A method for predicting the scale of rural construction land based on digital twin

Abstract

The present invention relates to the technical field of land use planning prediction, and specifically discloses a method for predicting the scale of rural construction land based on digital twins. By collecting geographical information data of N alternative villages, a terrain twin model of each alternative village is constructed; based on the terrain twin model of each alternative village, each environmental parameter corresponding to each alternative village is extracted; the possibility of the expansion of the construction land of each alternative village is analyzed to identify the expansion area of each alternative village corresponding to each future time point; thereby screening out the target expansion villages, which helps to concentrate resources for key development, improve the efficiency and effect of rural construction, and also promote regional balanced development; helps to evaluate the development potential of each village, and can also provide a scientific basis for the rational allocation of land resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of land use planning prediction, and relates to a method for predicting the scale of rural construction land based on digital twins. Background Art

[0002] The prediction of the scale of rural construction land is of extremely important necessity and significance in modern society. With the growth of the population and the acceleration of the urbanization process, the land demand in rural areas is constantly increasing. Through accurate prediction, waste and unreasonable utilization of land resources can be avoided, and the sustainable development of land resources can be ensured. Secondly, the prediction of the scale of rural construction land helps to improve rural infrastructure and public services. By predicting future land demand, necessary infrastructure such as roads, water and electricity, and communication can be planned and constructed in advance, thereby improving the living quality of rural residents and promoting the development of the rural economy.

[0003] The current technology still has certain limitations in selecting target expanding villages. It is unable to effectively screen target expanding villages through the analysis of alternative villages, which is not conducive to improving the selection efficiency and intelligent level of target expanding villages to a certain extent. It cannot effectively guarantee the comprehensiveness of the selection of target expanding villages, cannot effectively ensure the scientificity and reliability of the selection of target expanding villages, and cannot provide accurate data for the follow-up, and cannot improve the accuracy of the selection of target expanding villages. Summary of the Invention

[0004] In view of the above problems existing in the prior art, the present invention provides a method for predicting the scale of rural construction land based on digital twins to solve the above technical problems.

[0005] In order to achieve the above object and other objects, the technical solution adopted by the present invention is as follows:

[0006] The present invention provides a method for predicting the scale of rural construction land based on digital twins, and the method includes the following steps:

[0007] Step 1: Collect geographical information data of N alternative villages, and thus construct a terrain twin model for each alternative village;

[0008] Step 2: Based on the terrain twin model of each alternative village, extract each environmental parameter corresponding to each alternative village;

[0009] Step 3: Analyze the possibility of the expansion of the construction land of each alternative village, and identify the expansion area corresponding to each alternative village at each future time point;

[0010] Step 4: Based on the expansion area corresponding to each alternative village at each future time point, screen out the target expanding villages.

[0011] In the preferred technical solution of the present application, the geographical information data of each alternative village includes altitude, slope, vegetation coverage area, geographical location superiority degree, and population quantity.

[0012] In the preferred technical solution of the present application, constructing the terrain twin models of each alternative village includes:

[0013] Interpolating the altitude and slope data of each alternative village to fill in missing values and eliminate noise; the vegetation coverage area data needs to be classified and filtered to determine the distribution of different types of vegetation; spatially registering the geographical location superiority degree data of each alternative village to ensure the spatial consistency between different data layers; spatially processing the population data of each alternative village;

[0014] Integrating the altitude and slope data of each alternative village through a GIS platform to generate the three-dimensional terrain model of each alternative village; and overlaying the vegetation coverage area data on the three-dimensional terrain model of each alternative village to generate a terrain model with the actual vegetation coverage area situation; combining the geographical location superiority degree data of each alternative village to enrich the details of the model; finally, combining the population quantity data of each alternative village with the model to generate the terrain twin model of each alternative village.

[0015] Verifying the constructed terrain twin models of each alternative village, validating and correcting the models; conducting on-site investigations on the actual geographical information data of each alternative village, and modifying and adjusting the terrain twin models of each alternative village based on it.

[0016] In the preferred technical solution of the present application, extracting the corresponding environmental parameters of each alternative village includes:

[0017] The specific environmental parameters are divided into patch density evaluation coefficient, landscape shape evaluation coefficient, and patch cohesion evaluation coefficient;

[0018] Based on the terrain twin models of each alternative village, obtaining the total number of land patches M within each alternative village, the area 、perimeter and frequency of each land patch, where i is the number of each alternative village, i = 1, 2,... N, and j is the number of each land patch, j = 1, 2,... M;

[0019] Thus, obtaining the corresponding patch density evaluation coefficient 、landscape shape evaluation coefficient and patch cohesion evaluation coefficient of each alternative village in turn, being the total area of the i-th alternative village.

[0020] In a preferred technical solution of the present application, the possibilities of the expansion of each alternative rural construction land are analyzed, including:

[0021] Obtaining the rural expansion influence index corresponding to the geographic information data;

[0022] Combining the patch density evaluation coefficient, landscape shape evaluation coefficient, and patch cohesion evaluation coefficient corresponding to each alternative rural area, and comprehensively calculating the expansion possibility evaluation coefficient of each alternative rural area , is the rural expansion influence index corresponding to the geographic information data, are respectively the weight factors corresponding to the patch density evaluation coefficient, landscape shape evaluation coefficient, patch cohesion evaluation coefficient, and rural expansion influence index.

[0023] In a preferred technical solution of the present application, obtaining the rural expansion influence index corresponding to the geographic information data includes:

[0024] Extracting the altitude, slope, vegetation coverage area, geographical location superiority, and population quantity of each expanded rural area from the historical database;

[0025] Performing standardization processing on the altitude of each expanded rural area , and obtaining the standardized altitude of each expanded rural area ; d is the number of each expanded rural area, d = 1, 2,... D; ;

[0026] Similarly, obtaining the standardized slope, standardized vegetation coverage area, standardized geographical location superiority, and standardized population quantity of each expanded rural area;

[0027] Determining the weights of each geographical information data, and performing weighted summation of the standardized altitude, standardized slope, standardized vegetation coverage area, standardized geographical location superiority, and standardized population quantity of each expanded rural area with the corresponding weights to obtain the rural expansion influence index corresponding to the geographical information data.

[0028] In a preferred technical solution of the present application, the specific calculation processes of the weight factors corresponding to the patch density evaluation coefficient, landscape shape evaluation coefficient, patch cohesion evaluation coefficient, and rural expansion influence index are as follows:

[0029] Obtaining the initial patch density evaluation coefficient of each expanded rural area during each expansion , the initial landscape shape evaluation coefficient and the initial patch cohesion evaluation coefficient , where k is the number of the expansion times, k = 1, 2,... B;

[0030] Obtaining the expansion frequency of each expanded rural area during each expansion ;

[0031] By analyzing the formula , the correlation coefficient between the initial patch density evaluation coefficient and the expansion frequency during each expansion of the expanded villages is analyzed and obtained . Summing them up, the total correlation coefficient between the initial patch density evaluation coefficient and the expansion frequency corresponding to the expanded villages is obtained ;

[0032] Similarly, the total correlation coefficients between the initial landscape shape evaluation coefficient and the initial patch cohesion evaluation coefficient corresponding to the expanded villages and the expansion frequency are calculated respectively, and they are denoted as and ;

[0033] The initial altitude, initial slope, initial vegetation coverage area, initial geographical location superiority, and initial population quantity during each expansion of each expanded village are obtained, and the initial rural expansion impact index during each expansion of each expanded village is calculated. Similarly, the total correlation coefficient between the initial rural expansion impact index corresponding to the expanded villages and the expansion frequency is calculated ;

[0034] Furthermore, the weight factors corresponding to the patch density evaluation coefficient are solved ; the weight factors corresponding to the landscape shape evaluation coefficient ; the weight factors corresponding to the patch cohesion evaluation coefficient ; the weight factors corresponding to the rural expansion impact index .

[0035] In a preferred technical solution of the present application, the expansion areas corresponding to each alternative village at each future time point are identified, including:

[0036] According to the expansion possibility evaluation coefficients of each alternative village, each preparatory expansion village is screened out;

[0037] The area of each preparatory expansion village is divided into several cells, and each cell has a corresponding initial state, where the initial state values are 0 and 1; 0 represents undeveloped land, and 1 represents construction land;

[0038] The cells within the area of each preparatory expansion village are numbered in sequence, numbered 1, 2,... u,... q in sequence;

[0039] represents the initial state of the u-th cell corresponding to the f-th preparatory expansion village at the current time point t, represents the number of construction land cells within the neighborhood of the u-th cell corresponding to the f-th preparatory expansion village at the current time point t, and f is the number of the preparatory expansion village;

[0040] Then, establish the state transition rules for the corresponding cells of each preparatory expansion village ; n is the number of intervals of the preset interval duration h between the current time point t and each future time point, and h is the preset interval duration;

[0041] Thus, calculate the expansion area of each preparatory expansion village corresponding to each future time point , is an indicator function, which takes the value of 1 when its condition is true, otherwise 0.

[0042] In a preferred technical solution of the present application, screening out the target expansion villages includes:

[0043] Obtain the expansion area of each preparatory expansion village corresponding to each future time point , calculate the expansion rate of each preparatory expansion village corresponding to each future time point , is the expansion area of the f-th preparatory expansion village corresponding to the (t+(n + 1)) h-th future time point;

[0044] And calculate the average value of the expansion rates of each preparatory expansion village corresponding to each future time point to obtain the average expansion rate of each preparatory expansion village corresponding to the future time point. Sort each preparatory expansion village according to it, and select the preparatory expansion village with the first-ranked average expansion rate at the future time point as the target expansion village.

[0045] As described above, a method for predicting the scale of rural construction land based on digital twin provided by the present invention has at least the following beneficial effects:

[0046] (1) A method for predicting the scale of rural construction land based on digital twin provided by the present invention can provide a scientific and systematic method for rural planning and development by collecting the geographical information data of N alternative villages and constructing the terrain twin models of each alternative village. The terrain twin model can accurately simulate and reproduce the terrain and landforms of each village, enabling planners to more intuitively understand the geographical features and environmental conditions of each village. This accurate terrain simulation not only helps to identify the advantages and disadvantages of the terrain, but also provides a reliable data basis for the subsequent extraction of environmental parameters.

[0047] (2) By analyzing the expansion possibilities of each alternative rural construction land in the embodiments of the present invention, it is possible to predict the expansion area of each rural area at different future time points. Such prediction not only helps to evaluate the development potential of each rural area, but also provides a scientific basis for the rational allocation of land resources. By identifying the possible expansion areas of each rural area in the future, planners can formulate land use plans in advance to avoid waste of resources and environmental damage caused by disorderly expansion; this not only helps to concentrate resources for key development, improve the efficiency and effect of rural construction, but also promotes regional balanced development and narrows the urban-rural gap. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 Schematic diagram of the connection of each step of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by this claims, they should all belong to the protection scope of the present invention.

[0051] Embodiment 1

[0052] Please refer to Figure 1 As shown, a method for predicting the scale of rural construction land based on digital twin, the method includes the following steps:

[0053] Step 1: Collect geographical information data of N alternative rural areas, and thus construct a terrain twin model for each alternative rural area;

[0054] Preferably based on the above solution, the geographical information data of each alternative rural area includes altitude, slope, vegetation coverage area, geographical location superiority, and population quantity.

[0055] The geographical location superiority refers to the position of a region in the geographical space and its related natural, economic, and social characteristics, which have an impact on aspects such as the development potential, economic activities, and social structure of the region; specifically, it can include natural location elements: including climate conditions, hydrological resources, etc.; economic location elements: involving traffic conditions, labor supply, technical level, etc.

[0056] Elevation data can be obtained through a Digital Elevation Model (DEM), usually collected by technical means such as satellite remote sensing, Light Detection and Ranging (LiDAR), or unmanned aerial vehicle (UAV) aerial photography. Slope data is calculated by performing spatial analysis on elevation data. Vegetation coverage area data can be classified and analyzed through remote sensing images. Common remote sensing data sources include Landsat, Sentinel, etc. The geographical location superiority involves information such as geographical coordinates, transportation networks, and water source distributions, which can be integrated through a Geographic Information System (GIS) platform. Population quantity data can be obtained from the statistical data of the National Bureau of Statistics or local governments.

[0057] Preferably, based on the above scheme, a terrain twin model of each alternative village is constructed, including:

[0058] Interpolate the elevation and slope data of each alternative village to fill in missing values and eliminate noise; the vegetation coverage area data needs to be classified and filtered to determine the distribution of different types of vegetation; perform spatial registration on the geographical location superiority data of each alternative village to ensure spatial consistency between different data layers; perform spatial processing on the population data of each alternative village;

[0059] Integrate the elevation and slope data of each alternative village through a GIS platform to generate a three-dimensional terrain model of each alternative village; and overlay the vegetation coverage area data on the three-dimensional terrain model of each alternative village to generate a terrain model with the actual vegetation coverage area situation; combine the geographical location superiority data of each alternative village to enrich the details of the model; finally, combine the population quantity data of each alternative village with the model to generate a terrain twin model of each alternative village.

[0060] Verify the constructed terrain twin model of each alternative village, verify and correct the model; conduct on-site investigations on the actual geographical information data of each alternative village, and modify and adjust the terrain twin model of each alternative village based on it.

[0061] Step 2: Based on the terrain twin model of each alternative village, extract the corresponding environmental parameters of each alternative village;

[0062] Preferably, based on the above scheme, extract the corresponding environmental parameters of each alternative village, including:

[0063] The specific environmental parameters are divided into patch density evaluation coefficients, landscape shape evaluation coefficients, and patch cohesion evaluation coefficients;

[0064] Based on the terrain twin model of each alternative village, obtain the total number of land patches M within each alternative village, the area of each land patch perimeter and frequency , where \(i\) is the number of each alternative village, \(i = 1, 2, \cdots, N\), and \(j\) is the number of each land patch, \(j = 1, 2, \cdots, M\);

[0065] Based on the terrain twin models of each alternative village, establish the reverse digital terrain model (RDTM) of each alternative village, with the average elevation of the DTM as the benchmark; within the reverse digital terrain model of each alternative village, extract the elevation maximum points of each alternative village, which are the key to identifying patches; select three terrain factors: elevation, terrain undulation degree, and slope, and determine the weights of these terrain factors by the entropy weight method; use the weighted method to obtain the comprehensive membership degree of terrain factors, and determine the threshold through the comprehensive membership degree of terrain factors in multiple windows; thereby locate the positions of each land patch within each alternative village, and further obtain the area and perimeter of each land patch within each alternative village.

[0066] Thus, the patch density evaluation coefficients corresponding to each alternative village are obtained in sequence , the landscape shape evaluation coefficient and the patch cohesion evaluation coefficient , is the total area of the \(i\)-th alternative village.

[0067] Step 3: Analyze the possibility of construction land expansion in each alternative village, and identify the expansion area corresponding to each future time point for each alternative village;

[0068] Based on the above scheme, optimize and analyze the possibility of construction land expansion in each alternative village, including:

[0069] Obtain the rural expansion impact index corresponding to the geographic information data;

[0070] Combined with the patch density evaluation coefficient, landscape shape evaluation coefficient, and patch cohesion evaluation coefficient corresponding to each alternative village, comprehensively calculate the expansion possibility evaluation coefficient of each alternative village , is the rural expansion impact index corresponding to the geographic information data, are the weight factors corresponding to the patch density evaluation coefficient, landscape shape evaluation coefficient, patch cohesion evaluation coefficient, and rural expansion impact index respectively.

[0071] Based on the above scheme, optimize and obtain the rural expansion impact index corresponding to the geographic information data, including:

[0072] Extract the altitude, slope, vegetation coverage area, geographical location superiority, and population quantity of each expanded village from the historical database;

[0073] For the altitude of each expanded village perform standardization processing, , the standardized elevation of each expanded village is obtained ; d is the number of each expanded village, d = 1, 2,... D;

[0074] Similarly, the standardized slope, standardized vegetation coverage area, standardized geographical location superiority degree, and standardized population quantity of each expanded village are obtained;

[0075] Determine the weights of each geographical information data, and perform weighted summation on the standardized elevation, standardized slope, standardized vegetation coverage area, standardized geographical location superiority degree, and standardized population quantity of each expanded village with the corresponding weights to obtain the rural expansion impact index corresponding to the geographical information data.

[0076] The calculation method of the weights of each geographical information data is the same as that of the weight factors corresponding to the patch density evaluation coefficient.

[0077] On the basis of the above scheme, optimize the weight factors corresponding to the patch density evaluation coefficient, landscape shape evaluation coefficient, patch cohesion evaluation coefficient, and rural expansion impact index. The specific calculation process is as follows:

[0078] Obtain the initial patch density evaluation coefficient of each expanded village during each expansion , initial landscape shape evaluation coefficient and initial patch cohesion evaluation coefficient , k is the number of the expansion times, k = 1, 2,... B;

[0079] Calculate the population growth rate and land use change rate of each expanded village during each expansion; use the multivariate regression model

[0080] Bring the population growth rate and land use change rate of each expanded village during each historical expansion back into the multivariate regression model to obtain the value of the constant term and the regression coefficients and 2 values, and import the population growth rate and land use change rate of each expanded village during each expansion into the multivariate regression model to obtain the expansion frequency of each expanded village during each expansion.

[0081] Obtain the expansion frequency of each expanded village during each expansion ;

[0082] By analyzing the formula , analyze and obtain the correlation coefficient between the initial patch density evaluation coefficient and the expansion frequency of the expanded village during each expansion, sum them up to obtain the total correlation coefficient between the corresponding initial patch density evaluation coefficient and the expansion frequency of the expanded village ;

[0083] Similarly, calculate the sum of the correlation coefficients between the initial landscape shape evaluation coefficient, the initial patch cohesion evaluation coefficient corresponding to the expanded villages and the expansion frequency respectively, and denote them as and ;

[0084] Obtain the initial altitude, initial slope, initial vegetation coverage area, initial geographical location superiority and initial population of each expanded village at each expansion, calculate the initial rural expansion impact index of each expanded village at each expansion, and similarly calculate the sum of the correlation coefficients between the initial rural expansion impact index corresponding to the expanded villages and the expansion frequency ;

[0085] Furthermore, solve the weight factor corresponding to the patch density evaluation coefficient ; the weight factor corresponding to the landscape shape evaluation coefficient ; the weight factor corresponding to the patch cohesion evaluation coefficient ; the weight factor corresponding to the rural expansion impact index 。

[0086] Based on the above scheme, optimize and identify the expansion area of each alternative village corresponding to each future time point, including:

[0087] According to the expansion possibility evaluation coefficient of each alternative village, screen out each preparatory expansion village;

[0088] Divide the area of each preparatory expansion village into several cells, and let each cell have a corresponding initial state, where the initial state values are 0 and 1; 0 represents undeveloped land, and 1 represents construction land;

[0089] Number each cell in the area of each preparatory expansion village in sequence, numbered 1, 2,... u,... q in sequence;

[0090] represents the initial state of the u-th cell corresponding to the f-th preparatory expansion village at the current time point t, represents the number of construction land cells in the neighborhood of the u-th cell corresponding to the f-th preparatory expansion village at the current time point t, and f is the number of the preparatory expansion village;

[0091] Then establish the state transition rule of each cell corresponding to each preparatory expansion village ; n is the number of the preset interval duration h between the current time point t and each future time point, and h is the preset interval duration;

[0092] The state transition rule is that the condition for an undeveloped land cell (state 0) to be converted into a construction land cell (state 1) is that at least 3 of its neighborhoods are construction land cells (state 1).

[0093] From this, calculate the expansion area of each candidate expanding village corresponding to each future time point. , is an indicator function, which takes the value of 1 when its condition is true, otherwise 0.

[0094] Indicator function is used to check whether each cell is in a construction land cell (state 1). If the state of cell p at the current time point t is 1, then the indicator function is 1; otherwise the indicator function is 0;

[0095] This summation operation traverses the entire grid to count the number of cells in the rural land state; multiply the number of rural land cells counted by the area of each cell to obtain the expansion area of each candidate village corresponding to each future time point.

[0096] Step 4: Based on the expansion area of each candidate expanding village corresponding to each future time point, screen out the target expanding village.

[0097] On the basis of the above scheme, preferably, screen out the target expanding village, including:

[0098] Obtain the expansion area of each candidate expanding village corresponding to each future time point , calculate the expansion rate of each candidate expanding village corresponding to each future time point , is the expansion area of the f-th candidate expanding village corresponding to the (t+(n + 1)) h-th future time point;

[0099] And calculate the average value of the expansion rates of each candidate expanding village corresponding to each future time point to obtain the average expansion rate of each candidate expanding village corresponding to the future time point. Sort each candidate expanding village according to it, and select the candidate expanding village with the first average expansion rate at the future time point as the target expanding village.

[0100] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0101] It should be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0102] As described above, this is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

[0103] 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 shall be included within the protection scope of the present invention.

Claims

1. A method for predicting the scale of rural construction land based on digital twins, characterized in that: include: Collect geographic information data of N candidate villages, and construct terrain twin models of each candidate village; Based on the terrain twin model of each candidate village, the environmental parameters corresponding to each candidate village are extracted; Analyze the possibility of expansion of construction land in each candidate village, and identify the expansion area of ​​each candidate village at each future time point; Analyze the possibility of expansion of each alternative rural construction land, including: Obtain the rural expansion impact index corresponding to geographic information data; Combined with the patch density assessment coefficient, landscape shape assessment coefficient and patch integration assessment coefficient corresponding to each candidate village, the expansion potential assessment coefficient of each candidate village is comprehensively calculated. , is the rural expansion impact index corresponding to the geographic information data, They are the weight factors corresponding to the patch density assessment coefficient, landscape shape assessment coefficient, patch integration assessment coefficient and rural expansion impact index; Based on the expansion area of ​​each candidate village at each future time point, the target expansion village is selected; Identify the expansion area of ​​each candidate village at each future time point, including: Based on the expansion potential evaluation coefficient of each candidate village, select the villages to be expanded; Each rural area to be expanded is divided into a number of cells, and each cell has a corresponding initial state, where the initial state value is divided into 0 and 1; 0 represents undeveloped land, and 1 represents construction land; Number each cell in each rural area to be expanded in sequence, 1, 2, ...u, ...q; Indicates that at the current time point t, the initial state of the u-th cell corresponding to the f-th prepared expansion village, It indicates the number of construction land cells in the neighborhood of the u-th cell corresponding to the f-th village to be expanded at the current time point t, where f is the number of the village to be expanded; Then establish the state transition rules for the corresponding cells of each prepared expansion village ; n is the number of preset intervals h between the current time point t and each future time point, and h is the preset interval length; The expansion area of ​​each planned expansion village corresponding to each future time point is calculated , is an indicator function, which takes the value 1 when its condition is true, otherwise it is 0; Select target villages for expansion, including: Get the expansion area of ​​each planned expansion village at each future time point , calculate the expansion rate of each planned expansion village at each future time point , The fth prepared expansion village corresponds to the t+(n+1)th The expansion area at h future time points; The expansion rates of each planned expansion village corresponding to each future time point are averaged and calculated to obtain the average expansion rate of each planned expansion village corresponding to the future time point. The planned expansion villages are ranked based on the average expansion rate, and the planned expansion village with the highest average expansion rate at the future time point is selected as the target expansion village.

2. According to a method for predicting the scale of rural construction land based on digital twins according to claim 1, it is characterized in that: The geographic information data of each candidate village includes altitude, slope, vegetation coverage area, geographical location superiority and population size.

3. According to a method for predicting the scale of rural construction land based on digital twins according to claim 2, it is characterized in that: Construct terrain twin models for each candidate village, including: Interpolate the altitude and slope data of each candidate village to fill in missing values ​​and eliminate noise; classify and filter the vegetation coverage data to determine the distribution of different types of vegetation; spatially register the geographic location superiority data of each candidate village to ensure spatial consistency between different data layers; and spatially process the population data of each candidate village; The altitude and slope data of each candidate village are integrated through the GIS platform to generate a three-dimensional terrain model of each candidate village; the vegetation coverage area data is superimposed on the three-dimensional terrain model of each candidate village to generate a terrain model with real vegetation coverage area; the geographical location superiority data of each candidate village is combined to enrich the details of the model; finally, the population data of each candidate village is combined with the model to generate a terrain twin model of each candidate village; Verify the constructed terrain twin models of each candidate village, and verify and calibrate the models; conduct field surveys on the actual geographic information data of each candidate village, and revise and adjust the terrain twin models of each candidate village based on the data.

4. The method for predicting the scale of rural construction land based on digital twin according to claim 1 is characterized in that: Extract the environmental parameters corresponding to each candidate village, including: Each environmental parameter is specifically divided into patch density assessment coefficient, landscape shape assessment coefficient and patch cohesion assessment coefficient; Based on the terrain twin model of each candidate village, the total number of land patches M and the area of ​​each land patch in each candidate village are obtained. ,perimeter and frequency , i is the number of each candidate village, i=1,2,...N, j is the number of each land patch, j=1,2,...M; Thus, the patch density assessment coefficients corresponding to each candidate village are obtained in turn. , Landscape shape evaluation coefficient and plaque binding evaluation coefficient , is the total area of ​​the i-th alternative village.

5. The method for predicting the scale of rural construction land based on digital twin according to claim 1 is characterized in that: Obtain the rural expansion impact index corresponding to geographic information data, including: Extract the altitude, slope, vegetation coverage, geographical location and population of each expanded village from the historical database; The altitude of each expanded village To standardize the process, , get the standardized altitude of each expanded village ; d is the number of each expanded village, d=1,2,...D; Similarly, the standardized slope, standardized vegetation coverage, standardized geographical location superiority and standardized population of each expanded village are obtained; Determine the weight of each geographic information data, and take the weighted sum of the standardized altitude, standardized slope, standardized vegetation coverage area, standardized geographical location superiority and standardized population size of each expanded village with the corresponding weight to obtain the rural expansion impact index corresponding to the geographic information data.

6. The method for predicting the scale of rural construction land based on digital twin according to claim 1 is characterized by: The weight factors corresponding to the patch density assessment coefficient, landscape shape assessment coefficient, patch integration assessment coefficient and rural expansion impact index are calculated as follows: Obtain the initial patch density assessment coefficient of each expanded village at each expansion , initial landscape shape evaluation coefficient and initial plaque binding evaluation coefficient , k is the number of expansion times, k=1,2,...B; Get the expansion frequency of each expanded village at each expansion , the specific way to obtain it is: using the multivariate regression model ; The population growth rate and land use change rate of each expanded village in each historical expansion are brought back to the multivariate regression model to obtain the constant term The value and regression coefficient and 2, and import the population growth rate and land use change rate of each expanded village in each expansion into the multivariate regression model to obtain the expansion frequency of each expanded village in each expansion; By analyzing the formula The correlation coefficient between the initial patch density assessment coefficient of the expanded villages at each expansion and the expansion frequency was obtained by analysis. , sum them up to get the sum of the correlation coefficients between the initial patch density assessment coefficient and the expansion frequency corresponding to the expanded village ; Similarly, the sum of the correlation coefficients of the initial landscape shape assessment coefficient and the initial patch integration assessment coefficient corresponding to the expanded village and the expansion frequency are calculated and recorded as and ; Obtain the initial altitude, initial slope, initial vegetation coverage, initial geographical location superiority and initial population of each expanded village at each expansion, calculate the initial rural expansion impact index of each expanded village at each expansion, and similarly calculate the sum of the correlation coefficients between the initial rural expansion impact index and the expansion frequency of the expanded village ; Then solve the weight factor corresponding to the plaque density assessment coefficient ; Weight factor corresponding to the landscape shape assessment coefficient ; Weight factor corresponding to the plaque binding evaluation coefficient ; Weight factor corresponding to the rural expansion impact index .

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