Conflict Patch Management Method and Equipment Based on Spatial Database Topology Graph Model

Abstract

This application discloses a method and apparatus for conflict patch governance based on a spatial database topology graph model, relating to the field of spatial database technology. The method includes: preprocessing patch data in a spatial database to obtain a standardized patch dataset, and analyzing the spatial relationships between patches in the standardized patch dataset to identify conflict patches; constructing a corresponding spatial database topology graph model; calculating the collaborative governance strength between conflict patches, and dividing the conflict patches into at least one collaborative governance unit based on the collaborative governance strength; identifying whether there are governance dependencies between conflict patches within each collaborative governance unit, and performing topological sorting of the collaborative governance units based on the governance dependencies to determine the governance path for each collaborative governance unit; and generating a collaborative governance scheme for conflict patches corresponding to the spatial database based on the governance path.

Description

Technical Field

[0001] This application relates to the field of spatial database technology, specifically to a method and device for conflict patch management based on a spatial database topology graph model. Background Technology

[0002] With the development of geographic information systems, spatial databases, and natural resource informatization, spatial parcel management has been widely applied in fields such as land spatial planning, ecological protection, urban renewal, and mine restoration. Spatial parcels are typically stored as objects in spatial databases. By using geographic information systems and spatial databases to identify conflicts in multi-source parcel data, spatial conflicts, planning conflicts, and ownership disputes between parcels can be identified, thus providing basic technical support for spatial governance.

[0003] However, existing spatial conflict governance technologies mostly output conflict identification results in list form, lacking a unified structured expression of the relationships between multiple conflict patches. As a result, it is impossible to analyze and process interconnected conflict patches as a whole. Consequently, governance tasks can usually only be assigned and handled item by item according to individual patches, leading to repeated investigations, repeated coordination, repeated approvals and repeated rectifications, increasing governance costs and reducing governance efficiency. Summary of the Invention

[0004] To address the aforementioned issues, this application proposes a conflict patch management method based on a spatial database topology graph model, including: The spatial data of the map features in the spatial database are preprocessed to obtain a standardized map feature dataset, and the spatial relationships between the map features in the standardized map feature dataset are analyzed to identify conflicting map features. Using the conflicting features as nodes and the relationships between the conflicting features as edges, a corresponding spatial database topology graph model is constructed. Based on the association relationships represented by each edge in the spatial database topology graph model, the collaborative governance strength between the conflicting patches is calculated, and the conflicting patches are divided into at least one collaborative governance unit according to the collaborative governance strength. For each conflicting patch within a collaborative governance unit, identify whether there is a governance dependency relationship between the conflicting patches, and perform topological sorting of the collaborative governance units based on the governance dependency relationship to determine the governance path of each collaborative governance unit; Based on the governance path, a collaborative governance scheme for conflict patches corresponding to the spatial database is generated.

[0005] This application provides a conflict patch management device based on a spatial database topology graph model, characterized in that the device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a conflict patch management method based on a spatial database topology graph model as described above.

[0006] The conflict patch management method based on the spatial database topology graph model proposed in this application can bring the following beneficial effects: By constructing a spatial database topology graph model, the relationships between conflicting features are uniformly and structurally expressed as nodes and edges, overcoming the limitations of existing technologies that only present results as lists or conflict relationships. Based on the relationships in the model, the collaborative governance strength between conflicting features is calculated, automatically merging multiple spatially adjacent and rule-related conflicting features into collaborative governance units. This avoids the repetitive work caused by assigning each conflicting feature individually in traditional methods. Furthermore, based on the governance dependencies in the topology graph model, the governance order is automatically derived and collaborative governance schemes are generated, realizing a shift from governing single features one by one to multi-feature collaborative governance. This significantly reduces governance costs and improves the efficiency of spatial conflict governance. Attached Figure Description

[0007] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating the conflict patch management method based on a spatial database topology graph model provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of a conflict patch management device based on a spatial database topology graph model, provided in an embodiment of this application. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0009] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0010] like Figure 1As shown in the embodiments of this application, the conflict patch management method based on a spatial database topology graph model includes: S101: Preprocess the patch data in the spatial database to obtain a standardized patch dataset, and analyze the spatial relationships between patches in the standardized patch dataset to identify conflicting patches.

[0011] Spatial databases are used to store data with spatial characteristics. They can store the location, shape, size, and related attribute information of geospatial entities, forming the data foundation of Geographic Information Systems (GIS). Common spatial databases include PostGIS and Oracle Spatial. Spatial data is typically organized using polygons as the basic unit. Polygon data includes spatial data representing the boundaries and extent of geographic entities, as well as attribute data such as land use type, planning type, ownership unit, administrative division, conflict type, and rectification status. Due to differences in planning, ownership registration, and control requirements from different sources and periods, polygons may exhibit spatial relationships such as intersection, overlap, adjacency, or excessive proximity. In terms of rules, incompatible uses, planning inconsistencies, or control conflicts may arise. To accurately identify conflict resolution targets and provide a basis for post-resolution path derivation, the polygon data in the spatial database needs to be preprocessed first. This involves coordinate unification, field mapping, anomaly geometric removal, and basic cleaning operations to form a standardized polygon dataset. Based on the spatial relationships between polygons in the preprocessed standardized polygon dataset, conflicting polygons are identified.

[0012] Specifically, after obtaining the standardized patch dataset, it is necessary to analyze the spatial relationships between the patches to identify conflicting patches with spatial conflicts. This application employs a dual judgment mechanism of spatial conditions and rule conditions to identify conflicting relationships between patches. First, at the spatial condition judgment level, based on preset judgment conditions, it detects whether there are spatial relationships between patches in the standardized patch dataset. The judgment conditions include four conditions: spatial intersection, spatial overlap, spatial adjacency, and distance threshold. Spatial intersection refers to the existence of intersecting or internally overlapping areas between the geometric boundaries of two patches. Spatial overlap refers to the non-zero area overlap between the geometric regions of one patch and another patch. Spatial adjacency refers to the existence of common edges or common vertices but no internal overlap between the geometric boundaries of two patches. The distance threshold judgment refers to whether the minimum distance between the geometric boundaries of two patches is less than a preset buffer distance, such as the legal setback distance between an ecological protection red line and a construction patch. In this case, although no physical contact has occurred, a spatial proximity conflict has been constituted. At the rule-based matching level, pre-defined constraint rules—namely, land use compatibility rules, planning consistency rules, and regulatory constraint rules—are used to further match spatially related patches. Land use compatibility rules determine whether the land use of adjacent or overlapping patches falls under legally prohibited or restricted combinations of land categories. Planning consistency rules determine whether there is a directional contradiction between the current land category of a patch and the planned land use of its region. Regulatory constraint rules determine whether the development intensity, boundary setbacks, or indicator configuration of a patch violate specific regulatory requirements. Only when a patch simultaneously meets any one of the above criteria and constraint rules is it identified as a conflicting patch. Through this dual-judgment mechanism, the final output set of conflicting patches not only covers intuitive geometric overlap scenarios but also identifies implicit conflicts arising from mismatches in spatial planning, land use, or regulatory rules, providing a foundation for subsequent spatial modeling.

[0013] S102: Construct the corresponding spatial database topology graph model by using conflicting polygons as nodes and the relationships between conflicting polygons as edges.

[0014] After identifying conflicting features, the relationships between them are determined. These relationships include at least adjacency, ownership, planning constraints, and governance dependencies. A spatial database topology model of the conflicting features is constructed, using conflicting features as nodes and their relationships as edges. Adjacency reflects the degree of intersection, overlap, proximity, or closeness between features; ownership reflects the consistency or relevance of coordinating entities; planning reflects the consistency of rule sources; and governance dependencies reflect the sequential governance constraints between features. Spatial relationship edges are established when two conflicting features are spatially adjacent, spatially overlapping, or less than a preset threshold in distance. Attribute relationship edges are established when two conflicting features belong to the same ownership entity, the same management entity, or the same planning area. Directed governance dependencies are established when the rectification of feature A is a prerequisite for the rectification of feature B. Regardless of the type of association mentioned above, the conflict patches will ultimately be structured and stored in the spatial database topology graph model in the form of nodes and edges, realizing a unified expression of the association relationship of conflict patches. This avoids the fragmentation of association information caused by the original list-style storage, and can intuitively present the overall association structure of all conflict patches, providing a data foundation for the subsequent division of collaborative governance units.

[0015] S103: Based on the association relationships represented by each edge in the spatial database topology graph model, calculate the collaborative governance strength between conflicting patches, and divide the conflicting patches into at least one collaborative governance unit according to the collaborative governance strength.

[0016] Based on the relationships represented by the edges in the spatial database topology graph model, the collaborative governance strength between conflicting patches is calculated. According to the collaborative governance strength, the nodes in the spatial database topology graph model are clustered, thereby dividing the conflicting patches into at least one collaborative governance unit. This ensures that the connections within the same collaborative governance unit are close, the governance subjects are unified, and the control rules are related, thus integrating the scattered individual conflicting patches into governance objects that can be uniformly handled.

[0017] Specifically, based on preset governance dimensions, the collaborative governance strength between conflicting patches is weighted and calculated. These dimensions include spatial proximity, ownership consistency, planning relevance, and governance dependency. After obtaining the collaborative governance strength of each pair of conflicting patches, the patches are clustered and merged based on a preset collaborative governance strength threshold. The calculated collaborative governance strength is compared with the preset threshold to identify edges with collaborative governance strengths higher than the threshold as strongly correlated edges. Based on these strongly correlated edges, the corresponding connected subgraphs in the spatial database topology model are identified. Each connected subgraph serves as a collaborative governance unit, and the conflicting patches within each unit are spatially adjacent, rule-related, or governance-dependent, making them suitable for unified governance as a whole.

[0018] It should be noted that when the number of nodes or the total area within the collaborative governance unit corresponding to the connected subgraph exceeds a preset threshold, the collaborative governance unit can be further split based on differences in ownership, planning, or administrative boundaries.

[0019] Specifically, it is determined whether the number of nodes within the collaborative governance unit exceeds a preset node number threshold, and / or whether the total area of ​​the patches corresponding to each node within the collaborative governance unit exceeds a preset total area threshold. If so, the collaborative governance unit needs to be progressively split according to a preset specified governance dimension to form several sub-units.

[0020] First, ownership differences are decomposed. Nodes within the collaborative governance unit are grouped according to the ownership unit of the map features, such as landowners, users, and management units. The ownership unit identifiers of all nodes within the collaborative governance unit are extracted, establishing an ownership unit set O = {o_1, o_2, ..., o_k}. Nodes with the same ownership unit are grouped into the same subset, forming ownership subgroups V_o1, V_o2, ..., V_ok. The connectivity of nodes within each ownership subgroup is rechecked. If nodes within a certain ownership subgroup are not directly connected in the original topology graph, they are further split into multiple sub-units. Each split sub-unit is checked to see if it still exceeds a threshold; if it does, the second dimension of splitting begins.

[0021] Next, the planning difference dimension is broken down. Based on the planning type of the map patch, such as inside or outside urban development boundaries, basic farmland protection areas, ecological protection red line areas, and general agricultural land areas, sub-units that still exceed the threshold are further broken down. The planning type identifiers of all nodes within the sub-unit are extracted, establishing a planning type set P = {p_1, p_2,..., p_m}. Nodes with the same planning type are grouped into the same subset, forming planning subgroups. The connectivity of nodes within each planning subgroup is also checked to ensure spatial continuity within each sub-unit. Each sub-unit after splitting is checked to see if it still exceeds the threshold; if it does, it proceeds to the third dimension of splitting.

[0022] Finally, the administrative boundary dimension is used for further splitting. Based on administrative boundaries, such as township boundaries, street boundaries, and village boundaries, sub-units that still exceed the node number threshold or total area threshold are split into multiple subgroups. Using the administrative boundary as the cutting line, sub-units that cross the administrative boundary are split into multiple subgroups along the boundary. The connectivity of each split subgroup is rechecked. If there are still sub-units exceeding the threshold after splitting, a finer-grained administrative level can be used for further splitting.

[0023] It should be noted that after splitting, the relationships between units need to be maintained. For the sub-units generated by the split, if there are connecting edges in the original spatial database topology model, cross-unit coordination edges should be established between the sub-units to record the governance dependencies between them. At the same time, the collaborative governance strength of each sub-unit should be recalculated to ensure that the split sub-units still have the necessity for internal collaborative governance.

[0024] For example, suppose a collaborative governance unit contains 35 conflict-affected nodes with a total area of ​​1200 mu (approximately 80 hectares), exceeding a pre-set threshold (node ​​count threshold N_max = 30, total area threshold A_max = 1000 mu). First split: The 35 nodes belong to two ownership units: state-owned land (22 nodes) and collectively owned land (13 nodes). The collectively owned land subgroup does not exceed the threshold and is directly treated as an independent subunit. The state-owned land subgroup proceeds to the next dimension. Second split: Of the 22 state-owned land nodes, 14 are located within the urban development boundary, and 8 are located outside the urban development boundary. These form two subunits, neither exceeding the threshold. Final result: The original large collaborative governance unit is split into three subunits: a collectively owned land subunit (13 nodes), a state-owned land-within-city subunit (14 nodes), and a state-owned land-outside-city subunit (8 nodes). Coordination edges are retained between units to ensure that cross-unit dependencies are properly handled during the governance process.

[0025] In one embodiment, after completing the initial grouping based on collaborative governance strength, it is necessary to further identify potential conflict relationships between conflicting patches in order to uncover potential conflicts among them. A potential conflict refers to a conflict relationship that has not yet reached the conflict identification threshold, but may be triggered or exacerbated during the governance process due to changes in the governance outcome of a particular patch. Potential conflict relationships can be categorized into four types. The first type is spatial proximity-based potential conflict, where two map patches currently do not have direct spatial conflict, but their buffer distance is less than a preset distance threshold. When one of the map patches undergoes boundary retreat or morphological adjustment after governance, new spatial overlap may occur. The second type is planning change-based potential conflict, where the planned use of a map patch is under adjustment or has been included in the adjustment plan. After the planning change, new planning conflicts may arise with other map patches. The third type is governance transmission-based potential conflict, where map patch A and map patch B have direct conflict, map patch B and map patch C have direct conflict, while map patch A and map patch C currently do not have direct conflict. However, when map patch B is governed, changes in its boundary or attributes may trigger indirect conflict between A and C. The fourth type is resource competition-based potential conflict, where governance schemes for multiple map patches may compete for the same governance resources, such as the same supplementary arable land quota, the same supporting road, or the same ecological compensation area, thus forming implicit resource competition conflicts.

[0026] After identifying potential conflict relationships, it is determined whether the two conflicting patches belong to different collaborative governance units. If so, the manifestation probability and impact degree of each potential conflict relationship need to be calculated, and the product of the two is used as the potential conflict risk value corresponding to that conflicting patch. The manifestation probability represents the likelihood that the potential conflict relationship will actually evolve into a real conflict during the governance process. Its value is determined based on the type difference of the potential conflict. For spatial proximity type, the manifestation probability depends on the ratio of the buffer distance to a preset distance threshold; the closer the distance, the higher the probability. For planning change type, it depends on the certainty of the planning adjustment scheme and the approval progress. For governance transmission type, it depends on the length of the transmission path and the governance certainty of intermediate patches. For resource competition type, it depends on the scarcity of the target resource and the number of competing patches. The impact degree is determined comprehensively based on the number of patches that may be affected after the conflict manifests, the scale of the area involved, and the increase in governance costs.

[0027] Furthermore, it is determined whether the potential conflict risk value corresponding to the potential conflict relationship exceeds a preset potential conflict risk threshold. This preset threshold is a threshold used to determine whether a potential conflict is sufficient to drive regrouping adjustments, and can be pre-configured based on the risk appetite and resource capacity of the governance scenario. If two conflicting patches belong to different collaborative governance units, it indicates that the current grouping result has not included these patches with the risk of becoming explicit in the same governance object. Once the potential conflict becomes explicit, it will generate cross-unit coordination costs and repeated adjustments. Simultaneously, if the potential conflict risk value exceeds the preset threshold, it indicates that the likelihood and severity of the potential conflict's manifestation have reached a level requiring proactive intervention. When both conditions are met, the collaborative governance strength between conflicting patches in the spatial database topology model needs to be adjusted based on the potential conflict risk value to obtain the adjusted collaborative governance strength. Specifically, the adjusted collaborative governance strength is obtained through the following formula: W_adjusted(i,j) = W_original(i,j) + β × R_potential(i,j) Where W_adjusted(i,j) represents the original collaborative governance strength, R_potential(i,j) represents the potential conflict risk value, and β represents the potential conflict impact coefficient. The potential conflict impact coefficient is used to control the gain of potential conflict risk on the original collaborative governance strength. Generally, its value ranges from 0.3 to 0.5 to avoid excessive interference from potential conflicts with confirmed real conflict relationships. While adjusting the collaborative governance strength, it is also necessary to add a new edge between the two conflict patches corresponding to the potential conflict relationship in the spatial database topology graph model to explicitly express the association constraint of the potential conflict in the topology graph model.

[0028] Based on the adjusted collaborative governance strength, the corresponding connected subgraphs in the spatial database topology model are re-identified to adjust the divided collaborative governance units. Specifically, using the adjusted collaborative governance strength as the edge weight, the connected subgraph identification algorithm is re-executed. At this point, due to the connecting effect of newly added edges or changes in connectivity caused by enhanced edge weights, conflicting features originally belonging to different collaborative governance units may be grouped into the same connected subgraph, thus triggering the merging and adjustment of collaborative governance units. Through this potential conflict-driven grouping and adjustment mechanism, dynamic optimization of collaborative governance units is achieved, ensuring that conflicting features with explicit risks are included in the same collaborative governance object for unified handling before governance begins.

[0029] S104: For conflict patches within each collaborative governance unit, identify whether there are governance dependencies between conflict patches, and perform topological sorting of collaborative governance units based on governance dependencies to determine the governance path of each collaborative governance unit.

[0030] After the division of collaborative governance units, the order of governance execution among conflicting features within each unit remains unclear. This is because there are often objective constraints on the order of governance among conflicting features within the same collaborative governance unit, and between different collaborative governance units. For example, land ownership disputes must be resolved before land use planning adjustments, and upstream river channel regulation must be completed before downstream flood control projects commence. Therefore, it is necessary to further identify whether governance dependencies exist among the conflicting features within each collaborative governance unit. Governance dependencies characterize the constraints on the order of governance execution among conflicting features. Based on these dependencies, it can be determined whether there are prioritization relationships within and between collaborative governance units, allowing for topological sorting of the collaborative governance units and the determination of the corresponding governance paths for each unit.

[0031] In one embodiment, it is determined whether there is a governance dependency relationship between conflicting features. If no governance dependency relationship exists, it indicates that the governance execution of each conflicting feature within the collaborative governance unit is independent and there are no pre-constraints. In this case, the governance path of the collaborative governance unit is determined to be parallel governance, meaning that all conflicting features within the collaborative governance unit can be governed simultaneously. Furthermore, since there is no governance dependency relationship between conflicting features in different collaborative governance units, multiple collaborative governance units can also perform parallel governance. Conversely, if a governance dependency relationship is identified between conflicting features, it is necessary to further determine whether this governance dependency relationship is a simple sequential dependency or a circular dependency. A circular dependency refers to a closed dependency chain formed by a group of features or collaborative governance units. For example, the governance of feature A depends on the governance result of feature B, and the governance of feature B depends on the governance result of feature A, thus forming a closed dependency chain. The cycle, and more complex cases include Multiple nodes with cycles. Due to the existence of cyclic dependencies, each feature cannot be managed in a simple sequential order. Traditional topological sorting algorithms cannot be directly applied to directed graphs containing cycles, so the cyclic structure must be eliminated first.

[0032] Therefore, it is necessary to first detect cyclic dependencies between conflicting morphologies. Cyclic dependency detection employs depth-first search (DFS) or strongly connected component analysis (FT-COM) algorithms to traverse the directed dependency graph, such as the Tarjan algorithm and the Kosaraju algorithm. DFS maintains the access status of nodes (unvisited, being visited, completed), and identifies cyclic dependencies when a back edge pointing to a node in the "being visited" state is detected during the traversal. FT-COM algorithms, on the other hand, calculate all mutually reachable subsets of nodes in the graph, identifying each maximal strongly connected component as a cyclic dependency group. Once cyclic dependencies are detected, conflicting morphologies with cyclic dependencies are aggregated into a joint coordination and governance object, which is then replaced by a supernode in the spatial database topology graph model. The supernode is an abstract representation of the joint coordination and governance object in the topology graph model, inheriting all governance dependencies of its internal member nodes and appearing as a single node externally, thus transforming the original cyclic structure into a sortable acyclic structure. A joint coordination and governance object refers to a group of conflicting features or collaborative governance units that exhibit circular dependencies in their governance relationships. Because their governance results influence each other and cannot be governed sequentially, they must be treated as a whole and coordinated in a unified manner. The core characteristics of a joint coordination and governance object include: internal dependencies forming a closed loop, meaning any change in a member's governance plan can affect other members; governance plans must be formulated synchronously, not independently; and it participates as a supernode in subsequent topology sorting during governance order derivation.

[0033] After aggregating cyclic dependencies into supernodes, a directed acyclic graph (DAG) is constructed based on the replaced supernodes and the remaining governance dependencies to determine the governance order. A DAG is a directed graph data structure where nodes are connected by directed edges but there are no closed loops. Nodes include the supernodes corresponding to collaborative governance units that do not participate in cycles and the jointly coordinated governance objects. Directed edges represent the direction of governance dependencies; that is, the starting point of an edge is the upstream governance object, and the ending point is the downstream governance object, indicating that downstream governance must wait for upstream governance to complete before it can begin. Based on this DAG, the governance path of each collaborative governance unit can be further determined even when cyclic governance dependencies exist within conflicting patches. It should be noted that if there are only simple sequential dependencies between conflicting patches, then sequential governance can be performed on each collaborative governance unit and its internal conflicting patches according to this sequential constraint.

[0034] In one embodiment, when determining the governance path based on a directed acyclic graph (DAG), the DAG first needs to be topologically layered. The specific execution logic of topological layering is as follows: traverse the in-degree of all nodes in the DAG (the number of directed edges pointing to that node), assign nodes with an in-degree of zero to level zero, and then remove all nodes and their outgoing edges from level zero. At this point, the in-degree of some remaining nodes decreases to zero due to the removal of their predecessor nodes. These newly generated nodes with an in-degree of zero are assigned to level one. This process is iterated until all nodes are assigned to their corresponding levels. Through topological layering, a hierarchical execution framework for the DAG is established. Nodes within the same level correspond to collaborative governance units or jointly coordinated governance objects, forming parallel task groups. This means that these governance objects do not have mutual dependencies and are capable of starting governance simultaneously. Different levels form a serial execution sequence, meaning that a downstream level's governance object must wait for all its upstream level nodes' corresponding governance objects to complete before it can start.

[0035] Furthermore, after determining the hierarchical structure, for each node within each level, a corresponding governance task is generated, and an estimated governance duration is allocated. The estimated governance duration is an estimate of the duration of the governance task, obtained by comprehensively evaluating factors such as the actual time consumption of similar historical governance tasks, the scale of the governance area, the technical complexity, and the intensity of resource input, expressed in days or months. After allocating the estimated governance duration, based on the critical path method, the earliest start time and latest start time of each node are calculated, thereby calculating the float time of the estimated governance duration. The float time represents the maximum time margin that the corresponding governance task can be delayed without affecting the overall governance duration. Nodes with zero float time are identified as critical nodes, and these critical nodes are connected along directed governance dependency edges to form the critical path. The critical path is the longest path that runs through all levels and consists of nodes with zero float time. Governance tasks on the critical path have no time margin, and any delay will directly lead to an extension of the overall governance duration. Therefore, governance tasks on the critical path must prioritize governance resources and be executed strictly in the sequential order of adjacent levels.

[0036] Specifically, the critical path method (CPM) calculation process includes two stages: forward traversal and backward traversal. Forward traversal starts from level zero and calculates the earliest start time and earliest finish time of each node sequentially along the directed edges. The earliest start time is the maximum of the earliest finish times of all predecessor nodes of that node. If multiple predecessors exist, the latest finisher is selected. The earliest finish time is the sum of the earliest start time and the estimated governance duration. Backward traversal starts from the highest level and calculates the latest finish time and latest start time of each node sequentially in the reverse direction of the directed edges. The latest finish time is the minimum of the latest start times of all successor nodes of that node. If multiple successors exist, the earliest starter is selected. The latest start time is the difference between the latest finish time and the estimated governance duration. Floating time refers to the difference between the latest and earliest start times of a node. At least one node in the directed acyclic graph with a floating time of zero is identified as a critical node. These critical nodes are then linked together along the directed edges to form the critical path for the corresponding governance task.

[0037] Furthermore, for nodes outside the critical path, the dependency strength between them and other nodes is analyzed. Dependency strength characterizes the constraint strength of governance dependencies, including strong and weak dependencies. Strong dependencies require downstream governance tasks to wait for upstream governance tasks to be fully completed before they can start; for example, land ownership disputes must be resolved before land use planning adjustments. Weak dependencies allow downstream governance tasks to start when upstream governance tasks are completed to a predetermined percentage, without waiting for the upstream to be fully completed; for example, greening construction can begin after the main building demolition is completed, without waiting for site clearing. Based on dependency strength, the conditional parallel trigger points for other nodes outside the critical path are determined, which can fully realize the parallelization of governance tasks while ensuring governance quality and improving governance efficiency.

[0038] Specifically, for nodes outside the critical path, corresponding progress trigger thresholds are set. The progress trigger threshold is a proportional parameter used to quantify whether the upstream governance progress has met the downstream startup conditions. For example, it can be set to 60%. When the actual completion progress of the collaborative governance unit corresponding to the upstream node reaches this progress trigger threshold, the downstream node is allowed to start ahead of schedule within the allowable floating time, thereby transforming the originally sequential governance tasks into conditionally parallel execution, achieving weakly dependent conditional parallelization. Therefore, based on the set progress trigger threshold, the conditional parallel trigger point for each other node can be determined. The conditional parallel trigger point refers to the decision node that allows the governance task corresponding to that other node to switch from its original sequential waiting state to a parallel execution state when the actual completion progress of the upstream governance task meets the progress trigger threshold. During actual execution, the actual completion progress of the upstream governance task is continuously monitored. When the actual completion progress reaches or exceeds the progress trigger threshold, the conditional parallel trigger point is triggered, determining that the governance task of that other node is executed in parallel with the upstream governance task. By combining the setting of progress trigger thresholds under weak dependencies with real-time progress monitoring, conditional parallelization is achieved while ensuring governance quality, effectively compressing the overall governance period and improving collaborative governance efficiency.

[0039] Furthermore, based on the topology layering results, critical paths, and parallel triggering points, the governance paths of each collaborative governance unit are determined. Specifically, based on the topology layering results, the execution order of nodes within the same layer is orchestrated. Since topology layering has already grouped nodes without prior governance dependencies into the same layer, nodes within this layer theoretically possess the conditions for parallel governance. However, considering the limited availability of resources in the actual governance process, such as funds, approval authority, construction manpower, and supplementary farmland quotas, as well as the differences among nodes in terms of governance urgency, conflict severity, and resource demand, it is necessary to further calculate the execution order priority of nodes within the same layer based on preset evaluation indicators. The preset evaluation indicators include at least one of the following: dependency indicators, urgency indicators, conflict severity indicators, governance effectiveness indicators, and resource availability indicators. After normalization, each indicator is weighted and summed according to preset weights to obtain a comprehensive priority score. Among them, the dependency index is determined by the ratio between the number of downstream dependent nodes and the total number of nodes in the entire directed acyclic graph; the urgency index is calculated by weighting urgency factors such as policy deadline and ecological equivalence, and the specific weights can be set according to actual needs; the conflict severity index is obtained by weighted summation of the products of the proportion of each conflict area and the weight of the conflict type; the governance effectiveness index is calculated by the ratio between the number of conflicts expected to be resolved after governance and the total number of conflicts; and the resource availability index is calculated by the ratio between the currently available resources and the resources required for governance.

[0040] Based on the comprehensive priority score, nodes within the same level are sorted from highest to lowest score to form an execution order priority. Subsequently, the governance mode of the nodes within that level is determined according to the sufficiency of current governance resources. If the current governance resources are sufficient, the governance path corresponding to the nodes within that level is determined to be parallel governance, that is, multiple nodes start execution simultaneously; if the current governance resources are insufficient, serial governance is performed according to the execution order priority, that is, resources are allocated to high-priority nodes first, and the next priority node is started in sequence after the node completes or releases its resources.

[0041] For nodes at different levels, since the topology layering establishes strict pre- and post-order relationships between levels, in principle, each level forms a serial execution sequence. Under this constraint, governance tasks on the critical path and task sequences composed of governance tasks from each level are governed serially. The critical path is the longest path consisting of nodes with zero float time; governance tasks on it have no time margin and must be executed serially in strict hierarchical order. A task sequence refers to the chain of governance tasks formed by arranging the remaining nodes in each level according to their execution priority, excluding the critical path; it also follows the serial constraint between levels. However, during serial execution, when a conditional parallel trigger point is detected—that is, when the actual completion progress of the upstream governance task corresponding to a downstream node with a weak dependency reaches a preset progress trigger threshold—the designated governance task is switched from a serial waiting state to a parallel governance state, allowing the downstream task to start execution ahead of schedule. Throughout the execution of the governance path, the governance priority of the critical path is always higher than that of the task sequence. That is, when there is resource competition between governance tasks on the critical path and governance tasks in the task sequence, priority is given to ensuring the resource supply and execution sequence of tasks on the critical path to ensure that the overall governance schedule is not delayed. By combining the prioritization and resource adaptation within the same level with the critical path guarantee and conditional parallel triggering between different levels, a collaborative governance unit governance path that takes into account both time constraints and flexible optimization space is finally generated.

[0042] S105: Based on the governance path, generate a collaborative governance scheme for conflict patches corresponding to the spatial database.

[0043] After determining the governance paths for each collaborative governance unit, a collaborative governance plan for conflict patches corresponding to the spatial database is generated based on these paths. The collaborative governance plan for conflict patches includes at least a governance timeline plan, a governance task list, critical path identifiers, and resource guarantee strategies. This plan is written into the governance sequence table and governance unit table of the spatial database in structured data form, and a visual thematic layer is generated simultaneously to intuitively display the spatial distribution, governance timeline, and execution status of each collaborative governance unit in the geographic information system. This embodiment of the application significantly improves the automation level and implementation efficiency of spatial conflict governance by transforming abstract topological sorting results into governance action instructions.

[0044] The above are embodiments of the methods proposed in this application. Based on the same idea, some embodiments of this application also provide devices corresponding to the above methods.

[0045] Figure 2 This is a schematic diagram of the structure of a conflict patch management device based on a spatial database topology graph model, provided in an embodiment of this application. Figure 2 As shown, it includes: At least one processor; and, At least one processor-communication-connected memory; wherein, The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the conflict patch management method based on the spatial database topology model as described in any of the preceding claims.

[0046] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device is described simply because it is basically similar to the method embodiments; relevant parts can be referred to the descriptions of the method embodiments.

[0047] The devices and methods provided in this application are one-to-one correspondences. Therefore, the devices also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices will not be repeated here.

[0048] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A conflict patch management method based on a spatial database topology graph model, characterized in that, The method includes: The spatial data of the map features in the spatial database are preprocessed to obtain a standardized map feature dataset, and the spatial relationships between the map features in the standardized map feature dataset are analyzed to identify conflicting map features. Using the conflicting features as nodes and the relationships between the conflicting features as edges, a corresponding spatial database topology graph model is constructed. Based on the association relationships represented by each edge in the spatial database topology graph model, the collaborative governance strength between the conflicting patches is calculated, and the conflicting patches are divided into at least one collaborative governance unit according to the collaborative governance strength. For each conflicting patch within a collaborative governance unit, identify whether there is a governance dependency relationship between the conflicting patches, and perform topological sorting of the collaborative governance units based on the governance dependency relationship to determine the governance path of each collaborative governance unit; Based on the governance path, a collaborative governance scheme for conflict patches corresponding to the spatial database is generated; Identifying whether governance dependencies exist between the conflicting patches, and based on these dependencies, performing topological sorting of the collaborative governance units to determine the governance path for each collaborative governance unit, specifically includes: Identify whether there are governance dependencies between the conflicting patches; If not, the governance path of each of the aforementioned collaborative governance units is determined to be parallel governance; If so, the circular dependencies between the conflicting patches are detected, and the conflicting patches with circular dependencies are aggregated into a joint coordination and governance object. The joint coordination and governance object is then replaced with a super node in the spatial database topology graph model. The super node inherits the governance dependencies of each member node within the joint coordination and governance object. Based on the supernodes and the governance dependencies, a directed acyclic graph is constructed to determine the governance order. The governance path of each collaborative governance unit is determined through the directed acyclic graph. The governance path for each collaborative governance unit is determined using the directed acyclic graph, specifically including: The directed acyclic graph is topologically layered to divide the nodes in the directed acyclic graph into several levels, and the corresponding topological layering results are obtained. For each node in each level, a corresponding governance task is generated, and a corresponding estimated governance period is assigned to the governance task. Based on the floating time of the estimated governance period, a critical path consisting of at least one critical governance task is determined. Determine the dependency strength between other nodes located outside the critical path in the directed acyclic graph, and determine the conditional parallel triggering points corresponding to the other nodes based on the dependency strength; Based on the topology layering results, the critical path, and the conditional parallel triggering point, the governance path of each collaborative governance unit is determined; The dependency strength includes strong dependency and weak dependency. Based on the dependency strength, the conditional parallel triggering points corresponding to the other nodes are determined, specifically including: When the dependency strength is weak, determine the progress trigger threshold corresponding to the other nodes; For each other node, based on the progress trigger threshold, a conditional parallel trigger point is determined for the other node, so that if the actual completion progress of the upstream governance task located upstream of the other node meets the progress trigger threshold, the governance task of the other node and the upstream governance task are executed in parallel.

2. The conflict patch management method based on a spatial database topology graph model according to claim 1, characterized in that, Based on the estimated floating time of the remediation period, a critical path consisting of at least one key remediation task is determined, specifically including: Based on the governance dependency relationship, the nodes in each level are traversed bidirectionally in hierarchical order to calculate the earliest start time and latest start time of the governance task corresponding to each node. The difference between the latest start time and the earliest start time is used as the floating time for the estimated treatment period; Identify at least one critical node in the directed acyclic graph whose floating time is zero, and generate a critical path consisting of the critical governance tasks corresponding to the critical node.

3. The conflict patch management method based on a spatial database topology graph model according to claim 1, characterized in that, Based on the topology layering results, the critical path, and the conditional parallel triggering points, the governance path for each collaborative governance unit is determined, specifically including: Based on the topology layering results, according to the preset evaluation indicators, the execution order priority of the nodes in the same layer is determined, and according to the current governance resources, the governance path of the nodes in the same layer is determined to be parallel governance or serial governance according to the execution order priority. For nodes at different levels, the governance tasks on the critical path and the task sequences composed of governance tasks at each level are governed sequentially, and when the conditional parallel trigger point is reached, the specified governance task is switched to parallel governance; wherein, the governance priority of the critical path is higher than that of the task sequence.

4. The conflict patch management method based on a spatial database topology graph model according to claim 1, characterized in that, Based on the association relationships represented by each edge in the spatial database topology graph model, the collaborative governance strength among the conflicting patches is calculated, and according to the collaborative governance strength, the conflicting patches are divided into at least one collaborative governance unit, specifically including: Based on the relationships represented by each edge in the spatial database topology model, the collaborative governance strength between the conflicting patches is weighted and calculated according to a preset governance dimension; wherein, the governance dimension includes spatial proximity, ownership consistency, planning relevance, and governance dependency. The collaborative governance strength is compared with a preset collaborative governance strength threshold, and the edges with collaborative governance strength higher than the collaborative governance strength threshold are regarded as strongly associated edges. Based on the strongly correlated edges, the corresponding connected subgraphs in the spatial database topology graph model are identified, and each connected subgraph is treated as a collaborative governance unit.

5. The conflict patch management method based on a spatial database topology graph model according to claim 1, characterized in that, After dividing the conflict patches into at least one collaborative governance unit based on the collaborative governance strength, the method further includes: The potential conflict relationships between the conflicting patches are identified, and the potential conflict risk value corresponding to the conflicting patches is calculated based on the manifestation probability and influence degree of the potential conflict relationships. Determine whether the two conflict patches corresponding to the potential conflict relationship belong to different collaborative governance units, and whether the potential conflict risk value exceeds a preset potential conflict risk threshold; If so, based on the potential conflict risk value, the collaborative governance strength among the conflict patches in the spatial database topology model is adjusted to obtain the adjusted collaborative governance strength; Based on the adjusted collaborative governance strength, the corresponding connected subgraphs in the spatial database topology model are re-identified in order to adjust the divided collaborative governance units.

6. The conflict patch management method based on a spatial database topology graph model according to claim 1, characterized in that, The spatial relationships between the patches in the standardized patch dataset are analyzed to identify conflicting patches, specifically including: Based on preset judgment conditions, the system detects whether there is a spatial relationship between the patches in the standardized patch dataset; wherein, the judgment conditions include spatial intersection, spatial overlap, spatial adjacency, and distance threshold. If so, the spatial relationships between the polygons are matched using preset constraint rules, and the polygons that simultaneously satisfy the judgment conditions and the constraint rules are identified as conflicting polygons.

7. A conflict patch management device based on a spatial database topology model, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a conflict patch management method based on a spatial database topology graph model as described in any one of claims 1-6.

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