Method and system for defining the attraction range of an agricultural field based on an improved breaking point theory
By improving the breakpoint theory and combining multi-source data with terrain slope correction, the actual accessibility distance of agricultural fields is calculated, which solves the problem of ignoring terrain resistance in traditional division methods, realizes a more reasonable division of the radiation range of agricultural fields, and supports the layout and planning of county-level facilities.
Patent Information
- Application Number
- CN202610774408.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-01
- Publication Date
- 2026-08-25
AI Technical Summary
Traditional methods for defining the attraction range of agricultural fields ignore differences in transportation networks and terrain resistance, resulting in highly subjective radiation boundaries that differ significantly from the actual coverage of agricultural technology services and are difficult to adapt to complex geographical environments.
By adopting the improved breakpoint theory and combining multi-source agricultural geographic data and socio-economic data, a comprehensive quality evaluation index system is constructed to calculate the actual accessibility distance, correct the terrain slope, and generate an objective agricultural field attraction range.
It provides a more reasonable division of agricultural field coverage, adapts to agricultural product transportation and agricultural technology services, supports county-level facility layout and planning, and improves the rationality and reliability of the division results.
Smart Images
Figure CN122633787A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of agricultural field technology, and in particular to a method and system for defining the attraction range of agricultural fields based on an improved breakpoint theory. Background Technology
[0002] As the overall planning of rural agriculture in counties continues to advance, the layout of agricultural development under the background of rural revitalization is becoming more and more refined. The establishment of grassroots agricultural service stations, the circulation and allocation of agricultural materials, the zoning planning of characteristic planting and breeding industries, and the implementation of agricultural technology extension services all urgently require a precise clarification of the radiation service scope corresponding to the agricultural fields in each township.
[0003] Traditional methods of defining the attraction range of agricultural fields often suffer from the core problem of relying solely on straight-line distances. This approach ignores differences in regional transportation networks and the accessibility caused by terrain such as mountains and slopes. It also fails to differentiate the strength of radiation capacity based on the actual agricultural development capabilities of various regions. Consequently, the resulting radiation boundaries are highly subjective and differ significantly from the actual coverage of agricultural technology services and agricultural product transportation and distribution. This makes it difficult to adapt to the complex geographical environment of rural areas. Summary of the Invention
[0004] The purpose of this invention is to address the core problem of existing technologies that rely solely on straight-line distances in a plane for delineation, neglecting differences in regional road networks and the traffic resistance caused by terrain such as mountains and slopes, failing to differentiate the strength of radiation capacity based on the actual agricultural development strength of various regions, resulting in highly subjective radiation boundaries that differ significantly from the actual coverage of agricultural technology services and agricultural product transportation and distribution. Therefore, this invention proposes a method and system for defining the agricultural field attraction range based on an improved breakpoint theory.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: The method for defining the attraction range of agricultural fields based on the improved breakpoint theory includes the following steps: S1: Collect multi-source agricultural geospatial data and socioeconomic data within the study area, and perform data cleaning, spatial coordinate matching and attribute standardization processing in sequence to establish a standardized spatial database; S2: Construct a comprehensive quality evaluation index system for agricultural fields that includes four dimensions: agricultural production capacity, infrastructure level, scientific and technological service capacity, and ecological environment quality. Use a combination of the analytic hierarchy process and the entropy weight method to calculate the weight of each index, and sum the weights to obtain the comprehensive quality index of each agricultural field. S3: Construct a comprehensive transportation network dataset for the study area, calculate the shortest time distance between any two agricultural fields, and obtain the actual reachability distance by combining the slope data of the digital elevation model. Substitute the improved breakpoint formula into the breakpoint location between adjacent agricultural fields. S4: Generate a weighted average using all agricultural fields as the originating element and the comprehensive quality index as the weight. The initial attraction range is obtained from the graph. Natural geographical boundaries and administrative boundaries are superimposed to make reasonable corrections, and the final spatial distribution result of the agricultural field attraction range is output.
[0006] The above technical solution further includes: Specifically, the attribute standardization process in step S1 uses the range standardization method to map all attribute data to the [0, 1] interval; the spatial coordinate matching unifies all spatial data to the CGCS2000 national geodetic coordinate system.
[0007] Specifically, the comprehensive quality evaluation index system described in step S2 includes 4 primary indicators and 12 secondary indicators; Among them, agricultural production capacity indicators include cultivated land area, total grain output, agricultural mechanization level and the proportion of facility agriculture area; Infrastructure level indicators include road network density, irrigation guarantee rate, power supply coverage, and communication network coverage; Indicators of science and technology service capacity include the number of agricultural technology extension personnel, the number of agricultural research institutions, and the number of farmers' professional cooperatives; Ecological and environmental quality indicators include forest coverage, soil organic matter content, and the intensity of fertilizer and pesticide use.
[0008] Specifically, the combined weighting method described in step S2 integrates subjective and objective weights through linear weighting, as shown in the formula: ,in For the first The combined weights of the indicators The subjective weights calculated by the analytic hierarchy process (AHP). The objective weights calculated using the entropy weight method. The value range is 0.4-0.6.
[0009] Specifically, the comprehensive transportation network dataset mentioned in step S3 includes highways, railways, and waterways, and adopts... The algorithm calculates the shortest time distance between any two agricultural fields.
[0010] Specifically, in step S3, the slope correction is assigned a corresponding correction coefficient based on the slope grade; The correction factor is 1.0 when the slope is less than 5°, 1.2 when the slope is between 5° and 15°, 1.5 when the slope is between 15° and 25°, and 2.0 when the slope is greater than 25°.
[0011] Specifically, the improved break point formula described in step S3 is as follows: ,in From the breakpoint to the agricultural field distance, For agricultural fields and The actual reachability distance between them and Agricultural fields and The overall quality index.
[0012] Specifically, the boundary correction in step S4 involves overlaying the initial attraction range with the natural geographical boundary to adjust the area divided by rivers and mountains; then overlaying it with the administrative boundary to make the attraction range consistent with the boundaries of towns and counties.
[0013] Specifically, the agricultural field attraction range definition system based on the improved breakpoint theory is used to implement the method described above. It includes a data acquisition and preprocessing module, a comprehensive quality evaluation module, a breakpoint calculation module, an attraction range generation module, and a result output module. These modules are connected in sequence and work together.
[0014] Specifically, it also includes a results visualization and analysis module, which displays the attraction range in map form, provides statistical analysis functions for area, population, and cultivated land area, and supports recalculating the attraction range after data updates.
[0015] The present invention has the following beneficial effects: In this invention, the actual traversable distance can be calculated by combining regional transportation network and terrain slope conditions, breaking away from the previous single model of defining the radiation range solely based on straight-line distance on a plane. At the same time, it relies on a multi-dimensional evaluation system to objectively quantify the comprehensive agricultural development strength of different regions.
[0016] This effectively solves the problems of traditional division results being out of touch with the current situation in the region and boundary delineation not conforming to the actual agricultural service radiation pattern. It makes the final determined agricultural field attraction range more in line with the real-world scenarios of agricultural product transportation, grassroots agricultural technology extension, and the implementation of agricultural convenience services. The division results are more reasonable and can provide a real and reliable spatial reference for the layout of county-level agricultural facilities and the overall planning of characteristic agriculture, and adapt to the actual use needs of grassroots agricultural planning. Attached Figure Description
[0017] Figure 1 This is a schematic diagram of the data acquisition and standardization process of the agricultural field attraction range definition method and system based on the improved breakpoint theory proposed in this invention. Figure 2 This is a schematic diagram of the process for comprehensive quality evaluation and breakpoint calculation in this invention; Figure 3 This is a schematic diagram illustrating the process of generating, modifying, and applying the attraction range in this invention; Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Example
[0020] like Figures 1-3 As shown, the method for defining the attraction range of agricultural fields based on the improved breakpoint theory proposed in this invention includes the following steps: S1: Collect multi-source agricultural geospatial data and socioeconomic data within the study area, and perform data cleaning, spatial coordinate matching and attribute standardization processing in sequence to establish a standardized spatial database; S2: Construct a comprehensive quality evaluation index system for agricultural fields that includes four dimensions: agricultural production capacity, infrastructure level, scientific and technological service capacity, and ecological environment quality. Use a combination of the analytic hierarchy process and the entropy weight method to calculate the weight of each index, and sum the weights to obtain the comprehensive quality index of each agricultural field. S3: Construct a comprehensive transportation network dataset for the study area, calculate the shortest time distance between any two agricultural fields, and obtain the actual reachability distance by combining the slope data of the digital elevation model. Substitute the improved breakpoint formula into the breakpoint location between adjacent agricultural fields. S4: Generate a weighted average using all agricultural fields as the originating element and the comprehensive quality index as the weight. The initial attraction range is obtained from the graph. Natural geographical boundaries and administrative boundaries are superimposed to make reasonable corrections, and the final spatial distribution result of the agricultural field attraction range is output.
[0021] Furthermore, the specific workflow of (S1) is as follows: First, multi-source data collection is carried out, such as collecting the latest land use status map of a certain county (to obtain the spatial distribution and area of cultivated land plots), statistical yearbook (to obtain socio-economic data such as total grain output and total power of agricultural machinery in each township), traffic network vector map, township administrative division boundary map, 30-meter resolution digital elevation model (DEM), and soil survey data (to obtain organic matter content, etc.); The next step is data processing: the first step is "data cleaning", which involves topological checking of the above data, removing redundant or erroneous patches, and processing missing and outlier values in the statistical tables. The second step is "spatial coordinate matching". Using GIS software, the coordinate systems of all vector and raster data are uniformly converted to the CGCS2000 national geodetic coordinate system to ensure that all layers can be accurately superimposed in space. The third step is "attribute standardization", which uses the range standardization formula to linearly transform the original values of each indicator (such as grain output from 50,000 tons to 200,000 tons) to the range of [0, 1] to eliminate the influence of dimensions; Finally, all processed spatial layers and attribute tables are linked and integrated to form a structured and standardized spatial database, in which each township unit is associated with its spatial geometric features and a set of standardized attribute values, thus preparing for the next step of comprehensive quality evaluation.
[0022] The core of (S2) is to quantify the comprehensive attractiveness of each agricultural field (such as a township); The specific workflow is as follows: First, based on the database constructed in step S1, an evaluation system containing 4 primary indicators and 12 secondary indicators is established. For example, the specific values of the secondary indicators such as "total grain output" and "arable land area" under the primary indicator "agricultural production capacity" have been obtained from the database and standardized. Next, a combined weighting method was used to determine the weights of each indicator: on the one hand, experts in the field of agricultural planning were invited to construct a judgment matrix by comparing the importance of indicators pairwise, and the subjective weights of each indicator were calculated using the Analytic Hierarchy Process (AHP). ); On the other hand, based on the actual data of each indicator in all fields, the objective weight reflecting the degree of data dispersion is obtained by calculating the information entropy. Then, with a preset balance coefficient. (For example, take 0.5), according to the formula =0.5 +0.5 Perform linear weighting to obtain the combined weight of each indicator; Finally, a weighted summation is performed: the standardized values of each secondary indicator for each agricultural field are multiplied by their corresponding combined weights and summed to obtain the comprehensive quality index of that field. For example, if a township's standardized values for 12 indicators are [0.8, 0.6, ...], and its corresponding combined weights are [0.1, 0.08, ...], then its comprehensive quality index is... =0.8*0.1+0.6*0.08+..., this index will serve as the core basis for measuring its attractiveness in subsequent steps.
[0023] The core of (S3) is to accurately calculate the theoretical boundary points between them; The specific workflow is as follows: First, based on the database in step S1, a comprehensive transportation network dataset containing elements such as highways and railways of different levels is constructed, and a design driving speed is assigned to each road type (for example, 100 km / h for expressways, 80 km / h for national highways, and 30 km / h for rural roads). Next, using each agricultural field (e.g., the administrative center of each township) as a node, we utilize graph theory... The algorithm searches the network and calculates the shortest travel time between any two nodes to obtain the "shortest time distance".
[0024] Then, terrain resistance factors are introduced: the slope of the area traversed by the route is extracted from the digital elevation model, and the above shortest time is corrected according to the preset slope-speed correction coefficient (for example, for road sections with a slope greater than 25°, the travel time cost is multiplied by a coefficient of 2.0), so as to obtain an "actual accessibility distance" that is more in line with the actual travel difficulty.
[0025] Finally, the "actual accessibility distance" of any two adjacent fields (such as towns A and B) and their respective "comprehensive quality indices" calculated in step S2 are substituted into the improved breakpoint formula. This allows us to calculate the value from the field. The theoretical distance from (Town A) to the gravitational equilibrium point (breakpoint) between these two fields This allows us to determine the specific geographical location of the break point on the line.
[0026] (S4) Transform the point fracture theory obtained from the previous steps into a complete, reasonable and usable planar spatial range; The specific workflow is as follows: First, the spatial geometric center points of all agricultural fields in step S1 (e.g., the administrative center points of each township) are used as generators, and the comprehensive quality index of each field calculated in step S2 is used as its influence weight. Weighted averages are then applied in the GIS platform. Graph algorithms are used for spatial partitioning; This process allows a stronger field with higher weights to gain a wider range of influence than a geometric Thiessen polygon, thus generating a preliminary distribution map of the attraction range. Next, this initial boundary map is overlaid with high-precision natural geographic features (such as the median lines of major rivers and continuous ridgelines) and administrative boundaries (such as township boundaries) for analysis. Reasonable modifications are made based on the principle of "natural obstacles are insurmountable and management units are relatively intact": for example, if the initial boundary line crosses a wide river without a bridge, the line is adjusted to the center line of the river. If an initial range contains fragmented areas belonging to two different townships, then, without deviating significantly from the theoretical breakpoint, the area should be merged and aligned with the boundary as much as possible, based on the township boundary. Ultimately, an agricultural field attraction range spatial distribution layer (usually a shapefile or geodatabase feature class) is output, which both follows the principles of spatial interaction mechanics and respects actual geographical constraints and management patterns. This provides direct digital spatial basis for subsequent decisions such as the layout of agricultural service facilities and resource allocation.
[0027] In step S1, the attribute standardization process uses the range standardization method to map all attribute data to the [0, 1] interval; spatial coordinate matching unifies all spatial data to the CGCS2000 national geodetic coordinate system.
[0028] Furthermore, the specific workflow is as follows: After data cleaning is completed, the first step is to perform "spatial coordinate matching". This involves using professional tools such as ArcGIS or QGIS to unify all original spatial data from different sources and with different coordinate systems (for example, land use data uses the WGS84 coordinate system, while road data uses a local independent coordinate system) into the nationally recognized CGCS2000 geodetic coordinate system through projection transformation. This ensures that all geographic elements can be accurately superimposed at the millimeter level in spatial location, which is the foundation for all subsequent spatial analysis and calculations. Next, "attribute standardization processing" is carried out. For the original values of various socio-economic and resource and environmental indicators of each agricultural field (such as township), the range standardization method is used to process them.
[0029] For example, if the original value of the "arable land area" of each township in the study area ranges from 5,000 to 50,000 mu, then for a township with an area of 20,000 mu, the formula for calculating its standardized value is: (20,000-5,000) / (50,000-5,000)=0.333.
[0030] This formula linearly maps all the original values of the indicators to the interval [0, 1], completely eliminating the incomparability between different indicators caused by differences in dimensions and orders of magnitude, thus enabling the subsequent calculation of the comprehensive quality index to be carried out objectively and fairly.
[0031] The comprehensive quality evaluation index system in step S2 includes 4 primary indicators and 12 secondary indicators; Among them, agricultural production capacity indicators include cultivated land area, total grain output, agricultural mechanization level and the proportion of facility agriculture area; Infrastructure level indicators include road network density, irrigation guarantee rate, power supply coverage, and communication network coverage; Indicators of science and technology service capacity include the number of agricultural technology extension personnel, the number of agricultural research institutions, and the number of farmers' professional cooperatives; Ecological and environmental quality indicators include forest coverage, soil organic matter content, and the intensity of fertilizer and pesticide use.
[0032] Furthermore, specifically, for each agricultural field (such as a township), the specific values corresponding to the 12 secondary indicators listed in this paragraph are first extracted from the established standardized spatial database.
[0033] For example, when evaluating "Town A", all 12 standardized indicators, such as "arable land area" (e.g., 0.75), "total grain output" (e.g., 0.60), "road network density" (e.g., 0.85), "number of agricultural technology extension personnel" (e.g., 0.40), and "forest coverage rate" (e.g., 0.90), will be retrieved from the database.
[0034] These indicator values will then serve as inputs for calculations, awaiting multiplication by weights determined through a combined weighting method (e.g., the weight for "arable land area" might be assigned as 0.12). These 12 indicators collectively constitute a complete evaluation "check-up," ensuring that the evaluation of an agricultural field considers not only its productivity (e.g., arable land and yield) but also its supporting conditions (e.g., roads and irrigation), development potential (e.g., technological services), and sustainability (e.g., the ecological environment), thus laying the foundation for generating a comprehensive and balanced overall quality index. This index is the core basis for subsequently judging the relative attractiveness between fields.
[0035] In step S2, the combined weighting method integrates subjective and objective weights through linear weighting, as shown in the formula: ,in For the first The combined weights of the indicators The subjective weights calculated by the analytic hierarchy process (AHP). The objective weights calculated using the entropy weight method. The value range is 0.4-0.6.
[0036] Further, the specific operations are as follows: First, calculate the subjective and objective weights separately: On the one hand, invite multiple experts in the agricultural field to construct a judgment matrix by comparing the importance of each indicator pairwise, and use the Analytic Hierarchy Process (AHP) to calculate the set of subjective weights reflecting expert consensus { }; On the other hand, based on the actual data of various indicators in all agricultural fields (such as townships), the degree of dispersion is measured by calculating the entropy value of the indicators, and then the entropy weight method is used to calculate the objective weight set that is completely determined by the data distribution. }
[0037] Then, linear weighted fusion is performed: a balance coefficient is set. (For example, taking the median value of 0.5 to give equal weight to subjective and objective opinions), for the first Each indicator (e.g., "arable land area") has its subjective weight. (Assuming a value of 0.18) and objective weight (Assuming the value is 0.08) According to the formula =0.5*0.18+(1-0.5)*0.08=0.13 Combining these values, we obtain the final combined weights used for calculation. .
[0038] This process examines each indicator in the system one by one, ultimately generating a set of combined weights that integrate expert wisdom and data objectivity. These weights are then used to calculate the comprehensive quality index, aiming to minimize the bias that may result from a single weighting method.
[0039] The integrated transportation network dataset in step S3 includes highways, railways, and waterways, and adopts... The algorithm calculates the shortest time distance between any two agricultural fields.
[0040] Furthermore, specifically: First, based on a unified spatial database, a vector network dataset containing multimodal traffic routes is constructed.
[0041] For example, this network layer would include all highways, national roads, provincial roads, county and township roads (with different design speeds, such as 120, 80, and 40 km / h), railway lines (mixed passenger and freight speeds), and navigable waterways within the study area.
[0042] Each line segment (edge) is assigned attributes such as travel speed and one-way restriction, while network nodes (junctions) represent intersections, bridges, ports, or stations.
[0043] Subsequently, representative locations within each agricultural field (typically administrative or economic centers) are used as the starting and ending points for path calculation within the network. The algorithm performs optimal path search.
[0044] The algorithm traverses the network to find and calculate the path with the shortest total travel time between any two points.
[0045] For example, when calculating the shortest travel time from town A to town B, the algorithm does not calculate the straight-line distance, but takes into account the road network structure, road grade, and speed, and may arrive at the result that "the journey takes 45 minutes, passing through a highway and a provincial road".
[0046] This "45 minutes" is the "shortest time distance" between the two towns based on the current transportation network. As basic data, it will be combined with the terrain slope factor in the next step to further revise it into the "actual accessibility distance" that better reflects the real difficulty of travel. It is one of the key input parameters in the breakpoint formula.
[0047] In step S3, the slope correction is assigned a corresponding correction coefficient based on the slope grade; The correction factor is 1.0 when the slope is less than 5°, 1.2 when the slope is between 5° and 15°, 1.5 when the slope is between 15° and 25°, and 2.0 when the slope is greater than 25°.
[0048] Furthermore, specifically, after calculating the shortest path between two points through the traffic network, the system will extract the slope information of the area traversed by the path based on the digital elevation model (DEM), and perform segmented corrections to the path according to the preset slope level-correction coefficient comparison table.
[0049] For example, in a planned route from town A to town B, a 5-kilometer section has a gradient of less than 5°, with a correction factor of 1.0, and the time cost remains unchanged. The gradient of the 3-kilometer section is between 5° and 15°, with a coefficient of 1.2. The travel time for this section needs to be multiplied by 1.2. The gradient of the 2-kilometer section is between 15° and 25°, with a coefficient of 1.5, and the time is multiplied by 1.5. If there is a steep slope section of 1 kilometer with a gradient greater than 25°, its time cost needs to be multiplied by a factor of 2.0.
[0050] Finally, the corrected times for each segment are summed to obtain the final actual reachability distance considering terrain resistance. This result will serve as the core input parameter for improving the breakpoint formula.
[0051] The improved fracture point formula in step S3 is as follows: ,in From the breakpoint to the agricultural field distance, For agricultural fields and The actual reachability distance between them and Agricultural fields and The overall quality index.
[0052] Furthermore, firstly, three core input parameters are extracted from the established database: actual reachability distance. (For example, the travel time cost between the center points of town A and town B, after road network and slope correction, is 90 minutes), and the overall quality index of the two towns. and (For example, Town A has an index of 2.5, and Town B has an index of 0.5).
[0053] Next, substitute these parameters into the formula. Perform the calculation. The calculation steps are as follows: 1) Calculate the square root of the mass ratio. = ≈0.4472; 2) Calculate the denominator: 1 + 0.4472 = 1.4472; 3) Calculate the distance to the break point. = ≈62.2 minutes.
[0054] 4) Its physical meaning is: from the stronger town A ( Starting from point A (=2.5), following the actual travel path from A to B, the geographical location reached after approximately 62.2 minutes (about 69% of the total distance) is the theoretical breakpoint. This point signifies that the attractiveness from town A diminishes to a point where it balances with the attractiveness from town B. In GIS, this calculation result is combined with the path's coordinate information, automatically marking a point on the path 62.2 minutes from town A. The coordinates of this point are the precise spatial location of the breakpoint, which will be used in subsequent weighted average calculations. One of the core control points of the diagram.
[0055] In step S4, the boundary correction specifically involves overlaying the initial attraction range with the natural geographical boundary to adjust the area divided by rivers and mountains; then overlaying it with the administrative boundary to make the attraction range consistent with the boundaries of townships and counties.
[0056] Furthermore, the boundary correction in this step follows a workflow based on the initial weighted average generated theoretically. Based on the map, real-world geographical and administrative constraints are superimposed to make reasonable adjustments to the spatial scope and align the boundaries.
[0057] The specific steps are as follows: First, correct the natural geographical boundaries by overlaying the initial attraction range polygon layer with the vector layers of natural barrier elements such as rivers and mountains for analysis.
[0058] For example, if an initial area is traversed by a major river without bridges (such as the main stream of the Yangtze River), the area is divided at the river, and the areas on both sides of the river are assigned to adjacent, more easily accessible areas, with the river's main channel centerline or shoreline as the boundary.
[0059] Next, administrative boundaries are corrected by overlaying the results from the previous step with the administrative boundary layers of townships, counties, etc.
[0060] For example, if an initial attraction zone includes fragmented areas belonging to two townships, A and B, it will be integrated based on administrative boundaries, without significantly violating the core breakpoint location. All villages belonging to township A will be included in the same attraction zone, ensuring the final division is largely consistent with existing administrative units, thereby enhancing the practical application value of the results. This process is completed manually or semi-automatically based on rules using GIS spatial editing functions, ultimately outputting a practical agricultural field attraction zone planning map that conforms to spatial interaction theory while respecting natural geographical barriers and administrative realities.
[0061] The agricultural field attraction range definition system based on the improved breakpoint theory includes a data acquisition and preprocessing module, a comprehensive quality evaluation module, a breakpoint calculation module, an attraction range generation module, and a result output module. These modules are connected in sequence and work together.
[0062] Furthermore, the system achieves full-process automation of the definition method through the linear connection and data flow of five functional modules.
[0063] The specific workflow is as follows: Users first import or connect multi-source data in the "Data Acquisition and Preprocessing Module" (for example, importing the administrative divisions, land use, road network, DEM and socio-economic statistics tables of a certain county). This module automatically performs cleaning, coordinate unification and standardization, and outputs a standard spatial database.
[0064] Subsequently, the "Comprehensive Quality Evaluation Module" reads the indicator data in the database, calls the built-in AHP-entropy weight combination weighting model, and automatically calculates and outputs the comprehensive quality index for each agricultural field.
[0065] Next, the "Breakpoint Calculation Module" uses the road network and DEM data in the database, as well as the quality index obtained in the previous step, to automatically perform shortest path analysis, slope correction, and improved breakpoint formula calculation, and outputs a set of breakpoint coordinates between all adjacent fields.
[0066] Then, the "Attraction Range Generation Module" automatically generates a weighted average based on the field locations as the generating elements and the quality index as the weight. The image is plotted, and an overlay analysis tool is invoked to perform semi-automatic or automatic fusion and correction with preset natural and administrative boundary layers to form the final attraction range polygon.
[0067] Finally, the "Results Output Module" outputs the final range layer, statistical reports, and intermediate process data in a specified format (such as Shapefile, map document, report).
[0068] Each module strictly depends on the output of the upstream module as its input, forming an automated processing chain from raw data to final results.
[0069] It also includes a results visualization and analysis module, which displays the attraction range in map form, provides statistical analysis functions for area, population, and cultivated land area, and supports recalculating the attraction range after data updates.
[0070] Furthermore, the results visualization and analysis module in the system has a workflow of dynamically displaying, quantitatively statistically analyzing, and iteratively managing the final generated attraction range results.
[0071] Specifically, the module first reads and loads the final attraction range spatial layer (such as Shapefile format) generated by the result output module, and then uses symbolic rendering in the GIS platform to intuitively display the influence range of each agricultural field in the form of a map.
[0072] For example, the attraction range of different towns can be filled with different colors, and users can click to query its attributes. At the same time, the module provides statistical analysis functions, which can automatically link basic data such as population and cultivated land, calculate and generate reports, such as statistics on the total area of the attraction range of "Town A", the total population covered, and the cultivated land area.
[0073] This module has dynamic update capabilities: when users update the underlying data (such as building a new highway or grain production data of a certain township), they can trigger the system to re-execute the complete calculation process from comprehensive evaluation to scope generation through this module, so as to realize the synchronous update of the attraction scope results, thereby forming a closed-loop analysis system of "data update - model recalculation - result visualization", which provides support for the dynamic adjustment of planning.
[0074] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A method for defining the attraction range of agricultural fields based on improved breakpoint theory, characterized in that, Includes the following steps: S1: Collect multi-source agricultural geospatial data and socioeconomic data within the study area, and perform data cleaning, spatial coordinate matching and attribute standardization processing in sequence to establish a standardized spatial database; S2: Construct a comprehensive quality evaluation index system for agricultural fields that includes four dimensions: agricultural production capacity, infrastructure level, scientific and technological service capacity, and ecological environment quality. Use a combination of the analytic hierarchy process and the entropy weight method to calculate the weight of each index, and sum the weights to obtain the comprehensive quality index of each agricultural field. S3: Construct a comprehensive transportation network dataset for the study area, calculate the shortest time distance between any two agricultural fields, and obtain the actual reachability distance by combining the slope data of the digital elevation model. Substitute the improved breakpoint formula into the breakpoint location between adjacent agricultural fields. S4: Generate a weighted average using all agricultural fields as the originating element and the comprehensive quality index as the weight. The initial attraction range is obtained from the graph. Natural geographical boundaries and administrative boundaries are superimposed for rationality correction, and the final spatial distribution result of the agricultural field attraction range is output.
2. The method according to claim 1, characterized in that, The attribute standardization process in step S1 uses the range standardization method to map all attribute data to the [0, 1] interval; the spatial coordinate matching unifies all spatial data to the CGCS2000 national geodetic coordinate system.
3. The method according to claim 1, characterized in that, The comprehensive quality evaluation index system described in step S2 includes 4 primary indicators and 12 secondary indicators; Among them, agricultural production capacity indicators include cultivated land area, total grain output, agricultural mechanization level and the proportion of facility agriculture area; Infrastructure level indicators include road network density, irrigation guarantee rate, power supply coverage, and communication network coverage; Indicators of science and technology service capacity include the number of agricultural technology extension personnel, the number of agricultural research institutions, and the number of farmers' professional cooperatives; Ecological and environmental quality indicators include forest coverage, soil organic matter content, and the intensity of fertilizer and pesticide use.
4. The method according to claim 1, characterized in that, The combined weighting method described in step S2 integrates subjective and objective weights through linear weighting, as shown in the formula: ,in For the first The combined weights of the indicators The subjective weights calculated by the analytic hierarchy process (AHP). The objective weights calculated using the entropy weight method. The value range is 0.4-0.
6.
5. The method according to claim 1, characterized in that, The integrated transportation network dataset mentioned in step S3 includes highways, railways, and waterways, and adopts... The algorithm calculates the shortest time distance between any two agricultural fields.
6. The method according to claim 1, characterized in that, The slope correction described in step S3 is based on the slope grade and is assigned a corresponding correction coefficient. The correction factor is 1.0 when the slope is less than 5°, 1.2 when the slope is between 5° and 15°, 1.5 when the slope is between 15° and 25°, and 2.0 when the slope is greater than 25°.
7. The method according to claim 1, characterized in that, The improved fracture point formula mentioned in step S3 is as follows: ,in From the breakpoint to the agricultural field distance, For agricultural fields and The actual reachability distance between them and Agricultural fields and The overall quality index.
8. The method according to claim 1, characterized in that, The boundary correction mentioned in step S4 specifically involves overlaying the initial attraction range with the natural geographical boundary to adjust the area divided by rivers and mountains; then overlaying it with the administrative boundary to make the attraction range consistent with the boundaries of townships and counties.
9. An agricultural field attraction range delineation system based on improved breakpoint theory, used to implement the method described in any one of claims 1-8, characterized in that, It includes a data acquisition and preprocessing module, a comprehensive quality evaluation module, a fracture point calculation module, an attraction range generation module, and a result output module. These modules are connected in sequence and work together.
10. The system according to claim 9, characterized in that, It also includes a results visualization and analysis module, which displays the attraction range in map form, provides statistical analysis functions for area, population, and cultivated land area, and supports recalculating the attraction range after data updates.