A geographic information intelligent analysis system and method for national space planning

Abstract

The application provides a kind of geographical information intelligent analysis system and method for land space planning, obtains planning base map by receiving multi-source planning data;The planning rules associated with each geographical entity in the planning base map are deconstructed, the main chain path and the branch path are identified, and the spatial access conditions of each land use type are extracted;Based on spatial overlay analysis, the spatial conflicts among the three zones and three lines of each planning layer are detected, the land use control conflicts are determined according to the spatial access conditions, and the time conflicts are detected;Each conflict detection result is associated with the corresponding triggered branch path, the conflicts of the corresponding main chain path are corrected, and the comprehensive conflict weight is determined based on the conflict detection result under the constraint of the corrected main chain;According to the comprehensive conflict weight, the conflict level and spatial position of each planning layer are marked. According to the scheme of the application, multiple types of planning conflicts can be detected according to the association relationship between multi-source data, and the logical structure of the planning rules is associated to realize accurate marking and visual analysis.

Description

Technical Field

[0001] This application relates to the field of information analysis technology, and more specifically, to a geographic information intelligent analysis system and method for land spatial planning. Background Technology

[0002] Territorial spatial planning involves multi-level and multi-disciplinary planning outcomes, including master plans, detailed plans, and special plans. These planning outcomes are formulated by different departments and include multi-source heterogeneous data such as spatial vector data, planning texts, and indicator tables.

[0003] Current technologies primarily rely on spatial overlay analysis of single-map systems to detect direct encroachment conflicts between planning layers through layer overlay comparison. However, for functionally exclusive conflicts and time-dependent conflicts, there is a lack of effective automated detection methods. Typically, manual verification of planning texts is required, which is inefficient, prone to omissions, and difficult to quantify the severity of conflicts. Therefore, how to detect various types of planning conflicts based on the correlations between multi-source data and how to correlate the logical structure of planning rules to achieve accurate annotation and visual analysis has become a challenge for the industry. Summary of the Invention

[0004] This application provides a geographic information intelligent analysis system and method for land spatial planning, which can detect various planning conflicts based on the correlation between multi-source data and associate the logical structure of planning rules to achieve accurate annotation and visualization analysis.

[0005] Firstly, this application provides a geographic information intelligent analysis method for land spatial planning, comprising the following steps: Receive multi-source planning data and perform spatial benchmark unification and semantic standardization to obtain a planning base map with multiple planning layers of geographic information; The planning rules associated with each geographic entity in the planning base map are deconstructed, the main chain path and branch path in the planning rules are identified, and the spatial access conditions of each land use type are extracted from the planning text of multi-source planning data. Based on spatial overlay analysis, spatial conflicts of three zones and three lines between planning layers are detected. Based on the spatial access conditions, land use control conflicts between planning layers are determined. Temporal conflicts of planning arrangements in multi-source planning data are detected, and multiple conflict detection results are obtained. Each conflict detection result is associated with the corresponding triggered branch path, thereby correcting the conflict in the corresponding main chain path, and determining the comprehensive conflict weight based on the conflict detection results under the corrected main chain constraints. The conflict levels and spatial locations of each planning layer are labeled according to the comprehensive conflict weight, and the analysis results of the geographic information of the national land spatial planning are obtained.

[0006] In some embodiments, deconstructing the planning rules associated with each geographic entity in the planning base map and identifying the main chain path and branch paths in the planning rules specifically includes: Identify the geographic entities in the planning base map and extract the planning text from the multi-source planning data; Determine multiple planning rules associated with each geographic entity, and represent each planning rule and its corresponding geographic entity as a directed path graph; Extract the longest continuous path from the head entity to the tail entity from each directed path graph as the main chain path to obtain the main chain path of the corresponding directed path graph. Mark the sub-paths that originate from each node on the main chain path and whose endpoints are outside the main chain as conditional branch paths; A sub-path that starts from the same node, passes through different intermediate nodes, and then rejoins the main chain is marked as a parallel branch path. All conditional branch paths and parallel branch paths are treated as branch paths. The branch point position information of each branch path relative to the main chain, as well as the topological connection relationship between each branch path and the main chain, are recorded.

[0007] In some embodiments, extracting spatial access conditions for each land use type from the planning text of multi-source planning data specifically includes: Obtain planning text and multiple planning rules associated with each geographic entity from multi-source planning data; From all planning rules, select the constraint rules and identify the land use type and activity type in each constraint rule; Identify all feasible and prohibited activities for each land use type, thereby obtaining the spatial access conditions for each land use type.

[0008] In some embodiments, detecting spatial conflicts between three zones and three lines in different planning layers based on spatial overlay analysis specifically includes: Load the planning layers corresponding to the three zones and three lines from the aforementioned planning base map; Perform spatial overlay calculations on any two planning layers and mark any spatial conflicts; Record the name, conflict area, and occupied area of ​​each planning layer marked as having a spatial conflict; Based on the priority of the three-zone, three-line control rules, a preliminary assessment of the severity of each conflict area is made.

[0009] In some embodiments, determining land use control conflicts between planning layers based on the spatial access conditions, detecting temporal conflicts in planning arrangements in multi-source planning data, and obtaining multiple conflict detection results specifically include: Obtain spatial access conditions for each land use type and multiple planning rules associated with each geographic entity; For different planning layers at the same spatial location, extract the land use type and spatial access conditions corresponding to that spatial location: If the activities allowed by the land use types corresponding to the two planning layers in the spatial access conditions conflict, it is determined to be a conflict of land use control in the spatial location, and the conflict location, the land use type pair involved and the reason for the conflict are recorded. Extract time-constrained phrases and sentences from the planning text to analyze the temporal dependencies between planning arrangements; Each planning arrangement is treated as a node, and the temporal dependency relationship is treated as an edge. A time dependency graph is constructed. If a directed cycle exists in the time dependency graph, it is determined to be a time conflict. The sequence of planning items that form the cycle is recorded. By summarizing land use control conflicts, temporal conflicts, and spatial conflicts, multiple conflict detection results were obtained.

[0010] In some embodiments, associating each conflict detection result with the corresponding triggered branch path, and thereby correcting the conflict in the corresponding main chain path, specifically includes: Based on the branch point location information of each branch path, each conflict detection result is traced back to the branch path that triggered the corresponding conflict. For the conflict detection results of one or more conditional branch paths associated with the same main chain path, determine the overall conflict severity of the conditional branch path; The overall conflict severity of the conditional branch path is used as an external control parameter to correct the conflict severity of the corresponding main chain path; For conflicts directly arising from the main chain path itself, no branch-level corrections are made, and their original severity is preserved. Output the severity of conflicts in each main chain path after correction, as well as conflict information that remains at the branch level and did not participate in the main chain correction.

[0011] In some embodiments, determining the comprehensive conflict weight based on the conflict detection results under the modified main chain constraints specifically includes: Collect conflict detection results of all parallel branch paths associated with the same main chain path, and determine the conflict score of each parallel branch; The contribution weight of each parallel branch path is calculated using a self-attention mechanism; The conflict score for each parallel branch path is weighted and summed using the corresponding contribution weight to obtain the fusion conflict weight of the parallel branch under the corresponding main chain. The corrected conflict severity of each main chain is combined with all the merged conflict weights to obtain the comprehensive conflict weight of each main chain path.

[0012] Secondly, this application provides a geographic information intelligent analysis system for land spatial planning, comprising: The acquisition module is used to receive multi-source planning data and perform spatial benchmark unification and semantic standardization to obtain a planning base map of multiple planning layers of geographic information. The processing module is used to deconstruct the planning rules associated with each geographic entity in the planning base map, identify the main chain path and branch path in the planning rules, and extract the spatial access conditions of each land use type from the planning text of multi-source planning data. The processing module is also used to detect spatial conflicts of the three zones and three lines between planning layers based on spatial overlay analysis, determine land use control conflicts between planning layers according to the spatial access conditions, detect time conflicts of planning arrangements in multi-source planning data, and obtain multiple conflict detection results. The processing module is also used to associate each conflict detection result with the corresponding triggered branch path, thereby correcting the conflict of the corresponding main chain path, and determining the comprehensive conflict weight based on the conflict detection result under the corrected main chain constraint. The execution module is used to label the conflict level and spatial location of each planning layer according to the comprehensive conflict weight, and obtain the analysis results of the geographic information of the national spatial planning.

[0013] Thirdly, this application provides a computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described intelligent geographic information analysis method for territorial spatial planning.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described intelligent geographic information analysis method for territorial spatial planning.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides a geographic information intelligent analysis system and method for territorial spatial planning. First, it receives multi-source planning data and performs spatial benchmark unification and semantic standardization to obtain a planning base map of multiple planning layers. Then, it deconstructs the planning rules associated with each geographic entity in the planning base map, identifies the main chain path and branch paths in the planning rules, and extracts spatial access conditions for each land use type from the planning text of the multi-source planning data. Based on spatial overlay analysis, it detects spatial conflicts of the three zones and three lines between planning layers, determines land use control conflicts between planning layers according to the spatial access conditions, detects temporal conflicts in planning arrangements in the multi-source planning data, and obtains multiple conflict detection results. Each conflict detection result is associated with the corresponding triggered branch path, thereby correcting the conflict of the corresponding main chain path. Based on the conflict detection results under the corrected main chain constraints, a comprehensive conflict weight is determined. Finally, based on the comprehensive conflict weight, the conflict level and spatial location of each planning layer are labeled to obtain the analysis results of the geographic information for territorial spatial planning.

[0016] Therefore, this application's intelligent geographic information analysis method for territorial spatial planning achieves hierarchical labeling and visual output of conflicts by standardizing the benchmarks and semantics of multi-source planning data, deconstructing planning rules and distinguishing between main and branch paths, jointly detecting spatial, land use control, and temporal conflicts, tracing conflicts back to branch paths and correcting main-chain conflicts, and then obtaining comprehensive conflict weights based on self-attention fusion of parallel branch weights. This not only overcomes the limitations of traditional overlay analysis, which can only identify explicit spatial conflicts and effectively solves the problem of difficulty in identifying implicit conflicts such as functional exclusivity and temporal contradictions, but also achieves traceable conflict sources, quantifiable conflict degrees, and locatable conflict positions, significantly improving the comprehensiveness, accuracy, and intelligence level of territorial spatial planning conflict detection. The above scheme can detect multiple types of planning conflicts based on the correlation between multi-source data and associate the logical structure of planning rules to achieve accurate labeling and visual analysis. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a geographic information intelligent analysis method for territorial spatial planning, as shown in some embodiments of this application. Figure 2 This is a scene interaction diagram for detecting spatial conflicts according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of a geographic information intelligent analysis system according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for implementing a geographic information intelligent analysis method for land spatial planning, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a geographic information intelligent analysis method for land spatial planning according to some embodiments of this application. The geographic information intelligent analysis method for land spatial planning mainly includes the following steps: In step 101, multi-source planning data is received and spatial benchmark unification and semantic standardization are performed to obtain a planning base map of geographic information multi-planning layers.

[0020] In practice, the process of receiving multi-source planning data and unifying spatial benchmarks and semantics to obtain a planning base map of geographic information multi-planning layers can be achieved in the following way: Collecting land spatial master plan texts, detailed planning maps, special planning data, results of the Third National Land Survey, remote sensing images, digital elevation models, and control tables of planning indicators at all levels from departments such as natural resources, transportation, water resources, and ecological environment, forming multi-source planning data; uniformly converting the above data into the 2000 National Geodetic Coordinate System and the 1985 National Elevation Datum, and uniformly storing vector data (such as Shapefile, GeoJSON) and raster data (such as GeoTIFF) of different formats as spatial data. The system uses a library format and performs semantic alignment on the attribute field names and land use classification codes of each layer to ultimately generate a spatial database containing multiple planning layers as the planning base map. The multi-source planning data is a collection of original planning data from different departments and in different formats, including planning text, vector layers, raster images, and indicator tables, used to provide the original data source for the planning base map construction. The planning base map is a multi-layered spatial database formed after spatial benchmark unification and semantic standardization, containing various planning layers such as the three zones and three lines, current land use, and transportation and water conservancy, used as a unified spatial data base for subsequent rule deconstruction and conflict detection. Other embodiments may also use other methods, which are not limited here. In step 102, the planning rules associated with each geographic entity in the planning base map are deconstructed, the main chain path and branch path in the planning rules are identified, and the spatial access conditions of each land use type are extracted from the planning text of the multi-source planning data.

[0021] In some embodiments, deconstructing the planning rules associated with each geographic entity in the planning base map and identifying the main chain path and branch path in the planning rules can be achieved by the following steps: Identify the geographic entities in the planning base map and extract the planning text from the multi-source planning data; Determine multiple planning rules associated with each geographic entity, and represent each planning rule and its corresponding geographic entity as a directed path graph; Extract the longest continuous path from the head entity to the tail entity from each directed path graph as the main chain path to obtain the main chain path of the corresponding directed path graph. Mark the sub-paths that originate from each node on the main chain path and whose endpoints are outside the main chain as conditional branch paths; A sub-path that starts from the same node, passes through different intermediate nodes, and then rejoins the main chain is marked as a parallel branch path. All conditional branch paths and parallel branch paths are treated as branch paths. The branch point position information of each branch path relative to the main chain, as well as the topological connection relationship between each branch path and the main chain, are recorded.

[0022] In specific implementation, identifying each geographic entity in the planning base map and extracting the planning text from the multi-source planning data can be achieved in the following way: Read the planning base map file from computer storage, traverse each geographic entity in the base map, and obtain the unique identifier and spatial extent of the entity; simultaneously load all planning text files from the multi-source planning data folder, including the overall plan, detailed plan, and special plan, to obtain the planning text; determine the multiple planning rules associated with each geographic entity, and represent each planning rule and its corresponding geographic entity as a directed path graph, which can be achieved in the following way: use the name or plot number of each geographic entity as a keyword, perform a full-text search in the planning text, and extract the paragraphs containing the keyword as the association rule for that geographic entity; for each For each geographic entity, each associated planning rule is parsed into a series of spatial relationship predicates, such as "contains," "intersects," and "proximity." Each geographic entity or planning area appearing in the rule is treated as a node in the graph, and the spatial relationship between any two nodes is treated as a directed edge, the direction of which is determined by the order of the subject and object in the rule. All nodes and edges are combined to form a directed path graph for the geographic entity. Here, a geographic entity is an independent spatial object in the planning base map with a clear spatial location and boundaries, and with attached attribute information such as land use classification. It includes cultivated land plots, construction land plots, ecological protection patches, road segments, river areas, etc., and is used as the basic unit for planning rule association and conflict detection. Other embodiments may also use other methods, which are not limited here.

[0023] It should be noted that planning rules are usually scattered in a large amount of text in the form of natural language, lacking explicit association with geographic entities; establishing associations through spatial location and name matching provides structured input for subsequent rule deconstruction; secondly, traditional methods directly match keywords in the text, losing the directionality and logical order of spatial relationships; while through directed graph modeling, the main constraint semantics of the rules are preserved, laying the foundation for the identification of the main chain and branches.

[0024] In specific implementation, the longest continuous path from the head entity to the tail entity is extracted from each directed path graph as the main chain path. The main chain path of the corresponding directed path graph can be obtained as follows: For each directed path graph, starting from the starting node (head entity), traverse along the directed edges until the ending node (tail entity) is reached; record each possible traversal path and calculate the number of nodes in the path; select the path with the most nodes as the main chain path; if multiple longest paths of equal length exist, select the one with the highest frequency of spatial relation predicates as the main chain path. In planning rules, there is often a core constraint chain that runs throughout, for example: from plot A through road B to area C; the extracted longest path can preserve the complete logical chain, avoiding truncation of core semantics due to branches; marking sub-paths originating from each node on the main chain path and ending outside the main chain as conditional branch paths can be done as follows: traverse each node on the main chain path and check if the node points to the main chain. For each node outside the main chain, a directed edge is formed. If it exists, the path extends outward from that node along the directed edge until it can no longer be extended. All nodes and edges passed during the extension are marked as conditional branch paths. The starting node of the conditional branch path is recorded as the index of its position on the main chain. The condition is explicitly extracted as a conditional branch, so that the conditional semantics participate in subsequent reasoning as a whole, rather than being broken down into isolated judgments. Marking a sub-path on the main chain path that starts from the same node, passes through different intermediate nodes, and then merges back into the main chain as a parallel branch path can be implemented as follows: traverse each node on the main chain path and check whether there are two or more directed edges starting from the same node, and these directed edges merge back into the same subsequent node on the main chain after passing through a series of nodes. If they exist, each sub-path from the starting point to the merging point is marked as a parallel branch path. The starting and ending points of each parallel branch path are recorded as the indexes of their positions on the main chain. Other implementations can also be used, which are not limited here.

[0025] In specific implementation, all conditional branch paths and parallel branch paths are treated as branch paths. The branch point position information of each branch path relative to the main chain, as well as the topological connection relationship between each branch path and the main chain, can be recorded in the following way: all marked conditional branch paths and parallel branch paths are uniformly stored in a branch path list; for each branch path, its type is recorded, namely conditional branch or parallel branch, the node number of the starting point on the main chain, and the node number of the ending point on the main chain. For conditional branches, the ending point is recorded as empty; at the same time, the original information of all nodes and edges in the branch path is recorded; the main chain path, the branch path list, and the topological connection relationship of each branch are combined; other implementation methods can also be used in other embodiments, which are not limited here.

[0026] It should be noted that the topological connection relationship in this application is metadata used to describe the connection method between the branch path and the main chain, which facilitates the correct attribution of conflict results to the corresponding branch in the future; the parallel branch path is a sub-path used to provide multi-path collaborative support; the conditional branch path is a sub-path used to provide external constraint conditions, which facilitates the separation of preconditions and main constraints; the main chain path is the longest continuous path used to carry the semantics of the main constraint of the rule, which facilitates the extraction of the most core spatial constraint logic in the rule; and the planning text is textual material used to record the requirements of land and space planning and control, which facilitates the extraction of rule information related to geographic entities from unstructured text.

[0027] In some embodiments, extracting spatial access criteria for each land use type from the planning text of multi-source planning data can be achieved using the following steps: Obtain planning text and multiple planning rules associated with each geographic entity from multi-source planning data; From all planning rules, select the constraint rules and identify the land use type and activity type in each constraint rule; Identify all feasible and prohibited activities for each land use type, thereby obtaining the spatial access conditions for each land use type.

[0028] In practice, the process of filtering constraint rules from all planning rules and identifying the land use type and activity type within each constraint rule can be achieved as follows: Iterate through each planning rule, using regular expressions to match whether it contains positive modal words such as "permitted," "may," or "able to," or negative modal words such as "prohibited," "must not," or "strictly prohibited." If a match is successful, mark the rule as a constraint rule. Further extract the land use type name (e.g., "arable land," "construction land," "ecological protection zone") and the activity type name (e.g., "building," "mining," "tree planting") from the rule text. Record the extracted results as triplets of (land use type, modal word, activity type). Constraint rules are planning clauses used to explicitly allow or prohibit specific activities. Determine all feasible and prohibited activities corresponding to each land use type, thereby obtaining the... Spatial access conditions for land use types can be implemented as follows: For each land use type, collect and record all triples related to that land use type; classify all activities corresponding to positive modal words into the feasible activity set, and all activities corresponding to negative modal words into the prohibited activity set; if a rule contains "conditionally permitted", then record the activity and its conditions separately as the conditional activity set; merge and store these three sets into a compatibility table, where each row corresponds to a land use type, each column corresponds to a type of activity, and the cell value is "permitted", "prohibited", or "conditionally permitted", which serves as the spatial access condition; wherein, the spatial access condition is structured data used to describe the set of activities that each land use type can engage in, facilitating quick querying during subsequent functional conflict determination; other embodiments may also use other methods to implement this, which are not limited here.

[0029] In step 103, spatial conflicts of the three zones and three lines between planning layers are detected based on spatial overlay analysis. Land use control conflicts between planning layers are determined according to the spatial access conditions. Temporal conflicts of planning arrangements in multi-source planning data are detected, and multiple conflict detection results are obtained.

[0030] In some embodiments, detecting spatial conflicts between three zones and three lines in different planning layers based on spatial overlay analysis can be achieved using the following steps: Load the planning layers corresponding to the three zones and three lines from the aforementioned planning base map; Perform spatial overlay calculations on any two planning layers and mark any spatial conflicts; Record the name, conflict area, and occupied area of ​​each planning layer marked as having a spatial conflict; Based on the priority of the three-zone, three-line control rules, a preliminary assessment of the severity of each conflict area is made.

[0031] In specific implementation, loading the planning layers corresponding to the three zones and three lines from the planning base map can be achieved in the following way: Read the layer files corresponding to the three zones from the planning base map data, including the ecological space layer, agricultural space layer, and urban space layer; simultaneously read the layer files corresponding to the three lines, including the ecological protection red line layer, the permanent basic farmland protection red line layer, and the urban development boundary layer; load the six layers corresponding to these three zones and three lines into memory, maintaining their original spatial reference coordinate system; perform spatial overlay calculations on any two planning layers and mark spatial conflicts, which can be achieved in the following way: For any two loaded planning layers, take each polygon in the first layer and perform spatial intersection calculations with each polygon in the second layer; calculate the area of ​​the intersection area; if the area of ​​the intersection area is greater than a preset threshold (e.g., 1 square meter)... If a spatial conflict is identified, it is marked on the intersecting area. The conflict marker includes the names of the two layers involved in the conflict and the geometric boundary of the intersecting area. The name, conflict area, and occupied area of ​​each planning layer marked as a spatial conflict can be recorded in the following way: For each marked spatial conflict, record the name of the first planning layer, the name of the second planning layer, the polygon coordinate string of the conflict area, and the area value of the polygon. Store this information as a conflict record. Each record contains four fields, such as: layer A name, layer B name, conflict area, and occupied area. The conflict area is a geometric object used to record the specific range of the spatial conflict. The spatial conflict is a marker used to identify that two planning layers overlap in the same spatial location, which is convenient for subsequent classification of conflict types. Other embodiments can also be implemented in other ways, which are not limited here.

[0032] In specific implementation, the initial severity assessment of each conflict area can be achieved according to the priority rules of the three zones and three lines. This can be done in the following way: Based on the priority rules of the three zones and three lines in the national land spatial planning, the ecological protection red line is set as the highest priority and assigned a value of 3; permanent basic farmland is set as the second highest priority and assigned a value of 2; urban development boundaries are set as the third priority and assigned a value of 1; the three zones are unified as the basic level and assigned a value of 0. For each spatial conflict, the level value of the layer with the higher priority among the two layers involved in the conflict is taken as the initial severity assessment value of the conflict. The initial assessment value is then appended to the conflict record. Other implementation methods can also be used in other embodiments, which are not limited here.

[0033] It should be noted that the reference Figure 2 As shown, this figure is a scene interaction diagram for detecting spatial conflicts in some embodiments of this application. The left side of the figure is the planning layer of three zones and three lines, the middle is the calculation example of spatial overlap, and the right side is the conflict area marked after spatial conflict detection.

[0034] In some embodiments, determining land use control conflicts between planning layers based on the spatial access conditions and detecting time conflicts in planning arrangements in multi-source planning data to obtain multiple conflict detection results can be achieved through the following steps: Obtain spatial access conditions for each land use type and multiple planning rules associated with each geographic entity; For different planning layers at the same spatial location, extract the land use type and spatial access conditions corresponding to that spatial location: If the activities allowed by the land use types corresponding to the two planning layers in the spatial access conditions conflict, it is determined to be a conflict of land use control in the spatial location, and the conflict location, the land use type pair involved and the reason for the conflict are recorded. Extract time-constrained phrases and sentences from the planning text to analyze the temporal dependencies between planning arrangements; Each planning arrangement is treated as a node, and the temporal dependency relationship is treated as an edge. A time dependency graph is constructed. If a directed cycle exists in the time dependency graph, it is determined to be a time conflict. The sequence of planning items that form the cycle is recorded. By summarizing land use control conflicts, temporal conflicts, and spatial conflicts, multiple conflict detection results were obtained.

[0035] In specific implementation, for different planning layers at the same spatial location, extracting the land use type and corresponding spatial access conditions for each spatial location can be achieved as follows: Traverse each spatial location on the planning base map and find all planning layers covering that location; for each layer, obtain the land use type corresponding to that location, and then query the set of prohibited activities for that land use type in the compatibility table; take any two layers and check whether the planning use of the first layer belongs to the prohibited activities of the land use type corresponding to the second layer; if so, record a land use control conflict at that location, with the conflict reason recorded as "functional mutual exclusion". Record the names of the two planning layers and their corresponding land use types; among them, land use control conflict is used to identify abnormal situations where two planning layers propose mutually exclusive use requirements for the same plot; extract time-constrained phrases from the planning text, and parse the temporal dependencies between planning arrangements in the following way: extract all sentences containing time relation words such as "first," "later," "before," "after," "no earlier than," "no later than," "following," and "next" from the planning text; for each sentence, parse out the planning item A represented by the subject and the planning item B represented by the object, as well as the sequential direction indicated by the time relation words; if the relation word list If A precedes B, a directed dependency from A to B is recorded; if A follows B, a directed dependency from B to A is recorded. Temporal dependencies describe the order of two planning events, facilitating subsequent detection of scheduling conflicts. Each planning event is treated as a node, and temporal dependencies as edges, constructing a temporal dependency graph. If a directed cycle exists in the time dependency graph, a time conflict is identified, and the sequence of planning events forming a cycle is recorded. This can be achieved by treating all parsed planning events as nodes in the graph and all temporal dependencies as directed edges, constructing a time dependency graph. Starting from each node, perform a depth-first traversal to check if it is possible to return to a previously visited node along a directed edge. If such a cycle is found, mark all nodes and edges on the cycle as a time conflict and record the sequence of planning events that form the cycle. Next, create an empty list of conflict results. Add all detected spatial conflict records to the list. Add all detected land use control conflict records to the list. Add all detected time conflict records to the list. Finally, output this list containing the three types of conflicts as multiple conflict detection results. Other implementations can also use other methods, which are not limited here.

[0036] It should be noted that existing planning conflict detection technologies mainly rely on layer overlay analysis, which can only detect direct spatial encroachment but cannot identify two types of implicit conflicts: first, functional logical contradictions, where the boundaries of two layers do not overlap but their uses are mutually exclusive; second, temporal contradictions, such as "build roads first, then factories" and "build factories first, then roads" appearing simultaneously in different planning documents, forming a circular dependency. Existing methods either ignore these implicit conflicts or rely on manual verification, which is inefficient and prone to omissions. This step addresses these two pain points separately: First, by using pre-constructed land use types and activity compatibility, the planned uses are mapped to a set of permitted / prohibited activities, and mutual exclusion of activities is judged for different layers at the same spatial location, thereby discovering functional mutual exclusion conflicts; second, by extracting time constraint phrases from the planning documents and constructing a directed graph, a cycle detection algorithm is used to automatically discover temporal circular dependencies. The two detections are executed in parallel and then summarized, realizing the joint detection of spatial, functional, and temporal conflicts; thus making implicit logical contradictions explicit and calculable.

[0037] In step 104, each conflict detection result is associated with the corresponding triggered branch path, thereby correcting the conflict of the corresponding main chain path, and determining the comprehensive conflict weight based on the conflict detection results under the corrected main chain constraints.

[0038] In some embodiments, associating each conflict detection result with the corresponding triggered branch path, and thereby correcting the conflict in the corresponding main chain path, can be achieved through the following steps: Based on the branch point location information of each branch path, each conflict detection result is traced back to the branch path that triggered the corresponding conflict. For the conflict detection results of one or more conditional branch paths associated with the same main chain path, determine the overall conflict severity of the conditional branch path; The overall conflict severity of the conditional branch path is used as an external control parameter to correct the conflict severity of the corresponding main chain path; For conflicts directly arising from the main chain path itself, no branch-level corrections are made, and their original severity is preserved. Output the severity of conflicts in each main chain path after correction, as well as conflict information that remains at the branch level and did not participate in the main chain correction.

[0039] In specific implementation, the following method can be used to trace back each conflict detection result to the branch path that triggered the corresponding conflict based on the branch point location information of each branch path: Read the list of branch paths and the branch point location information of each branch path; read multiple conflict detection result lists, each conflict result containing the geometric coordinates of the occurrence location and the conflict type; for each conflict detection result, obtain the coordinates of its geometric center point, and then traverse all branch paths, performing the following attribution judgments: if the branch path is a conditional branch, check whether the coordinates of the conflict location are within the spatial range covered by the branch path, i.e., the polygon union of all geographic entities corresponding to all nodes in the branch path; if they are within... If the conflict falls within the specified range, the conflict result is added to the associated conflict list of the branch path. If the branch path is a parallel branch, spatial inclusion judgment is also performed. When the conflict location falls within the range of multiple branch paths, it is preferentially assigned to the path with the closest branch point location, that is, the path with the minimum spatial distance from the conflict location to the branch point node. The preset threshold uses Euclidean distance for distance comparison. If the shortest distance is less than 10 meters, it is assigned to that branch. If the distance of all branches is greater than 10 meters, the conflict result is assigned to the main chain path. The 10-meter threshold is derived from the estimation of the side length corresponding to the minimum area of ​​the planned patch and can be configured in practice. Other methods can also be used in other embodiments, which are not limited here.

[0040] It should be noted that tracing the conflict results back to the design concept of the triggering branch and establishing the attribution relationship using the branch point location information allows us to distinguish whether the conflict is caused by the failure to meet the premise of the conditional branch or by the multi-path contradiction of the parallel branch, providing a basis for differentiated correction.

[0041] In practice, for the conflict detection results of one or more conditional branch paths associated with the same main chain path, the overall conflict severity of the conditional branch path can be determined in the following way: for each conditional branch path, obtain a list of all conflict results associated with that branch; extract the initial severity value from each conflict result; sum these severity values ​​to obtain the overall conflict severity of the conditional branch path. If there are no conflicts associated with the conditional branch path, the overall conflict severity is set to zero: time conflicts often lead to project stoppages or delays, and their severity is higher than that of functional conflicts, so they are given a higher weight; this empirical value can be adjusted according to the planning and management methods of different regions, but a fixed value is used in this step to simplify implementation; the summation is used instead of averaging because multiple conflicts in the branch have a cumulative effect; among them, for spatial conflicts, the set priority values ​​are used: ecological protection red line = 3, permanent basic farmland = 2, urban development boundary = 1, three zones = 0; for functional conflicts, the default severity is uniformly set to 1; for time conflicts, the default severity is uniformly set to 2; preset explanation: the default severity values ​​of functional conflict = 1 and time conflict = 2 are derived from empirical judgment in the planning field; other implementation methods can also be used in other embodiments, which are not limited here.

[0042] In specific implementation, the overall conflict severity of the conditional branch path can be used as an external control parameter to adjust the conflict severity of the corresponding main chain path. This can be achieved in the following way: In practice, for conflicts directly generated by the main chain path itself, no branch-level correction is made, and the original severity is retained. This can be achieved in the following way: Find the main chain path to which the conditional branch path belongs; read the current conflict severity of the main chain path; use the overall conflict severity as an external control parameter, and correct the main chain conflict severity according to the following rules: if the overall conflict severity is greater than zero and less than or equal to 3, increase the main chain conflict severity by 1; if the overall conflict severity is greater than 3 and less than or equal to 6, increase the main chain conflict severity by 2; if the overall conflict severity is greater than 6, increase the main chain conflict severity by 3; if the overall conflict severity is equal to zero, the main chain conflict severity remains unchanged. The revised upper limit of conflict severity is 10; if it exceeds 10, it is truncated to 10. The preset threshold is derived from the statistical analysis of 1000 planning conflict cases: the original conflict severity of each main chain is distributed between 0 and 5, and the overall conflict severity of conditional branches is distributed between 0 and 10. Using 10 as the upper limit can cover most cases. The division of the revision gradient is obtained by fitting the natural logarithm curve and rounding it to ensure that the revision is mild when there is low conflict and significant when there is high conflict. Other implementation methods can also be used in other embodiments, which are not limited here.

[0043] In specific implementation, the output of the conflict severity of each main chain path after correction, as well as the conflict information that remains at the branch level and did not participate in the main chain correction, can be achieved in the following way: For each main chain path, check whether there are any branch paths associated with that main chain, i.e., conditional branches or parallel branches; if there are no branch paths, or the existing branch paths have not produced any conflict results, i.e., the associated conflict list of all branches is empty, then it is determined that all conflicts on that main chain path are directly generated by the main chain itself; for such conflicts, no correction operation is performed, but the initial judgment value of the conflict severity corresponding to that main chain path is directly extracted from the conflict results (if there are multiple conflicts, the maximum value is taken), as the final conflict severity of that main chain path; this value is marked as the original uncorrected; where the maximum value is taken instead of the average value, because any severe conflict on the main chain is enough to affect the entire planning chain; other implementation methods can also be used in other embodiments, which are not limited here.

[0044] It should be noted that the original severity in this application is a baseline value used to retain the main chain's own conflict information. It is used to maintain the original result of conflict determination in the absence of branch constraints, making it easier to distinguish which conflicts are directly generated by the main chain and which are introduced by branches. The overall conflict severity is a comprehensive index used to quantify the strength of the constraint of the conditional branch on the main chain. It is used to reflect the cumulative effect of the degree of non-compliance of all premises in the conditional path, making it easy to use as an external control parameter to adjust the final level of the main chain conflict. Among them, the conflict detection results are only scattered problem points, which lack a connection with the logical structure of the planning rules. This means that we only know the location of the conflict, but not the cause and effect of the conflict, and we cannot distinguish the degree of influence of different branches on the main chain. This step uses the pre-deconstructed rule topology to back-attribute the detected conflicts according to the branch point location: the conflict of the conditional branch is used as an overall parameter to correct the main chain severity, reflecting the rule logic that the main chain conclusion is weakened when the premise is not met; the conflict of the parallel branch is not corrected for the main chain and is retained for the next step of fusion. The beneficial effects are: elevating conflicts from spatial points to the rule chain level for attribution, enabling the cumulative constraint effect of conditional branches to be quantified and transmitted, while avoiding the main chain's own conflicts from being mistakenly corrected by branches, thus laying a structured foundation for subsequent differentiated integration.

[0045] In some embodiments, determining the comprehensive conflict weight based on the conflict detection results under the modified main chain constraints can be achieved through the following steps: Collect conflict detection results of all parallel branch paths associated with the same main chain path, and determine the conflict score of each parallel branch; The contribution weight of each parallel branch path is calculated using a self-attention mechanism; The conflict score for each parallel branch path is weighted and summed using the corresponding contribution weight to obtain the fusion conflict weight of the parallel branch under the corresponding main chain. The corrected conflict severity of each main chain is combined with all the merged conflict weights to obtain the comprehensive conflict weight of each main chain path.

[0046] In practice, the conflict detection results of all parallel branch paths associated with the same main chain path are collected, and the conflict score of each parallel branch is determined in the following way: In practical implementation, the contribution weight of each parallel branch path can be calculated using a self-attention mechanism as follows: For each parallel branch path, obtain a list of all conflict results associated with that branch; extract a severity value from each conflict result: for spatial conflicts, use the set priority values: ecological protection red line = 3, permanent basic farmland = 2, urban development boundary = 1, three zones = 0; for functional conflicts, set a severity value of 1; for temporal conflicts, set a severity value of 2; then, take the maximum value from all conflict severity values ​​of that branch path as the conflict score of that parallel branch path; if there are no conflicts associated with that parallel branch path, the conflict score is set to zero; for the conflict score of each parallel branch path, use the corresponding contribution weight. The weighted summation of the fusion conflict weights for parallel branches under the corresponding main chain can be achieved as follows: For any two parallel branch paths, extract three feature values: The first feature is the consistency of conflict type; if the main conflict types of the two branches are the same, the value is 1, otherwise it is 0. The second feature is the overlap of involved layers; calculate the intersection size of the planning layer sets involved by the two branches divided by the union size to obtain a ratio between 0 and 1. The third feature is the spatial overlap; calculate the intersection area of ​​the conflict occurrence areas of the two branches divided by the union area; if both branches have multiple conflict areas, take the pair with the largest overlap among all areas. Sum these three feature values ​​to obtain a total between 0 and 3, which is used as the semantic similarity between the two branches. The higher the similarity, the more semantically related the two branches are. Preset thresholds: Among the three features, the consistency of conflict type uses discrete 0 / 1 values, derived from logical judgment; layer overlap and spatial overlap are continuous values, using the standard formula of intersection divided by union, without the need for additional thresholds. This step does not set a high or low threshold for similarity because the subsequent self-attention mechanism will use the original values ​​for calculation; other embodiments may also use other methods to achieve this, which are not limited here.

[0047] In specific implementation, the corrected conflict severity of each main chain is combined with all the merged conflict weights to obtain the comprehensive conflict weight of each main chain path. This can be achieved in the following way: For each main chain path, the corrected conflict severity and the merged conflict weight of the parallel branches under that main chain are obtained; these two values ​​are combined by weighted summation: the corrected conflict severity is multiplied by a coefficient of 0.6, the merged conflict weight is multiplied by a coefficient of 0.4, and then the two products are added together to obtain the comprehensive conflict weight; the larger the weight value, the more severe the planning conflict faced by the main chain path; other implementation methods can also be used in other embodiments, which are not limited here.

[0048] It should be noted that the comprehensive conflict weight in this application is the final value used to evaluate the overall conflict level of each main chain path. It is used to merge the main chain conflict after conditional branch correction and the conflict after parallel branch fusion into a scalar, which facilitates subsequent classification of conflict levels according to thresholds and output of visualization results. The fusion conflict weight is a comprehensive indicator used to integrate the conflict levels of multiple parallel branches. It is used to aggregate the conflicts of all parallel branches under the same main chain into a single value through weighted summation, which facilitates unified merging with the conflict severity after main chain correction. Semantic similarity is a value used to measure the degree of correlation between two parallel branch paths, which is used to characterize the cooperative or conflict relationship between different constraint dimensions.

[0049] In step 105, the conflict level and spatial location of each planning layer are labeled according to the comprehensive conflict weight, and the analysis results of the geographic information of the national land spatial planning are obtained.

[0050] In some embodiments, the analysis results of geographic information for territorial spatial planning, obtained by labeling the conflict level and spatial location of each planning layer according to the comprehensive conflict weight, can be achieved through the following steps: Three conflict level thresholds are preset. The comprehensive conflict weight of each main chain path is compared with the threshold to determine its conflict level. For main chain paths that are determined to be of a high conflict level, the geometric boundaries of all conflict areas associated with them are highlighted and marked on the planning base map. For main chain paths with medium conflict levels, they are highlighted in yellow and accompanied by the main branch types resulting from the conflict; For main chain paths with low conflict levels, no special marking is made; compliance status is only recorded in their attribute table. The output includes a statistical table of conflict level classifications, a thematic map of conflict distribution, and a detailed attribute list for each conflict parcel.

[0051] In practical implementation, for main chain paths determined to have a high conflict level, highlighting the geometric boundaries of all associated conflict areas on the planning base map can be achieved as follows: Based on the actual needs of land spatial planning review, two thresholds are pre-set: a low threshold of 0.3 and a high threshold of 0.7. For the comprehensive conflict weight of each main chain path, if the weight is greater than or equal to 0.7, it is determined to be a high conflict level; if the weight is between 0.3 and 0.7, it is determined to be a medium conflict level; if the weight is less than 0.3, it is determined to be a low conflict level. For main chain paths with a medium conflict level, highlighting them in yellow and including the main branch types that caused the conflict can be achieved as follows: Traverse all main chain paths determined to have a high conflict level, and find the associated information of that path... The polygonal geometric boundaries of all conflict areas covered by the map are rendered on the display canvas of the planning base map, using red as the fill color and setting a semi-transparent fill style. At the same time, a red warning icon is added to the center of the polygon to enhance the visualization effect. For main chain paths with low conflict levels, no special marking is made, and compliance status is only recorded in their attribute table. This can be achieved by: traversing all main chain paths judged to have medium conflict levels and rendering the polygonal boundaries of their conflict areas using yellow as the fill color; at the same time, extracting the branch type (conditional branch or parallel branch) with the highest contribution weight from the association information of the main chain path and attaching the branch type as a text label next to the rendering area; other embodiments can also be implemented in other ways, which are not limited here.

[0052] Furthermore, in another aspect of this application, in some embodiments, this application provides a geographic information intelligent analysis system for land spatial planning, with reference to... Figure 3 The figure is a schematic diagram of the structure of a geographic information intelligent analysis system according to some embodiments of this application. The geographic information intelligent analysis system includes: an acquisition module 301, a processing module 302, and an execution module 303, which are described below: The acquisition module 301 in this application is mainly used to receive multi-source planning data and perform spatial benchmark unification and semantic standardization to obtain a planning base map of geographic information multi-planning layers. Processing module 302, in this application, is used to deconstruct the planning rules associated with each geographic entity in the planning base map, identify the main chain path and branch path in the planning rules, and extract the spatial access conditions of each land use type from the planning text of multi-source planning data. It should be noted that the processing module 302 in this application is also used to detect spatial conflicts of the three zones and three lines between planning layers based on spatial overlay analysis, determine land use control conflicts between planning layers according to the spatial access conditions, detect time conflicts of planning arrangements in multi-source planning data, and obtain multiple conflict detection results. In addition, it should be noted that the processing module 302 in this application is also used to associate each conflict detection result with the corresponding triggered branch path, thereby correcting the conflict of the corresponding main chain path, and determining the comprehensive conflict weight based on the conflict detection result under the corrected main chain constraint. The execution module 303 in this application is mainly used to mark the conflict level and spatial location of each planning layer according to the comprehensive conflict weight, so as to obtain the analysis results of the geographic information of the land spatial planning.

[0053] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described intelligent geographic information analysis method for territorial spatial planning.

[0054] In some embodiments, reference Figure 4 The figure is a schematic diagram of the structure of a computer device for implementing a geographic information intelligent analysis method for land spatial planning, according to some embodiments of this application. The geographic information intelligent analysis method for land spatial planning in the above embodiments can... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 401, a communication bus 402, a memory 403, and at least one communication interface 404.

[0055] Processor 401 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0056] The communication bus 402 can be used to transmit information between the aforementioned components.

[0057] The memory 403 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 403 may exist independently and be connected to the processor 401 via the communication bus 402. The memory 403 may also be integrated with the processor 401.

[0058] The memory 403 stores program code for executing the scheme of this application, and its execution is controlled by the processor 401. The processor 401 executes the program code stored in the memory 403. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 401 and one or more software modules in the program code in the memory 403.

[0059] Communication interface 404 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0060] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0061] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0062] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described intelligent geographic information analysis method for land spatial planning.

[0063] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0064] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A geographic information intelligent analysis method for land spatial planning, characterized in that, The method includes the following steps: Receive multi-source planning data and perform spatial benchmark unification and semantic standardization to obtain a planning base map with multiple planning layers of geographic information; The planning rules associated with each geographic entity in the planning base map are deconstructed, the main chain path and branch path in the planning rules are identified, and the spatial access conditions of each land use type are extracted from the planning text of multi-source planning data. Based on spatial overlay analysis, spatial conflicts of three zones and three lines between planning layers are detected. Based on the spatial access conditions, land use control conflicts between planning layers are determined. Temporal conflicts of planning arrangements in multi-source planning data are detected, and multiple conflict detection results are obtained. Each conflict detection result is associated with the corresponding triggered branch path, thereby correcting the conflict in the corresponding main chain path, and determining the comprehensive conflict weight based on the conflict detection results under the corrected main chain constraints. The conflict levels and spatial locations of each planning layer are labeled according to the comprehensive conflict weight, and the analysis results of the geographic information of the national land spatial planning are obtained.

2. The method as described in claim 1, characterized in that, The planning rules relating to each geographic entity in the planning base map are deconstructed, and the main chain path and branch path in the planning rules are identified, specifically including: Identify the geographic entities in the planning base map and extract the planning text from the multi-source planning data; Determine multiple planning rules associated with each geographic entity, and represent each planning rule and its corresponding geographic entity as a directed path graph; Extract the longest continuous path from the head entity to the tail entity from each directed path graph as the main chain path to obtain the main chain path of the corresponding directed path graph. Mark the sub-paths that originate from each node on the main chain path and whose endpoints are outside the main chain as conditional branch paths; A sub-path that starts from the same node, passes through different intermediate nodes, and then rejoins the main chain is marked as a parallel branch path. All conditional branch paths and parallel branch paths are treated as branch paths. The branch point position information of each branch path relative to the main chain, as well as the topological connection relationship between each branch path and the main chain, are recorded.

3. The method as described in claim 1, characterized in that, The spatial access conditions for each land use type extracted from the planning text of multi-source planning data specifically include: Obtain planning text and multiple planning rules associated with each geographic entity from multi-source planning data; From all planning rules, select the constraint rules and identify the land use type and activity type in each constraint rule; Identify all feasible and prohibited activities for each land use type, thereby obtaining the spatial access conditions for each land use type.

4. The method as described in claim 1, characterized in that, Spatial conflicts between planning layers based on spatial overlay analysis specifically include: Load the planning layers corresponding to the three zones and three lines from the aforementioned planning base map; Perform spatial overlay calculations on any two planning layers and mark any spatial conflicts; Record the name, conflict area, and occupied area of ​​each planning layer marked as having a spatial conflict; Based on the priority of the three-zone, three-line control rules, a preliminary assessment of the severity of each conflict area is made.

5. The method as described in claim 1, characterized in that, Based on the aforementioned spatial access conditions, land use control conflicts between planning layers are determined, and temporal conflicts in planning arrangements are detected in multi-source planning data. Multiple conflict detection results are obtained, including: Obtain spatial access conditions for each land use type and multiple planning rules associated with each geographic entity; For different planning layers at the same spatial location, extract the land use type and spatial access conditions corresponding to that spatial location: If the activities allowed by the land use types corresponding to the two planning layers in the spatial access conditions conflict, it is determined to be a conflict of land use control in the spatial location, and the conflict location, the land use type pair involved and the reason for the conflict are recorded. Extract time-constrained phrases and sentences from the planning text to analyze the temporal dependencies between planning arrangements; Each planning arrangement is treated as a node, and the temporal dependency relationship is treated as an edge. A time dependency graph is constructed. If a directed cycle exists in the time dependency graph, it is determined to be a time conflict. The sequence of planning items that form the cycle is recorded. By summarizing land use control conflicts, temporal conflicts, and spatial conflicts, multiple conflict detection results were obtained.

6. The method as described in claim 1, characterized in that, Associating each conflict detection result with the corresponding triggered branch path, and then correcting the conflict in the corresponding main chain path, specifically includes: Based on the branch point location information of each branch path, each conflict detection result is traced back to the branch path that triggered the corresponding conflict. For the conflict detection results of one or more conditional branch paths associated with the same main chain path, determine the overall conflict severity of the conditional branch path; The overall conflict severity of the conditional branch path is used as an external control parameter to correct the conflict severity of the corresponding main chain path; For conflicts directly arising from the main chain path itself, no branch-level corrections are made, and their original severity is preserved. Output the severity of conflicts in each main chain path after correction, as well as conflict information that remains at the branch level and did not participate in the main chain correction.

7. The method as described in claim 1, characterized in that, The determination of the comprehensive conflict weight based on the conflict detection results under the modified main chain constraints specifically includes: Collect conflict detection results of all parallel branch paths associated with the same main chain path, and determine the conflict score of each parallel branch; The contribution weight of each parallel branch path is calculated using a self-attention mechanism; The conflict score for each parallel branch path is weighted and summed using the corresponding contribution weight to obtain the fusion conflict weight of the parallel branch under the corresponding main chain. The corrected conflict severity of each main chain is combined with all the merged conflict weights to obtain the comprehensive conflict weight of each main chain path.

8. A geographic information intelligent analysis system for land spatial planning, characterized in that, include: The acquisition module is used to receive multi-source planning data and perform spatial benchmark unification and semantic standardization to obtain a planning base map of multiple planning layers of geographic information. The processing module is used to deconstruct the planning rules associated with each geographic entity in the planning base map, identify the main chain path and branch path in the planning rules, and extract the spatial access conditions of each land use type from the planning text of multi-source planning data. The processing module is also used to detect spatial conflicts of the three zones and three lines between planning layers based on spatial overlay analysis, determine land use control conflicts between planning layers according to the spatial access conditions, detect time conflicts of planning arrangements in multi-source planning data, and obtain multiple conflict detection results. The processing module is also used to associate each conflict detection result with the corresponding triggered branch path, thereby correcting the conflict of the corresponding main chain path, and determining the comprehensive conflict weight based on the conflict detection result under the corrected main chain constraint. The execution module is used to label the conflict level and spatial location of each planning layer according to the comprehensive conflict weight, and obtain the analysis results of the geographic information of the national spatial planning.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the intelligent geographic information analysis method for territorial spatial planning as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent geographic information analysis method for territorial spatial planning as described in any one of claims 1 to 7.

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