Land planning method based on data analysis

Abstract

The invention provides a land planning method based on data analysis, and the method comprises the steps: obtaining an initial distance matrix between land parcels in a farmland land parcel set around a town, calculating the direct connectivity between the land parcels and other land parcels based on a distance threshold value, and constructing an initial farmland ecological network topological structure diagram; the overall network connectivity, the node centrality and the average path length of the initial farmland ecological network topological structure diagram are calculated through a graph theory algorithm, so that the structural integrity of the ecological network is evaluated, and an ecological network structural evaluation index initial value is obtained; according to the adjusted distance matrix between the land parcels, recalculating the connectivity between the land parcels, reconstructing a farmland ecological network topology structure, and calculating various indexes of network structure integrity to assess an ecological network structure; and combining the plot connectivity and the land utilization efficiency to evaluate the feasibility and cost effectiveness of plough layer stripping and reutilization, and determining an optimal adjustment scheme.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a land planning method based on data analysis. Background Art

[0002] In the land planning of the farmland ecological network around towns, when the connectivity of local plots is blocked, for example, due to the obstruction of artificial buildings, roads or other obstacles, the ecological corridors between plots may be cut off, and the planning scheme needs to be adjusted based on the distance factor between plots. However, this adjustment may have an impact on the stripping and reuse of the farmland tillage layer. Specifically, when the distance between two plots exceeds a certain threshold, in order to maintain the structural integrity of the farmland ecological network, it may be necessary to merge or split the local plots. Although this adjustment can optimize the connectivity of the plots, it may also break the soil structure of the original plots, leading to the stripping and redistribution of the tillage layer. The stripped tillage layer soil may be used to backfill other plots or for other purposes, changing the original soil environment. Due to the differences in soil texture, fertility and other characteristics of different plots, the redistribution of the tillage layer may affect the balanced distribution of soil nutrients, thereby affecting the growth and yield of crops. In addition, frequent stripping and reuse of the tillage layer may also increase the risk of soil erosion and nutrient loss. Therefore, when making land planning adjustments based on plot connectivity, it is necessary to take into account the integrity of the farmland ecological network and the sustainability of the reuse of the cultivated layer, weigh the impact of the adjustment plan on soil resources, and take appropriate measures to mitigate the negative impact to ensure the long-term stability of the farmland ecosystem and the sustainable development of agricultural production. Summary of the invention

[0003] The present invention provides a land planning method based on data analysis, which mainly includes: Obtain the initial distance matrix between each plot in the set of farmland plots around towns, calculate the direct connectivity between the plots and other plots based on the distance threshold, and construct the initial farmland ecological network topology structure diagram; The overall network connectivity, node centrality and average path length of the initial farmland ecological network topology structure diagram are calculated through graph theory algorithms to evaluate the integrity of the ecological network structure and obtain the initial values ​​of the ecological network structure evaluation indicators. Based on the overall network connectivity, it is determined whether the actual connectivity reaches the expected connectivity level. For the plot pairs that do not reach the expected connectivity level, they are identified as having blocked connectivity. When there is a local block connectivity obstruction in the initial farmland ecological network topology diagram, according to the initial distance matrix between the blocks, if the distance between the blocks is greater than the distance threshold between the blocks, it is judged that the connection between the corresponding pairs of blocks is obstructed, so as to identify the obstructed blocks and form a set; For the obstructed plots, a distance decay-based connectivity adjustment method is applied to adjust the distance matrix according to the distance factor between plots, where the distance factor is calculated based on the plot area, the type and number of obstacles between plots, and the functional attributes of the plots; According to the adjusted inter-plot distance matrix, the connectivity between plots is recalculated, the topological structure of the farmland ecological network is reconstructed, and the evaluation index of the adjusted ecological network structure is calculated; Based on the changes in the ecological network structure assessment indicators before and after the adjustment, the impact of the blocked connectivity of local plots on the integrity of the ecological network structure is analyzed. If the impact exceeds the preset change threshold, the dynamic adjustment of the planning scheme is triggered to reallocate the functional attributes and / or spatial layout of the plots surrounding the blocked plots until the final land planning scheme is obtained; The feasibility and cost-effectiveness of stripping and reusing the cultivated layer are evaluated by combining the connectivity of the plots, the distance between the plots and the land use efficiency, and the optimal earthwork allocation plan for the final land planning scheme is determined.

[0004] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses a land planning method based on data analysis. The method first constructs an initial farmland ecological network topology diagram, and calculates indicators such as network connectivity to evaluate its integrity. When it is found that the connectivity of a local plot is blocked, the present invention applies a connectivity adjustment method based on distance attenuation, adjusts the distance matrix considering factors such as plot area, obstacles and functional attributes, and reconstructs and evaluates the network topology. If the impact of the blocked connectivity on the network integrity exceeds a threshold, the dynamic adjustment of the planning scheme is triggered to reallocate the functions and layouts of the plots surrounding the blocked plot. The present invention also combines connectivity and land use efficiency to evaluate the feasibility of stripping and reusing the cultivated layer, and determines the optimal adjustment scheme. The method can effectively optimize the farmland ecological network structure, improve the connectivity and stability of the farmland ecosystem, and provide a scientific basis for farmland ecological planning and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 A schematic diagram of a land planning method based on data analysis of the present invention; Figure 2 It is another schematic diagram of the land planning method based on data analysis of the present invention. DETAILED DESCRIPTION

[0006] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0007] like Figure 1-2 The land planning method based on data analysis in this embodiment may specifically include: Step S101, obtaining the initial distance matrix between each plot in the set of farmland plots around the town, calculating the direct connectivity between the plots and other plots based on the distance threshold, and constructing an initial farmland ecological network topology structure diagram.

[0008] A digital elevation processing tool is used to obtain the terrain undulation data of farmland plots, and the initial distance matrix between plots is calculated according to the coordinates of the center points of the farmland plots; the distance value between plots is judged according to the initial distance matrix, and if the distance between plots is less than the distance threshold between plots, it is marked as directly connected, and the area value and ecological resistance value of the plot are obtained through geographic information processing tools; the edge weights are assigned to the connectivity map constructed by the minimum spanning tree algorithm according to the area value of the plot and the ecological resistance value, and the weighted connectivity matrix of the plot is obtained using the proximity calculation tool; the spatial clustering tool based on geographic distance is used to divide the farmland plot groups according to the weighted connectivity matrix, and the topological structure map of the farmland plots is obtained by calculating the internal connectivity distance of the plot group.

[0009] Specifically, digital elevation processing is used to collect terrain data of farmland plots, the coordinates of the center points of farmland plots are obtained from the farmland geographic information database, the distances between the center points of farmland plots are calculated according to the Euclidean distance calculation tool, the initial distance matrix of farmland plots is generated, and the boundary contour data of plots are obtained through the farmland plot boundary polygon calculation tool. For the basic distance matrix of farmland plots, when the distance between two plots is less than the preset threshold of 10,000 meters, it is assigned as direct connection, and when it is greater than the preset threshold, it is marked as indirect connection. The connectivity relationship between the plots is marked, the plot area calculation module in the geographic information processing tool is used to calculate the area of ​​each plot, and the ecological resistance value of each plot is obtained from the landscape index calculation module in the geographic information processing tool. The connectivity map between farmland plots is constructed from the minimum spanning tree algorithm, the edges in the connectivity map are weighted according to the area data of each plot and the landscape ecological resistance value, and the proximity calculation tool is used on the connectivity map to calculate the degree of connectivity between any two farmland plots to form a weighted farmland plot connectivity matrix. According to the weighted farmland plot connectivity matrix, the spatial clustering tool based on geographic distance is used to divide the farmland plot groups. The maximum and minimum connectivity distances of the internal plots are calculated for each farmland plot group, and the farmland plot topological structure map is constructed. The connectivity status between the plots is marked on the plot topological structure map. In digital elevation processing, the terrain undulation data of the farmland plot area is reflected as elevation point data. Each elevation point contains longitude, latitude and altitude data. For example, the point elevation data on the farmland plot is 120.5, 30.6, and the altitude is 85 meters. The gridded terrain data is generated through the data set, and then the topographic map of the farmland plot is obtained. The coordinate data of the center point of each farmland plot is recorded in the geographic information database. For example, the center point of farmland plot No. 5 is located at 120 degrees 31 minutes east longitude and 30 degrees 15 minutes north latitude. In the Euclidean distance calculation, the straight-line distance between the center points of any two farmland plots is calculated based on longitude and latitude, thereby generating the initial distance matrix of farmland plots containing the distances between all plots. The boundary polygon data records the longitude and latitude coordinate sequence of the contour points of the plots. In the basic distance matrix between farmland plots, 10,000 meters is used as the threshold for direct connectivity. When the distance between two plots is less than 10,000 meters, it is marked as 1 on the connectivity relationship mark, and when it is greater than 10,000 meters, it is marked as 0, and a binary direct connectivity relationship matrix is ​​constructed. In the process of geographic information processing, the area of ​​each plot is calculated. The area of ​​plot No. 5 is 80,000 square meters. At the same time, the ecological resistance value is calculated based on the soil conditions and crop types of each farmland, and the value range is between 0 and 100. The minimum spanning tree algorithm constructs a connectivity map with farmland plots as nodes, determines the connectivity edges of the minimum spanning tree based on the straight-line distance between plots, and then assigns weights to the connectivity edges based on the area and ecological resistance value of each plot. The weight value reflects the actual connectivity difficulty between farmland plots. The weighted connectivity distance between any two farmland plots is obtained by using proximity calculation and recorded in the weighted connectivity matrix.For example, the weighted connectivity distance between plots 5 and 8 is 15,000 meters. Based on the weighted connectivity matrix, spatial clustering based on geographic distance divides farmland plots with similar distances and strong connectivity into the same plot group. The maximum and minimum connectivity distances are calculated within each plot group. For example, the maximum connectivity distance between plots within the plot group 1 is 8,000 meters, and the minimum connectivity distance is 2,000 meters. Finally, in the topological structure map of farmland plots, lines of different thicknesses are used to represent the connectivity strength between plots. The thicker the line, the stronger the connectivity. Different colors are used to mark the plot group to which the plot belongs.

[0010] Step S102, calculating the overall network connectivity, node centrality and average path length of the initial farmland ecological network topology structure diagram through a graph theory algorithm, thereby evaluating the integrity of the ecological network structure and obtaining the initial value of the ecological network structure evaluation index.

[0011] The Dijkstra path calculation tool is used to obtain the path length between node pairs in the farmland ecological network, and the node degree matrix is ​​obtained according to the path length statistics; for the nodes in the node degree matrix, if the node degree value is greater than the node degree value threshold, the node weight value is assigned to a first preset value, if the node degree value is less than the node degree value threshold, the node weight value is assigned to a second preset value, and the node centrality value is obtained according to the product of the node weight value and the number of connecting edges; the shortest path length between node pairs is calculated according to the node degree matrix, and the path distance summation tool is used to accumulate the product of the shortest path length and the node centrality value to obtain the weighted average path length index; the network connectivity calculation module is used to obtain the connectivity strength value between node pairs according to the product of the node centrality value and the weighted average path length index, and the farmland ecological network structure evaluation index is calibrated.

[0012] Specifically, the Dijkstra shortest path tool is used to calculate the path length between any two points in the topological structure of the agroecological network. The number of connected edges of each node is obtained from the node degree statistics tool and a node degree matrix is ​​generated. The number of edges of all nodes is counted on the basic topological structure to obtain the overall connectivity of the agroecological network. For each node in the topological structure of the agroecological network, the node weight is assigned according to the node degree matrix. When the node degree value is greater than the preset threshold of 4, the weight value is 1, and when it is less than the preset threshold, the weight value is 0.5. For each node, the node centrality value is obtained by multiplying the node weight value and the number of connected edges. According to the node degree matrix, the path distance sum tool is used to calculate the shortest path length between any two nodes in turn. Each path length value is multiplied by the node centrality value of each node on the path. The path length between all node pairs is averaged to obtain the weighted average path length index. The network connectivity calculation module is used in the topological structure of the farmland ecological network. The connectivity strength between each node pair is calculated according to the product of the node centrality value and the weighted average path length index, and the farmland ecological network structure evaluation index is calibrated according to the connectivity strength value. The nodes in the topological structure of the farmland ecological network represent farmland plots, and the edges represent the connectivity relationship between plots. In the Dijkstra shortest path calculation, the shortest connectivity distance between any two farmland plots is calculated. For example, the shortest path length from plot 1 to plot 5 is 2000 meters, passing through the two middle plots 2 and 3. The node degree statistics show the number of connections between each plot and the surrounding plots. For example, plot 5 is connected to 6 adjacent plots, and its node degree value is 6. The degrees of all nodes are recorded in the node degree matrix. The overall connectivity reflects the ratio of the number of edges actually connected in the farmland ecological network to the theoretical maximum possible number of edges, and the value is between 0 and 1. In the process of assigning node weights, plots with node degree values ​​exceeding the preset threshold of 4 are considered to have strong ecological connectivity and are assigned a weight of 1. Plots with lower degree values ​​are assigned a weight of 0.5, which reflects the importance of different plots in the ecological network. For example, the degree of plot 5 is 6, and the weight value is 1. Multiplying it by the number of connected edges 6 gives a node centrality value of 6, while the degree of plot 8 is 3, and the weight value is 0.5. The node centrality value is 1.5, indicating that plot 5 has a more important connectivity function in the network. When calculating the weighted average path length, the centrality of each node on the path is considered. For example, the path from plot 1 to plot 5 passes through plot 2 and plot 3. The centrality of plot 2 is 4, and the centrality of plot 3 is 5. The weighted length of the path is 2000 multiplied by 4 and then multiplied by 5, resulting in a weighted path length of 40000. The weighted path lengths of all node pairs in the network are averaged to obtain a weighted average path length indicator that reflects the overall network connectivity performance.The assessment of the farmland ecological network structure is based on the node centrality and the weighted average path length, and the connectivity strength between each node pair is calculated. For example, if the product of the node pair centrality is 24 and the weighted average path length is 40,000, the connectivity strength is 0.0006. The larger the value, the better the ecological connectivity between the two nodes. By statistically analyzing the connectivity strength distribution of all node pairs, the overall structural integrity of the farmland ecological network is quantitatively assessed, and the assessment results reflect the connectivity status of the farmland ecological corridor. The assessment indicators can be divided into four levels: 0 to 0.0002 for weak connectivity, 0.0002 to 0.0004 for moderate connectivity, 0.0004 to 0.0006 for good connectivity, and above 0.0006 for strong connectivity.

[0013] Step S103, based on the overall network connectivity, determine whether the actual connectivity reaches the expected connectivity level, and identify the plot pairs that do not reach the expected connectivity level as having blocked connectivity; when there is blocked connectivity of local plots in the initial farmland ecological network topology structure diagram, based on the initial distance matrix between plots, if the distance between plots is greater than the distance threshold between plots, it is determined that the connection between the corresponding plot pairs is blocked, so as to identify the blocked plots and form a set.

[0014] The ratio of the actual number of connected edges to the maximum possible number of connected edges in the topological structure of the farmland ecological network is obtained, and the ratio is compared with the preset connectivity threshold to obtain the marking results of the plots in the state to be judged; based on the marking results of the plots in the state to be judged and the initial distance matrix between the plots, for the plot pairs marked as the state to be judged, the standard connectivity distance threshold is obtained from the geographic spatial database to obtain the markings of the plots in the blocked connectivity state; the spatial clustering analysis tool is used to calculate the spatial correlation of the plots in the blocked connectivity state, and the plot connectivity resistance value is generated according to the plot boundary distance data to obtain the blocked plot set; the connectivity resistance index is calculated for the plots in the blocked plot set, and the connectivity blocking level classification result is obtained according to the connectivity resistance index and the connectivity distance weighted value of the surrounding plots.

[0015] Specifically, the network connectivity statistics tool is used to calculate the ratio of the actual number of connected edges to the maximum number of possible connected edges in the topological structure of the farmland ecological network, and the overall network connectivity value is obtained. If the obtained connectivity value is less than the preset connectivity threshold of 0.6, the corresponding plot is marked as a pending state in the plot connectivity state matrix. According to the initial distance matrix between farmland plots, for the plot pairs marked as pending states, the standard connectivity distance threshold of 8,000 meters of the current regional plots is obtained from the geospatial database. Based on the Euclidean distance calculation between the boundaries of adjacent plots, the plot pairs with distance values ​​exceeding the threshold are marked as blocked connectivity. On the farmland ecological network topological structure diagram, the spatial clustering analysis tool is used to calculate the spatial correlation of the plots marked as blocked connectivity, and the plot boundary distance data is used to generate the plot connectivity resistance value. The blocked plots are grouped according to the resistance value to form a blocked plot set. The connectivity resistance index of the plots in the blocked plot set was calculated using the plot connectivity assessment tool. The weighted value of the connectivity distance between each blocked plot and the surrounding plots was calculated. The connectivity obstruction level of each blocked plot in the plot set was divided according to the connectivity resistance index. In the farmland ecological network, the overall network connectivity reflects the level of ecological connectivity between farmland plots. Taking 8 farmland plots as an example, the maximum possible number of connected edges is 28, and the actual number of connected edges is 14. The calculated overall network connectivity is 0.5, which is lower than the preset connectivity threshold of 0.6 and marked as pending. The lower the connectivity value, the higher the degree of fragmentation of the farmland ecosystem and the more discontinuous the ecological corridors between plots. The initial distance matrix records the actual boundary distance between adjacent plots. For example, the boundary distance between plots 5 and 6 is 10,000 meters, which exceeds the standard connectivity distance threshold of 8,000 meters, indicating that the connectivity between the two plots is blocked by geographic space. In the distance threshold judgment, the Euclidean distance of each pair of adjacent plots is calculated, and the plot pairs exceeding the threshold are marked as blocked connectivity, such as the 5-6 plot pair and the 7-8 plot pair. In the spatial clustering analysis, the spatial correlation degree of the blocked connectivity plots is calculated. For example, there are three similarly blocked plots around the blocked plot 5, with a high spatial correlation, while there is only one blocked plot around the blocked plot 8, with a low spatial correlation. The connectivity resistance value is calculated based on the plot boundary distance. The corresponding resistance value for the boundary distance of 10,000 meters is 1.25, and the corresponding resistance value for 12,000 meters is 1.5. The blocked plots are divided into multiple sets according to the resistance value. The connectivity resistance index comprehensively considers the distance resistance and spatial aggregation degree between plots. For example, the connectivity resistance index of plot 5 is 1.8, which is higher than the 1.5 of the surrounding plots, indicating that its connectivity is more seriously blocked. By calculating the weighted connectivity distance between each obstructed plot and its surrounding plots, the connectivity obstruction levels of the plots are divided: a connectivity resistance index of 1.2 to 1.4 is mildly obstructed, 1.4 to 1.6 is moderately obstructed, 1.6 to 1.8 is severely obstructed, and above 1.8 is severely obstructed.In actual application, plots 5 and 6 are both severely obstructed, and the ecological corridor between them needs to be improved urgently, while plot 7 is slightly obstructed, indicating that its connectivity problem is relatively mild.

[0016] Step S104, for the obstructed plot set, a connectivity adjustment method based on distance attenuation is applied to adjust the distance matrix according to the distance factor between plots, wherein the distance factor is calculated in combination with the plot area, the type and number of obstacles between plots, and the functional attributes of the plots.

[0017] An area calculation tool is used to obtain the area of ​​the plots in the obstructed plot set, and the farmland function attribute values ​​are extracted from the geospatial database according to the area to obtain the basic weight of the plot; for the obstructed plot set, obstacle distribution data is extracted from the remote sensing image database, and the product of the obstacle density value and the obstacle strength value is calculated according to the obstacle distribution data to obtain the spatial barrier coefficient; an initial distance weight matrix is ​​generated according to the Euclidean distance values ​​between the obstructed plots, and the initial distance weight matrix is ​​multiplied by the spatial barrier coefficient and the basic weight of the plot to obtain the distance attenuation coefficient; for adjacent plot pairs in the obstructed plot set, a spatial distance weighting tool is used to multiply the initial distance matrix by the distance attenuation coefficient to obtain a weighted distance matrix.

[0018] Specifically, the area calculation tool is used to measure the area of ​​the plots in the obstructed plot set, the farmland functional attributes are extracted from the geospatial database to assign functions to each plot, and the basic weight of the plot is generated according to the area size and functional attributes. The obstacle information between the obstructed plots is extracted from the remote sensing image database, and the obstacle density value is obtained by dividing the distribution area of ​​the obstacles between the farmlands by the total area of ​​the plots. The obstacle strength value is assigned according to the degree of obstruction between the two plots, and the product of the obstacle density value and the obstacle strength value is used as the spatial obstruction coefficient. According to the Euclidean distance value between the obstructed plots, the distance weight calculator is used to generate the initial distance weight matrix, the weight value of the adjacent plot pairs is multiplied by the spatial obstruction coefficient, and the distance weight value is corrected according to the basic weight of the plot to obtain the weighted distance attenuation coefficient. The spatial distance weighting tool is applied to the obstructed plot set. For each pair of adjacent plots, the distance attenuation coefficient is used to weight the initial distance matrix, and the actual distance value between the plots is multiplied by the corresponding distance attenuation coefficient to generate a new weighted distance matrix. In the area calculation, the size of the farmland plot directly affects its ecological connectivity. For example, the area of ​​plot 5 is 80,000 square meters, which belongs to large-scale farmland. Its functional attribute is a grain production area, and the basic weight value is set to 1.2. The area of ​​plot 7 is 20,000 square meters, and its functional attribute is an economic crop area. The basic weight value is set to 0.8. The larger the area and the more important the function, the higher the basic weight value. Remote sensing image interpretation shows that there are obstacles such as roads and buildings between plots 5 and 6. The obstacle covers an area of ​​4,000 square meters. Relative to the total area of ​​100,000 square meters of the plot, the obstacle density value is calculated to be 0.04. Considering that the road is a completely blocking obstacle, its obstacle strength value is given 1.5. The multiplication of the two gives a spatial barrier coefficient of 0.06, which reflects the actual impact of the obstacle on the connectivity of the plot. During the distance weight calculation process, the Euclidean distance between plots 5 and 6 is 8,000 meters, corresponding to an initial distance weight value of 0.8, which is multiplied by the spatial barrier coefficient of 0.06 to obtain 0.048. Combined with the basic weights of the two plots of 1.2 and 1.0, the distance attenuation coefficient of 0.0576 is finally calculated. The smaller the distance attenuation coefficient, the greater the actual connectivity difficulty between the two plots. In the spatial distance weighted correction, the distance between plots 5 and 6 recorded in the original distance matrix is ​​8,000 meters. After multiplying by the distance attenuation coefficient of 0.0576, it becomes 460.8 meters in the new weighted distance matrix. This weighted correction takes into account the influence of plot area, functional attributes and obstacles, so that the distance value more accurately reflects the actual connectivity difficulty between plots. In contrast, although the actual distance between plots 8 and 9 is close to 7,500 meters, there are no obstacles in the middle, the spatial barrier coefficient is 0, the two plots are similar in area and function, and the basic weights are close. The final weighted distance is only 300 meters, indicating that the connectivity between the two plots is good.This distance attenuation adjustment based on multiple factors provides a more accurate spatial reference for subsequent improvement of plot connectivity.

[0019] Step S105, recalculating the connectivity between the plots according to the adjusted distance matrix between the plots, reconstructing the farmland ecological network topology structure, and calculating the evaluation index of the adjusted ecological network structure.

[0020] A connectivity calculator is used to obtain the distance value between adjacent plots based on the distance matrix between plots. If the distance value is less than a preset distance threshold, the adjacent plot pair is determined to be a directly connected plot pair. A farmland ecological network structure diagram is generated for the directly connected plot pairs through a topological map construction tool, and a connectivity calculation module is used to calculate the number of directly connected plots of each plot to obtain the plot connectivity value. A connectivity strength statistical tool is used to quantify the directly connected plot pairs for the farmland ecological network structure diagram, and the total network connectivity value is obtained from the connectivity strength value of the directly connected plot pairs. Connectivity relationship data is obtained from the structural integrity assessment tool based on the total network connectivity value, and an assessment calculation module is used to combine the plot connectivity value and the connectivity strength value to obtain a network integrity assessment index.

[0021] Specifically, according to the adjusted distance matrix between plots, the connectivity calculator is used to calculate the connectivity relationship of any adjacent plots. If the distance value between plots is less than the preset distance threshold of 8,000 meters, the plot pair is marked as directly connected on the topological relationship diagram, and the connectivity strength value between the connected plot pairs is quantified and assigned. Based on the marked connected plots, a new farmland ecological network structure diagram is generated through the topological map construction tool, and the number of directly connected plots of each plot is calculated to obtain the connectivity value, and the total network connectivity value is calculated based on the connectivity strength value between the connected plot pairs. The connectivity strength statistics tool is used to quantify the degree of connectivity between plots in the topological structure, and the connectivity strength value of each plot and its directly connected plot is accumulated, and the network connectivity is calculated from the total connectivity strength value of all plots. For the generated farmland ecological network structure diagram, the connectivity relationship data is obtained from the structural integrity assessment tool, the plot connectivity value and the plot connectivity strength value are combined, and the network integrity assessment index is numerically calculated. In the adjusted farmland ecological network, the connectivity relationship between adjacent plots is recalculated based on the distance matrix. Taking plot 5 as an example, the distance values ​​of its adjacent plots 6, 7, and 8 are 7500 meters, 6800 meters, and 8200 meters, respectively. Since the preset distance threshold is 8000 meters, plot 5 is marked as directly connected to plots 6 and 7, but not directly connected to plot 8. For the connected plot pairs, the connectivity strength value is calculated according to the proportion of the actual distance to the threshold, such as the connectivity strength value of the 5-6 plot pair is 0.94, and the 5-7 plot pair is 0.85. In the new farmland ecological network structure diagram, the connectivity value of each plot reflects the number of plots it is directly connected to. For example, plot 5 is directly connected to 2 plots, with a connectivity value of 2, while plot 3 is directly connected to 4 plots, with a connectivity value of 4. The total value of network connectivity is calculated by accumulating the connectivity strength values ​​of all connected plot pairs. For example, the current network contains 12 pairs of connected plots, and the total connectivity strength value is 10.2, indicating that the overall connectivity of the network is good. Connectivity strength statistics show that the cumulative connectivity strength value of plot 5 is 1.79, which comes from 0.94 of plot 6 and 0.85 of plot 7. In contrast, due to the large number of connected plots, the cumulative connectivity strength value of plot 3 reaches 3.45. The connectivity of the entire network is calculated by dividing the sum of the connectivity strength values ​​of all plots by the number of plots. The current network connectivity is 0.85, indicating that the network structure is relatively complete. In the structural integrity assessment, the connectivity value and connectivity strength value of each plot are weighted and combined, with weights of 0.4 and 0.6 respectively. Taking plot 5 as an example, the connectivity value 2 multiplied by the weight 0.4 is 0.8, and the connectivity strength value 1.79 multiplied by the weight 0.6 is 1.074. The sum of the two gives the integrity index value of the plot 1.874. The average integrity index value of all plots, 2.1, is used as the overall integrity evaluation index of the network, reflecting the overall connectivity level of the farmland ecological network structure.

[0022] Step S106, based on the changes before and after the adjustment of the ecological network structure evaluation indicators, analyze the impact of the obstructed connectivity of local plots on the integrity of the ecological network structure. If the impact exceeds the preset change threshold, the dynamic adjustment of the planning scheme is triggered to reallocate the functional attributes and / or spatial layout of the plots surrounding the obstructed plots until the final land planning scheme is obtained.

[0023] The change amplitude value of the network structure evaluation index is obtained from the index change calculator. If the change amplitude value exceeds the preset change threshold, the functional attributes of the obstructed plot and the surrounding plots are annotated; based on the functional attribute annotation, the spatial distance of the surrounding plots of the obstructed plot is calculated by the spatial adjacency analysis tool, and the spatial distance is used to classify the adjacent plots; for the adjacent plots after the classification, the adaptability score of the alternative functional attribute is calculated by the functional matching optimizer, and if the adaptability score exceeds the adaptability score threshold, a functional attribute label of the plot group is generated; based on the functional attribute label of the plot group, the layout adjustment calculation tool is used to calculate the relative position of the spatial layout of the plot, and the spatial distance between the plots is judged by the layout conflict verifier.

[0024] Specifically, the difference operation is performed on the initial value and the adjustment value of the ecological network structure evaluation index from the indicator change calculator to obtain the change amplitude value of the network structure evaluation index. If the indicator change amplitude value exceeds the preset change threshold of 0.3, the functional attributes of the blocked plot and the surrounding plots are annotated. The spatial distance of the surrounding plots of the blocked plot is calculated by the spatial adjacency analysis tool, the adjacent plots are graded based on the distance value between the plots, the functional combination adaptability score is calculated according to the existing functional attribute assignment of the plot, and the functional attribute conversion rule of the plot is read from the farmland function rule library. In the group of plots around the blocked plot, the functional matching optimizer is used to calculate the adaptability score of the alternative functional attributes for each plot, and the functional attributes with adaptability scores exceeding the preset threshold of 0.6 are recorded, and new functional attribute labels of the plot group are generated according to the functional attribute conversion rules. For the new functional attribute labels of the plot group, the layout adjustment calculation tool is used to calculate the spatial layout of the plot, and the relative position of each plot is calculated based on the functional attribute labels of the plots. The spatial distance between the plots in the generated layout is judged by the layout conflict verifier. In the evaluation of ecological network structure, the calculation of index changes reflects the degree of impact of connectivity obstruction. For example, the initial evaluation index value of plot 5 was 0.85, which dropped to 0.45 after adjustment, with a change of 0.4, exceeding the preset threshold of 0.3, indicating that the connectivity of the area was seriously affected, and the functional attributes of plot 5 and its surrounding plots needed to be adjusted. The functional attributes of plots include types such as grain production area, cash crop area, and ecological protection area. Each plot has its original functional attribute label. Spatial adjacency analysis shows that there are three adjacent plots within 500 meters around plot 5, namely plots 6, 7 and 8, which are divided into two levels according to the adjacency distance: plot 6 is 320 meters away from the first level, and plots 7 and 8 are 480 meters and 490 meters away from the second level. Currently, plot 6 is a grain production area, and plots 7 and 8 are cash crop areas. According to the functional combination rule, the adaptability score is calculated, and the adaptability score of the grain area adjacent to the cash crop area is 0.7. During the functional matching optimization process, the adaptability scores of different functional attributes of Plot 5 were calculated: 0.65 as a grain production area, 0.55 as a cash crop area, and 0.75 as an ecological protection area. Since the adaptability score of the ecological protection area exceeded the preset threshold of 0.6 and was higher than the scores of other functional attributes, the functional attribute of Plot 5 was adjusted from the original cash crop area to the ecological protection area. At the same time, the function of Plot 8 was also adjusted from the cash crop area to the grain production area, and the adaptability score was increased from 0.55 to 0.68. The layout adjustment calculation spatially reorganized the plot group according to the new functional attribute labels. Plot 5 in the ecological protection area is the core, and the grain production area and cash crop area are arranged around it to form functional complementarity. The minimum spacing between plots must be kept above 300 meters to ensure space for agricultural machinery operations.The layout conflict check shows that the distances between the adjusted Plot 5 and the surrounding plots are 350 meters, 420 meters and 380 meters respectively, which meets the spatial layout requirements, and the area and shape of each functional block meet the needs of agricultural production.

[0025] Step S107, combining the connectivity of the plots, the distance between the plots and the land use efficiency to evaluate the feasibility and cost-effectiveness of stripping and reusing the cultivated layer, and determine the optimal earthwork allocation plan for the final land planning scheme.

[0026] The connectivity value between the plots is obtained, and the current utilization data of the plots is extracted from the current land resource utilization database according to the connectivity value, and the total value of regional land resource utilization is calculated based on the current utilization data of the plots; the current utilization data of the plots is processed by the engineering quantity accounting tool to obtain the earthwork allocation quantity between the plots, and a transportation route map is generated according to the earthwork allocation quantity and the actual transportation distance between the plots, and the transportation cost value is calculated based on the transportation route map; the distance factor value is obtained according to the actual transportation distance between the plots, and the transportation cost value is processed by the scheme fitness evaluator to obtain the fitness score of the earthwork allocation scheme; based on the fitness score of the earthwork allocation scheme, the connectivity value of the plots, the total land resource utilization value and the transportation cost value are normalized and calculated by the weighted sorting tool to obtain the optimal earthwork allocation scheme. Specifically, the dynamically adjusted connectivity value between plots is obtained from the connectivity calculator, the current utilization data of plots is extracted according to the current land resource utilization database, the land utilization degree of each plot is scored, and the total value of regional land resource utilization is calculated based on the utilization degree scores of all plots. The engineering quantity accounting tool is used to calculate the earthwork allocation quantity between plots, the earthwork allocation cost is quantified based on the actual transportation distance between plots, a transportation path map is generated for the earthwork allocation plan, and the transportation cost value is recorded in different earthwork allocation plans. According to the distance factor value between plots, the scheme fitness evaluator is used to evaluate different earthwork allocation plans, and the fitness score of each plan is calculated according to the connectivity value of the plot, the total land utilization value, and the transportation cost value. In the allocation plan library, the fitness score of each earthwork allocation plan is sorted by the weighted sorting tool, the three indicators of plot connectivity, land utilization, and transportation cost are normalized and calculated, and the optimal allocation plan is selected by the weighted combination value of the three indicators.

[0027] , C i represents the comprehensive score of the ith solution, P i Represents the connectivity index value, L i represents the land use index value, T irepresents the value of the transport cost index, α1, α2, and α3 are the weight coefficients of the three indicators and the sum is 1, and max and min represent the maximum and minimum values ​​of each indicator respectively. The connectivity value of the plot reflects the connection status of the farmland ecological network. Taking plots 5 and 6 as examples, the connectivity between the two plots increased from 0.45 to 0.75 after dynamic adjustment, indicating that the connectivity has improved after adjustment. The current status of land resource utilization shows that the cultivated land utilization rate of plot 5 is 85%, and that of plot 6 is 78%. The land utilization score is calculated based on the cultivated land area and utilization intensity. Plot 5 scores 0.85, and Plot 6 scores 0.78. The overall land resource utilization value of the region is 0.82. During the engineering quantity accounting process, the earthwork allocation from plot 5 to plot 6 involves an earthwork volume of 5,000 cubic meters and a transportation distance of 800 meters. According to the unit transportation distance cost, the transportation cost of this scheme is 4,000 yuan. There are other allocation plans, such as allocating 3,000 cubic meters from Plot 5 to Plot 7, with a distance of 1,200 meters and a transportation cost of 3,600 yuan. The transportation route map shows the specific routes of different allocation plans, including information such as passing roads and transfer points. In the fitness evaluation of the plan, the three indicator values ​​of the allocation plan for Plots 5-6 are: connectivity 0.75, land use 0.82, and transportation cost 4,000 yuan. In contrast, the indicator values ​​of the allocation plan for Plots 5-7 are: connectivity 0.65, land use 0.79, and transportation cost 3,600 yuan. By setting the connectivity weight of 0.4, the land use weight of 0.3, and the transportation cost weight of 0.3, the fitness score of Plan 5-6 is calculated to be 0.72, and the score of Plan 5-7 is 0.68. Among all the allocation plans, the plans were compared through weighted sorting. First, the three indicators were normalized: connectivity and land use were directly used in their original values, and transportation costs were converted by comparing them with the benchmark cost of 5,000 yuan. For example, the normalized index values ​​of Plan 5-6 were 0.75, 0.82, and 0.80, and the final score after weighted combination was 0.79, which was at the top of all plans. Through this multi-indicator evaluation method, the improvement of the connectivity of the ecological network was taken into account, while the efficient use of land resources and the economic efficiency of project implementation were also taken into account. The optimal plan finally selected was able to achieve a good balance in these three aspects.

[0028] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A land planning method based on data analysis, characterized in that: The method comprises: Obtain the initial distance matrix between each plot in the set of farmland plots around towns, calculate the direct connectivity between the plots and other plots based on the distance threshold, and construct the initial farmland ecological network topology structure diagram; The overall network connectivity, node centrality and average path length of the initial farmland ecological network topology structure diagram are calculated through graph theory algorithms to evaluate the integrity of the ecological network structure and obtain the initial values ​​of the ecological network structure evaluation indicators. Based on the overall network connectivity, it is determined whether the actual connectivity reaches the expected connectivity level. For the plot pairs that do not reach the expected connectivity level, they are identified as having blocked connectivity. When there is a local block connectivity obstruction in the initial farmland ecological network topology diagram, according to the initial distance matrix between the blocks, if the distance between the blocks is greater than the distance threshold between the blocks, it is judged that the connection between the corresponding pairs of blocks is obstructed, so as to identify the obstructed blocks and form a set; For the obstructed plots, a distance decay-based connectivity adjustment method is applied to adjust the distance matrix according to the distance factor between plots, where the distance factor is calculated based on the plot area, the type and number of obstacles between plots, and the functional attributes of the plots; According to the adjusted inter-plot distance matrix, the connectivity between plots is recalculated, the topological structure of the farmland ecological network is reconstructed, and the evaluation index of the adjusted ecological network structure is calculated; Based on the changes in the ecological network structure assessment indicators before and after the adjustment, the impact of the blocked connectivity of local plots on the integrity of the ecological network structure is analyzed. If the impact exceeds the preset change threshold, the dynamic adjustment of the planning scheme is triggered to reallocate the functional attributes and / or spatial layout of the plots surrounding the blocked plots until the final land planning scheme is obtained; The feasibility and cost-effectiveness of stripping and reusing the cultivated layer are evaluated by combining the connectivity of the plots, the distance between the plots and the land use efficiency, and the optimal earthwork allocation plan for the final land planning scheme is determined.

2. The method according to claim 1, characterized in that The method of obtaining the initial distance matrix between each plot in the set of farmland plots around the town, respectively calculating the direct connectivity between the plot and other plots based on the distance threshold, and constructing the initial farmland ecological network topology structure diagram includes: Digital elevation processing tools are used to obtain the topographic data of farmland plots, and the initial distance matrix between plots is calculated based on the coordinates of the center points of the farmland plots; Determine the distance value between plots according to the initial distance matrix. If the distance between plots is less than the distance threshold between plots, mark them as directly connected. Obtain the area value and ecological resistance value of the plot through geographic information processing tools. Assign edge weights to the connectivity graph constructed by the minimum spanning tree algorithm according to the land area value and the ecological resistance value, and use a proximity calculation tool to obtain a weighted land connectivity matrix; The weighted connectivity matrix is ​​divided into farmland plots using a spatial clustering tool based on geographic distance, and the farmland plot topological structure map is obtained by calculating the internal connectivity distance of the plot group.

3. The method according to claim 1, characterized in that The overall network connectivity, node centrality and average path length of the initial farmland ecological network topology structure diagram are calculated by graph theory algorithm to evaluate the integrity of the ecological network structure and obtain the initial value of the ecological network structure evaluation index, including: The Dijkstra path calculation tool is used to obtain the path length between node pairs in the agroecological network, and the node degree matrix is ​​obtained according to the path length statistics; For the nodes in the node degree matrix, if the node degree value is greater than the node degree value threshold, the node weight value is assigned to be a first preset value; if the node degree value is less than the node degree value threshold, the node weight value is assigned to be a second preset value, and the node centrality value is obtained according to the product of the node weight value and the number of connected edges; Calculating the shortest path length between node pairs according to the node degree matrix, and using a path distance summation tool to accumulate the product of the shortest path length and the node centrality value to obtain a weighted average path length index; The network connectivity calculation module is used to obtain the connectivity strength value between node pairs according to the product of the node centrality value and the weighted average path length index, and the farmland ecological network structure evaluation index is calibrated.

4. The method according to claim 1, characterized in that According to the overall network connectivity, it is judged whether the actual connectivity reaches the expected connectivity level, and for the plot pairs that do not reach the expected connectivity level, they are identified as having blocked connectivity; when there is blocked connectivity of local plots in the initial farmland ecological network topology structure diagram, according to the initial distance matrix between plots, if the distance between plots is greater than the distance threshold between plots, it is judged that the connection between the corresponding plot pairs is blocked, so as to identify the blocked plots and form a set, including: Obtaining the ratio of the actual number of connected edges to the maximum possible number of connected edges in the farmland ecological network topology structure, and comparing the ratio with a preset connectivity threshold to obtain a marking result of the land parcel to be judged; According to the marking results of the land parcels in the state to be judged and the initial distance matrix between the land parcels, for the land parcel pairs marked as the state to be judged, a standard connectivity distance threshold is obtained from the geospatial database to obtain the marking of the land parcels in the state of blocked connectivity; A spatial clustering analysis tool is used to calculate the spatial correlation of the blocked-connectivity plots, and a block connectivity resistance value is generated according to the block boundary distance data to obtain a set of blocked plots; A connectivity resistance index is calculated for the plots in the obstructed plot set, and a connectivity obstruction level classification result is obtained based on the connectivity resistance index and the weighted value of the connectivity distance of the surrounding plots.

5. The method according to claim 1, characterized in that For the obstructed plot set, the connectivity adjustment method based on distance decay is applied to adjust the distance matrix according to the distance factor between plots, wherein the distance factor is calculated by combining the plot area, the type and number of obstacles between plots, and the functional attributes of the plots, including: Using an area calculation tool to obtain the area of ​​the plots in the obstructed plot set, extracting farmland function attribute values ​​from a geospatial database based on the area, and obtaining the basic weight of the plot; For the obstructed land set, obstacle distribution data is extracted from the remote sensing image database, and the product of the obstacle density value and the obstacle strength value is calculated according to the obstacle distribution data to obtain the spatial barrier coefficient; Generate an initial distance weight matrix according to the Euclidean distance values ​​between the blocked plots, and multiply the initial distance weight matrix by the spatial barrier coefficient and the plot basic weight to obtain a distance attenuation coefficient; For the adjacent plot pairs in the obstructed plot set, a spatial distance weighting tool is used to multiply the initial distance matrix by the distance attenuation coefficient to obtain a weighted distance matrix.

6. The method according to claim 1, characterized in that According to the adjusted inter-plot distance matrix, the connectivity between plots is recalculated, the farmland ecological network topology is reconstructed, and the evaluation indicators of the adjusted ecological network structure are calculated, including: A connectivity calculator is used to obtain the distance value between adjacent plots according to the distance matrix between plots. If the distance value is less than a preset distance threshold, the adjacent plot pair is determined to be a directly connected plot pair. For the directly connected plots, a topology map construction tool is used to generate a farmland ecological network structure map, and a connectivity calculation module is used to calculate the number of directly connected plots of each plot to obtain a plot connectivity value; A connectivity strength statistical tool is used for the farmland ecological network structure diagram to quantitatively calculate the directly connected plot pairs, and a total network connectivity value is obtained from the connectivity strength values ​​of the directly connected plot pairs; The connectivity relationship data is obtained from the structural integrity assessment tool according to the total network connectivity value, and the network integrity assessment index is obtained by combining the connectivity value of the plot and the connectivity strength value using an assessment calculation module.

7. The method according to claim 1, characterized in that The above mentioned method is to analyze the impact of the blocked connectivity of local plots on the integrity of the ecological network structure based on the changes before and after the adjustment of the ecological network structure assessment indicators. If the impact exceeds the preset change threshold, the dynamic adjustment of the planning scheme is triggered to reallocate the functional attributes and / or spatial layout of the plots surrounding the blocked plots until the final land planning scheme is obtained, including: Obtaining a change amplitude value of the network structure evaluation index from the index change calculator, and if the change amplitude value exceeds a preset change threshold, marking the functional attributes of the blocked plot and the surrounding plots; According to the functional attribute annotation, the spatial distance of the surrounding plots of the blocked plot is calculated by using the spatial adjacency analysis tool, and the spatial distance is used to classify the adjacent plots; For the classified adjacent plots, a functional matching optimizer is used to calculate the adaptability score of the candidate functional attributes, and if the adaptability score exceeds the adaptability score threshold, a plot group functional attribute label is generated; According to the functional attribute labels of the plot group, a layout adjustment calculation tool is used to calculate the relative position of the plot spatial layout, and a layout conflict verifier is used to determine the spatial distance between the plots.

8. The method according to claim 1, characterized in that The feasibility and cost-effectiveness of stripping and reusing the cultivated layer are evaluated by combining the connectivity of the plots, the distance between the plots and the land use efficiency, and determining the optimal earthwork allocation plan for the final land planning scheme, including: Obtaining connectivity values ​​between plots, extracting plot utilization status data from a land resource utilization status database according to the connectivity values, and calculating the total value of regional land resource utilization based on the plot utilization status data; The engineering quantity accounting tool is used to process the current utilization data of the plots to obtain the earthwork allocation quantity between the plots, a transportation route map is generated according to the earthwork allocation quantity and the actual transportation distance between the plots, and a transportation cost value is calculated based on the transportation route map; Obtaining a distance factor value according to the actual transportation distance between the plots, processing the transportation cost value using a scheme fitness evaluator, and obtaining a fitness score for the earthwork allocation scheme; According to the fitness score of the earthwork allocation plan, the connectivity value of the land parcel, the total value of land resource utilization and the transportation cost value are normalized and calculated through a weighted sorting tool to obtain the optimal earthwork allocation plan.

Images