A farmland road accessibility measurement method and system based on spatial topology reconstruction

Abstract

The application provides a farmland road accessibility measurement method and system based on spatial topology reconstruction, and the method comprises the following steps: S1: breaking road intersections to construct a regional road network diagram, and projecting field area points to the nearest road section to realize the threshold-free topological connectivity between the field area points and the road section; S2: constructing a field block group and establishing a field access relationship based on the reverse erasing method: filtering out ditch obstacles affecting traffic based on the reverse erasing method to construct a connected field block group, and mapping the field area points to the target field block group by combining a spatial index algorithm to establish the relationship between the field area across the obstacles and the field block group; S3: constructing the complete connectivity relationship of the road network, the field area and the field block group, transferring the road network traffic attribute to the field block group, and finally outputting the farmland road accessibility evaluation result. The method and system of the application are free from the geometric distance restriction, and the bottom layer is constructed without missing the high-robust field area and road topology association.

Description

Technical Field

[0001] This invention relates to the field of farmland road measurement technology, specifically to a method and system for measuring farmland road accessibility based on spatial topology reconstruction. Background Technology

[0002] Farmland roads are a crucial component of farmland infrastructure, and their accessibility directly impacts the efficiency of agricultural machinery operations, the cost of transporting agricultural supplies, and emergency response capabilities. During the construction and verification of high-standard farmland projects, it is typically necessary to clearly define the distribution and accessibility of farmland roads to support infrastructure assessment and management decisions. To meet these needs, current methods typically employ road data processing and spatial analysis based on Geographic Information Systems (GIS). Compared to manual on-site inspections and calculations, this approach significantly improves the efficiency and accuracy of farmland road accessibility calculations.

[0003] Existing technology 1: Invention patent CN115062967A uses plots of land (fields) within a project area as evaluation objects, road networks as proximity elements, sets a search radius, and performs a proximity search. Plots with proximity road elements within the search radius are counted as "accessible plots," while plots without proximity road elements are counted as "inaccessible plots." The final output is the "proportion of accessible plots to the total number of plots in the project area." This proximity search method typically requires a preset fixed search radius or distance threshold to determine whether a plot is "accessible." This type of threshold is highly sensitive to the accuracy and spatial deviation of road data: when road data has breaks, misaligned endpoints, offsets, or errors in plot boundaries, even small changes in the threshold can cause significant fluctuations in accessibility determination results, making it difficult to reuse uniform parameters across regions and data sources. Furthermore, this method primarily relies on a binary judgment of "whether there are adjacent roads," making it difficult to characterize the impact of the overall road network connectivity on accessibility. This can easily lead to misjudgments such as "locally adjacent but actually inaccessible via the network" or "locally distant but accessible via a connecting path." In addition, this method ignores the pivotal role of the field area as the only legitimate entry point for actual agricultural machinery. If vehicles are physically adjacent but obstructed by ditches, agricultural machinery cannot operate in the field.

[0004] Existing technology 2: Invention patent CN103886479A divides the area to be evaluated into grids, forming multiple land grid units; acquires road vector data and classifies roads by level, assigning road scores to different levels of roads; counts the number of roads of different levels within each grid unit, and calculates the road accessibility index of the grid based on the number of roads and road scores. Existing technology 2 uses a grid-based weighted statistical method to construct an accessibility index based on the number of roads and level scores, which can form a comprehensive index at the regional scale. However, its results reflect more the road distribution density and level structure, and it is difficult to directly correspond to the actual operational needs of whether the plots are accessible or whether agricultural machinery can reach them. Since the grid division scale, road level scoring rules, and weight settings are highly empirical, differences in road level systems, road widths, and traffic conditions in different regions will lead to insufficient comparability of the indicators. In addition, this type of method usually treats road elements as area coverage or counting factors, without considering the topological connectivity between roads and fields, as well as the blocking effect of obstacles such as ditches on the passage path, thus affecting the interpretability of the evaluation results.

[0005] In summary, existing methods suffer from two major common shortcomings: 1. Algorithm logic level: The above two methods ignore the objective obstacles of ditches and roads in physical space, and also lack consideration of the field area, which is the key physical entrance for achieving cross-obstacle connectivity. This evaluation mode that relies heavily on geometric calculation and distance parameters will lead to problems such as threshold sensitivity and poor computational robustness in the evaluation results in practical applications, and is very likely to cause business misjudgment such as "the distance is close but it is actually impossible to enter the field".

[0006] 2. Implementation and Automation: Existing methods generally consist of independent GIS scripts or fragmented processes relying on manual operation, lacking unified standardized calling interfaces and intelligent task scheduling mechanisms. When faced with varying data quality and evaluation requirements across different project areas, it is difficult to achieve dynamic arrangement and adaptive flow of the measurement toolchain, and thus cannot support the routine, automated verification and maintenance needs of large-scale, high-standard farmland construction. Summary of the Invention

[0007] To address the problems of existing technologies relying on geometric distance thresholds and ignoring natural physical barriers in farmland road accessibility evaluation, this invention proposes a farmland road accessibility measurement method based on spatial topology reconstruction, comprising: S1: breaking road intersections to construct a regional road network map, and projecting the points in the field access area onto the nearest road segment to achieve threshold-free topological connectivity between the points in the field access area and the road segment; S2: constructing field clusters and establishing field access connectivity based on the reverse erasure method: filtering out ditch obstacles affecting passage using the reverse erasure method to construct connected field clusters, and combining a spatial indexing algorithm to map the points in the field access area to the target field clusters, establishing the relationship between the field access area that crosses the obstacle and the field cluster; S3: constructing a complete connectivity relationship between the road network, the field access area, and the field clusters, transferring the road network accessibility attributes to the field clusters, and finally outputting the farmland road accessibility evaluation result.

[0008] In one implementation, step S1 includes: S11: Performing road geometric validity verification and topological preprocessing on the road vector dataset, including: extracting the geometric intersection points of the road vector data, inserting shared nodes at these points to establish topological associations between different road segments; dividing the original long line segment that originally passed through the intersection point into multiple independent road segments connected end to end, using the newly inserted shared nodes as boundaries; S12: Constructing a regional road network graph, the regional road network graph including: a node set, an edge set, and regional road network connectivity statistics, the node set including the start and end nodes of the road segments, the edge set including the nodeized road segments, and the regional road network connectivity statistics including the total number of connected components and the number of isolated nodes; S13: Connecting the Xiatian area points to the regional road network graph, so that a one-to-one connection relationship is established between the Xiatian area points and the road segments.

[0009] In one implementation, step S13 includes: 1) For each field point in the original field point dataset within the region, determining the connecting road segment in the edge set of the regional road network map; 2) After determining the connecting road segment, further determining the exact connecting position of the field point on the road segment, using the vertical projection of the nearest point from the field point to the connecting road segment as the logical anchor point; 3) Performing attribute connection through a relational data structure: the field point directly inherits the topological attributes of the connecting road segment through its logical anchor point, generating a dataset of connected field points. Each field object in the dataset of connected field points includes: a unique element number, physical entity point coordinates, logical anchor point coordinates, connecting road segment identifier, and its inherited road material type attribute.

[0010] In one implementation, step S2 includes: S21: constructing an initial farmland area and a physical topological barrier network; S22: dividing the field into groups based on the reverse erasure method; S23: extracting the point attributes of the field area and constructing a field group area index; S24: performing threshold-free pure topological mapping between the field area points and the field group area.

[0011] In one implementation, step S21 includes: 1) constructing an initial surface of the farmland area using the union of the fields, and performing a closing operation on the initial surface of the farmland area to obtain a continuous surface region as the macroscopic coverage surface of the fields; 2) using the set of edges as a reference, expanding vertically outward by a set of buffer distances to generate a continuous polygonal coverage area defined as the road obstacle surface area; using the ditch line vector data of the farmland area as a reference, expanding vertically outward by a set of buffer distances to generate a continuous polygonal coverage area defined as the ditch obstacle surface area; performing validity screening on the ditch obstacle surface area, retaining only the effective blocking ditches that intersect with the boundary of the macroscopic coverage surface or are connected to the road obstacle surface area; and spatially merging the road obstacle surface area with the filtered effective ditch area to generate a physical topology obstacle network.

[0012] In one implementation, step S22 includes: 1) Based on the macroscopic coverage area of ​​the field and the physical topological barrier network, the physical topological barrier network is erased in reverse through spatial difference operation in the macroscopic coverage area of ​​the field to obtain an effective passable farmland area; 2) All independent polygons in the passable farmland area are extracted to form a set of passable field group areas. Within any field group area in the set of passable field group areas, agricultural machinery can pass freely, while crossing different areas is blocked; 3) A spatial index of the set of passable field group areas is constructed. For any field, the spatial intersection area between it and each candidate area in the passable field group area is calculated. The unique field group to which the field belongs is the area where the intersection area is the largest.

[0013] In one implementation, step S23 includes: extracting the outer envelope rectangle of each polygon in the set of accessible field area regions, constructing an STRtree spatial index tree, and calculating the minimum Euclidean distance from the original point of the lower field area to each candidate area in the set of accessible field area regions based on the index tree.

[0014] In one implementation, step S24 includes: 1) For any field area object, calculate the minimum Euclidean distance from its physical entity point to each candidate surface in the set of accessible field area regions, where the distance function is the infimum of the physical entity point to each candidate surface region; 2) Based on the above distance function, find the nearest target field area region in the spatial topology for the original point of the field area.

[0015] In one implementation, step S3 includes: S31: Aggregating the accessibility of a group of fields based on the mapping relationship between the fields: For any accessible field group area, extract the set of road network types inherited by all fields connected to that field group area; Construct Boolean logic judgment rules for the accessibility of the field group: Determine whether the field group has the accessibility of farm roads and production roads. If the type set contains farm road attribute identifiers and production road attribute identifiers, it is considered to have the ability to achieve the accessibility of the field group; S32: Performing field attribute inheritance and accessibility rating: Merging the relationships between data tables; Rating the accessibility of the field by performing logical operations on the farm road accessibility and production road accessibility of the field.

[0016] The present invention also proposes a farmland road accessibility measurement system based on spatial topology reconstruction, comprising: a processor and a computer executable program, wherein when the processor executes the program, it performs the method described above.

[0017] The method of this invention transforms spatial geometric computation into graph-theoretic topological relationships. By setting the field region as the core connectivity hub that overcomes physical obstacles, each region point is addressed within the road network, and its optimal connecting road segment and projection position are determined, without being negated by the distance between the field region and the road. This eliminates the limitations of geometric distance and constructs a comprehensive and robust topological association between the field region and the road at the underlying level.

[0018] Furthermore, by reverse erasing physical barriers to construct barrier-free field clusters, and using nearest neighbor spatial indexing, points in the field area and representative points of the fields are topologically addressed to their corresponding surface regions. This avoids geometric intersection calculations, and the inheritance of road network accessibility attributes can be achieved solely through relational data connections, greatly improving the efficiency of automated evaluation across the entire region.

[0019] In addition, the entire set of underlying GIS algorithms is semantically orchestrated and constructed into standardized specialized skills. This ensures the reusability and efficient implementation of the overall evaluation process across project areas and data quality conditions. Attached Figure Description

[0020] To facilitate understanding of the invention, it will be described in more detail with reference to the specific embodiments shown in the accompanying drawings. These drawings depict only typical embodiments of the invention and should not be considered as limiting the scope of protection of the invention.

[0021] Figure 1 This is a flowchart of the method of the present invention.

[0022] Figure 2 This is a schematic diagram of the experimental area for one embodiment.

[0023] Figure 3This is a topological diagram of the rural area and road network in one embodiment.

[0024] Figure 4 This is the result of field group division in one embodiment.

[0025] Figure 5 This is a topological diagram of the relationship between the lower field area and the field group in one embodiment.

[0026] Figure 6 This is a classification map of the accessibility of regional farmland roads, as shown in one embodiment. Detailed Implementation

[0027] The embodiments of the present invention are described below with reference to the accompanying drawings to enable those skilled in the art to better understand and implement the present invention. However, the listed embodiments are not intended to limit the present invention. In the absence of conflict, the following embodiments and the technical features in the embodiments can be combined with each other, wherein the same components are indicated by the same reference numerals.

[0028] Figure 1 This is a flowchart of the method of the present invention. The core idea of ​​the present invention is to abandon the traditional evaluation model that relies on geometric distance thresholds and spatial intersection operations, and transform the physical space of farmland into a graph-theoretic topological network. Specifically, the present invention uses the field area as a connectivity hub to overcome physical obstacles, and uses the reverse erasure method to construct barrier-free access propagation units (field groups); then, through threshold-free pure topological spatial addressing and relational data merging, the road network accessibility attributes are transmitted to the micro-fields. On this basis, the entire set of core algorithms is standardized and packaged into a dedicated toolset (system) for accessibility calculation, realizing automated verification across project areas.

[0029] The farmland road accessibility measurement method described in this invention uses multi-source spatial vector data within a region as its basic input. Before performing the calculation, it first acquires the road line vector data, ditch line vector data, field area vector data, and field point vector dataset for the entire region. The aforementioned multi-source basic spatial data can be obtained through conventional techniques in this field, including but not limited to: directly acquiring the completion and acceptance data of high-standard farmland construction projects; acquiring data based on high-resolution satellite remote sensing imagery; acquiring data based on high-precision UAV remote sensing orthophotos combined with deep learning target extraction algorithms; or conducting on-site point mapping using high-precision positioning equipment such as RTK. The acquired data is uniformly converted into standard GeoJSON and Shapefile formats.

[0030] like Figure 1 As shown, the farmland road accessibility measurement method based on spatial topology reconstruction proposed in this invention includes steps S1-S3.

[0031] S1: Perform road intersection interruption to construct a regional road network graph, and project the Xiatian area points to the nearest road segment to achieve threshold-free topological connectivity between Xiatian area points and road segments.

[0032] Specifically, the geometric intersection points of road vector data are extracted, and shared nodes (topological nodes) are inserted at these points to establish topological associations between different road segments. Using the newly inserted shared nodes as boundaries, the original long line segments that continuously pass through the intersection points are divided into multiple independent road segments connected end to end. The Xiatian area is projected onto the nearest road segment to achieve threshold-free topological connectivity between the Xiatian area points and the road network.

[0033] Specifically, step S1 includes steps S11-S14.

[0034] S11: Perform road geometry validity verification and topology preprocessing on the road vector dataset.

[0035] 1) Perform data cleaning on the road vector dataset (including road geometry and material type attributes). For outliers in the dataset, set specific geometric and attribute filtering rules to remove them. Specifically, this includes: removing invalid line segments with zero geometric length, removing redundant road features lacking material type attribute labels, and removing isolated suspended line segments with a length less than a set threshold that do not intersect with other roads.

[0036] 2) Perform geometric validity recovery operations, that is, detect and repair topological errors such as geometric self-intersection in road vector lines.

[0037] 3) For the original data using geographic coordinate system (latitude and longitude), uniformly reproject to the metric projection coordinate system (UTM projection partition) applicable to the project area to ensure the accuracy of subsequent road length spatial calculation and topological relationship determination.

[0038] In the projected coordinate system, for road geometric discontinuities (such as false dead ends or small gaps) caused by vectorization errors, a tolerance-based endpoint snapping operation is used to implement topology repair: the spatial Euclidean distance between adjacent road endpoints is calculated, and when the distance is less than the set snapping tolerance threshold (preferably 0.5m), the spatial coordinates of the two endpoints are forcibly merged and updated to the same coordinate position, thereby closing the small physical gaps at the data level and ensuring that physically connected roads establish a real adjacency relationship in the topology network.

[0039] S12: Construct a regional road network map.

[0040] Farmland road accessibility is essentially the ability of roads to provide access to and services to farmland, and this ability depends primarily on whether the road system itself forms a usable network structure.

[0041] 1) Perform node-based and segmented extraction processing on the road vector dataset after S11 processing to obtain a node-based road segment dataset: By identifying the geometric intersection positions of road segments on the two-dimensional plane, a shared node is inserted at the geometric intersection point, and the original long line segment is divided into a set of road segments connected by adjacent nodes to ensure that physically intersecting roads establish a topological connection relationship.

[0042] Based on the node-based road segment dataset, the start and end nodes of the road segments are extracted, and a node set V and an edge set E are constructed to build a regional road network graph G: G=(V, E).

[0043] 2) To ensure that the semantic attributes of the original road data are preserved in the topology, for each edge e in the set of road segments E after segmentation, the "maximum overlap matching" principle is adopted to calculate the maximum overlap length between it and the original road line vector data to match the road type. Finally, the extracted road type information is used as the attribute field of edge e and directly written into and updated in the data of edge set E.

[0044] Thus, in the regional road network graph G, the node set V records the endpoint coordinates of each road segment. The edge set E records the node-based road segment dataset, and each edge e has attributes such as location, length, and material type.

[0045] 3) For the constructed regional road network graph G, perform topological traversal calculations to obtain the regional road network connectivity statistics (including the total number of connected components and the number of isolated nodes). Thus, the regional road network graph G includes: a set of nodes V, a set of edges E, and the regional road network connectivity statistics.

[0046] S13: Connect the points in the Xiatian area to the road network map to establish a one-to-one connection between the points in the Xiatian area and the road segments.

[0047] After obtaining the regional road network map G, this step uses the road network map as a reference to establish a one-to-one correspondence between the field access area points and road segments. The field access area refers to an effective transitional carrier that allows agricultural machinery to smoothly and safely enter the farmland from the road network. Physically, it includes not only artificially constructed field access facilities (such as bridges and culverts, ramps, etc.) but also connected areas where agricultural machinery can directly access the field due to flat terrain and the absence of physical barriers such as ditches. Because there may be a real offset distance between the field access area points and the road centerline (or extracted road line) during actual construction, using a fixed distance threshold to "determine whether" the field access area points "belong to / do not belong to the road" can easily lead to the erroneous exclusion of field access area points due to differences in the project area's environment.

[0048] Therefore, this invention adopts a "threshold-free connection" method: for each field point, its connection road segment and connection position are determined in the regional road network map G, and its effectiveness is not denied due to the distance between the field point and the road.

[0049] 1) For each field point in the original field point dataset within the region, determine the connecting road segments in the edge set E of the regional road network graph G.

[0050] Specifically, the original dataset of field points within the region is denoted as A. .in For the Xiatian area, M Let E be the total number of points in the Xiatian facility area. Using the edge set E of the regional road network graph G as the candidate road segment set, for each Xiatian area point... Calculate the shortest distance from it to each road segment, and select the one with the smallest spatial Euclidean distance as its unique connecting road segment. Defined as: (1), in Point The shortest Euclidean distance to road segment e.

[0051] 2) Determine the connecting road section Subsequently, the location of the Xiatian area was further determined. The exact attachment point on this road section is defined as the Xiatian area point. To the connecting road section The nearest point is vertically projected to generate logical anchor points. : (2), At this point, the Shimoda area point also includes: physical entity points whose original spatial locations have been preserved. And logical anchor points projected onto the road network to inherit accessibility attributes. .

[0052] 3) Establish the connection between the field area points and road sections.

[0053] After establishing the physical entity points in the Xiatian area with their original coordinate information and the logical anchor points projected onto the road network, attribute joins are performed using a relational data structure. Points in the Xiatian area are joined through their logical anchor points. Directly inherit the attached road section The topological attributes include, but are not limited to: road material type (farm road or production road), road connectivity component identifiers, etc. After completing the above operations, a structured "Field Area-Road Segment Connection Table" is generated. This yields the dataset A of connected field area points. * , recorded as (in, This represents a field area object that integrates physical entity coordinates, logical anchor point coordinates, and attached road segment attributes. M (Total number of points in the field facility area). The "Field Area - Road Section Connection Relationship Table" and set A * Complete record of each field area object Unique element number, physical entity point coordinates Logical anchor point coordinates , the road section signs and their inherited road material type attributes.

[0054] Data set A of the connected field area points * All field access points within the project area are stably mapped to the road network structure, and road material type information can be transferred to the field access level along with the connection relationship, providing a consistent data foundation for subsequent evaluation calculations based on "field access as the entrance to the field".

[0055] S2: Constructing field clusters and establishing field access connectivity based on reverse erasure method: Filter out ditch obstacles that affect passage based on reverse erasure method to construct connected field clusters, and combine spatial indexing algorithm to map the field access area points to the target field clusters, establishing the relationship between the field access area that crosses the obstacles and the field clusters.

[0056] After completing the structured representation of the road network in step S1, this step combines the acquired regional ditch line vector data and field surface vector data to construct a group of fields within the region capable of accessibility propagation. Considering that accessibility propagation between fields depends not only on geometric proximity but also on spatial obstacles formed by linear facilities such as roads and ditches, this invention abstracts roads and ditches into an obstacle network. A reverse erasure method is used to identify connected surfaces segmented by the obstacle network within the farmland coverage area. These connected surfaces serve as the spatial carriers of the field groups, thereby obtaining the accessible field group division results and the mapping relationship between fields and groups. Simultaneously, to establish cross-obstacle connectivity between the road network and farmland, this invention uses the area below the field as a node crossing spatial obstacles, adaptively searching for the nearest field group surface without a threshold.

[0057] S21: Construct the initial farmland area and physical topological barrier network.

[0058] First, obtain the set of field features F = {F i}, construct the initial surface of the farmland region by the union of the field plots: (3), Among them, F iThis represents a field plot. Because the field plot surface vector data may contain tiny gaps and narrow voids, this step, to eliminate spatial logic blockages caused by such minor geometric discontinuities, initializes the surface of the aforementioned farmland area. A closing operation is performed to repair small cracks. The closing operation adopts an outward expansion and then contraction method. The closing operation parameter δ (preferably δ=4.0 m) is set so that small cracks with a width of no more than 2δ are repaired at the area level, thereby ensuring the overall continuity of the farmland area. Finally, a continuous area is output as the macroscopic coverage area S of the field.

[0059] After determining the macroscopic coverage area S of the fields, considering that roads and ditches are expressed as line elements and cannot reflect the physical isolation width in the real world, the line elements are transformed into area elements with actual blocking widths. Using the edge set E of the road segment as a reference, a buffer distance (preferably an expansion radius of 2 meters) is set vertically outwards to both sides, generating a continuous polygonal coverage area defined as the road obstacle surface region. Similarly, using the ditch line vector data (ditch line set D) as a reference, a buffer distance (preferably an expansion radius of 1 meter) is set vertically outwards to both sides, generating a continuous polygonal coverage area defined as the ditch obstacle surface region. To avoid mistakenly including ditches that do not have spatial blocking and separation functions in the obstacle network, thus causing excessive fragmentation of the field clusters, this step performs effectiveness screening on the ditch obstacle surface regions: only those with boundaries to the macroscopic coverage area are retained. Intersecting or connected to the road obstacle surface area are effective blocking ditches. The road obstacle surface area and the filtered effective ditch area are spatially merged to generate the final overall physical topology obstacle network B.

[0060] S22: Field grouping based on reverse erasure.

[0061] 1) Call the field macro-coverage surface S and physical topological barrier network B output in S21. In the macro-coverage surface S, erase the overall physical topological barrier network B in reverse through spatial difference operation to obtain the effective passable farmland area M: M = S\B.

[0062] 2) After the erasure operation, the accessible farmland region M naturally splits into multiple non-overlapping connected components in physical space. Extracting all independent polygons from the accessible farmland region M constitutes the set of accessible field plot regions. In any field cluster surface region Z k Inside, agricultural machinery can move freely without obstacles, but crossing different areas will inevitably be blocked by road or water networks.

[0063] 3) Construct a spatial index for the set of accessible field regions Z. For any field... Calculate its relationship with the surface region Z of each candidate field group. k The spatial intersection area. Define field F.i The unique group of fields g(i) is identified by the region containing the largest intersecting area: (4), S23: Extract the point attributes of the lower field area and construct the field group area index.

[0064] For dataset A of connected field points that have completed road network connection... * For any field area object It is clear that it has two spatial node attributes: 1) Original point of Xiatian area : Represents the physical coordinates of the point in the Shimoda area in real space.

[0065] 2) Road network connection points : Represents the nearest road section to the Shimoda area Vertical projection logical anchor point.

[0066] Next, the outer envelope rectangles of each polygon in the set Z of accessible field clusters are extracted, and an STRtree spatial index tree is constructed to improve the efficiency of topological relationship retrieval. Based on this index tree, the original points of the lower field area are calculated. Candidate surface regions Z in the set of accessible field clusters Z k The minimum Euclidean distance. Define the distance function. Origin point To the Z-region k The lower bound of the closure: (5), S24: Perform threshold-free pure topological mapping between the points in the field area and the surface region of the field group.

[0067] The core of this step lies in performing a threshold-free pure topological nearest neighbor mapping. Based on the aforementioned distance function, the original points of the Shimoda region are... Find the nearest target field group surface region Z in the spatial topology. * The mapping model is: (6), In this logical mapping, adaptation covers both physical forms without introducing any manual distance threshold settings. If the infimum... Determine the original point of the Xiatian area Crossing the physical isolation zone, directly located in the target field area Z. * The interior of the polygon; if Determine the original point of the Xiatian area Located within a physical spatial isolation zone created by roads or ditches. In this case, the mapping model is equivalent to implicitly generating Thiessen polygons within the isolation zone, and then using the original points of the lower field area. Deterministically mapped to the nearest field surface region Z * On the border.

[0068] Finally, the surface region Z of the target field group hit by the above mapping is obtained. * The unique identifier is used as an attribute field and concatenated into the already linked field point dataset A. * Ultimately, this allows the physical units that are interrupted by buffer zones in physical space to be connected logically in graph theory, establishing a connectivity logical chain from the road to the underlying farmland units.

[0069] S3: Farmland Road Accessibility Evaluation and Index Calculation: Construct a complete connectivity relationship between the road network, the field area, and the field cluster, transmit the road network accessibility attributes to the field cluster, and finally output the farmland road accessibility evaluation results.

[0070] This step aims to transform the established logical chain into the final road accessibility business evaluation index, and achieve a four-level accessibility automatic evaluation that strictly aligns with national standards based on the attribute inheritance mechanism of relational topology.

[0071] S31: Aggregate the accessibility of field groups based on the mapping relationship of the field area.

[0072] Based on the relationship between the lower field area and the field group obtained from S2, for any traversable field group area... Extract the set of road network types T(Z) inherited by all downstream areas attached to the field cluster area. k Based on this, a Boolean logic rule for determining the accessibility of a cluster of farm plots is constructed: Whether the cluster of farm plots has the capability for mechanized road access is determined by M(Z). k ) and production road capacity P(Z) k If the type set T(Z) k If M(Z) contains the attribute identifier for farm roads, then M(Z) k =True if P(Z) = True, otherwise False; similarly, if it includes the production path attribute identifier, then P(Z) = True. k =True, otherwise False. This operation maps the road network capacity to the attributes of the field cluster region.

[0073] S32: Perform field attribute inheritance and accessibility rating.

[0074] Based on the established field F in S2 i The unique ownership identifier of the uniquely owned field group g(i) is merged through the relationship between data tables, and field F is... i It can directly inherit the accessibility of its own field group g(i).

[0075] To achieve standardized and efficient evaluation output, this invention establishes a structured four-level accessibility evaluation logic, directly comparing the road type attributes inherited by the fields, thus avoiding complex spatial geometric overlay analysis. This invention achieves its rating based on the following logical operation rules.

[0076] 1) If If all conditions are true, meaning the road is determined to be adjacent to farm roads and production roads in terms of spatial topology, then the rating is marked as "fully accessible".

[0077] 2) If If true, meaning that the location is determined to be adjacent only to a farm road in terms of spatial topology, then the rating is marked as "accessible".

[0078] 3) If If true, meaning it is determined in the spatial topology to be only adjacent to the production road, then the rating is marked as "basically reachable".

[0079] 4) If If true, it means that there is a certain distance between the road and the farm road and the production road and no effective connection has been established, then the rating is marked as "unreachable".

[0080] Among them, M(F) i ) represents field F i The traffic capacity of farm roads, P(F i ) represents field F i The production road traffic capacity.

[0081] According to another aspect, the present invention provides a system for measuring the accessibility of farmland roads based on spatial topology reconstruction.

[0082] The system encapsulates the algorithms of each step using the Model Context Protocol and constructs a reusable farmland road accessibility measurement skill through semantic orchestration: it builds dedicated tools and skill-driven toolchain orchestration based on the MCP protocol.

[0083] The system of this invention encapsulates the key GIS algorithms in steps S1 to S3 as tools and provides a unified calling interface through the Model Context Protocol (MCP); it drives the large language model to stably complete the calculation under different data quality conditions through semantic skills.

[0084] The system of this invention includes MCP tool encapsulation and a unified interface specification.

[0085] To ensure that the aforementioned tools can be uniformly invoked across different software platforms and computing environments, the system of this invention deploys the dedicated tools as an MCP server. The MCP server is used for: publishing a tool list, providing tool parameter patterns (preferably JSON Schema or equivalent structures), receiving invocation requests, and returning structured responses. The MCP server can provide service interfaces using transmission methods such as STDIO or HTTP, thereby ensuring compatibility with different client integration methods.

[0086] Preferably, each tool Ti has the following interface triplet: (7), in Strict constraints were defined, including spatial vector data paths, coordinate systems, and topological tolerances. This refers to the corresponding underlying topology calculation process; This outputs a structured spatial relationship table or feature set. This encapsulation method does not directly depend on the underlying GIS library or script details; it only requires parameter population and result parsing based on the schema, thus decoupling the algorithm implementation from the calling logic.

[0087] The system of this invention has semantic encapsulation of the skill layer and toolchain orchestration capabilities.

[0088] Above the MCP interface, the system of this invention includes a dedicated semantic skill library for farmland road accessibility measurement scenarios. This library is not merely a list of tools, but encompasses the business intent, spatial input constraints, and contextual dependencies of each tool, specifically including: Road network construction and threshold-free topological connectivity module for Xiatian area (corresponding to S1): It has the semantic ability to transform linear vectors into node and edge topological graphs and to address and generate logical anchor points in the road network.

[0089] Obstacle Reverse Erasure and Cross-Boundary Topology Mapping Module (corresponding to S2): It has the semantic ability to identify physical obstacles such as water systems / ditches and perform reverse erasure to construct the smallest accessible evaluation unit.

[0090] Accessibility index calculation module (corresponding to S3): performs logical relationship merging and semantic capabilities for farmland road accessibility compliance rating.

[0091] The system of this invention has workflow orchestration capabilities based on the Skills library.

[0092] Based on the aforementioned Skills library, the system of this invention rigidly orchestrates these five core Skills into an execution chain according to the inherent dependency logic of farmland road accessibility calculation. To address the process interruption issue caused by heterogeneous data across project areas, this toolchain incorporates a proprietary anomaly circuit breaker and degradation mechanism for geospatial data between nodes: 1) Context Loading: Skills are executed sequentially according to their order. The topology table output by the preceding skill (e.g., GeoJSON or GraphML format) serves as the context state, which is then injected into the subsequent skill. .

[0093] 2) Spatial data compliance check: Perform geometric validity checks (such as self-intersection detection and empty geometry detection) before moving on to the next skill.

[0094] 3) Local adaptive blocking of spatial data: If a topological chain breaks in a local space (such as the complete isolation of points in the Xiatian area), the system will no longer perform global calculation backtracking, but will mark it as an invalid node and block the mapping branch to ensure that the remaining topological chains are resolved normally.

[0095] Through the arrangement of the above skills, this invention ultimately forms an automated calculation pipeline for farmland road accessibility.

[0096] Figures 2-6 An example of the application of the method of the present invention is shown.

[0097] Figure 2 This is a schematic diagram of the original orthophoto of the experimental area. The image shows the real environment of the project area, indicating that before processing by the system of this invention, the infrastructure (roads, fields, ditches, etc.) in the area has a complex shape and irregular distribution, and there is strong dispersion in the topological relationship between roads and fields, and between fields.

[0098] Figure 3 This is a topological diagram showing the relationship between the field area and the road network. This diagram corresponds to step S1 of the method in this invention, illustrating the connected road network constructed by the system using the acquired vector data of farm roads and production roads. The red nodes in the field area are connected to the nearest road segment via threshold-free anchoring, forming a topological association between the field area and the road segment.

[0099] Figure 4 This is a diagram showing the results of field group division based on reverse erasure. This diagram corresponds to the first half of step S2 of the method of this invention. The continuous color blocks distinguished by different colors in the diagram represent the passable field groups that are naturally formed after the reverse erasure of physical obstacles (ditches).

[0100] Figure 5 This is a topological diagram showing the relationship between the lower field area and the group of field plots. This diagram corresponds to the latter half of step S2 in the method of this invention. In the diagram, the points in the lower field area have been mapped to the logically belonging group of field plots. This establishes the topological connectivity between the lower field area and the traversable group of field plots, overcoming existing physical barriers.

[0101] Figure 6This is the final output map showing the accessibility of farmland roads in the region. This map corresponds to the final output step S3 of this invention. The map uses four standard color levels—dark green (fully accessible), light green (accessible), orange (mostly accessible), and red (inaccessible)—to classify each micro-plot. This indicates that the road network accessibility attributes have been successfully transferred to the plots.

[0102] Table 1 Calculation results of regional farmland road accessibility

[0103] The embodiments described above are merely preferred embodiments of the present invention. The terms "in one embodiment," "in another embodiment," "in yet another embodiment," or "in still another embodiment" used in this specification all refer to one or more of the same or different embodiments according to this disclosure. Ordinary variations and substitutions made by those skilled in the art within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating the accessibility of farmland roads based on spatial topology reconstruction, characterized in that, include: S1: Break the road intersections to construct a regional road network graph, and project the Xiatian area points to the nearest road segments to achieve threshold-free topological connectivity between Xiatian area points and road segments. The regional road network graph includes: a set of nodes, a set of edges, and regional road network connectivity statistics. The set of nodes includes the start and end nodes of the road segments, the set of edges includes the nodeized road segments, and the regional road network connectivity statistics include the total number of connected components and the number of isolated nodes. S2: Constructing field clusters and establishing field access connectivity based on reverse erasure method: Filtering out ditch obstacles that affect passage based on reverse erasure method to construct connected field clusters, and combining spatial indexing algorithm to map the field access area points to the target field clusters, establishing the relationship between the field access area that crosses the obstacles and the field clusters. S3: Construct a complete connectivity relationship between the road network, the field area, and the field clusters, transmit the road network accessibility attributes to the field clusters, and finally output the farmland road accessibility evaluation results. Step S2 includes: S21: Constructing the initial farmland surface and physical topological barrier network: 1) Construct the initial farmland surface using the union of field plots, and perform a closing operation on the initial farmland surface to obtain a continuous surface as the macroscopic coverage surface of the field plots; 2) Using the edge set as a reference, extend vertically outwards to both sides by a set of buffer distances to generate a continuous polygonal coverage area defined as the road barrier surface area; Using the ditch line vector data of the farmland area as a reference, extend vertically outwards to both sides by a set of buffer distances to generate a continuous polygonal coverage area defined as the ditch barrier surface area; Perform validity screening on the ditch barrier surface area, retaining only effective blocking ditches that intersect with the boundary of the macroscopic coverage surface or are connected to the road barrier surface area; Spatially merge the road barrier surface area with the filtered effective ditch area to generate a physical topological barrier network; S22: Field group division based on reverse erasure method: 1) Based on the macroscopic coverage of the field and the physical topological barrier network, the physical topological barrier network is reverse-erased in the macroscopic coverage of the field through spatial difference operation to obtain effective passable farmland areas; 2) All independent polygons in the passable farmland areas are extracted to form a set of passable field group areas. Within any field group area in the set of passable field group areas, agricultural machinery can pass freely, while crossing different areas is blocked; 3) Construct a spatial index for the set of passable field group areas: For any field, calculate the spatial intersection area between it and each candidate area in the passable field group area. The unique field group to which the field belongs is the area containing the largest intersection area. S23: Extract the point attributes of the field area and construct the field group area index; S24: Perform threshold-free pure topological mapping between the points in the field area and the surface region of the field group.

2. The method for calculating farmland road accessibility based on spatial topology reconstruction according to claim 1, characterized in that, Step S1 includes: S11: Perform road geometry validity verification and topology preprocessing on the road vector dataset, including: extracting the geometric intersection points of the road vector data, inserting shared nodes at these points to establish topological associations between different road segments; using the newly inserted shared nodes as boundaries, dividing the original long line segments that continuously pass through the intersection points into multiple independent road segments connected end to end; S12: Construct a regional road network map based on step S11; S13: Connect the Xiatian area points to the regional road network map to establish a one-to-one connection between the Xiatian area points and road segments.

3. The method for calculating farmland road accessibility based on spatial topology reconstruction according to claim 2, characterized in that, Step S13 includes: 1) For each field point in the original field point dataset within the region, determine the connecting road segments in the edge set of the regional road network graph. 2) After determining the connecting road section, further determine the exact connecting position of the Xiatian area point on the road section, and use the vertical projection of the Xiatian area point to the nearest point of the connecting road section as the logical anchor point. 3) Perform attribute connection through relational data structure: The points in the field area directly inherit the topological attributes of the attached road segments through their logical anchor points to generate a dataset of attached field area points. Each field area object in the dataset of attached field area points includes: unique element number, physical entity point coordinates, logical anchor point coordinates, attached road segment identifier and its inherited road material type attribute.

4. The method for calculating farmland road accessibility based on spatial topology reconstruction according to claim 1, characterized in that, Step S23 includes: Extract the outer envelope rectangle of each polygon in the set of accessible field area regions, construct the STRtree spatial index tree, and calculate the minimum Euclidean distance from the original point of the field area to each candidate area in the set of accessible field area regions based on the index tree.

5. The method for calculating farmland road accessibility based on spatial topology reconstruction according to claim 1, characterized in that, Step S24 includes: 1) For any field area object, calculate the minimum Euclidean distance from its physical entity point to each candidate surface in the set of accessible field area groups, where the distance function is the infimum of the physical entity point to each candidate surface. 2) Based on the above distance function, find the nearest target field group surface in the spatial topology for the original point of the Xiatian area.

6. The method for calculating farmland road accessibility based on spatial topology reconstruction according to claim 5, characterized in that, Step S3 includes: S31: Aggregate the accessibility of field clusters based on the mapping relationship of the field area: For any accessible field cluster area, extract the set of road network types inherited by all the field areas attached to the field cluster area; construct a Boolean logic judgment rule for the accessibility of the field cluster to determine whether the field cluster has the accessibility of farm roads and production roads. If the type set contains farm road attribute identifiers and production road attribute identifiers, it is considered that the field cluster has the ability to realize the accessibility of the field cluster. S32: Perform field attribute inheritance and accessibility rating: merge the relationships between data tables; and determine the accessibility rating of the field by performing logical operations on the accessibility of farm roads and production roads.

7. A system for calculating the accessibility of farmland roads based on spatial topology reconstruction, characterized in that, include: A processor and a computer executable program, which, when executed by the processor, performs the method according to any one of claims 1-6.

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