Land planning method based on data analysis

Abstract

The application provides a land planning method based on data analysis, comprising: obtaining an initial distance matrix between each plot in a set of farmland plots around a town, respectively calculating the direct connectivity of each plot with other plots based on a distance threshold, and constructing an initial farmland ecological network topology structure diagram; 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 structural integrity of the ecological network to obtain initial values of ecological network structure evaluation indexes; recalculating the connectivity between each plot according to an adjusted distance matrix between plots, reconstructing the farmland ecological network topology structure, and calculating various indexes of network structure integrity to evaluate the ecological network structure; and combining the plot connectivity with the land use efficiency to evaluate the feasibility and cost-effectiveness of the tillage layer stripping and reuse, 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 land planning for peri-urban agroecological networks, when connectivity is impeded in some plots, for example due to artificial buildings, roads, or other obstacles, ecological corridors between plots may be severed, necessitating adjustments to the land planning scheme based on inter-plot distance factors. However, such adjustments may impact the stripping and reuse of the cultivated layer of farmland. Specifically, when the distance between two plots exceeds a certain threshold, local plots may need to be merged or split to maintain the structural integrity of the agroecological network. While such adjustments can optimize plot connectivity, they can also disrupt the soil structure of the original plots, leading to the stripping and redistribution of the cultivated layer. The stripped cultivated layer soil may be used to backfill other plots or for other uses, altering the original soil environment. Due to differences in soil texture, fertility, and other characteristics across plots, the redistribution of the cultivated layer may affect the balanced distribution of soil nutrients, thereby impacting crop growth and yield. Furthermore, frequent stripping and reuse of the cultivated layer may 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 corresponding 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:

[0004] Obtain the initial distance matrix between each plot in the collection of farmland plots around towns, calculate the direct connectivity between each plot and other plots based on the distance threshold, and construct the initial farmland ecological network topology structure diagram;

[0005] The overall network connectivity, node centrality, and average path length of the initial farmland ecological network topology structure graph are calculated using graph theory algorithms to evaluate the integrity of the ecological network structure and obtain the initial values ​​of the ecological network structure evaluation indicators.

[0006] Based on the overall network connectivity, it is determined whether the actual connectivity reaches the expected connectivity level. For plot pairs that do not reach the expected connectivity level, they are identified as having blocked connectivity.

[0007] When there is a local block connectivity obstruction in the initial agroecological network topology, based on the initial distance matrix between blocks, if the distance between blocks is greater than the distance threshold between blocks, it is determined that the connection between the corresponding blocks is obstructed, so as to identify the obstructed blocks and form a set;

[0008] For the set of obstructed plots, a connectivity adjustment method based on distance decay is applied to adjust the distance matrix according to the distance factor between plots. 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.

[0009] Based on 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;

[0010] Based on the changes in ecological network structure assessment indicators before and after adjustment, the impact of localized land parcel connectivity obstruction on the integrity of the ecological network structure is analyzed. If the impact exceeds the preset change threshold, the dynamic adjustment of the land planning scheme is triggered, and the functional attributes and / or spatial layout of the surrounding parcels of the obstructed parcel are reallocated until the final land planning scheme is obtained.

[0011] The feasibility and cost-effectiveness of stripping and reusing the cultivated layer are evaluated by combining the connectivity of plots, the distance between plots and land use efficiency, and the optimal earthwork allocation plan for the final land planning scheme is determined.

[0012] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0013] 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 the network topology and evaluates it. If the impact of the connectivity obstruction on the network integrity exceeds a threshold, the dynamic adjustment of the land planning scheme is triggered, and the functions and layout of the plots around the obstructed plot are reallocated. The present invention also combines connectivity and land use efficiency to evaluate the feasibility of arable layer stripping and reuse, and determines the optimal adjustment scheme. This 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

[0014] Figure 1 A schematic diagram of a land planning method based on data analysis according to the present invention;

[0015] Figure 2It is another schematic diagram of the land planning method based on data analysis of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, 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.

[0017] like Figure 1-2 The land planning method based on data analysis in this embodiment may specifically include:

[0018] Step S101: obtain the initial distance matrix between each plot in the set of farmland plots around the town, calculate the direct connectivity between each plot and other plots based on the distance threshold, and construct an initial farmland ecological network topology structure diagram.

[0019] 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 based on the center point coordinates of the farmland plots; the distance value between plots is judged based on 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 a geographic information processing tool; the edge weights are assigned to the connectivity map constructed by the minimum spanning tree algorithm based on the area value and the ecological resistance value, and the weighted plot connectivity matrix is ​​obtained using a proximity calculation tool; the weighted connectivity matrix is ​​divided into farmland plot groups using a spatial clustering tool based on geographic distance, and the topological structure map of the farmland plot is obtained by calculating the internal connectivity distance of the plot group.

[0020] Specifically, digital elevation processing was used to collect topographic data for farmland plots. The coordinates of the farmland plot centers were obtained from the farmland geographic information database. The distances between each farmland plot center point were calculated using the Euclidean distance calculation tool to generate an initial farmland plot distance matrix. The farmland plot boundary polygon calculation tool was used to obtain the plot boundary contour data. For the farmland plot basic distance matrix, when the distance between two plots was less than a preset threshold of 10,000 meters, the distance was assigned as directly connected, and when it was greater than the preset threshold, it was marked as indirect connected. The connectivity relationships between the plots were marked, and the plot area calculation module in the geographic information processing tool was used to calculate the area of ​​each plot. The ecological resistance value of each plot was obtained from the landscape index calculation module in the geographic information processing tool. A connectivity map between the farmland plots was constructed using the minimum spanning tree algorithm. The edges in the connectivity map were weighted based on the plot area data and the landscape ecological resistance value. The proximity calculation tool was used on the connectivity map to calculate the degree of connectivity between any two farmland plots, forming a weighted farmland plot connectivity matrix. Based on the weighted farmland parcel connectivity matrix, a spatial clustering tool based on geographic distance is used to divide farmland parcels into clusters. For each parcel cluster, the maximum and minimum connectivity distances within the parcels are calculated. A topological map of the farmland parcels is constructed, and the connectivity between the parcels is annotated on the topological map. In digital elevation processing, the topographic data of the farmland parcel area is represented as elevation points. Each elevation point contains longitude, latitude, and altitude data. For example, the elevation data for points on a farmland parcel is 120.5 and 30.6, which is 85 meters above sea level. Gridded terrain data is generated from this dataset, and a topographic map of the farmland parcels is then generated. The coordinates of the center point of each farmland parcel are recorded in a geographic information database. For example, the center point of farmland parcel 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 any two farmland parcel centers is calculated based on longitude and latitude, thus generating an initial farmland parcel distance matrix containing the distances between all parcels. Boundary polygon data records the longitude and latitude coordinate sequences of the plot's contour points. 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, the connectivity marker is marked as 1, and when it is greater than 10,000 meters, it is marked as 0. This constructs a binary direct connectivity matrix. During geographic information processing, the area of ​​each plot is calculated; Plot 5 has an area of ​​80,000 square meters. Ecological resistance values ​​are also calculated based on the soil conditions and crop types of each farmland, ranging from 0 to 100. The minimum spanning tree algorithm constructs a connectivity graph using farmland plots as nodes. Connected edges in the minimum spanning tree are determined based on the straight-line distances between plots. Weights are then assigned to connected edges based on the area and ecological resistance value of each plot. The weighted distances reflect the actual connectivity difficulty between farmland plots. Proximity is used to calculate the weighted connectivity distance between any two farmland plots and record it 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 cluster. The maximum and minimum connectivity distances are calculated within each plot cluster. For example, the maximum connectivity distance between plots within plot cluster 1 is 8,000 meters, and the minimum connectivity distance is 2,000 meters. Finally, in the farmland plot topology map, lines of varying thickness are used to represent the strength of connectivity between plots. Thicker lines indicate stronger connectivity, and different colors are used to mark the plot clusters to which the plots belong.

[0021] Step S102 , calculating the overall network connectivity, node centrality, and average path length of the initial farmland ecological network topology structure graph 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.

[0022] 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 based on 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, and 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 agroecological network structure evaluation index is calibrated.

[0023] Specifically, the Dijkstra Shortest Path tool was used to calculate the path length between any two points in the agroecological network topology. The Node Degree Statistics tool was used to obtain the number of edges connected to each node and generate a node degree matrix. The number of edges for all nodes in the drawn basic topology was then counted to determine the overall connectivity of the agroecological network. For each node in the agroecological network topology, a node weight was assigned based on the node degree matrix. A node degree value greater than a preset threshold of 4 was assigned a weight of 1, while a node degree value less than the preset threshold was assigned a weight of 0.5. For each node, the node centrality value was multiplied by the node weight and the number of edges connected to it. Based on the node degree matrix, the Path Distance Sum tool was used to sequentially calculate the shortest path length between any two nodes. Each path length was multiplied by the node centrality value of each node along the path. The path lengths between all node pairs were averaged to obtain the weighted average path length indicator. A network connectivity calculation module is used to calculate the topological structure of the agroecological network. The connectivity strength between each node pair is calculated by multiplying the node centrality value by the weighted average path length. The connectivity strength values ​​are then used to calibrate the agroecological network structure assessment indicators. Nodes in the agroecological network topology represent farmland plots, while edges represent the connectivity between plots. The Dijkstra shortest path calculation calculates the shortest distance between any two farmland plots. For example, the shortest path from Plot 1 to Plot 5 is 2000 meters, passing through Plots 2 and 3. Node degree statistics indicate the number of connections each plot has with its surrounding plots. For example, Plot 5 connects to six neighboring plots, resulting in a node degree of 6. The degrees of all nodes are recorded in a node degree matrix. Overall connectivity reflects the ratio of the number of edges actually connected to the theoretical maximum possible number of edges in the agroecological network, with a value between 0 and 1. When assigning node weights, parcels with node degrees exceeding a preset threshold of 4 are considered to have strong ecological connectivity and are assigned a weight of 1. Parcels with lower degrees receive a weight of 0.5, reflecting the importance of different parcels in the ecological network. For example, parcel 5 has a degree of 6 and a weight of 1. Multiplying this weight by the number of edges it connects to (6) yields a node centrality of 6. Parcel 8, with a degree of 3, receives a weight of 0.5 and a node centrality of 1.5, indicating that parcel 5 plays a more important role in network connectivity. The weighted average path length (WAL) is calculated by taking into account the node centrality of each node along the path. For example, if the path from parcel 1 to parcel 5 passes through parcels 2 and 3, where Plot 2 has a centrality of 4 and Plot 3 has a centrality of 5, the weighted length of this path is 2,000 times 4 times 5, resulting in a weighted path length of 40,000. The weighted path lengths of all node pairs in the network are averaged to obtain the weighted average path length metric, which reflects the overall network connectivity.The structural assessment of the agroecological network is based on node centrality and weighted average path length, calculating the connectivity strength between each node pair. For example, if the product of the node centralities is 24 and the weighted average path length is 40,000, the connectivity strength is 0.0006. Larger values ​​indicate greater ecological connectivity between the two nodes. By statistically analyzing the connectivity strength distribution of all node pairs, the overall structural integrity of the agroecological network is quantitatively assessed. The results reflect the connectivity status of the agroecological corridors. The assessment index 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 0.0006 and above for strong connectivity.

[0024] Step S103: Based on the overall network connectivity, determine whether the actual connectivity reaches the expected connectivity level. For the plot pairs that do not reach the expected connectivity level, identify them as having blocked connectivity. When there is blocked connectivity of local plots in the initial farmland ecological network topology 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.

[0025] Obtain 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, compare the ratio with the preset connectivity threshold, and 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, obtain the standard connectivity distance threshold from the geographic spatial database for the plot pairs marked as the state to be judged, and obtain the markings of the plots in the blocked connectivity state; use the spatial clustering analysis tool to calculate the spatial correlation of the plots in the blocked connectivity state, generate the plot connectivity resistance value according to the plot boundary distance data, and obtain the blocked plot set; calculate the connectivity resistance index for the plots in the blocked plot set, and obtain the connectivity blocking level classification result according to the connectivity resistance index and the weighted value of the connectivity distance of the surrounding plots.

[0026] Specifically, the network connectivity statistics tool is used to calculate 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 to obtain the overall network connectivity value. If the obtained connectivity value is less than the preset connectivity threshold of 0.6, the corresponding plot will be marked as a pending state in the plot connectivity matrix. According to the initial distance matrix between farmland plots, for the plot pairs marked as pending, the standard connectivity distance threshold of 8,000 meters for the current area 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. 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 set of blocked plots. A connectivity resistance index was calculated for each obstructed plot in the set using a parcel connectivity assessment tool. For each obstructed plot, a weighted value of the connectivity distance to surrounding plots was calculated. Each obstructed plot in the set was then assigned a connectivity resistance level based on the connectivity resistance index. In an agroecological network, overall network connectivity reflects the level of ecological connectivity between plots. For eight plots, the maximum possible number of connected edges is 28, while the actual number is 14. The calculated overall network connectivity is 0.5, which falls below the preset connectivity threshold of 0.6 and is marked as pending. Lower connectivity values ​​indicate greater fragmentation in the agroecological system and less continuous ecological corridors between plots. The initial distance matrix records the actual boundary distances between adjacent plots. For example, the boundary distance between plots 5 and 6 is 10,000 meters, exceeding the standard connectivity distance threshold of 8,000 meters, indicating that the connectivity between the two plots is geographically isolated. When determining the distance threshold, the Euclidean distance between each pair of adjacent plots is calculated. Plot pairs exceeding the threshold are marked as having blocked connectivity, such as Plots 5-6 and 7-8. During spatial cluster analysis, the spatial correlation of blocked plots is calculated. For example, Plot 5 is surrounded by three similarly blocked plots, indicating a high degree of spatial correlation, while Plot 8 is surrounded by only one blocked plot, indicating a low degree of spatial correlation. Connectivity resistance values ​​are calculated based on the distance to the plot boundaries. A boundary distance of 10,000 meters corresponds to a resistance value of 1.25, and a boundary distance of 12,000 meters corresponds to a resistance value of 1.5. Blocked plots are then divided into multiple clusters based on resistance values. The connectivity resistance index comprehensively considers the distance resistance and spatial clustering between plots. For example, Plot 5 has a connectivity resistance index of 1.8, which is higher than the 1.5 of its surrounding plots, indicating that its connectivity is more severely blocked. By calculating the weighted connectivity distance between each obstructed plot and its surrounding plots, the connectivity obstruction level of the plots is 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 is in urgent need of improvement, while plot 7 is slightly obstructed, indicating that its connectivity problem is relatively mild.

[0027] Step S104 , for the obstructed plot set, a 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 in combination with the plot area, the type and number of obstacles between plots, and the functional attributes of the plots.

[0028] An area calculation tool is used to obtain the occupied area of ​​the plots in the obstructed plot set, and the farmland functional attribute values ​​are extracted from the geographic spatial database based on the occupied 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 intensity value is calculated based on the obstacle distribution data to obtain the spatial barrier coefficient; an initial distance weight matrix is ​​generated based on 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.

[0029] Specifically, an area calculation tool was used to measure the area of ​​the plots in the obstructed plot set. Farmland functional attributes were extracted from a geospatial database to assign a functional value to each plot, and a basic weight for the plots was generated based on the area and functional attributes. Obstacle information between the obstructed plots was extracted from a remote sensing image database. Obstacle density was calculated by dividing the area of ​​the obstacles within the farmland by the total area of ​​the plots. Obstacle strength values ​​were assigned based on the degree of barrier between the two plots, and the product of the obstacle density and obstacle strength values ​​was used as the spatial barrier coefficient. Based on the Euclidean distance between the obstructed plots, an initial distance weight matrix was generated using a distance weight calculator. The weights of adjacent plot pairs were multiplied by the spatial barrier coefficient and then corrected according to the plot basic weight to obtain a weighted distance decay coefficient. The spatial distance weighting tool was then applied to the obstructed plot set. For each pair of adjacent plots, the initial distance matrix was weighted and corrected using the distance decay coefficient. The actual distance between the plots was multiplied by the corresponding distance decay coefficient to generate a new weighted distance matrix. In the area calculation, the size of farmland plots directly influences their ecological connectivity. For example, Plot 5, with an area of ​​80,000 square meters, is classified as large farmland and has a grain production function, so its base weight is set at 1.2. Plot 7, with an area of ​​20,000 square meters and a cash crop function, has a base weight of 0.8. Larger plots with more important functions receive higher base weights. Remote sensing image interpretation reveals that there are obstacles, such as roads and buildings, between Plots 5 and 6. The obstacles cover an area of ​​4,000 square meters. Relative to the total area of ​​100,000 square meters, the calculated obstacle density is 0.04. Considering the road as a complete barrier, its barrier strength is assigned a value of 1.5. Multiplying the two results in a spatial barrier coefficient of 0.06, reflecting the actual impact of the obstacles on the connectivity of the plots. During the distance weight calculation process, the Euclidean distance between plots 5 and 6 was 8,000 meters, corresponding to an initial distance weight of 0.8. Multiplying this with the spatial barrier coefficient of 0.06 yielded a value of 0.048. Combined with the two plots' base weights of 1.2 and 1.0, the final calculated distance attenuation coefficient was 0.0576. The smaller the distance attenuation coefficient, the greater the actual connectivity difficulty between the two plots. In the weighted spatial distance correction, the original distance matrix recorded a distance of 8,000 meters between plots 5 and 6. After multiplying this distance by the distance attenuation coefficient of 0.0576, it became 460.8 meters in the new weighted distance matrix. This weighted correction takes into account the impact of plot area, functional attributes, and obstacles, allowing the distance value to more accurately reflect 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 have similar areas and the same functions, 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 multi-factor-based distance attenuation adjustment provides a more accurate spatial reference for subsequent improvements in plot connectivity.

[0030] Step S105 , recalculating the connectivity between the plots based on the adjusted inter-plot distance matrix, reconstructing the farmland ecological network topology, and calculating the evaluation index of the adjusted ecological network structure.

[0031] A connectivity calculator is used based on the distance matrix between plots to obtain the distance value between adjacent 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 using a topology map construction tool, and a connectivity calculation module is used to calculate the number of directly connected plots for each plot to obtain the plot connectivity value; a connectivity strength statistical tool is used for the farmland ecological network structure diagram to quantify the directly connected plot pairs, 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.

[0032] Specifically, based on the adjusted inter-plot distance matrix, a connectivity calculator is used to calculate the connectivity of any adjacent plots. If the inter-plot distance is less than a preset distance threshold of 8,000 meters, the plot pair is marked as directly connected on the topological relationship diagram, and the connectivity strength between the connected plot pairs is quantified. Based on the marked connected plots, a new agroecological network structure diagram is generated using the topological map construction tool. For each plot, the number of directly connected plots is calculated to obtain a connectivity value, and the total network connectivity value is calculated based on the connectivity strength values ​​between the connected plot pairs. A connectivity strength statistics tool is used to quantify the degree of connectivity between plots in the topological structure. The connectivity strength values ​​of each plot and its directly connected plots are accumulated, and the network connectivity is calculated from the total connectivity strength values ​​of all plots. For the generated agroecological network structure diagram, connectivity data is obtained from the structural integrity assessment tool. The plot connectivity values ​​and the plot connectivity strength values ​​are combined to numerically calculate the network integrity assessment index. In the adjusted agroecological network, the connectivity between adjacent plots was recalculated based on the distance matrix. For example, the distances between Plot 5 and its adjacent Plots 6, 7, and 8 are 7,500 meters, 6,800 meters, and 8,200 meters, respectively. Since the preset distance threshold is 8,000 meters, Plot 5 is marked as directly connected to Plots 6 and 7, but not directly connected to Plot 8. For connected pairs of plots, the connectivity strength is calculated based on the ratio of the actual distance to the threshold. For example, the connectivity strength for the Plot 5-6 pair is 0.94, and for the Plot 5-7 pair is 0.85. In the new agroecological network structure diagram, the connectivity value for each plot reflects the number of plots it is directly connected to. For example, Plot 5 is directly connected to two plots, with a connectivity value of 2, while Plot 3 is directly connected to four plots, with a connectivity value of 4. The total network connectivity is calculated by summing the connectivity strength values ​​of all connected pairs of plots. For example, the current network contains 12 pairs of connected plots, with a total connectivity strength of 10.2, indicating good overall network connectivity. Connectivity strength statistics show that Plot 5 has a cumulative connectivity strength of 1.79, which is derived from Plot 6's 0.94 and Plot 7's 0.85. In contrast, Plot 3, due to its greater number of connected plots, has a cumulative connectivity strength of 3.45. The overall network connectivity is calculated by summing the connectivity strength values ​​of all plots and dividing it by the number of plots. The current network connectivity is 0.85, indicating a relatively complete network structure. For the structural integrity assessment, the connectivity and connectivity strength values ​​for each plot are weighted together, with weights of 0.4 and 0.6, respectively. Taking plot 5 as an example, the connectivity value of 2 multiplied by a weight of 0.4 equals 0.8, while the connectivity strength value of 1.79 multiplied by a weight of 0.6 equals 1.074. The sum of these two gives the plot's integrity index of 1.874. The average integrity index of all plots, 2.1, serves as the overall network integrity assessment indicator, reflecting the overall connectivity level of the farmland ecological network structure.

[0033] Step S106: Based on the changes in the ecological network structure assessment indicators before and after adjustment, the impact of the obstructed 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 land planning scheme is triggered, and the functional attributes and / or spatial layout of the plots surrounding the obstructed plots are reallocated until the final land planning scheme is obtained.

[0034] 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 labeled; based on the functional attribute labeling, the spatial distance of the plots surrounding 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 plot group functional attribute label is generated; based on the plot group functional attribute label, the layout adjustment calculation tool is used to calculate the relative position of the plot spatial layout, and the spatial distance between the plots is determined by the layout conflict verifier.

[0035] Specifically, the indicator change calculator calculates the difference between the initial and adjusted values ​​of the ecological network structure assessment indicators to determine the magnitude of the change. If the magnitude of the change exceeds a preset threshold of 0.3, the obstructed plot and surrounding plots are labeled with their functional attributes. The spatial distances of the surrounding plots are calculated using the spatial adjacency analysis tool. Adjacent plots are classified based on their inter-plot distances. Functional combination adaptability scores are calculated based on the existing functional attribute values ​​assigned to the plots. The functional attribute conversion rules for the plots are retrieved from the farmland functional rule library. Within the cluster of plots surrounding the obstructed plot, the functional matching optimizer calculates the adaptability scores of alternative functional attributes for each plot. Functional attributes with adaptability scores exceeding the preset threshold of 0.6 are recorded, and new functional attribute labels are generated for the cluster according to the functional attribute conversion rules. Based on the new functional attribute labels for the cluster, the layout adjustment calculation tool calculates the spatial layout of the plots. The relative positions of the plots are calculated based on the functional attribute labels, and the layout conflict checker verifies the spatial distances between the plots in the generated layout. In the ecological network structure assessment, changes in indicators reflect the extent of connectivity disruption. For example, the initial assessment indicator value for Plot 5 was 0.85, but after adjustment, it dropped to 0.45, a change of 0.4, exceeding the preset threshold of 0.3. This indicates that connectivity in this area is severely impacted, and adjustments to the functional attributes of Plot 5 and its surrounding plots are necessary. Plot functional attributes include types such as grain production area, cash crop area, and ecological protection area, and each plot is labeled with its original functional attributes. Spatial adjacency analysis shows that Plot 5 has three adjacent plots within 500 meters: Plots 6, 7, and 8. These adjacencies are categorized into two levels based on distance: Plot 6 is 320 meters away, with first-level adjacency, while Plots 7 and 8 are 480 and 490 meters away, respectively, with second-level adjacency. Plot 6 is currently a grain production area, while Plots 7 and 8 are cash crop areas. Based on the functional combination rule, the adaptability score for adjacent grain and cash crop areas 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 based on the new functional attribute labels. Plot 5 in the ecological protection area serves as the core, and the grain production area and cash crop area are arranged around it to form functional complementarity. The minimum distance between plots must be kept above 300 meters to ensure space for agricultural machinery operations.Layout conflict verification 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.

[0036] Step S107 , combining the connectivity of plots, the distance between plots and land use efficiency to evaluate the feasibility and cost-effectiveness of stripping and reusing the cultivated layer, and determining the optimal earthwork allocation plan for the final land planning scheme.

[0037] Obtain connectivity values ​​between plots, extract plot utilization status data from a land resource utilization status database based on the connectivity values, and calculate the total value of regional land resource utilization based on the plot utilization status data; use a quantity accounting tool to process the plot utilization status data to obtain the earthwork allocation quantity between plots, generate a transportation route map based on the earthwork allocation quantity and the actual transportation distance between plots, and calculate the transportation cost value based on the transportation route map; obtain a distance factor value based on the actual transportation distance between the plots, use a scheme fitness evaluator to process the transportation cost value, and obtain the earthwork allocation scheme fitness score; based on the earthwork allocation scheme fitness score, use a weighted sorting tool to normalize the plot connectivity values, the total land resource utilization value, and the transportation cost value to obtain the optimal earthwork allocation scheme.

[0038] Specifically, the dynamically adjusted inter-plot connectivity values ​​are obtained from the connectivity calculator. Plot utilization data is extracted from the current land resource utilization database, and the land utilization level of each plot is scored. The regional land resource utilization value is then calculated based on the utilization level scores of all plots. A quantity accounting tool is used to calculate the earthwork transfer volume between plots. The earthwork transfer costs are quantified based on the actual transportation distances between plots. A transportation route map is generated for each earthwork transfer scheme, and transportation costs are recorded for each earthwork transfer scheme. Based on the distance factor between plots, a scheme fitness evaluator is used to evaluate different earthwork transfer schemes. The fitness score of each scheme is calculated based on the plot connectivity value, total land use value, and transportation cost. Within the transfer scheme library, a weighted ranking tool is used to rank the fitness scores of each earthwork transfer scheme. The three indicators of plot connectivity, land use, and transportation cost are normalized and calculated, and the optimal transfer scheme is selected using the weighted combination of the three indicators.

[0039]

[0040] , C i represents the comprehensive score of the i-th solution, P i Represents the connectivity index value, L i Represents the land use index value, T irepresents the transport cost indicator value. α1, α2, and α3 are the weight coefficients of the three indicators, summing to 1. max and min represent the maximum and minimum values ​​of each indicator, respectively. The connectivity value of the plot reflects the connectivity status of the agroecological network. Taking plots 5 and 6 as an example, the connectivity between the two plots increased from 0.45 to 0.75 after dynamic adjustment, indicating improved connectivity. The current land resource utilization status shows that the cultivated land utilization rate of plot 5 is 85%, and that of plot 6 is 78%. Land use scores calculated based on cultivated land area and utilization intensity yielded a score of 0.85 for plot 5 and 0.78 for plot 6, resulting in an overall regional land resource utilization value of 0.82. During the engineering quantity calculation process, the earthwork transfer from plot 5 to plot 6 involved a volume of 5,000 cubic meters, transported over a distance of 800 meters. Based on the unit transportation cost, the transportation cost of this solution was calculated to be 4,000 yuan. There are also other allocation plans, such as allocating 3,000 cubic meters from plot 5 to plot 7, a distance of 1,200 meters, with a transportation cost of 3,600 yuan. The transportation route map shows the specific routes of different allocation plans, including information such as the roads passed through and transfer points. In the scheme fitness evaluation, 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 to 0.4, the land use weight to 0.3, and the transportation cost weight to 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 options, each was compared using a weighted ranking. First, three indicators were normalized: connectivity and land use were used directly at their original values, while transportation costs were converted by comparing them to a benchmark cost of 5,000 yuan. For example, the normalized indicator values ​​for options 5-6 were 0.75, 0.82, and 0.80, respectively. The final weighted combined score was 0.79, placing it at the top of the list. This multi-metric evaluation approach considered both improved ecological network connectivity and the efficient use of land resources and economical project implementation. The optimal option ultimately achieved a good balance between these three aspects.

[0041] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments 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 scope of protection 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 collection of farmland plots around towns, calculate the direct connectivity between each plot and other plots based on the distance threshold, and construct the initial farmland ecological network topology structure diagram; The Dijkstra path calculation tool is used to obtain the path length between node pairs in the agroecological network, and a node degree matrix is ​​obtained based on 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 by multiplying the node weight value by the number of connected edges; the shortest path length between node pairs is calculated based on 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 a weighted average path length index; the network connectivity calculation module is used to obtain the connectivity strength value between node pairs based on the product of the node centrality value and the weighted average path length index, and the agroecological network structure evaluation index is calibrated; Obtain 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, compare the ratio with a preset connectivity threshold, and 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, obtain the standard connectivity distance threshold from the geospatial database for the plot pairs marked as the state to be judged, and obtain the markings of the plots in the state of blocked connectivity; use a spatial clustering analysis tool to calculate the spatial correlation of the plots in the state of blocked connectivity, generate the plot connectivity resistance value based on the plot boundary distance data, and obtain the set of blocked plots; calculate the connectivity resistance index for the plots in the set of blocked plots, and obtain the connectivity blocking level classification results based on the connectivity resistance index and the weighted value of the connectivity distance of the surrounding plots; When there is a local block connectivity obstruction in the initial agroecological network topology, based on the initial distance matrix between blocks, if the distance between blocks is greater than the distance threshold between blocks, it is determined that the connection between the corresponding blocks is obstructed, so as to identify the obstructed blocks and form a set; For the set of obstructed plots, a connectivity adjustment method based on distance decay is applied to adjust the distance matrix according to the distance factor between plots. 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. Based on 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; Based on the changes in ecological network structure assessment indicators before and after adjustment, the impact of localized land parcel connectivity obstruction on the integrity of the ecological network structure is analyzed. If the impact exceeds the preset change threshold, the dynamic adjustment of the land planning scheme is triggered, and the functional attributes and / or spatial layout of the surrounding parcels of the obstructed parcel are reallocated 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 plots, the distance between plots and land use efficiency, and the optimal earthwork allocation plan for the final land planning scheme is determined; The method of obtaining an initial distance matrix between each plot in a set of farmland plots around towns, calculating the direct connectivity between each plot and other plots based on a distance threshold, and constructing an initial farmland ecological network topology diagram includes: Digital elevation processing tools were used to obtain the topographic data of farmland plots, and the initial distance matrix between plots was calculated based on the coordinates of the center points of the farmland plots. Determine the distance between plots based on 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. Assigning edge weights to the connectivity graph constructed by the minimum spanning tree algorithm according to the plot area value and the ecological resistance value, and obtaining a weighted plot connectivity matrix using a proximity calculation tool; The weighted plot connectivity matrix is ​​used to divide farmland plots into clusters using a spatial clustering tool based on geographic distance, and the topological structure map of farmland plots is obtained by calculating the internal connectivity distance of the plot clusters. For the obstructed plot set, a 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 based on 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 occupied areas of the plots in the obstructed plot set, extracting farmland functional attribute values ​​from a geospatial database based on the occupied areas to obtain the basic weights of the plots; For the obstructed land parcel set, obstacle distribution data is extracted from a 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 a 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 base weight to obtain a distance attenuation coefficient; For adjacent pairs of plots in the obstructed plot set, a spatial distance weighting tool is used to multiply the initial distance matrix by the distance decay coefficient to obtain a weighted distance matrix; Based on 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 pairs are determined to be directly connected plot pairs. For the directly connected plots, a topology map construction tool is used to generate an agroecological 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 to quantitatively calculate the directly connected plot pairs based on the farmland ecological network structure diagram, and a total network connectivity value is obtained from the connectivity strength values ​​of the directly connected plot pairs; Obtaining connectivity relationship data from a structural integrity assessment tool based on the total network connectivity value, and using an assessment calculation module to perform a combined operation on the connectivity value of the plot and the connectivity strength value to obtain a network integrity assessment index; Based on the changes in ecological network structure assessment indicators before and after adjustment, the impact of local land parcel connectivity obstruction on the integrity of the ecological network structure is analyzed. If the impact exceeds a preset change threshold, the dynamic adjustment of the land planning scheme is triggered, and the functional attributes and / or spatial layout of the land parcels surrounding the obstructed land parcel are reallocated until the final land planning scheme is obtained, including: Obtaining a change magnitude value of a network structure evaluation index from an index change calculator, and if the change magnitude value exceeds a preset change threshold, labeling the blocked plot and surrounding plots with functional attributes; Based on the functional attribute annotations, the spatial distance of the surrounding plots of the obstructed plot is calculated using a 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 scores of the candidate functional attributes, and if the adaptability scores exceed an adaptability score threshold, a plot group functional attribute label is generated; According to the functional attribute labels of the plot group, the relative position of the plot spatial layout is calculated using the layout adjustment calculation tool, and the spatial distance between the plots is determined by the layout conflict checker; 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 based on the connectivity values, and calculating the total value of regional land resource utilization based on the plot utilization status data; Using a quantity accounting tool to process the current utilization data of the plots, obtain the earthwork allocation quantity between the plots, generate a transportation route map based on the earthwork allocation quantity and the actual transportation distance between the plots, and calculate the transportation cost value based on the transportation route map; Obtaining a distance factor value based on the actual transportation distance between the plots, processing the transportation cost value using a scheme fitness evaluator to obtain a fitness score for the earthwork allocation scheme; With respect to the fitness score of the earthwork allocation scheme, the connectivity value of the land parcel, the total value of land resource utilization and the transportation cost value are normalized and calculated using a weighted sorting tool to obtain the optimal earthwork allocation scheme.

Images