Land space planning data multi-dimensional analogue simulation method and system

Abstract

The invention belongs to the technical field of territorial space planning, and discloses a territorial space planning data multi-dimensional simulation method and system, and the method comprises the following steps: S1, obtaining the multi-dimensional feature data of a main urban area, presetting the region type, and dividing a plurality of unit regions through combining the multi-dimensional feature data with the region type; s2, constructing a plurality of unit areas according to the multi-dimensional feature data to obtain a plurality of unit simulation models; s3, combining the plurality of unit simulation models to obtain a three-dimensional simulation model; according to the invention, seamless joint of two unit simulation models can be realized; through the setting of the fitness function and the deficient area, the decision maker is helped to accurately position the position of the new urban area, and the optimal coordinate distribution combination of each area category in the new urban area is selected, so that the balance between the new urban area and the main urban area is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of territorial space planning. More specifically, the present invention relates to a multi-dimensional simulation method and system for territorial space planning data. Background Art

[0002] Territorial space planning is a comprehensive planning method that involves overall arrangements and long-term planning for land use, natural resource management, ecological environment protection, economic and social development, etc. in a country or region. With the advent of the information age, the rapid development of technologies such as big data, 3D GIS, spatial data mining and analysis has brought unprecedented opportunities and challenges to the field of territorial space planning. As an important basic work for national development, the scientificity and accuracy of territorial space planning are directly related to the rational allocation of national resources, the protection of the ecological environment and the sustainable development of the regional economy. However, in the existing territorial space planning process, the workload of relevant planners is rather cumbersome, and it is also inconvenient to help decision-makers determine the location of the new urban area and the optimal distribution of various regional categories within the new urban area.

[0003] In view of this, the present invention proposes a multi-dimensional simulation method and system for territorial space planning data to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solutions: A multi-dimensional simulation method and system for territorial space planning data, comprising the following steps: S1. Obtain multi-dimensional characteristic data of the main urban area and preset regional categories, and divide a number of unit areas by combining the two; S2. Construct a number of unit areas according to the multi-dimensional characteristic data, and then obtain a number of unit simulation models; S3. Combine a number of unit simulation models to obtain a three-dimensional simulation model; S4. Preset a number of extended areas based on the planning requirements of the new urban area, and determine the optimal extended area and its corresponding optimal coordinate distribution combination from the number of extended areas by improving the genetic algorithm; S5. Construct the optimal extended area to obtain the final three-dimensional simulation model.

[0005] Further, the step of obtaining the multi-dimensional characteristic data of the main urban area is as follows: Determine the administrative scope of the main urban area, and obtain the geographical information within the administrative scope of the main urban area by combining GIS and satellite remote sensing data. The geographical information includes topography, ground slope and geological category; Obtain building information within the administrative scope of the main urban area through the combination of high-definition images taken by drones and GIS. The building information includes building specifications, building structures, and building textures. Obtain road information within the administrative scope of the main urban area through the combination of GIS and satellite remote sensing data. The road information includes road routes and their corresponding road specifications, transportation hubs, and transportation facilities. Obtain natural environment information within the administrative scope of the main urban area through the combination of GIS and satellite remote sensing data. The natural environment information includes plant species, vegetation density, plant area, and water area. Associate geographical information, building information, road information, and natural environment information according to geographical coordinates to form multi-dimensional feature data of the main urban area.

[0006] Furthermore, the steps for obtaining the several unit areas are as follows: Preset the area categories of the main urban area, and the area categories are respectively residential areas, commercial areas, industrial areas, and greening areas. Divide the main urban area according to the area categories and the multi-dimensional feature data. For each area category, determine the central coordinates of the area category through geographical information and road information, and find the road route closest to the perimeter. Form a closed loop with the road route closest to the perimeter to obtain a unit area. According to the above steps, sequentially obtain the remaining unit areas, that is, obtain several unit areas.

[0007] Furthermore, the steps for obtaining several unit simulation models are as follows: Construct a virtual space. Through geographical coordinates and the division of unit areas, distribute the multi-dimensional feature data of the main urban area to several unit areas to obtain the multi-dimensional feature data corresponding to several unit areas. Perform standardization processing on the multi-dimensional feature data corresponding to several unit areas in sequence and map them into the virtual space to obtain several unit simulation models. Among them, the construction method of the unit simulation model is as follows: Use the geographical information corresponding to the unit area to perform mapping in the virtual space to obtain a geographical three-dimensional model; use the building information corresponding to the unit area to perform mapping in the virtual space to obtain a building three-dimensional model; use the road information corresponding to the unit area to perform mapping in the virtual space to obtain a road three-dimensional model; use the natural environment information corresponding to the unit area to perform mapping in the virtual space to obtain a natural environment three-dimensional model; align and merge the geographical three-dimensional model, building three-dimensional model, road three-dimensional model, and natural environment three-dimensional model corresponding to the unit area in the virtual space from bottom to top according to geographical coordinates to obtain the unit simulation model.

[0008] Further, the steps of obtaining the 3D simulation model are as follows: Obtain and compare the edge lines of each unit simulation model, identify the common coordinate points, and then adjust the relative positions of adjacent unit simulation models based on the common coordinate points; Prioritize splicing the unit simulation models with the most common coordinate points. When splicing, first splice the road routes with common coordinate points in two unit simulation models, and then align the heights of the road surfaces of the road routes; Sequentially splice and combine the remaining unit simulation models according to the same process until all unit simulation models are combined, and the 3D simulation model is obtained.

[0009] Further, the steps of determining the optimal extension area and its corresponding optimal coordinate distribution combination from several extension areas through the improved genetic algorithm are as follows: Obtain the areas around the main urban area that meet the planning requirements to get m extension areas; For each extension area, randomly generate h coordinate distribution combinations, and then obtain m×h coordinate distribution combinations. Take the m×h coordinate distribution combinations as the initial population, where each coordinate distribution combination represents the relative distribution positions of four area categories in the same extension area; Take each coordinate distribution combination as an individual. Each individual is encoded by the coordinates of the area categories in a preset arrangement order, and take the coordinates of each area category as the genes of the individual; Define the fitness function and calculate the fitness value of each individual in the initial population; Select the elite individuals in the initial population as the parent individuals according to the fitness values, and perform crossover and mutation operations on the parent individuals; Repeat the selection, crossover, and mutation operations to generate a new population, and calculate the fitness value of the new population until the preset stop condition or the maximum number of iterations is reached; Select the individual with the highest fitness value from the final population as the optimal coordinate distribution combination among all extension areas, and take the extension area where the optimal coordinate distribution combination is located as the optimal extension area.

[0010] Further, the process of the selection, crossover, and mutation operations is as follows: At the beginning of the evolution of the current population, calculate the fitness value of each individual in the current population and sort them in descending order; Adopt the elite selection strategy, preset an elite ratio of k%, and select the individuals with fitness values in the top k% in the current population as elite individuals and directly retain them in the next generation population; In the crossover operation, only perform crossover on the individual genes corresponding to adjacent extension areas in the current population, and generate new individuals by exchanging part of the genes; In the mutation operation, the individual genes of the current population are mutated, and the mutation method is coordinate exchange, thereby generating new individuals.

[0011] Furthermore, the fitness function is as follows: ; In the formula, is the fitness value, is the distance between the residential area of the new city and the greening area of the new city, is the distance between the residential area of the new city and the industrial area of the new city, is the distance between the residential area of the new city and the commercial area of the new city, is the distance between the lacking area of the main city and the supplementary area of the new city. The supplementary area is the unit area with the same area category as the lacking area within the new city, is the distance between the residential area of the main city and the industrial area of the new city, is a small positive number; The steps for obtaining the lacking area are as follows: According to the actual quantity of the unit areas corresponding to each area category in the main city, calculate the Simpson index of the main city; Preset the reference Simpson index of the main city. Based on the reference Simpson index, obtain the reference quantity of the unit areas corresponding to each area category in the main city. Compare the reference quantity of the unit areas corresponding to each area category in the main city with the actual quantity of the unit areas corresponding to each area category in the main city, and regard the area category corresponding to the unit area with the largest difference in comparison quantity as the area category of the lacking area; Obtain the center point coordinates of the main city as the first coordinate point; With the first coordinate point as the center, and taking the center points of the unit areas of the same category as the lacking area as the second coordinate points, draw a circle that includes all the second coordinate points; Draw lines between the first coordinate point and the second coordinate points, and calculate the included angle between adjacent lines. Regard the area between the two adjacent lines with the largest included angle as the lacking range; Obtain the second coordinate points on the two adjacent lines that form the lacking range, and calculate the midpoint coordinates between the two second coordinate points. Take this midpoint coordinate as the position coordinate of the lacking area.

[0012] Furthermore, the steps for obtaining the final 3D simulation model are as follows: According to the planning requirements of the new city, calculate the area requirements of each area category, match the area requirements with the optimal coordinate distribution combination, and obtain the areas corresponding to each area category; According to the areas corresponding to each area category, select the unit simulation models with the same area category and the closest area for splicing and combination; Adjust the road routes of the unit simulation models in the splicing combination to connect with the road routes in the main urban area, and then obtain the final 3D simulation model.

[0013] A multi-dimensional simulation system for land and space planning data includes: A division module, configured to obtain multi-dimensional feature data of the main urban area and preset regional categories, and divide a number of unit areas by combining the two; A construction module, configured to construct a number of unit areas according to the multi-dimensional feature data, and then obtain a number of unit simulation models; A combination module, configured to combine a number of unit simulation models, and then obtain a 3D simulation model; An extension module, configured to preset a number of extension areas based on the planning requirements of the new urban area, and determine the optimal extension area and its corresponding optimal coordinate distribution combination from the number of extension areas through an improved genetic algorithm; A final construction module, configured to construct the optimal extension area, and then obtain the final 3D simulation model.

[0014] The technical effects and advantages of a multi-dimensional simulation method and system for land and space planning data of the present invention: 1. Through the division of unit areas and the construction of unit simulation models, the functionality and integrity of each unit simulation model can be ensured. By splicing and aligning based on the common coordinate points and the midline of the road routes during the combination process of the unit simulation models, it helps to achieve seamless docking of two unit simulation models, and enables the splicing and combination effect of the 3D simulation model to be realistic and continuous; 2. Under the combined action of the fitness function and the lacking areas, it can not only ensure the accurate docking of the new urban area with the lacking areas in the main urban area, achieve effective filling and balance of each regional category, but also enhance the unified coordination between the new urban area and the main urban area through simulation; it is beneficial to help decision-makers accurately locate the position of the new urban area and select the optimal coordinate distribution combination of each regional category in the new urban area to achieve the balance between the new urban area and the main urban area, providing a scientific and reasonable planning scheme for decision-makers. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flow chart of a multi-dimensional simulation method for land and space planning data of the present invention; Figure 2 It is a schematic structural diagram of a multi-dimensional simulation system for land and space planning data of the present invention; Figure 3 It is a combined schematic diagram of the position coordinates of the main urban area and the lacking areas in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention. Embodiment 1

[0017] Please refer to Figure 1 and Figure 2 As shown, a multi-dimensional simulation method for land space planning data in this embodiment includes the following steps: S1. Obtain multi-dimensional feature data of the main urban area and preset regional categories, and divide several unit areas by combining the two; S2. Construct several unit areas according to the multi-dimensional feature data, and then obtain several unit simulation models; S3. Combine several unit simulation models to obtain a three-dimensional simulation model.

[0018] The steps for obtaining the multi-dimensional feature data of the main urban area are as follows: Determine the administrative scope of the main urban area, and obtain the geographical information within the administrative scope of the main urban area through the combination of GIS (Geographic Information System) and satellite remote sensing data. The geographical information includes topography, ground slope, and geological category; Obtain the building information within the administrative scope of the main urban area through the combination of high-definition images taken by drones and GIS. The building information includes building specifications, building structures, and building textures; Obtain the road information within the administrative scope of the main urban area through the combination of GIS and satellite remote sensing data. The road information includes road routes and their corresponding road specifications, transportation hubs, and transportation facilities; Obtain the natural environment information within the administrative scope of the main urban area through the combination of GIS and satellite remote sensing data. The natural environment information includes plant species, vegetation density, plant area, and water area; Associate the geographical information, building information, road information, and natural environment information according to geographical coordinates to form the multi-dimensional feature data of the main urban area.

[0019] Among them, GIS provides a spatial analysis framework, while satellite remote sensing data provides detailed information on surface coverage and physical characteristics. By processing these remote sensing data with GIS software, a digital elevation model can be constructed, and geographical information, road information, and natural environment information can be obtained through the digital elevation model; by importing the high-definition image data taken by drones into the GIS system and using the geographical registration function of GIS to match the image data with the known geographical coordinate system, the building information can be obtained; Specifically, by integrating GIS, satellite remote sensing data, and high-definition images captured by drones, multi-dimensional feature data of the main urban area can be comprehensively and accurately obtained, including geographical information, building information, road information, and natural environment information. This not only improves the efficiency and accuracy of obtaining multi-dimensional feature data but also ensures the quality of the basic data for subsequent simulation model construction.

[0020] The steps for obtaining several unit areas are as follows: Preset the area categories of the main urban area, and the area categories are respectively residential areas (such as residential quarters, neighborhoods, etc.), commercial areas (such as commercial streets, shopping centers, markets, etc.), industrial areas (such as factories, etc.), and green areas (such as parks, lawns, rivers, etc.); Divide the main urban area according to the area categories and multi-dimensional feature data; For each area category, determine the central coordinates of the area category through geographical information and road information, and find the nearest road route around it. Form a closed loop with the nearest road route around it to obtain a unit area. According to the above steps, obtain the remaining unit areas in sequence, ensuring that each unit area is larger than the preset minimum area and the area ranges do not overlap, that is, several unit areas are obtained; Among them, when dividing the area, divide it according to the midline of the nearest road route.

[0021] Specifically, dividing the main urban area according to the preset area categories and using the surrounding road routes to form closed-loop unit areas can ensure the functionality and integrity of each unit area; at the same time, it can ensure that each unit area is larger than the preset minimum area and does not overlap, effectively avoiding the problem of increased complexity and computational cost of the unit simulation model caused by over-fine division or overlap.

[0022] The steps for obtaining several unit simulation models are as follows: Construct a virtual space. Through geographical coordinates and the division of unit areas, distribute the multi-dimensional feature data of the main urban area to several unit areas to obtain the multi-dimensional feature data corresponding to several unit areas; Perform standardization processing on the multi-dimensional feature data corresponding to several unit areas in sequence and map them into the virtual space, that is, several unit simulation models are obtained; Among them, the construction method of the unit simulation model is: The geographical information corresponding to the unit area is mapped in the virtual space to obtain a three-dimensional geographical model; the building information corresponding to the unit area is mapped in the virtual space to obtain a three-dimensional building model; the road information corresponding to the unit area is mapped in the virtual space to obtain a three-dimensional road model; the natural environment information corresponding to the unit area is mapped in the virtual space to obtain a three-dimensional natural environment model; according to the geographical coordinates, the three-dimensional geographical model, three-dimensional building model, three-dimensional road model and three-dimensional natural environment model corresponding to the unit area are aligned and merged in the virtual space from bottom to top, so as to obtain a unit simulation model; Among them, the quantifiable data defining the multi-dimensional feature data, such as the ground slope, the quantization code of the geological category, the building specifications (such as height, area), the road specifications (such as width, number of lanes), the quantization value of the plant density, the plant area and the water area, etc., uses the Min-Max normalization or Z-score normalization method to normalize the quantifiable data; before constructing the virtual space, it is necessary to ensure that all geographical space data is based on geographical coordinates, which includes the spatial position data in the geographical information, building information, road information and natural environment information: Specifically, by normalizing the multi-dimensional feature data, the efficiency and accuracy of the subsequent construction of the unit simulation model can be improved. In the virtual space, the three-dimensional models of geography, buildings, roads and natural environment are respectively mapped, and through precise vertical alignment and merging, a real and accurate unit simulation model can be obtained, which is conducive to shortening the construction time of the unit simulation model and improving the usability of the unit simulation model.

[0023] The steps to obtain the three-dimensional simulation model are as follows: Obtain and compare the edge lines of each unit simulation model, identify the common coordinate points, and then take the common coordinate points as the reference to adjust the relative positions of adjacent unit simulation models; Prioritize splicing the unit simulation models with the most common coordinate points. When splicing, first splice the road routes with common coordinate points in the two unit simulation models, and then align the heights of the road surfaces of the road routes; Sequentially splice and combine the remaining unit simulation models according to the same process until all unit simulation models are combined, that is, the three-dimensional simulation model is obtained; Among them, the edge line refers to a number of coordinate points set at the boundary of the unit simulation model, and a closed-loop line formed by connecting the coordinate points in sequence. The selection principle of the coordinate points is: ensure that after deleting any coordinate point, the overall shape or local curvature of the edge line changes; the common coordinate points are the coordinate points with the same coordinates in two unit simulation models, and the coordinate points are the specific positions of geographical coordinates.

[0024] Specifically, splicing and combining between unit simulation models based on common coordinate points can ensure the accuracy and automation of the splicing and combining process. Moreover, when splicing road routes, by splicing the roads first and then aligning the road surfaces, the unit simulation models can be spliced more precisely, further improving the continuity of the unit simulation models during splicing and combining. Additionally, by using the center line of the road route for splicing and combining, the splicing and combining process can be simplified, effectively avoiding problems such as discontinuous seams and misalignment that may occur during the splicing of unit simulation models, and ensuring the integrity and aesthetics of the three-dimensional simulation model.

[0025] In this embodiment, through the division of unit areas and the construction of unit simulation models, the functionality and integrity of each unit simulation model can be ensured. By performing splicing and alignment based on common coordinate points and the center line of the road route during the combination process of unit simulation models, it helps to achieve seamless docking of two unit simulation models, enabling the splicing and combination effect of the three-dimensional simulation model to be realistic and continuous. Embodiment 2

[0026] Please refer to Figure 1 、 Figure 2 and Figure 3 As shown in, the multi-dimensional simulation method for land space planning data in this embodiment includes the following steps: S4. Preset several extension areas based on the planning requirements of the new city, and determine the optimal extension area and its corresponding optimal coordinate distribution combination from the several extension areas through an improved genetic algorithm; S5. Construct the optimal extension area to obtain the final three-dimensional simulation model.

[0027] The steps of determining the optimal extension area and its corresponding optimal coordinate distribution combination from the several extension areas through an improved genetic algorithm are as follows: Obtain the areas around the main urban area that meet the planning requirements. The planning requirements are the minimum land area, being within the same administrative region as the main urban area, and having no unsuitable areas such as mountain rocks for utilization, to obtain m extension areas. Each extension area includes four area categories: residential area, commercial area, industrial area, and greening area; For each extension area, randomly generate h coordinate distribution combinations, and then obtain m×h coordinate distribution combinations. Take the m×h coordinate distribution combinations as the initial population. Among them, each coordinate distribution combination represents the relative distribution positions of the four area categories in the same extension area; Take each coordinate distribution combination as an individual. Each individual is encoded by the coordinates of the area categories in a preset arrangement order. The arrangement order is from left to right and then from top to bottom. Take the coordinates of each area category as the genes of the individual; Define the fitness function and calculate the fitness value of each individual in the initial population; Select the elite individuals in the initial population as the parental individuals according to the fitness values, and perform crossover and mutation operations on the parental individuals; Repeat the selection, crossover, and mutation operations to generate a new population, and calculate the fitness value of the new population until the preset stop condition or the maximum number of iterations is reached. The maximum number of iterations is then set to 500 or 1000, and the preset stop condition is to reach the preset fitness threshold or the distribution of the individual fitness values in the population tends to be stable; Select the individual with the highest fitness value from the final population as the optimal coordinate distribution combination in all extended areas, take the extended area where the optimal coordinate distribution combination is located as the optimal extended area, and take the location of the optimal extended area as the location of the new city.

[0028] Specifically, by presetting several extended areas and using the improved genetic algorithm to search for the optimal coordinate distribution combination, it is possible to accurately determine the specific distribution of residential areas, commercial areas, industrial areas, and greening areas in each extended area, consider the relative positional relationship between regions, and ensure the functional complementarity and spatial coordination between the new city and the main city through the setting of the fitness function, thus realizing the scientificity and rationality of the overall national territorial space planning of the new city and the main city.

[0029] The process of selection, crossover, and mutation operations is as follows: At the beginning of the evolution of the current population, calculate the fitness value of each individual in the current population and perform a descending order sorting; Adopt the elite selection strategy, preset the elite ratio of k%, and select the individuals with fitness values in the top k% in the current population as elite individuals and directly retain them in the next generation population; In the crossover operation, only cross the individual genes corresponding to adjacent extended areas in the current population, generate new individuals by exchanging some genes, and the number of some genes is greater than 0 and less than or equal to 3; In the mutation operation, mutate the individual genes of the current population, and the mutation method is coordinate swapping, thereby generating new individuals.

[0030] Specifically, through the application of the improved genetic algorithm, the time for finding the optimal solution (i.e., the optimal coordinate distribution combination) is greatly shortened, and the efficiency of the national territorial space planning work is improved; through the elite selection, crossover, and mutation operations in the genetic algorithm, while ensuring the diversity of the population, it also accelerates the convergence to the optimal solution; it can help decision-makers quickly obtain high-quality planning schemes and provide strong support for the development and construction of the new city.

[0031] The fitness function is: ; In the formula, is the fitness value, is the distance between the residential area and the greening area in the new city zone, is the distance between the residential area and the industrial area in the new city zone, is the distance between the residential area and the commercial area in the new city zone, is the distance between the lacking area in the main city zone and the supplementary area in the new city zone, and the supplementary area is the unit area with the same area category as the lacking area in the new city zone, is the distance between the residential area in the main city zone and the industrial area in the new city zone, is a small positive number; Among them, taking as an example, its distance calculation method is: Obtain the area corresponding to the planning requirements of the new city zone (optimal extension area), divide it according to the optimal distribution combination and the planned areas corresponding to each area in the new city zone, and calculate the distance between the center point of the residential area in the new city zone and the center point of the greening area in the new city zone, that is, obtain ; Taking as an example, its distance calculation method is: Calculate the distance between the center point of the residential area in the main city zone and the center point of the industrial area in the new city zone, that is, obtain ; Specifically, if the sum of the above distances is larger, the fitness value is lower; if the sum of the above distances is smaller, the fitness value is higher. Furthermore, the coordinate distribution combination corresponding to the minimum distance sum can be selected as the optimal coordinate distribution combination; enabling reasonable distribution of each area category, which is beneficial to improving the utilization efficiency of land resources, helping to enhance the interaction and communication between residents in the residential area and industrial, commercial, and greening areas, and enhancing the overall vitality of the main city zone and the new city zone.

[0032] The steps for obtaining the lacking area are as follows: Calculate the Simpson index of the main city zone according to the actual quantity of unit areas corresponding to each area category in the main city zone; Among them, the calculation formula of the Simpson index is , in the formula, is the Simpson index corresponding to the main city zone, represents the actual quantity of the th type of unit area, is the index of the area category, represents the total number of all unit areas in the main city zone.

[0033] The reference Simpson index of the preset main urban area is obtained, and based on the reference Simpson index, the reference quantity of the unit areas corresponding to each regional category within the main urban area is obtained (an initial weight is assigned to each regional category, and then by substituting the initial weight, the reference Simpson index, and the total number of all unit areas into the calculation formula of the Simpson index, the reference quantity corresponding to each regional category is calculated). The reference quantity of the unit areas corresponding to each regional category in the main urban area is compared with the actual quantity of the unit areas corresponding to each regional category in the main urban area, and the regional category corresponding to the unit area with the largest difference in the comparison quantity is used as the regional category of the lacking area; Obtain the central point coordinates of the main urban area as the first coordinate point; Taking the first coordinate point as the center of the circle and the central points of the unit areas of the same category in the lacking area as the second coordinate points, draw a circle that includes all the second coordinate points; Draw lines between the first coordinate point and the second coordinate points, and calculate the angles between adjacent lines. The area between the two adjacent lines with the largest angle is regarded as the lacking range; Obtain the second coordinate points on the two adjacent lines that form the lacking range, and calculate the midpoint coordinates between the two second coordinate points. Take this midpoint coordinate as the position coordinate of the lacking area.

[0034] Specifically, by calculating the Simpson index of the main urban area and comparing it with the preset reference Simpson index, the regional category of the lacking area can be accurately identified; thus, during the planning process of the new urban area, the relative positions of the supplementary areas can be set targeted, achieving seamless docking of functions between the main urban area and the new urban area; through the acquisition of the lacking area and the determination of its position coordinates, the integrity and coordination of urban functions can be effectively improved.

[0035] The steps to obtain the final 3D simulation model are as follows: According to the planning requirements of the new urban area, calculate the area requirements of each regional category, match the area requirements with the optimal coordinate distribution combination, and obtain the area corresponding to each regional category; According to the area corresponding to each regional category, select the unit simulation models with the same regional category and the closest area for splicing and combination. The closest unit simulation model is obtained by the minimum difference between the area corresponding to the regional category and the area of the unit simulation model; Adjust the road routes of the spliced and combined unit simulation models to be connected to the road routes of the main urban area, and then obtain the final 3D simulation model. Among them, the widest road route is preferentially selected for alignment and splicing.

[0036] Specifically, through the constructed final 3D simulation model, the planning effect of the new urban area can be intuitively demonstrated, ensuring the scientific nature of the functional connection and spatial layout among various unit areas. At the same time, through steps such as splicing combination and road alignment, the simulation model is seamlessly connected to the road system of the main urban area, providing strong support for subsequent planning implementation and urban management.

[0037] In this embodiment, under the combined action of the fitness function and the lacking areas, it can not only ensure that the new urban area accurately docks with the lacking areas of the main urban area, realizing the effective filling and balance of various area categories, but also enhance the unity and coordination between the new urban area and the main urban area through simulation; it is beneficial to help decision-makers accurately locate the position of the new urban area and select the optimal coordinate distribution combination of various area categories in the new urban area to achieve the balance between the new urban area and the main urban area, providing a scientific and reasonable planning scheme for decision-makers. Embodiment 3

[0038] Please refer to Figure 1 and Figure 2 As shown, a multi-dimensional simulation system for territorial space planning data in this embodiment includes: A division module, configured to obtain multi-dimensional feature data of the main urban area and preset area categories, and divide a number of unit areas by combining the two; A construction module, configured to construct a number of unit areas according to the multi-dimensional feature data, and then obtain a number of unit simulation models; A combination module, configured to combine a number of unit simulation models, and then obtain a 3D simulation model; An extension module, configured to preset a number of extension areas based on the planning requirements of the new urban area, and determine the optimal extension area and its corresponding optimal coordinate distribution combination from the number of extension areas through an improved genetic algorithm; A final construction module, configured to construct the optimal extension area, and then obtain a final 3D simulation model.

[0039] In this embodiment, through the division of unit areas and the construction of unit simulation models, the functionality and integrity of each unit simulation model can be ensured, which helps to achieve the seamless docking of two unit simulation models, and enables the splicing and combination effect of the 3D simulation model to be realistic and continuous; through the setting of the fitness function and the lacking areas, the effective filling and balance of various area categories can be realized, which is beneficial to help decision-makers accurately locate the position of the new urban area and select the optimal coordinate distribution combination of various area categories in the new urban area to achieve the balance between the new urban area and the main urban area, providing a scientific and reasonable planning scheme for decision-makers.

[0040] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0041] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0042] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

[0043] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included in the protection scope of the present invention.

Claims

1. A multi-dimensional simulation method for national land space planning data, characterized in that: The following steps are involved: S1. Obtain multi-dimensional feature data of the main urban area and preset regional categories, and combine the two to divide several unit areas; S2. constructing a plurality of unit regions according to the multi-dimensional feature data, thereby obtaining a plurality of unit simulation models; S3, combining several unit simulation models to obtain a three-dimensional simulation model; S4. Preset a number of extension areas based on the planning requirements of the new urban area, and determine the optimal extension area and its corresponding optimal coordinate distribution combination from the several extension areas by using an improved genetic algorithm; S5. Construct the optimal extension area to obtain the final three-dimensional simulation model.

2. A multi-dimensional simulation method for land space planning data according to claim 1, characterized in that: The steps for obtaining the multi-dimensional feature data of the main urban area are: Determine the administrative scope of the main urban area, and obtain geographic information within the administrative scope of the main urban area through the combination of GIS and satellite remote sensing data, the geographic information includes topography, ground slope and geological category; The building information within the administrative scope of the main urban area is obtained by combining high-definition images taken by drones with GIS, and the building information includes building specifications, building structures and building textures; Acquire road information within the administrative area of ​​the main urban area by combining GIS with satellite remote sensing data, the road information including road routes and their corresponding road specifications, transportation hubs and transportation facilities; The natural environment information within the administrative scope of the main urban area is obtained by combining GIS with satellite remote sensing data, and the natural environment information includes plant species, vegetation density, plant area and water area; The geographic information, building information, road information and natural environment information are associated according to geographic coordinates to form multi-dimensional characteristic data of the main urban area.

3. A multi-dimensional simulation method for land space planning data according to claim 2, characterized in that: The steps of obtaining the plurality of unit areas are as follows: The area categories of the main urban area are preset, and the area categories are respectively residential area, commercial area, industrial area and green area; Divide the main urban area into regions based on regional categories and multi-dimensional feature data; For each area category, the center coordinates of the area category are determined through geographic information and road information, and the road route closest to the surrounding area is found, and a closed loop is formed with the road route closest to the surrounding area, thereby obtaining a unit area; According to the above steps, the remaining unit regions are acquired in sequence, that is, a plurality of unit regions are obtained.

4. The multi-dimensional simulation method for national land space planning data according to claim 3 is characterized in that: The steps of obtaining several unit simulation models are: Construct a virtual space, distribute the multi-dimensional feature data of the main urban area to several unit areas through the division of geographic coordinates and unit areas, and obtain the multi-dimensional feature data corresponding to several unit areas; The multi-dimensional feature data corresponding to the plurality of unit areas are sequentially standardized and mapped into the virtual space, so as to obtain a plurality of unit simulation models; Among them, the construction method of the unit simulation model is: The geographic information corresponding to the unit area is mapped in the virtual space to obtain a geographic three-dimensional model; the building information corresponding to the unit area is mapped in the virtual space to obtain a building three-dimensional model; the road information corresponding to the unit area is mapped in the virtual space to obtain a road three-dimensional model; the natural environment information corresponding to the unit area is mapped in the virtual space to obtain a natural environment three-dimensional model; the geographic three-dimensional model, building three-dimensional model, road three-dimensional model and natural environment three-dimensional model corresponding to the unit area are aligned and merged in the virtual space from bottom to top according to the geographic coordinates to obtain a unit simulation model.

5. A multi-dimensional simulation method for national land space planning data according to claim 4, characterized in that: The steps of obtaining the three-dimensional simulation model are: Obtain and compare the edge lines of each unit simulation model, identify the common coordinate points, and then adjust the relative positions of adjacent unit simulation models based on the common coordinate points; Prioritize splicing the unit simulation models with the most common coordinate points. When splicing, first splice the road routes with common coordinate points in the two unit simulation models, and then align the road surfaces of the road routes; The remaining unit simulation models are spliced ​​and combined in turn according to the same process until all the unit simulation models are combined to obtain a three-dimensional simulation model.

6. A multi-dimensional simulation method for national land space planning data according to claim 5, characterized in that: The step of determining the optimal extension area and its corresponding optimal coordinate distribution combination from a plurality of extension areas by using the improved genetic algorithm is as follows: Obtain the areas around the main urban area that meet planning requirements and obtain m extended areas; For each extended area, h coordinate distribution combinations are randomly generated, and then m×h coordinate distribution combinations are obtained, and the m×h coordinate distribution combinations are used as the initial population, where each coordinate distribution combination represents the relative distribution position of the four area categories in the same extended area; Each coordinate distribution combination is regarded as an individual, each individual is encoded by the coordinates of the regional category according to the preset arrangement order, and the coordinates of each regional category are regarded as the gene of the individual; Define the fitness function and calculate the fitness value of each individual in the initial population; Select elite individuals in the initial population as parent individuals according to the fitness value, and perform crossover and mutation operations on the parent individuals; Repeat the selection, crossover, and mutation operations to generate a new population and calculate the fitness value of the new population until the preset stop condition or the maximum number of iterations is reached; The individual with the highest fitness value is selected from the final population as the optimal coordinate distribution combination in all extension areas, and the extension area where the optimal coordinate distribution combination is located is taken as the optimal extension area.

7. The multi-dimensional simulation method for national land space planning data according to claim 6 is characterized in that: The process of selection, crossover and mutation operation is as follows: At the beginning of the evolution of the current population, the fitness value of each individual in the current population is calculated and sorted in descending order; Adopting the elite selection strategy, presetting the elite ratio of k%, selecting individuals with fitness values ​​in the top k% in the current population as elite individuals and directly retaining them in the next generation population; In the crossover operation, only the individual genes corresponding to the adjacent extension region in the current population are crossed, and new individuals are generated by exchanging some genes; In the mutation operation, the individual genes of the current population are mutated by exchanging coordinates to generate new individuals.

8. The multi-dimensional simulation method for national land space planning data according to claim 6 is characterized in that: The fitness function is: ； In the formula, is the fitness value, is the distance between the residential area and the green area of ​​the new urban area, is the distance between the residential area of ​​the new city and the industrial area of ​​the new city, is the distance between the residential area of ​​the new city and the commercial area of ​​the new city. The distance between the lacking area in the main urban area and the supplementary area in the new urban area. The supplementary area is a unit area in the new urban area with the same area category as the lacking area. The distance between the residential area of ​​the main city and the industrial area of ​​the new city. is a small positive number; The steps for obtaining the defective area are as follows: According to the actual number of unit areas corresponding to each area category in the main urban area, the Simpson index of the main urban area is calculated; A reference Simpson index of the main urban area is preset, and a reference number of unit areas corresponding to each regional category in the main urban area is obtained based on the reference Simpson index. The reference number of unit areas corresponding to each regional category in the main urban area is compared with the actual number of unit areas corresponding to each regional category in the main urban area, and the regional category corresponding to the unit area with the largest difference in the compared number is taken as the regional category of the deficient area; Get the coordinates of the center point of the main urban area as the first coordinate point; With the first coordinate point as the center of the circle and the center point of the unit area of ​​the same category of the missing area as the second coordinate point, a circle is drawn that includes all the second coordinate points; Draw a line between the first coordinate point and the second coordinate point, and calculate the angle between adjacent lines, and regard the area between two adjacent lines with the largest angle as the defect range; The second coordinate points on two adjacent lines constituting the missing range are obtained, and the midpoint coordinates between the two second coordinate points are calculated, and the midpoint coordinates are used as the position coordinates of the missing area.

9. A multi-dimensional simulation method for national land space planning data according to claim 8, characterized in that: The steps of obtaining the final three-dimensional simulation model are: According to the planning requirements of the new urban area, the area requirements of each regional category are calculated, and the area requirements are matched with the optimal coordinate distribution combination to obtain the area corresponding to each regional category; According to the area corresponding to each regional category, select the unit simulation models with the same regional category and the closest area for splicing and combination; The road routes of the spliced ​​and combined unit simulation models are adjusted to connect with the road routes of the main urban area, thereby obtaining the final three-dimensional simulation model.

10. A multi-dimensional simulation system for land space planning data, used to implement a multi-dimensional simulation method for land space planning data according to any one of claims 1 to 9, characterized in that: include: The division module is used to obtain multi-dimensional feature data of the main urban area and preset regional categories, and combine the two to divide several unit areas; A construction module, used to construct a plurality of unit areas according to multi-dimensional feature data, thereby obtaining a plurality of unit simulation models; A combination module is used to combine several unit simulation models to obtain a three-dimensional simulation model; The extension module is used to preset several extension areas based on the planning requirements of the new urban area, and determine the optimal extension area and its corresponding optimal coordinate distribution combination from the several extension areas through an improved genetic algorithm; The final construction module is used to construct the optimal extension area to obtain the final three-dimensional simulation model.

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