for urban ancient ruins boundary reconstruction in an embodiment of the present application; Figure 2 This is another flow chart of the layout generation method for urban ancient ruins boundary reconstruction in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the urban planning system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] The ancient city wall ruins of a renowned historical and cultural city are undergoing renovation. The area encompasses numerous Ming and Qing dynasty city wall remnants and historical buildings, totaling approximately 30 hectares. With urban development, the density of construction surrounding the ruins continues to increase, leading to an increasing challenge in integrating the ancient ruins with modern urban space. Planning authorities are faced with the challenge of reorganizing the boundary space while preserving the historical remains, improving accessibility and usability within the ancient ruins. However, due to the complex spatial structure of the ancient ruins boundary and the interweaving of historical and cultural elements with modern functional requirements, traditional planning methods struggle to accurately capture the spatial characteristics and determine a scientific renovation strategy. Therefore, a technical approach is needed that can systematically analyze spatial characteristics and automatically generate layout plans.

[0032] In related technologies, spatial data can be acquired through a combination of 3D laser scanning and drone aerial photography, and then combined with CAD software for plan layout design to achieve the planning and design of ancient city boundary reconstruction. The following describes a scenario using the layout generation method for urban ancient city boundary reconstruction in related technologies.

[0033] In a renovation project for an ancient city ruins, the planning team employed traditional design techniques. They first acquired existing imagery through drones, then combined it with field measurements to create a 3D model. Designers then divided the site into functional zones based on their experience and used CAD software to create a floor plan. However, this approach had significant shortcomings: while accurate, the 3D model was difficult to translate into an effective design basis; the functional zones relied heavily on personal experience and lacked objective analysis; and the spatial layout required repeated revisions to meet various requirements, making the entire design process time-consuming and inefficient. The resulting renovation plan did not fully align with the spatial characteristics of the ancient ruins, making it difficult to preserve and enhance the historical environment.

[0034] The layout generation method for urban ancient ruins boundary reconstruction in the embodiment of this application is used to achieve automated layout generation by constructing a regional potential network and spatial flow analysis. This not only improves design efficiency but also ensures the scientific nature and feasibility of the solution. The following describes a scenario using the layout generation method for urban ancient ruins boundary reconstruction in this application.

[0035] This solution was used to plan the boundary reconstruction of a city wall ruins park in a provincial capital city. The system first acquired 3D scan data of the ruins area, extracted key control points, and calculated line-of-sight. Potential network analysis automatically generated activity paths and scientifically divided functional zones. During the plan generation process, the system fully considered factors such as topographic conditions, historical patterns, and the surrounding road network to ensure the rationality of the spatial layout. The resulting renovation plan not only preserved the historical features of the ancient ruins, but also effectively improved the spatial quality and usability of the boundary area. The entire planning process saved approximately 40% of working time compared to traditional methods, and the plan's feasibility was significantly improved.

[0036] It can be seen that the layout generation method for the reconstruction of the boundaries of urban ancient ruins in the embodiment of the present application can not only realize the automatic generation of spatial layout, but also effectively solve the problem that the traditional method relies on manual experience and lacks objective standards, thereby realizing the standardization and intelligence of the reconstruction planning of the boundaries of ancient ruins.

[0037] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a layout generation method for urban ancient ruins boundary reconstruction in an embodiment of the present application.

[0038] S101, obtaining three-dimensional scanning data of the ancient ruins area, extracting building corners, interface intersections and terrain feature points as spatial control points, and recording the spatial coordinates of each spatial control point.

[0039] Among them, the ancient ruins area refers to sites with historical and cultural value and their surrounding areas, including above-ground building remains, underground cultural relics burial areas and historical environmental elements; three-dimensional scanning data refers to a set of point cloud data obtained through laser scanning, photogrammetry and other technologies, which is used to represent the spatial geometric characteristics of the ancient ruins area; building corners refer to the turning point position of the plane outline of the building; interface intersections represent the intersection positions of different functional areas or spatial boundaries; terrain feature points refer to key nodes where the terrain undulations are obvious; spatial control points are used to represent key locations that have an important impact on the spatial pattern of the ancient ruins area.

[0040] This step is performed when the urban planning system needs to conduct spatial analysis and layout planning for the ancient ruins area. Specifically, the urban planning system first uses a three-dimensional laser scanning device to perform a full-scale scan of the ancient ruins area to collect point cloud data containing information such as spatial position and reflection intensity. The point cloud data is then subjected to noise reduction and registration processing to generate a high-precision three-dimensional model. Based on this model, the system identifies and extracts key points with spatial control functions, including the corner points of the building's exterior walls, the junctions of different functional areas, and the commanding heights and depressions of the terrain. Finally, the system records the three-dimensional coordinate information of these spatial control points and establishes a spatial control point database.

[0041] In some embodiments, the extraction and coordinate recording of spatial control points can be achieved in a variety of ways: Optionally, the system can use a method based on curvature analysis to calculate the local curvature value of each point in the point cloud data, identify the location of the curvature mutation as a potential spatial control point, and then use a density clustering algorithm to filter redundant points to ultimately determine the location of the key control point; Optionally, the system can first classify the point cloud data into different categories such as buildings and terrain based on a semantic segmentation algorithm, and then extract feature points from each category, and determine the final spatial control point through multi-level feature fusion. It is understandable that other spatial feature extraction and data processing methods can also be used to determine the spatial control points, which are not limited here.

[0042] S102 , with the spatial control point as the center, calculate the sight range of each spatial control point based on the three-dimensional scanning data and spatial coordinates.

[0043] Among them, the visual range refers to the spatial area that can be directly seen from the spatial control point; the spatial coordinates refer to the position information of the spatial control point in three-dimensional space.

[0044] This step is performed when the urban planning system completes the extraction of spatial control points and needs to analyze the spatial influence range of each control point. Specifically, the urban planning system uses each spatial control point as a viewpoint to generate a 360-degree ray scanning grid on the horizontal plane. The system performs line of sight analysis based on the three-dimensional model and calculates the first intersection point of each ray with surrounding buildings, terrain and other obstacles. By connecting all intersection points, the system generates the line of sight boundary of the control point. This boundary is then projected onto the horizontal plane to obtain a two-dimensional line of sight range. The system repeats this process for each spatial control point and ultimately obtains a complete line of sight analysis result.

[0045] In some embodiments, the calculation of the line of sight range can be achieved in a variety of ways: Optionally, the system can use a ray tracing algorithm to generate uniformly distributed rays in a spherical coordinate system, determine the line of sight obstruction position by calculating the intersection of the rays and the three-dimensional model, and then use a convex hull algorithm to generate the line of sight boundary; Optionally, the system can divide the space into grid cells based on the view cone culling algorithm, and determine the visible range by judging whether each grid cell is located within the view cone and is unobstructed. It is understandable that other line of sight analysis and spatial geometric operation methods can also be used to achieve the determination of the line of sight range, which is not limited here.

[0046] It should be noted that when calculating line-of-sight range, the system uses a polar coordinate system for ray tracing. Specifically, rays are generated with a spatial control point as the pole and true north as the polar axis, following a preset angular step size (e.g., 1 degree). For each ray, the system uses an iterative calculation method with an adaptive step size. A smaller detection step size (e.g., 0.1 meter) is used in close-range areas (e.g., 0-10 meters), and the step size is gradually increased as distance increases until the maximum search radius is reached. At each detection point, the system constructs a detection plane perpendicular to the ray direction and determines whether there is occlusion through line-plane intersection calculations. When occlusion is detected, the system uses trilinear interpolation to determine the exact intersection coordinates. This adaptive step size method ensures accuracy at close range while improving computational efficiency at longer distances. For example, a 5-meter-tall building requires centimeter-level accuracy at a distance of 10 meters, while meter-level accuracy is permitted at a distance of 100 meters.

[0047] S103. Convert the visual range into spatial potential energy value and construct a regional potential energy network.

[0048] Among them, the spatial potential energy value represents the influence intensity of the spatial control point on the surrounding area; the distance decay function refers to a mathematical model that describes the change of spatial influence with distance; the regional potential energy network is used to represent the spatial influence distribution relationship of the entire ancient ruins area; and the grid unit refers to the regular small areas into which the study area is divided.

[0049] When the urban planning system obtains the sight line range of all spatial control points and needs to quantify the spatial influence, this step will be executed. Specifically, the urban planning system first substitutes the sight line range of each spatial control point into a distance decay function such as Gaussian decay or exponential decay, and calculates the potential energy value of each position within the range. The potential energy fields generated by all spatial control points are then superimposed to obtain the potential energy distribution of the entire area. The system discretizes the continuous potential energy distribution into grid data and establishes a network structure containing nodes and connection relationships. Each node in the network stores the potential energy value of the corresponding position, and the connection between nodes reflects the spatial adjacency relationship.

[0050] In some embodiments, the construction of the potential energy network can be achieved in a variety of ways: optionally, the system can adopt a multi-scale grid division method, use a finer grid in areas where the potential energy value changes dramatically, ensure the accurate expression of the potential energy field through adaptive grid subdivision, and then build a network topology based on the adjacency relationship of the grid cells; optionally, the system can use the potential energy value as a node attribute based on Delaunay triangulation to construct an irregular triangulated network, and express the transmission characteristics of spatial influence through the weight of the edge. It is understandable that other spatial data structures and network construction methods can also be used to achieve the generation of regional potential energy networks, which are not limited here.

[0051] It should be noted that in the calculation of spatial potential energy values, the two decay functions, Gaussian decay and exponential decay, reflect different spatial influence decay characteristics. The Gaussian decay function follows the mathematical properties of the normal distribution. Its influence remains relatively stable in areas close to the source, then rapidly decreases within a medium distance, and finally slowly approaches zero at long distances. This gradual characteristic is particularly suitable for describing the range of human activity influence, because in daily activities, people's activity intensity near their residence or workplace tends to remain relatively stable, and then gradually decreases with increasing distance. For example, in the influence analysis of commercial facilities, the frequency of customer visits within a 5-minute walk is not much different, but it drops rapidly beyond this range.

[0052] In contrast, the exponential decay function exhibits a more aggressive decay characteristic, with its influence rapidly decreasing from the source. Although the decay rate decreases with distance, the overall decay curve is steeper. This characteristic is more suitable for describing visual impact or the spatial control of a building, as these factors are often most significant at close range. For example, the visual significance of a historic building is most prominent at close range and gradually weakens with increasing viewing distance.

[0053] In addition, the potential energy value is calculated using a modified Gaussian attenuation model. The system first calculates the normalized distance d = r / R, where r is the actual distance and R is the characteristic distance (determined by the influence range of the spatial control point). The potential energy value is then calculated using the formula E = E0exp(-αd²)(1+βcos θ), where E0 is the initial potential energy value, α is the attenuation coefficient, β is the directional adjustment coefficient, and θ is the angle with respect to the primary orientation. This model not only accounts for distance attenuation but also introduces directional influences. The attenuation coefficient α is dynamically adjusted based on the control point type, for example, 2.0 for landmark buildings and 1.5 for general buildings. The directional adjustment coefficient β reflects the heterogeneity of spatial influences, taking a larger value (e.g., 0.3) for the primary orientation and a smaller value (e.g., 0.1) for the opposite side.

[0054] S104, analyzing the regional potential energy network, generating a natural flow path within the region from high to low according to the spatial potential energy value, determining a preset activity channel based on the natural flow path, and generating a spatial separation line corresponding to the preset activity channel.

[0055] Among them, the natural flow path represents the optimal movement route that follows the potential energy gradient; the preset activity channel refers to the main pedestrian traffic path in the plan; and the spatial separation line is used to represent the boundary line between different functional areas.

[0056] This step is performed when the urban planning system completes the construction of the regional potential energy network and needs to determine the main activity paths. Specifically, based on the topological structure of the potential energy network, the urban planning system uses path search methods such as the steepest descent method or the A* algorithm to find the optimal path from the high potential energy value area to the low potential energy value area. The system spatially clusters and simplifies the multiple natural flow paths generated to form preset activity channels. Then, based on the direction and distribution characteristics of these channels, the system generates spatial separation lines perpendicular to the channel direction to divide different spatial areas.

[0057] In some embodiments, natural flow paths can be generated in a variety of ways: Alternatively, the system can use a modified Dijkstra algorithm, using potential energy differences as path costs, to iteratively optimize multiple paths with the steepest potential energy gradients, and then employ the Douglas-Peucker algorithm to smooth and simplify the paths. Alternatively, the system can convert the potential energy distribution into a vector field based on flow field analysis methods, generate natural flow paths through particle tracking simulation, and combine density clustering to determine the primary active channels. It is understood that other path planning and spatial analysis methods can also be used to determine active channels, and these are not limited here.

[0058] It should be noted that the A* algorithm optimizes paths by combining potential energy gradients and distance factors in natural flow path generation. The algorithm treats each grid cell in the potential energy network as a node in the state space and guides the search process through an evaluation function f(n). The evaluation function consists of two parts: a known cost g(n) and a heuristic estimate h(n). g(n) reflects the cumulative potential energy change from the starting point to the current node and is obtained by calculating and summing the potential energy differences between adjacent nodes on the path. The larger the potential energy difference, the more obvious the spatial flow trend and the smaller the corresponding path cost. h(n) uses the Euclidean distance from the current node to the target point as an estimate to ensure that the search process moves in the target direction.

[0059] In practice, the algorithm maintains a priority queue to store nodes to be expanded, selecting the node with the lowest evaluation value for expansion. For each expanded node, the algorithm calculates the evaluation values of all its neighboring nodes and updates the optimal path. To ensure that the generated path better reflects the characteristics of actual spatial flow, the algorithm also considers path smoothness and spatial accessibility. In this way, the resulting natural flow path both conforms to the distribution characteristics of the potential energy field and exhibits good spatial continuity.

[0060] S105. Divide the functional zones according to the jump positions of the potential energy values, and generate an initial spatial layout including space dividing lines and functional zones.

[0061] Among them, the jump position represents the area where the potential energy value changes significantly within a short distance; the functional zoning refers to the spatial area with similar usage characteristics; the initial spatial layout is used to represent the spatial organization plan of the transformation area.

[0062] This step is performed when the urban planning system determines the spatial separation lines and needs to carry out functional layout. Specifically, the urban planning system analyzes the distribution of potential energy values in the potential energy network and identifies locations where the potential energy values change by more than a threshold within a preset distance range. The system uses these jump locations as potential boundaries of functional zoning, and combines them with the previously generated spatial separation lines to form preliminary functional zoning through a region growing algorithm. The system then integrates the spatial separation line and functional zoning information to generate an initial spatial layout plan that includes spatial structure and functional layout.

[0063] In some embodiments, functional zoning can be achieved through a variety of methods: Optionally, the system can use a graph-cut-based zoning method, using the potential energy transition position as a constraint on the segmentation boundary, achieving optimal regional division through a minimum cut algorithm, and optimizing the zoning morphology based on spatial adjacency. Optionally, the system can use a clustering analysis method to group regions with similar potential energy values into functional zones, and control the scale and number of zones by adjusting clustering parameters. It is understood that other spatial zoning and layout optimization methods can also be used to determine functional zones, which are not limited here.

[0064] S106. Adjust the initial spatial layout based on urban planning control indicators and the surrounding road network to obtain a boundary transformation layout plan for the ancient ruins area.

[0065] Among them, urban planning control indicators refer to the various technical and economic indicators formulated by the planning management department; the surrounding road network refers to the existing traffic system around the transformation area; and the boundary transformation layout plan is used to represent the finalized spatial transformation plan.

[0066] This step occurs when the urban planning system generates an initial spatial layout and needs to optimize and adjust the plan. Specifically, the urban planning system first checks whether the initial layout meets planning control indicators such as floor area ratio, building density, and green space ratio. It then analyzes the hierarchical structure and connectivity of the surrounding road network and adjusts the entrance and exit locations of functional zones and the internal road system. Through multiple rounds of iterative optimization, the system continuously adjusts the spatial layout until all planning requirements are met, ultimately forming an implementable boundary transformation layout plan.

[0067] In some embodiments, layout optimization can be achieved through a variety of methods: Optionally, the system can employ a multi-objective optimization algorithm to convert planning indicators into constraints and optimization objectives, and then search for the optimal solution using a genetic algorithm or simulated annealing algorithm to achieve overall optimization of the layout. Optionally, the system can use a heuristic adjustment strategy to gradually adjust the boundaries of functional zones and building layouts based on the degree of deviation of different indicators until all planning requirements are met. It is understood that other scheme optimization and indicator control methods can also be used to determine the layout, and these are not limited here.

[0068] It's important to note that in layout optimization, multi-objective optimization algorithms simultaneously consider multiple planning objectives to find the optimal solution. The algorithm first converts various planning indicators into quantifiable objective functions, such as building density deviation, space utilization efficiency, and traffic flow conflicts. These objective functions often have constraints on each other; for example, increasing building density may reduce the quality of public space. The algorithm addresses this multi-objective trade-off using the concept of Pareto optimality, searching for a set of non-dominated solutions within the solution space—that is, solutions that cannot improve one objective without compromising others.

[0069] It should be noted that the layout optimization process employs an evolutionary computation framework, gradually improving the quality of solutions through population iteration. In each iteration, the algorithm first generates a set of candidate solutions, each corresponding to a possible layout solution. It then ranks each solution using non-dominated sorting and calculates the congestion level of the solutions to maintain population diversity. During selection, the algorithm prioritizes high-quality solutions on the Pareto front and generates new candidate solutions through crossover and mutation. Through multiple iterations, it ultimately converges to a set of optimal solutions that meet all planning criteria and are balanced.

[0070] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , which is another flow chart of the layout generation method for urban ancient ruins boundary reconstruction in an embodiment of the present application.

[0071] S201. Acquire three-dimensional scanning data of the ancient ruins area, extract building corners, interface intersections, and terrain feature points as spatial control points, and record the spatial coordinates of each spatial control point.

[0072] Referring to step S101 , the urban planning system records each spatial control point.

[0073] In some embodiments, the urban planning system will perform data processing based on aerial photography data and historical map data, that is, the urban planning system will read the aerial photography data and historical map data of the ancient ruins area; perform geometric correction on the aerial photography data to generate an orthophoto; perform coordinate conversion on the historical map data to generate a geographic information layer; and integrate and generate a spatial database based on the orthophoto and geographic information layers.

[0074] Among them, aerial photography data refers to aerial images of the ancient city area obtained using drones and other equipment; historical map data refers to map data that reflects the spatial pattern of the ancient city area during the historical period; geometric correction is used to eliminate the deformation and distortion of aerial images; orthophotos refer to corrected orthographic projection aerial images; geographic information layers refer to vector or raster data with geographic coordinates; spatial databases are used to store and manage various types of spatial data.

[0075] When preparing basic data, the urban planning system needs to process and integrate data from multiple sources. Specifically, the system first obtains aerial imagery and historical maps of the ancient ruins area. The aerial imagery is geometrically corrected and mosaicked to generate orthophotos covering the entire study area. The historical maps are then digitized and aligned, converting them into geographic information layers. Finally, the orthophotos and geographic information layers are spatially overlaid and analyzed to create a complete spatial database.

[0076] In some embodiments, spatial data processing and integration can be achieved through the following methods: Optionally, the urban planning system can use a control point method to perform geometric correction on aerial images, employ image registration technology to achieve coordinate transformation of historical maps, and construct a multidimensional database using a spatial data engine. Optionally, the urban planning system can also process aerial data based on photogrammetry, process historical maps using map projection transformation, and manage spatial data using a relational database. It is understood that other data processing methods can also be used to achieve spatial data integration, and these are not limited here.

[0077] S202: Calculate the sight range of each spatial control point based on the three-dimensional scanning data and spatial coordinates, with the spatial control point as the center.

[0078] Referring to step S102 , the urban planning system calculates the sight line range.

[0079] In some embodiments, the urban planning system calculates the line of sight range based on spatial collision simulation, that is, the urban planning system generates a fan-shaped scanning grid with each spatial control point as the center point to obtain a horizontal ray array; performs spatial collision simulation along the ray array to extract the three-dimensional coordinates of the ray array at each occlusion point; generates a line of sight boundary polygon based on the three-dimensional coordinates, and converts the line of sight boundary polygon into horizontal projection area data; vectorizes the projection area data to generate a line of sight range.

[0080] Among them, the fan-shaped scanning grid represents the grid division of the fan-shaped area within a preset radius with the spatial control point as the center; the ray array refers to multiple radial rays starting from the center point of the fan-shaped scanning grid; the blocking point represents the point where the ray intersects with obstacles such as buildings and terrain; the line-of-sight boundary polygon is used to represent the boundary outline of the visible range from the spatial control point; the projection area data refers to the projection data of the line-of-sight boundary polygon on the horizontal plane; the line-of-sight range represents the actual visible range boundary of the spatial control point.

[0081] When calculating the line-of-sight range of a spatial control point, the urban planning system first establishes a sector-shaped scanning grid and generates a ray array. Specifically, the system generates multiple rays at preset angle intervals to form a ray array. It then performs spatial collision detection along each ray, recording the three-dimensional coordinates of the intersection points between the ray and the obstacle. Based on these three-dimensional coordinates, it constructs a closed polygon as the line-of-sight boundary. Finally, it projects this polygon onto a horizontal plane and performs vector processing to obtain the boundary outline of the line-of-sight range.

[0082] In some embodiments, the calculation of the line of sight accessibility of the spatial control point can be achieved in the following ways: Optionally, the urban planning system can first set the scanning angle step size and the maximum scanning radius, generate a uniformly distributed fan-shaped grid with the spatial control point as the center, generate a ray at the center of each grid unit, use the ray projection method to detect the position of the obstacle, record the coordinates of the obstacle point and construct the boundary polygon; Optionally, the urban planning system can also calculate the visible range of the spatial control point based on the three-dimensional scanning point cloud data using the viewpoint visibility analysis algorithm, generate the visible boundary polygon through the convex hull algorithm, and perform a projection transformation to obtain the accessibility range. It is understandable that other line of sight analysis methods can also be used to achieve the calculation of the line of sight accessibility, which is not limited here.

[0083] S203. Substitute the sight range into the distance attenuation function to generate a potential energy distribution field.

[0084] Among them, the distance decay function represents a mathematical model that describes the attenuation of spatial influence with distance, including Gaussian decay function and exponential decay function; the potential energy distribution field refers to the continuous distribution of the influence of the spatial control point on the two-dimensional plane; the attenuation parameter is used to represent the rate and range of influence decay; the potential energy value represents the intensity of the influence of the control point on a certain point in space.

[0085] This step is performed when the urban planning system completes the calculation of the line of sight range and needs to convert the qualitative spatial visual relationship into a quantitative influence distribution. Specifically, the urban planning system first sets the initial potential energy value based on the nature and importance of the spatial control point, and then selects a suitable distance attenuation function. For active control points such as pedestrian gathering points, a Gaussian attenuation function is used; for visual control points such as landmark buildings, an exponential attenuation function is used. The urban planning system substitutes each location point within the line of sight range into the selected attenuation function and calculates the potential energy value of the point. By calculating all points within the line of sight range, a potential energy field reflecting the influence distribution of a single spatial control point is finally generated.

[0086] In some embodiments, the generation of the potential energy distribution field can be achieved in a variety of ways: Optionally, the urban planning system can use an adaptive attenuation parameter method to dynamically adjust the parameters of the attenuation function according to the size and shape of the line of sight, first calculate the potential energy value of the boundary point of the range to determine the attenuation coefficient, and then use the interpolation algorithm to generate a continuous potential energy distribution field; Optionally, the urban planning system can be based on a multi-level attenuation model, divide the influence of the spatial control point into three levels: short-range, medium-range and long-range, use different attenuation functions and parameters for each level, and generate the final potential energy distribution field through weighted superposition. It is understandable that other mathematical models and parameter optimization methods can also be used to achieve the generation of the potential energy distribution field, which is not limited here.

[0087] In some embodiments, the urban planning system generates a potential energy distribution field based on the attenuation function curve, that is, the urban planning system substitutes the boundary coordinate sequence of the line of sight range into the grid division function to generate a calculation grid unit matrix; reads the spatial distance value from each grid unit to the spatial control point in the calculation grid unit matrix to generate distance matrix data; calculates the potential energy attenuation coefficient of each grid unit based on the distance matrix data, generates an attenuation function curve, and generates a potential energy distribution field based on the attenuation function curve conversion.

[0088] Among them, the grid division function represents the mathematical function that divides the plane area into regular grid cells; the computational grid cell matrix refers to a two-dimensional array composed of multiple regular grid cells; the spatial distance value represents the Euclidean distance from the center point of the grid cell to the spatial control point; the potential energy attenuation coefficient is used to represent the proportional factor of the spatial potential energy attenuation with distance; the attenuation function curve refers to the mathematical curve that describes the relationship between potential energy value and distance; the potential energy distribution field data represents the distribution of spatial potential energy in the region.

[0089] When generating a potential energy distribution field, the urban planning system needs to convert the line of sight into specific potential energy values. Specifically, the urban planning system first grids the line of sight to establish a computational grid cell matrix. It then calculates the distance from each grid cell to the spatial control point to generate a distance matrix. Based on the distance matrix data, it uses functions such as exponential decay or power-law decay to calculate the potential energy attenuation coefficient and establish an attenuation function curve. Finally, the attenuation function is applied to each grid cell to obtain the complete potential energy distribution field data.

[0090] In some embodiments, the potential energy distribution field can be generated by the following methods: Optionally, the urban planning system can use a square grid division method, set the grid size and generate a uniform grid matrix, calculate the distance between grid centers, apply a Gaussian decay function to calculate the potential energy value, and generate a continuous potential energy field through interpolation. Optionally, the urban planning system can also perform spatial segmentation based on an irregular triangulated network (TIN), calculate node potential energy values using a distance weighting method, and solve the potential energy distribution using numerical simulation methods. It is understood that other numerical calculation methods can also be used to construct the potential energy field, which is not limited here.

[0091] S204: Perform a superposition operation on the potential energy distribution field of each spatial control point to determine the regional potential energy distribution.

[0092] Among them, the superposition operation represents the numerical synthesis of the influence of multiple potential energy distribution fields; the weight coefficient refers to the importance of the influence of different types of spatial control points; and the regional potential energy distribution is used to represent the comprehensive spatial influence field of the entire study area.

[0093] This step is performed when the urban planning system obtains the individual potential energy distribution fields of all spatial control points and needs to comprehensively evaluate the spatial influence of the region as a whole. Specifically, the urban planning system first assigns a weight coefficient to each potential energy distribution field based on the functional attributes and importance of the spatial control points. Then, at each location in the study area, the potential energy values generated by all control points are weighted and superimposed to obtain the comprehensive potential energy value of that location. By performing superposition calculations on the entire area, the urban planning system ultimately generates a regional potential energy distribution that reflects the overall distribution characteristics of spatial influence.

[0094] In some embodiments, the superposition of potential energy distribution fields can be achieved in a variety of ways: Optionally, the urban planning system can use the hierarchical analysis method to determine the weight coefficient, establish a judgment matrix for the importance of spatial control points, calculate the weight of each control point through eigenvalues, and then perform linear superposition of potential energy values according to the weight; Optionally, the urban planning system can use the fuzzy comprehensive evaluation method to fuzzily evaluate the influence of spatial control points according to different criteria, and achieve nonlinear superposition of potential energy values through membership functions and fuzzy operation rules. It is understandable that other multi-criteria decision-making and spatial superposition methods can also be used to determine the regional potential energy distribution, which is not limited here.

[0095] S205. Grid sampling is performed on the regional potential energy distribution to generate a potential energy data matrix.

[0096] Among them, grid sampling refers to the process of discretizing the continuous potential energy distribution into a regular grid; the potential energy data matrix refers to the two-dimensional array that stores the potential energy values of the grid cells; the sampling spacing is used to represent the distance between adjacent sampling points; and the sampling accuracy refers to the grid density of the discretization process.

[0097] This step is performed when the urban planning system obtains a continuous regional potential energy distribution and needs to convert it into calculable discrete data. Specifically, the urban planning system first determines the appropriate sampling spacing based on the scale of the study area and the potential energy variation characteristics, and establishes a regular sampling grid. The potential energy value is then obtained at each grid node position, and the potential energy value is calculated using the bilinear interpolation method for the area between the grid nodes. The urban planning system stores the potential energy values of all sampling points in a two-dimensional array according to their spatial position relationship, forming a data matrix that reflects the regional potential energy distribution characteristics.

[0098] In some embodiments, grid sampling of potential energy distribution can be achieved in a variety of ways: Optionally, the urban planning system can adopt an adaptive grid subdivision method, use denser sampling spacing in areas with larger potential energy gradients, realize dynamic subdivision of the grid through a quadtree structure, and finally uniformly store the sampling data of different scales in a multi-resolution data matrix; Optionally, the urban planning system can be based on surface fitting technology, first fit the potential energy distribution surface with a spline function, and then sample at regular grid points, and ensure that the sampling results retain key features and avoid data redundancy through numerical optimization. It is understandable that other spatial discretization and data compression methods can also be used to achieve the generation of the potential energy data matrix, which is not limited here.

[0099] S206. Construct a regional potential energy network including multiple grid cells based on the potential energy data matrix.

[0100] Among them, the grid unit represents the basic calculation unit in the potential energy data matrix; the regional potential energy network refers to the topological structure that describes the spatial correlation relationship between grid units; the connection relationship represents the adjacency between adjacent grid units; and the potential energy gradient is used to represent the rate of change of potential energy between adjacent units.

[0101] This step is performed when the urban planning system completes the construction of the potential energy data matrix and needs to establish a network structure that reflects spatial correlation. Specifically, the urban planning system uses each grid unit as a network node and establishes connections between nodes based on the spatial adjacency of the grid. For each pair of adjacent nodes, the system calculates the potential energy gradient between them and uses the gradient value as the weight attribute of the connection. In this way, the urban planning system converts discrete potential energy data into a potential energy network with complete topological structure and weight information.

[0102] In some embodiments, the construction of a regional potential energy network can be achieved in a variety of ways: Optionally, the urban planning system can adopt a multi-directional connection model, adding diagonal connections on the basis of an orthogonal grid, adjusting the potential energy gradient calculation in different directions through directional weighting coefficients, and ultimately constructing a network structure with richer spatial correlation; Optionally, the urban planning system can be based on a graph theory optimization method, screening key connections through a minimum spanning tree algorithm, and then combining the principle of spatial proximity to supplement local connections, forming a topological structure that can both reflect the main potential energy transfer path and maintain network integrity. It is understandable that other network construction and optimization methods can also be used to achieve the generation of a regional potential energy network, which is not limited here.

[0103] In some embodiments, the urban planning system will impose conditional constraints on the plan, that is, the urban planning system will substitute the three-dimensional coordinates of the topographic measurement points into the interpolation function to generate a surface elevation grid; perform Boolean operations on the point data of underground pipelines to generate a pipeline density grid; convert the geological structure data into a bearing capacity coefficient matrix; and generate a constraint condition data set based on the surface elevation grid, the pipeline density grid, and the bearing capacity coefficient matrix.

[0104] Among them, the surface elevation raster represents regular grid data describing the undulations of the terrain; the pipeline density raster refers to the raster data reflecting the distribution density of underground pipelines; the bearing capacity coefficient matrix is used to represent the spatial distribution of the foundation bearing capacity; and the constraint condition dataset represents a collection of various restrictive conditions that affect the planning layout.

[0105] When constructing constraints, the urban planning system needs to comprehensively consider factors such as topography, underground pipelines, and geological conditions. Specifically, the urban planning system first interpolates discrete topographic measurement points to generate a continuous surface elevation model; performs density analysis on underground pipeline data to calculate the distribution of pipelines per unit area; and converts geological survey data into a distribution of foundation bearing capacity. Finally, this data is integrated to generate a complete constraint dataset.

[0106] In some embodiments, constraints can be constructed using the following methods: Alternatively, the urban planning system can use Kriging interpolation to generate a terrain model, analyze pipeline distribution using kernel density estimation, and calculate bearing capacity distribution through geological parameter conversion. Alternatively, the urban planning system can construct terrain based on triangulation interpolation, assess pipeline impacts using buffer zone analysis, and determine foundation bearing capacity using fuzzy comprehensive evaluation. It is understood that other analytical methods can also be used to construct constraints, and these are not limited here.

[0107] S207, analyzing the regional potential energy network, generating a natural flow path within the region according to the spatial potential energy values from high to low, determining a preset activity channel based on the natural flow path, and generating a spatial separation line corresponding to the preset activity channel.

[0108] Referring to step S104 , the urban planning system generates space separation lines.

[0109] S208. Divide the functional zones according to the transition positions of the potential energy values, and generate an initial spatial layout including space separation lines and functional zones.

[0110] Referring to step S105 , the urban planning system generates an initial spatial layout.

[0111] It should be noted that the functional zoning is based on an improved region growing algorithm. The system first determines the partition seed points at the locations with the maximum potential energy gradient and then expands from these seed points. During the expansion process, potential energy similarity is used as the merging criterion. The similarity function is S = exp(-|ΔE| / σ), where ΔE is the potential energy difference and σ is the scale parameter. When the similarity exceeds a threshold (e.g., 0.75), regions are allowed to merge. To avoid overly complex partition boundaries, the system introduces shape constraints, which adjust the growth process by calculating the compactness and boundary smoothness of the partitions. For example, if growth in a certain direction causes the partition shape to become irregular (compactness below 0.6), the system will appropriately increase the merging threshold in that direction.

[0112] S209: Perform a topological operation on the boundary geometry data of multiple grid cells in the initial spatial layout to obtain connection relationship data of boundary line segments.

[0113] Among them, boundary geometry data represents the spatial coordinates and shape characteristics of the functional zoning boundary; topological operations refer to the mathematical process of analyzing the positional relationship between spatial elements; connection relationship data is used to represent the spatial relationships such as intersection and adjacency between boundary segments; boundary nodes represent the endpoint coordinates of boundary segments; boundary simplification parameters are used to control the complexity of the boundary shape.

[0114] This step is performed when the urban planning system generates the initial spatial layout and needs to clarify the spatial relationship between each partition. Specifically, the urban planning system first extracts the geometric information of the grid cell boundaries, including vertex coordinates and line segment directions. These boundaries are then topologically checked to identify intersections, overlapping segments, and endpoint connections between line segments. The system merges adjacent line segments through iterative operations, eliminating redundant nodes while maintaining the basic morphological characteristics of the boundaries. Finally, the urban planning system organizes the processed boundary data into a spatial data structure with complete topological properties.

[0115] In some embodiments, boundary topology operations can be implemented in a variety of ways: Optionally, the urban planning system can use a plane graph algorithm to construct boundary segments into a graph structure with nodes and edges, detect segment intersections through a plane scanning method, establish a complete topological relationship table, and then simplify the boundary based on the Douglas-Peucker algorithm to ultimately generate topologically correct boundary data; Optionally, the urban planning system can use a Delaunay triangulation method to construct a triangulated network using a boundary point set, reconstruct the connection relationship of the boundary segments by analyzing the triangle adjacency relationship, and then optimize the boundary shape in combination with morphological operations. It is understandable that other spatial data processing and topological analysis methods can also be used to determine boundary relationships, which are not limited here.

[0116] S210: Generate a three-dimensional grid model based on the connection relationship data and building volume parameters, and determine building density distribution data of the three-dimensional grid model.

[0117] Among them, building volume parameters represent the three-dimensional geometric characteristics of the building, such as height and area; the three-dimensional grid model refers to the three-dimensional grid structure that expresses the distribution of spatial volume; building density distribution data is used to represent the amount of buildings per unit area; the floor area ratio represents the ratio of the total building area to the land area; and the building spacing parameter is used to control the spatial relationship between buildings.

[0118] This step is performed when the urban planning system needs to perform three-dimensional spatial processing after completing the topological analysis of the plan layout. Specifically, the urban planning system determines the building volume control parameters for each area based on the attributes of the functional zoning and planning requirements. It then constructs a three-dimensional grid framework based on the boundary geometry data and maps the building volume parameters to the grid cells. The system calculates the building density value for each grid cell through spatial interpolation, taking into account the gradual relationship between building heights and the requirements of visual corridors. Finally, it generates a three-dimensional density model that reflects the spatial distribution characteristics of the buildings.

[0119] In some embodiments, the generation of a three-dimensional grid model can be achieved through a variety of methods: Optionally, the urban planning system can use a parametric modeling method to expand the three-dimensional space filling based on the building density control surface, control the growth process of the building volume through morphological grammar rules, and finally optimize the generated spatial structure to ensure that the sunlight and ventilation requirements are met; Optionally, the urban planning system can analyze the spatial integration and accessibility of the building layout based on space syntax theory, adjust the spatial position and volume relationship of the building through a multi-constraint optimization algorithm, and generate a three-dimensional model that meets the density requirements and has good spatial quality. It is understandable that other three-dimensional modeling and spatial optimization methods can also be used to determine the building density distribution, which is not limited here.

[0120] S211. Substitute the spatial coordinates of the natural flow path into the flow prediction function to calculate the traffic capacity parameters.

[0121] Among them, the traffic prediction function represents a mathematical model that describes the carrying capacity of a spatial path; the traffic capacity parameter refers to the maximum passenger flow that the path can accommodate; the spatial coordinates are used to represent the geometric location information of the path; the path grade represents the service level of the natural flow path; and the peak coefficient is used to reflect the temporal distribution characteristics of passenger flow.

[0122] This step is performed when the urban planning system determines the natural flow path and needs to evaluate the path's service capacity. Specifically, the urban planning system first determines an appropriate flow prediction model based on the path's spatial location and morphological characteristics. It then calculates the path's basic capacity, taking into account physical conditions such as path width and slope, and the flow generation intensity of surrounding functional zones. The system modifies the basic capacity by introducing a time variation coefficient and a directional distribution coefficient, ultimately obtaining a set of parameters that reflect the capacity at different time periods and sections.

[0123] In some embodiments, traffic capacity prediction can be achieved through a variety of methods: Alternatively, the urban planning system can employ a queuing theory model, treating the path as a service system. By establishing mathematical models of arrival and service flows, the system's service level and saturation can be calculated, and reasonable traffic capacity parameters can be determined based on simulation verification. Alternatively, the urban planning system can utilize historical data to train a traffic flow prediction model based on a neural network approach, and by considering multi-dimensional features such as spatial form and functional layout, dynamic prediction of path capacity can be achieved. It is understood that other traffic engineering and passenger flow prediction methods can also be used to achieve traffic capacity assessment, and these are not limited here.

[0124] S212. Calculate green space layout data based on the green space coverage threshold and constraint condition data set.

[0125] Among them, the green space coverage ratio threshold represents the minimum green space ratio required by the plan; the constraint condition dataset refers to the various restrictive requirements that affect the green space layout; the green space layout data is used to represent the spatial distribution plan of the green space; the ecological corridor represents the channel connecting each green space patch; and the buffer area is used to represent the transition space between green space and buildings.

[0126] This step is executed when the urban planning system needs to plan a green space system after completing the building density distribution analysis. Specifically, the urban planning system first determines the target green space coverage rate based on planning standards, while taking into account constraints such as terrain conditions and building layout. Then, based on the least resistance path analysis, it plans major green space patches in areas with low building density and connects these patches into a network through ecological corridors. The system adjusts the shape and position of the green space through a spatial optimization algorithm to ensure that the green space system not only meets the coverage requirements but also provides good landscape and ecological benefits.

[0127] In some embodiments, green space layout planning can be achieved through a variety of methods: Optionally, the urban planning system can adopt a landscape pattern optimization method to construct a green space spatial structure based on the patch-corridor-matrix theory, optimize the spatial organization of green spaces by setting connectivity indicators and landscape heterogeneity indicators, and ultimately form a complete green space network system; Optionally, the urban planning system can be based on a multi-scale analysis method, gradually refining the planning from the macro-ecological network to the micro-green space unit, and determining the layout plan of green spaces at all levels through hierarchical decomposition and comprehensive integration. It is understandable that other landscape ecology and spatial planning methods can also be used to achieve green space layout optimization, which is not limited here.

[0128] S213. Based on the building density distribution data, traffic capacity parameters and green space layout data, integrate and generate the boundary transformation layout plan of the ancient ruins area.

[0129] Among them, the boundary transformation layout plan represents a comprehensive spatial plan that integrates various planning elements; plan integration refers to the coordination and unification of planning results at different levels; spatial coordination is used to evaluate the spatial relationship between various elements; implementation guidelines represent the technical requirements for guiding the implementation of the plan; and plan evaluation indicators are used to measure the comprehensive benefits of the planning plan.

[0130] This step is executed when the urban planning system completes various special plans and needs to form a unified spatial plan. Specifically, the urban planning system first establishes a multi-level scheme integration framework, converting data such as building density, traffic capacity, and green space layout into a unified spatial reference system. Then, through a multi-objective optimization algorithm, the layout relationship of various spatial elements is adjusted to resolve potential conflicts. By setting spatial coordination evaluation indicators, the system repeatedly optimizes the plan until a balance is achieved, ultimately generating a holistic boundary transformation plan that meets all functional requirements.

[0131] In some embodiments, scheme integration and optimization can be achieved through a variety of methods: Alternatively, the urban planning system can employ system dynamics methods to establish a mathematical model reflecting the interactions between various spatial elements, verify the scheme's dynamic equilibrium through simulation analysis, and determine key control parameters based on sensitivity analysis, ultimately forming an adaptable planning scheme. Alternatively, the urban planning system can analyze the scheme's overall spatial structural characteristics based on space syntax theory, optimize the overall spatial organization by adjusting local spatial relationships, and achieve a unified functional layout, traffic organization, and landscape effect. It is understood that other comprehensive evaluation and scheme optimization methods can also be employed to generate boundary transformation layout schemes, which are not limited here.

[0132] In the embodiments of the present application, due to the innovative spatial analysis method based on potential energy network, the spatial characteristics of the ancient ruins boundary are quantitatively expressed through the line of sight range and potential energy value, and a complete automatic layout generation process is established, so that complex spatial relationships can be converted into computable data indicators, and the intelligent generation of layout plans is realized. By introducing key technologies such as spatial control point identification, line of sight analysis, and potential energy calculation, the problems of reliance on manual experience, lack of objective standards, and low efficiency in traditional planning methods are effectively solved. At the same time, by establishing a multi-level constraint system and optimization strategy, the feasibility of the generation plan is ensured, and the standardization, intelligence and efficiency of the ancient ruins boundary reconstruction plan are achieved, providing a new technical path for the protection and utilization of historical and cultural heritage.

[0133] The following describes the urban planning system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of the urban planning system in an embodiment of the present application.

[0134] It should be noted that Figure 3 The structure of the urban planning system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0135] like Figure 3 As shown, the urban planning system includes a CPU 301, which can perform various appropriate actions and processes based on programs stored in a ROM 302 or programs loaded from a storage unit 308 into a RAM 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0136] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, push button switches, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0137] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, the various functions defined in the present invention are performed.

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0139] Specifically, the urban planning system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the layout generation method for urban ancient ruins boundary reconstruction provided by the above embodiment is implemented.

[0140] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the urban planning system described in the above embodiments, or may exist independently and not incorporated into the urban planning system. The storage medium carries one or more computer programs, which, when executed by a processor of the urban planning system, enable the urban planning system to implement the layout generation method for urban ancient ruins boundary reconstruction provided in the above embodiments.

[0141] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0142] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

Claims

1. A layout generation method for urban ancient ruins boundary reconstruction, characterized in that: Applied to an urban planning system, the method comprises: Acquire three-dimensional scanning data of the ancient ruins area, extract building corners, interface intersections and terrain feature points as spatial control points, and record the spatial coordinates of each of the spatial control points; Taking the spatial control point as the center, calculating the sight range of each of the spatial control points based on the three-dimensional scanning data and the spatial coordinates; Convert the visual accessibility range into spatial potential energy values and construct a regional potential energy network; Analyzing the regional potential energy network, generating a natural flow path within the region according to spatial potential energy values from high to low, determining a preset activity channel based on the natural flow path, and generating a spatial separation line corresponding to the preset activity channel; Divide the functional areas according to the transition position of the potential energy value, and generate an initial spatial layout including the space separation line and the functional areas; the transition position is a position within a preset distance range where the potential energy difference value is greater than the maximum allowable difference; The initial spatial layout is adjusted based on urban planning control indicators and the surrounding road network to obtain a boundary reconstruction layout plan for the ancient ruins area.

2. The method according to claim 1, characterized in that The step of calculating the sight line range of each of the spatial control points based on the three-dimensional scanning data and the spatial coordinates with the spatial control point as the center specifically includes: Generating a fan-shaped scanning grid with each of the spatial control points as a center point to obtain a ray array in a horizontal direction; Performing spatial collision simulation along the ray array to extract the three-dimensional coordinates of each occlusion point of the ray array; Generating a line-of-sight boundary polygon according to the three-dimensional coordinates, and converting the line-of-sight boundary polygon into horizontal projection area data; Vectorization is performed on the projection area data to generate a sight line range.

3. The method according to claim 1, characterized in that The step of converting the visual range into a spatial potential energy value and constructing a regional potential energy network specifically includes: Substituting the sight range into the distance attenuation function to generate a potential energy distribution field; Performing a superposition operation on the potential energy distribution field of each of the spatial control points to determine the regional potential energy distribution; Performing grid sampling on the regional potential energy distribution to generate a potential energy data matrix; A regional potential energy network comprising a plurality of grid cells is constructed based on the potential energy data matrix.

4. The method according to claim 3, characterized in that The step of substituting the line of sight range into the distance attenuation function to generate a potential energy distribution field specifically includes: Substituting the boundary coordinate sequence of the sight-accessible range into the grid division function to generate a computational grid unit matrix; Reading the spatial distance value from each grid cell in the calculation grid cell matrix to the spatial control point to generate distance matrix data; The potential energy attenuation coefficient of each grid unit is calculated based on the distance matrix data to generate an attenuation function curve, and the potential energy distribution field is generated based on the attenuation function curve.

5. The method according to claim 1, wherein Before the steps of acquiring three-dimensional scanning data of the ancient ruins area, extracting building corners, interface intersections, and terrain feature points as spatial control points, and recording the spatial coordinates of each of the spatial control points, the method further includes: Reading aerial photography data and historical map data of the ancient ruins area; Performing geometric correction on the aerial photography data to generate an orthophoto; Performing coordinate conversion on the historical map data to generate a geographic information layer; Based on the orthophoto and geographic information layers, a spatial database is generated through integration.

6. The method according to claim 1, characterized in that After the step of converting the visual access range into a spatial potential energy value and constructing a regional potential energy network, the method further includes: Substitute the three-dimensional coordinates of the topographic measurement points into the interpolation function to generate the surface elevation grid; Perform Boolean operations on underground pipeline point data to generate pipeline density grids; Convert geological structure data into bearing capacity coefficient matrix; A constraint condition data set is generated based on the surface elevation grid, the pipeline density grid, and the bearing capacity coefficient matrix.

7. The method according to claim 1, characterized in that The step of adjusting the initial spatial layout based on urban planning control indicators and the surrounding road network to obtain a boundary reconstruction layout plan for the ancient ruins area specifically includes: Performing a topological operation on the boundary geometry data of the plurality of grid cells in the initial spatial layout to obtain connection relationship data of the boundary segments; generating a three-dimensional grid model according to the connection relationship data and building volume parameters, and determining building density distribution data of the three-dimensional grid model; Substituting the spatial coordinates of the natural flow path into the flow prediction function to calculate the traffic capacity parameters; Calculate green space layout data based on green space coverage threshold and constraint condition dataset; A boundary reconstruction layout plan for the ancient ruins area is generated based on the building density distribution data, the traffic capacity parameters and the green space layout data.

8. An urban planning system, characterized in that: The urban planning system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the urban planning system to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an urban planning system, the urban planning system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on an urban planning system, the urban planning system is caused to perform the method according to any one of claims 1 to 7.

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