Conduction model construction method and system based on territorial space planning element analysis

Abstract

The application discloses a method and system for constructing a conduction model based on element analysis of territorial space planning, relates to the technical field of model construction, and comprises the following steps: delimiting a target region to perform multi-level territorial space planning; performing element identification and classification based on a standard planning element dataset; setting a conduction rule library, associating the conduction rule library with a plurality of planning element conduction attribute parameters, and constructing a network of conduction relationships of territorial space planning elements; performing conduction simulation and conflict detection through iteration, and identifying abnormal conduction information; performing conflict optimization, generating planning conduction optimization suggestions, and performing correction guidance to construct a conduction model. The technical problems of insufficient standardization of the planning element conduction process, difficulty in foreseeing dynamic conflicts, and lack of scientific technical support for the conduction and feedback mechanism in the prior art are solved, a scientific, accurate and controllable intelligent conduction model is constructed, and the technical effects of improving the scientificity and coordination of the territorial space planning system are achieved.

Description

Technical Field

[0001] This invention relates to the technical field of model building, specifically to a method and system for constructing a transmission model based on the analysis of land and space planning elements. Background Technology

[0002] Traditional land spatial planning relies heavily on administrative directives and static indicator decomposition for transmission, resulting in a fragmented and mechanical process. On the one hand, planning elements are complex and diverse, including rigid constraints such as construction land scale and permanent basic farmland protection area, core spatial boundaries such as ecological protection red lines and urban development boundaries, as well as flexible guidance such as the layout of public service facilities and urban landscape guidelines. When transmitting across levels, the lack of systematic attribute definitions and correlation rules can easily lead to information attenuation, misunderstandings, or execution conflicts. On the other hand, when implementing the requirements of higher levels, lower-level plans need to be refined, deepened, and even made necessary adaptive adjustments based on local realities. This process may generate spatial contradictions that are inconsistent with the intentions of higher levels or between elements at the same level, i.e., transmission conflicts. These conflicts cannot be effectively identified and coordinated during the planning and review stages, seriously affecting the authority and operability of the plan, and even leading to disorder in the land spatial development and protection pattern. Current methods for assisting in the transmission of planning elements, such as indicator system construction, spatial overlay analysis, and scenario simulation, have significant limitations. They lack standardization and structural analysis of the planning elements themselves, and the inherent logical relationships between elements are not explicitly defined or formalized. Transmission rules are mostly implicit in policy texts or empirical judgments, lacking the support of a calculable and reasonable rule base. Simulation and conflict detection in the transmission process focus on post-event verification or partial links, lacking the ability to model and dynamically extrapolate the entire chain of element identification, rule association, network simulation, conflict detection, and optimization.

[0003] Therefore, the current technologies suffer from several technical problems, including insufficient standardization in the transmission process of planning elements, difficulty in predicting dynamic conflicts, and a lack of scientific and technological support for the transmission and feedback mechanisms. Summary of the Invention

[0004] This application provides a method and system for constructing a transmission model based on the analysis of territorial spatial planning elements. This solves the technical problems existing in the prior art, such as insufficient standardization of the transmission process of planning elements, difficulty in predicting dynamic conflicts, and lack of scientific and technical support for the transmission and feedback mechanism. It achieves the technical effect of constructing a scientific, accurate, and controllable intelligent transmission model, thereby improving the scientific nature and coordination of the territorial spatial planning system.

[0005] This application provides a method for constructing a transmission model based on the analysis of territorial spatial planning elements. The method includes: delineating a target area for multi-level territorial spatial planning and constructing a standard planning element dataset; identifying and classifying elements based on the standard planning element dataset to generate multiple planning element transmission attribute parameters; setting a transmission rule base and associating the transmission rule base with the multiple planning element transmission attribute parameters to construct a territorial spatial planning element transmission relationship network; traversing the territorial spatial planning element transmission relationship network to perform transmission simulation, and performing conflict detection based on the transmission simulation results to identify abnormal transmission information; optimizing conflicts based on the abnormal transmission information, generating planning transmission optimization suggestions for correction guidance, and constructing a transmission model.

[0006] In a possible implementation, element identification and classification are performed based on the standard planning element dataset to generate multiple planning element transmission attribute parameters. The method includes: reading and parsing planning data of the target area based on the standard planning element dataset to obtain element metadata; performing contextual analysis based on the element metadata, identifying elements based on the analysis results, and constructing multiple core element categories; performing semantic analysis based on the multiple core element categories to construct structured semantic information; parsing the transmission direction according to the structured semantic information to determine the transmission direction parameter; parsing the transmission carrier according to the structured semantic information to determine the transmission carrier type parameter; parsing the transmission constraint according to the structured semantic information to determine the transmission constraint strength parameter; and matching and binding the transmission direction parameter, the transmission carrier type parameter, and the transmission constraint strength parameter with the standard planning element dataset to construct the multiple planning element transmission attribute parameters.

[0007] In a possible implementation, contextual analysis is performed based on the element metadata, and element identification is performed based on the analysis results to construct multiple core element categories. The method includes: performing geometric analysis based on the element metadata to determine the element geometric type; reading the key attributes of the element metadata to determine the key attribute field values ​​of the element; matching and combining the element geometric type with the key attribute field values ​​of the element to construct a geometric-attribute combination feature library; matching the element metadata with the geometric-attribute combination feature library to determine classification feature labels; performing element identification on the element metadata according to the classification feature labels, constructing an element association network for element discrimination, and generating multi-source input evidence parameters; and using a machine learning classifier to perform fusion analysis on the multi-source input evidence parameters to construct the multiple core element categories.

[0008] In a possible implementation, the transmission rule base is associated with the transmission attribute parameters of the multiple planning elements to construct a transmission relationship network for territorial spatial planning elements. The method includes: traversing the transmission rule base according to the transmission carrier type parameter and the transmission direction parameter to perform a first-level matching and filtering, generating rule-element data pairs; performing data input format analysis based on the transmission rule base to determine the transmission carrier type parameter to be input; comparing the transmission carrier type parameter to be input with the transmission carrier type parameter based on the rule-element data pairs to perform a second-level matching and filtering, constructing a transmission rule list based on the filtering results; performing element adaptation identification based on the transmission rule list to construct a bidirectional mapping relationship table; using the standard planning element dataset as network nodes, and the transmission relationships of the network nodes as directed edges, connecting them according to the transmission direction parameter and the bidirectional mapping relationship table to construct a territorial spatial planning element transmission relationship network.

[0009] In a possible implementation, the standard planning element dataset is used as network nodes, and the transmission relationships of the network nodes are used as directed edges. These are connected according to the transmission direction parameters and the bidirectional mapping table to construct a transmission relationship network for land spatial planning elements. The method includes: performing semantic association analysis based on the network nodes to determine element semantic association data; performing spatial topology analysis based on the network nodes to determine element spatial topology relationships; performing transmission identification based on the element semantic association data and the element spatial topology relationships to generate multiple element pairs; traversing the multiple element pairs according to the transmission direction parameters to perform bidirectional transmission queries, determining the bidirectional transmission direction parameters, and determining the direction according to the bidirectional mapping table: when the bidirectional transmission direction parameters are determined according to the bidirectional mapping table... When the data is from top to bottom and the target element node is at a lower level, a directed edge is created from the source element node to the target element node. When the bidirectional transmission direction parameter is determined to be from bottom to top according to the bidirectional mapping relationship table, a reverse edge is created from the target element node to the source element node. Based on the directed edge and the reverse edge, an initial transmission network is constructed by automatic connection. The initial transmission network is traversed for verification, and a verification result is generated. The verification result includes isolated nodes and cyclic transmission paths. Based on the isolated nodes, missing associations are filled to generate first compensation data. Based on the cyclic transmission paths, closed-loop deconstruction is performed to generate second compensation data. The initial transmission network is updated according to the first compensation data and the second compensation data to construct the land and space planning element transmission relationship network.

[0010] In a possible implementation, the transmission simulation is performed by traversing the transmission relationship network of the land and space planning elements. The method includes: filtering from top to bottom based on the transmission attribute parameters of the planning elements according to the transmission direction parameters to construct a set of starting points, which contains multiple element nodes; performing a depth-first simulation search based on the multiple element nodes to traverse the transmission relationship network of the land and space planning elements to generate a first simulation search result; performing a breadth-first simulation search based on the multiple element nodes to traverse the transmission relationship network of the land and space planning elements to generate a second simulation search result; and performing data alignment compensation based on the first simulation search result and the second simulation search result to generate the transmission simulation result.

[0011] In a possible implementation, conflict detection is performed based on the transmission simulation results to identify abnormal transmission information. The method includes: performing state analysis based on the transmission simulation results to generate transmission state information; setting a preset transmission state threshold, comparing the transmission state information with the preset transmission state threshold, calculating the state deviation based on the comparison result, and generating a state difference degree; calculating the element transmission tolerance based on the transmission simulation results, and setting a global threshold based on the element transmission tolerance; when the state difference degree is greater than the global threshold, determining a transmission conflict, and generating transmission conflict parameters, the transmission conflict parameters including a set of conflict nodes; performing association extraction based on the set of conflict nodes to determine multiple conflict directed edges; performing conflict analysis based on the set of conflict nodes and the multiple conflict directed edges to generate multiple conflict categories, the multiple conflict categories including rigid conflict categories, elastic deviation categories, and element missing categories; and packaging the data according to the multiple conflict directed edges based on the rigid conflict categories, the elastic deviation categories, and the element missing categories to construct the abnormal transmission information.

[0012] In a possible implementation, conflict optimization is performed based on the abnormal transmission information to generate planning transmission optimization suggestions. The method includes: classifying planning according to the rigid conflict category, the elastic deviation category, and the element missing category in conjunction with an expert knowledge base, and determining multiple basic priority scores for each category; weighting and adjusting the multiple basic priority scores for each category according to the state difference degree to generate multiple category priority scores; arranging the multiple category priority scores in descending order to generate a priority sequence; mapping the abnormal transmission information sequentially according to the priority sequence to construct a queue of optimizations to be processed; constructing an optimization strategy rule base, and retrieving and querying the optimization strategy rule base according to the queue of optimizations to be processed to generate the planning transmission optimization suggestions.

[0013] In a possible implementation, generating planning transmission optimization suggestions for correction guidance and constructing a transmission model includes: performing mutual influence analysis based on the planning transmission optimization suggestions to identify interrelationships and generating suggestion groups based on the interrelationships; performing hierarchical division and reorganization based on the suggestion groups to generate segmented planning transmission correction guidance schemes; traversing the segmented planning transmission correction guidance schemes for correction guidance and constructing a linkage dataset; performing logical analysis based on the linkage dataset to construct transmission simulation logical parameters; integrating the segmented planning transmission correction guidance schemes into the transmission simulation logical parameters for automatic adjustment to generate initial transmission rule parameters; encapsulating the optimization strategy rule base as a core knowledge component and combining it with the initial transmission rule parameters to call the interface to construct the transmission model.

[0014] This application also provides a transmission model construction system based on the analysis of territorial spatial planning elements. The system includes: a territorial spatial planning module for delineating target areas for multi-level territorial spatial planning and constructing a standard planning element dataset; an element identification and classification module for identifying and classifying elements based on the standard planning element dataset and generating multiple planning element transmission attribute parameters; a transmission relationship network construction module for setting a transmission rule base and associating the transmission rule base with the multiple planning element transmission attribute parameters to construct a territorial spatial planning element transmission relationship network; an abnormal transmission information identification module for traversing the territorial spatial planning element transmission relationship network to perform transmission simulation, detecting conflicts based on the transmission simulation results, and identifying abnormal transmission information; and a transmission model construction module for performing conflict optimization based on the abnormal transmission information, generating planning transmission optimization suggestions for correction guidance, and constructing a transmission model.

[0015] This application proposes a method and system for constructing a transmission model based on the analysis of territorial spatial planning elements. This method involves delineating target areas for multi-level territorial spatial planning; classifying and identifying elements based on a standard planning element dataset; establishing a transmission rule base and associating it with the transmission attribute parameters of multiple planning elements to construct a network of transmission relationships between territorial spatial planning elements; traversing and simulating transmission processes and detecting conflicts to identify abnormal transmission information; optimizing conflicts, generating planning transmission optimization suggestions and providing correction guidance, and constructing a transmission model. This addresses the technical problems of insufficient standardization in the transmission process of planning elements, difficulty in predicting dynamic conflicts, and a lack of scientific and technical support for transmission and feedback mechanisms in existing technologies. It achieves the technical effect of constructing a scientific, accurate, and controllable intelligent transmission model, thereby improving the scientific nature and coordination of the territorial spatial planning system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 A schematic diagram of the method for constructing a transmission model based on the analysis of territorial spatial planning elements provided in this application embodiment.

[0018] Figure 2 A schematic diagram of the system structure for constructing a transmission model based on the analysis of land and space planning elements provided in this application embodiment.

[0019] Attached figure labels: Land and space planning module 10, element identification and classification module 20, transmission relationship network construction module 30, abnormal transmission information identification module 40, transmission model construction module 50. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0021] This application provides a method for constructing a transmission model based on the analysis of land and space planning elements, such as... Figure 1 As shown, the method includes:

[0022] Step S100: Delineate the target area for multi-level territorial spatial planning and construct a standard planning element dataset.

[0023] Preferably, based on administrative boundaries, natural geographical units, or specific functional zones such as river basins and economic zones, a specific geographic spatial range is determined as the target area for planning and transmission analysis. For this target area, its planning positioning, constraints, and requirements at different planning system levels (national, provincial, municipal, county, and township) are considered to form hierarchical planning tasks, ensuring that the spatial scope and control requirements of each level of planning are progressively connected. Planning elements refer to specific spatial or non-spatial objects involved in territorial spatial planning. Then, spatial element data and non-spatial element attribute data from each level of planning in the target area are collected, including ecological protection red lines, permanent bases, etc. This includes farmland, urban development boundaries, major infrastructure corridors, nature reserves, and other binding indicators such as construction land scale, arable land area, and forest coverage rate, as well as guiding parameters such as public service facility configuration standards. Then, planning data from different sources and formats, such as vector graphics, raster data, and tabular text, are converted into a unified format. Consistent attribute fields are defined for various planning elements, such as element codes, names, levels, control requirements, and transmission relationship identifiers, forming a structured attribute table. Finally, element classification, terminology, and coding are standardized to ensure that elements at different levels and in different plans are identifiable, correlated, and comparable, thus determining a standard planning element dataset.

[0024] Step S200: Based on the standard planning element dataset, perform element identification and classification to generate multiple planning element transmission attribute parameters.

[0025] Step S200 further includes: reading and parsing planning data of the target area based on the standard planning element dataset to obtain element metadata; performing contextual analysis based on the element metadata, identifying elements based on the analysis results, and constructing multiple core element categories; performing semantic analysis based on the multiple core element categories to construct structured semantic information; parsing the transmission direction according to the structured semantic information to determine the transmission direction parameters; parsing the transmission carrier according to the structured semantic information to determine the transmission carrier type parameters; parsing the transmission constraint according to the structured semantic information to determine the transmission constraint strength parameters; and matching and binding the transmission direction parameters, the transmission carrier type parameters, and the transmission constraint strength parameters with the standard planning element dataset to construct the multiple planning element transmission attribute parameters.

[0026] Preferably, relevant planning data for the target area, such as spatial graphics, attribute tables, and document descriptions, are read from the standard planning element dataset. This data is then parsed to identify basic descriptive information, including the spatial type (point, line, surface), coordinates, boundaries, and other geometric information of the elements; basic attribute information such as element name, code, planning level, compilation time, and version number; and implicit reference, association, or dependency information within the data. This data serves as element metadata describing the basic characteristics of the elements. Contextual analysis is then performed based on the element metadata, combining information such as planning level, geographical scope, and compilation background to understand the specific policy or planning context in which the elements appear. Analysis results are obtained, and element identification is performed based on these results. This includes determining the planning function and type of the elements and outputting multiple core element categories, such as constraint elements like ecological protection red lines and permanent basic farmland; indicator elements like total construction land area and forest land area; layout elements like transportation networks and public service facility locations; and use elements like residential land and industrial land.

[0027] Preferably, semantic analysis is performed based on multiple core element categories. This involves using natural language processing or rule parsing to extract and organize the key semantic features of each core element category, such as control requirements like prohibited construction, conditional construction, and encouraged construction; functional descriptions like ecological conservation, agricultural production, and urban development; and spatial or logical connections between elements, forming structured semantic information. Then, the structured semantic information is analyzed for transmission direction, transmission carrier, and transmission constraint to determine transmission direction parameters, transmission carrier type parameters, and transmission constraint strength parameters. Transmission direction analysis clarifies the flow of elements between levels, including the binding transmission from higher-level plans to lower-level plans (e.g., indicator decomposition); the feedback transmission from lower-level plans to higher-level plans (e.g., demand reporting); and the two-way negotiation transmission between higher and lower levels. Transmission carrier analysis clarifies the specific forms in which elements are transmitted, including indicator-type carriers transmitted in numerical form (e.g., area, proportion); boundary-type carriers transmitted in spatial graphic form (e.g., red lines, boundaries); and rule-type carriers transmitted in policy provisions or technical standards (e.g., construction intensity, style requirements). Transmission constraint analysis clarifies the mandatory and flexible aspects of elements in the transmission process. This includes rigid constraints that must be strictly enforced and cannot be violated, such as protection red lines; flexible constraints that allow adjustment within a certain range or under certain conditions, such as population forecasts; and guiding constraints that only provide a reference direction, such as development strategies. Finally, the transmission direction parameters, transmission carrier type parameters, and transmission constraint strength parameters are associated and matched with each specific element in the standard planning element dataset. Corresponding fields are added or updated in the element's attribute table to record its transmission attributes, thereby generating multiple planning element transmission attribute parameters.

[0028] Furthermore, step S200 also includes: performing geometric analysis based on the element metadata to determine the element geometric type; reading the key attributes of the element metadata to determine the key attribute field values; matching and combining the element geometric type with the key attribute field values ​​to construct a geometric-attribute combination feature library; matching the element metadata with the geometric-attribute combination feature library to determine classification feature labels; performing element identification on the element metadata according to the classification feature labels, constructing an element association network for element discrimination, and generating multi-source input evidence parameters; and using a machine learning classifier to perform fusion analysis on the multi-source input evidence parameters to construct the multiple core element categories.

[0029] Preferably, geometric analysis refers to analyzing the spatial data portion of the element metadata to identify the geometric type of each element, including points (such as transportation hubs and important facility locations), lines (such as road centerlines, rivers, and infrastructure corridors), areas (such as land parcels, administrative divisions, and ecological protection zones), and complex geometries (such as multi-point, multi-line, and polygons with holes). From the attribute table of the element metadata, fields that play a decisive role in element classification are extracted, and the values ​​of key attribute fields are determined, including land use and sea use classification codes, facility type codes, indicator names, planning zoning codes, and control line type identifiers, such as land use codes. The field value 0101 represents residential land, the control type field value is "prohibited construction area", and the planning level field value is "provincial master plan". The geometric type of the element is matched and combined with the key attribute field values ​​of the element. Among them, the combination of polygon geometry + land use classification code indicates land use control elements, the combination of point geometry + facility type code indicates facility configuration elements, and the combination of text annotation + strategic descriptive text indicates target strategic elements. A structured geometry-attribute combination feature library is constructed. For example, one record may be a geometry type "polygon", land use code "0301", and control type "permanent basic farmland".

[0030] Preferably, the metadata of the elements to be classified, containing geometric types and attribute values, is compared and matched in a geometric-attribute combination feature library. Each element is assigned a preliminary classification feature label, such as isal constraint elements or linear infrastructure elements. Based on the classification feature labels, the element metadata is used for element identification, i.e., analyzing spatial and logical relationships such as adjacency, inclusion, and intersection between elements. For example, elements belonging to the same planning section or having the same control requirements are considered. An element association network is constructed based on these spatial and logical relationships, where nodes are elements and edges represent relationships between elements. Then, element discrimination is performed within this association network, using graph algorithms to further determine or verify the category of an element based on its category and those of its associated elements. For example, an unclassified plot surrounded by multiple "ecological protection zone" is highly likely to be classified as an ecologically related element. Geometric features, attribute matching labels, and network association information are summarized and parameterized to generate a multi-dimensional feature vector for the final classification decision, serving as multi-source input evidence parameters.

[0031] Preferably, multi-source input evidence parameters are used as input features. Machine learning classifiers based on decision trees, random forests, support vector machines, or neural networks, pre-trained or online-trained, are used to comprehensively analyze these input features, learn the complex mapping relationship between different feature combinations and the final element category, and then fuse and output the core element category to which each element is most likely to belong, such as ecological protection red lines, urban development boundaries, major transportation corridors, and construction land indicators. This achieves automated and intelligent determination from multi-source feature evidence to the final standardized category, significantly improving the accuracy, robustness, and efficiency of automatic identification and classification of elements in complex and heterogeneous land spatial planning data.

[0032] Step S300: Set up a transmission rule base, associate the transmission rule base with the transmission attribute parameters of the multiple planning elements, and construct a transmission relationship network of land and space planning elements.

[0033] Step S300 further includes: traversing the transmission rule base according to the transmission carrier type parameter and the transmission direction parameter to perform a first-level matching and filtering, generating rule-element data pairs; performing data input format analysis based on the transmission rule base to determine the transmission carrier type parameter to be input; comparing the transmission carrier type parameter to be input with the transmission carrier type parameter based on the rule-element data pairs to perform a second-level matching and filtering, and constructing a transmission rule list based on the filtering results; performing element adaptation identification based on the transmission rule list to construct a bidirectional mapping relationship table; using the standard planning element dataset as network nodes, and the transmission relationship of the network nodes as directed edges, connecting them according to the transmission direction parameter and the bidirectional mapping relationship table to construct a land spatial planning element transmission relationship network.

[0034] Preferably, the technical standards, policy documents, historical cases, and expert experience of territorial spatial planning are reviewed to extract various rule requirements. These requirements are then expressed as logical statements in natural language using a condition-action format. Simultaneously, variables within the rules are identified, parameterized, and bound to the attribute parameters of planning elements to construct a transmission rule base. The core of this base includes rule identifiers, rule names, applicable planning levels, and rule sources. This base is stored using database tables or a knowledge graph with rules as nodes and conditions and actions as edges, ensuring efficient querying, matching, and management of rules. The conditional part includes element type conditions, transmission direction conditions, transmission carrier conditions, and spatial / attribute triggering conditions. The rule part includes specific transmission operations and logical calculations, such as indicator decomposition, boundary coordination, attribute inheritance, and the formula lower-level indicator = higher-level indicator × allocation coefficient ± adjustment amount, spatial relationship operators, and logical operators. Rule attributes include the rigidity, flexibility, and guiding nature of the rules, priority / conflict resolution strategies, and the time, space, or state conditions under which the rules are effective.

[0035] Preferably, the first-level matching and filtering involves traversing the transmission rule base, comparing the transmission carrier type parameters and transmission direction parameters of each planning element with the rule conditions in the transmission rule base, initially filtering out rules that may be related to the transmission direction and carrier form of the element, and generating rule-element data pairs, which include different rules applicable to different elements; performing data input format analysis based on the transmission rule base, that is, conducting a more in-depth structural analysis of the transmission rule base to determine the specific format and type of input data required by each rule, which serves as the transmission carrier type parameter to be input. For example, the rule about "decomposition of construction land index" may require the input data to be a numerical index type carrier; then, comparing the transmission carrier type parameter to be input with the transmission carrier type parameter according to the rule-element data pair, checking whether the two are precisely compatible in terms of data format and type, and performing a second-level matching and filtering based on the comparison results to determine the rules that are also completely matched at the data level, and then constructing a transmission rule list based on the filtering results, where each planning element corresponds to one transmission rule list.

[0036] Preferably, feature adaptation identification is performed based on the transmission rule list. That is, for each rule in the transmission rule list, the specific feature in the standard planning feature dataset to which it applies is specifically identified, and a structured bidirectional mapping relationship table is constructed. This table records the forward mapping relationship and the reverse mapping relationship. The forward mapping relationship is used to indicate the specific constraint or guidance from each feature to the rule, and the reverse mapping relationship is used to indicate the specific feature to which each rule is applied. The mapping relationship attributes may include transmission weight, priority, applicable conditions, etc. The bidirectional mapping relationship table is used to connect the "static feature data" and the "dynamic transmission logic" to ensure the accuracy of network construction.

[0037] Preferably, each element in the standard planning element dataset is used as a network node, and the transmission relationship between the network nodes is used as a directed edge connecting the nodes. The direction of the edge is determined by the transmission direction parameter. Then, based on the transmission direction parameter and the bidirectional mapping relationship table, it is determined whether the nodes need to be connected and the specific connection situation. Specifically, for source elements and target elements with transmission relationships, the bidirectional mapping relationship table is queried to confirm whether there is a transmission rule between the two elements. Then, combined with the transmission direction parameters of the two elements, the correct direction of the edge is determined. Then, all network nodes with transmission relationships are connected to form a complex, directed territorial spatial planning element transmission relationship network, which is used to intuitively show the transmission paths, transmission directions and constraint relationships between all elements across levels and between different types of planning elements.

[0038] Furthermore, step S300 also includes: performing semantic association analysis based on the network nodes to determine element semantic association data; performing spatial topology analysis based on the network nodes to determine element spatial topology relationships; performing transmission identification based on the element semantic association data and the element spatial topology relationships to generate multiple element pairs; traversing the multiple element pairs according to the transmission direction parameters to perform bidirectional transmission queries, determining the bidirectional transmission direction parameters, and determining the direction according to the bidirectional mapping relationship table: when the bidirectional transmission direction parameters are determined to be from top to bottom and the target element node is located at the lower level according to the bidirectional mapping relationship table, a directed edge is created from the source element node to the target element node. When the bidirectional transmission direction parameter is determined to be bottom-up according to the bidirectional mapping relationship table, a reverse edge is created pointing from the target element node to the source element node; an initial transmission network is constructed by automatically connecting the directed edge and the reverse edge; the initial transmission network is traversed for verification to generate verification results, which include isolated nodes and cyclic transmission paths; missing associations are filled based on the isolated nodes to generate first compensation data, and closed-loop deconstruction is performed based on the cyclic transmission paths to generate second compensation data; the initial transmission network is updated according to the first compensation data and the second compensation data to construct the territorial spatial planning element transmission relationship network.

[0039] Preferably, semantic association analysis analyzes the non-spatial logical relationships between network nodes, identifies the connections between elements in terms of policy, function, or management logic, and determines the semantic association data of elements. For example, there is a semantic association between the cultivated land area index and the permanent basic farmland boundary in terms of total quantity constraint spatial layout. Spatial topology analysis analyzes the spatial geometric relationships between network nodes, identifies the spatial positional relationships between elements, and determines the spatial topological relationships of elements, including inclusion relationships, adjacency relationships, and intersection / overlap relationships. Then, by combining the semantic association data and spatial topological relationships, multiple element pairs that may have transmission relationships are identified, such as provincial ecological red line-municipal ecological red line, transportation hub planning-surrounding residential land planning.

[0040] Preferably, bidirectional transmission query refers to determining the bidirectional transmission direction parameters for each generated element pair based on its respective transmission direction parameters, and querying the bidirectional mapping relationship table to accurately determine the actual transmission direction. Bidirectional transmission includes top-down transmission and bottom-up transmission. Top-down transmission means that when it is determined that the transmission is from the upper level to the lower level and the target element is missing in the lower level plan, a directed edge is created from the source element node to the target element node; when it is determined that the transmission is from the lower level to the upper level, an edge in the opposite direction is created, that is, a reverse edge from the target element node to the source element node. According to the determination result, all the created directed edges and reverse edges are connected to all network nodes to form a preliminary, directional transmission network graph, and the initial transmission network is determined.

[0041] Preferably, the initial transmission network is traversed for verification, generating verification results containing isolated nodes and cyclic transmission paths. Isolated nodes are nodes in the network without any connected edges, indicating that under the current rules and association analysis, this element has not been identified as having a transmission relationship with any other element, possibly due to missing associations. Cyclic transmission paths are closed-loop paths found in the network, indicating that there may be infinite loops or contradictions in the transmission logic, causing the simulation to fail to converge or produce unreasonable results. Then, based on isolated nodes, missing associations are filled in, i.e., an attempt is made to search for potentially overlooked general rules from the rule base, or to find other nodes with strong semantic / spatial similarity, automatically suggesting or supplementing missing transmission edges to generate the first compensation data. Based on cyclic transmission paths, closed-loop deconstruction is performed, i.e., each edge in the closed loop and the rules it is based on are analyzed, identifying the key links causing the loop, and deconstructing the cyclic path by adjusting the transmission direction, inserting interrupting nodes, or breaking the closed loop according to rule priority, ensuring the network's acyclicity, and generating the second compensation data. Finally, the first and second compensation data are used to correct and update the initial transmission network, ultimately constructing a logically consistent and structurally complete transmission relationship network for territorial spatial planning elements.

[0042] Step S400: Traverse the transmission relationship network of the land and space planning elements to perform transmission simulation, and perform conflict detection based on the transmission simulation results to identify abnormal transmission information.

[0043] Step S400 further includes: filtering from top to bottom based on the planning element transmission attribute parameters and the transmission direction parameters to construct a starting point set, the starting point set containing multiple element nodes; performing a depth-first simulation search based on the multiple element nodes to traverse the territorial spatial planning element transmission relationship network to generate a first simulation search result; performing a breadth-first simulation search based on the multiple element nodes to traverse the territorial spatial planning element transmission relationship network to generate a second simulation search result; and performing data alignment compensation based on the first simulation search result and the second simulation search result to generate the transmission simulation result.

[0044] Preferably, the transmission simulation is performed by traversing the transmission relationship network of land and space planning elements. Specifically, based on the transmission direction parameters of the planning elements, elements with a top-down transmission direction are selected from all element nodes, and multiple top-level element nodes are determined, such as the national farmland retention targets and the ecological protection red lines delineated by the provinces, thus forming a set of starting points, which includes the initial starting point for simulation. Then, based on multiple element nodes, a depth-first simulation search is performed by traversing the transmission relationship network of land and space planning elements. That is, starting from the set of starting points, the simulation explores as deeply as possible along each transmission path to the lower-level or related nodes, simulating the diffusion process of the transmission effect until the path reaches its end point. Then, other branch paths are explored backtracking. Among them, the end-to-end impact of a complete transmission chain is simulated first, such as the complete chain from national indicators → provincial indicators → municipal indicators → specific plots. The first simulation search result is output, clearly showing a few complete and in-depth transmission impact paths from the top to the bottom and their status.

[0045] Preferably, a breadth-first simulation search is performed based on traversing the transmission relationship network of territorial spatial planning elements through multiple element nodes. That is, starting from the starting node, all its direct subordinate or related nodes are visited first, and then the next level nodes of these nodes are visited layer by layer. Among them, priority is given to simulating the wide diffusion and concurrent impact of transmission effects at the same level. For example, at the provincial level, multiple constraints such as ecology, agriculture, and urban areas are transmitted to various cities and prefectures at the same time, and a second simulation search result is output to comprehensively show the coverage and horizontal correlation of transmission impact at each level.

[0046] Preferably, the first simulated search result from depth-first search and the second simulated search result from breadth-first search are integrated to identify the same nodes and paths simulated in both results, ensuring consistent descriptions of the state of the same element. Since depth-first search and breadth-first search have different focuses and their coverage varies, the advantages of the two simulated search results are combined and compensated by taking the union or weighted average, ultimately outputting a more comprehensive and robust transmission simulation result. This result includes both the in-depth impact details of key transmission paths and reflects the global distribution breadth of transmission effects, enabling a more realistic simulation of the complex dynamic process of planning elements being transmitted in the network.

[0047] Furthermore, step S400 also includes: performing state analysis based on the transmission simulation results to generate transmission state information; setting a preset transmission state threshold, comparing the transmission state information with the preset transmission state threshold, calculating the state deviation based on the comparison result, and generating a state difference degree; calculating the element transmission tolerance based on the transmission simulation results, and setting a global threshold based on the element transmission tolerance; when the state difference degree is greater than the global threshold, determining a transmission conflict, generating transmission conflict parameters, the transmission conflict parameters including a set of conflict nodes; performing association extraction based on the set of conflict nodes to determine multiple conflict directed edges, performing conflict analysis based on the set of conflict nodes and the multiple conflict directed edges to generate multiple conflict categories, the multiple conflict categories including rigid conflict categories, elastic deviation categories, and element missing categories; packaging the data according to the multiple conflict directed edges based on the rigid conflict categories, the elastic deviation categories, and the element missing categories to construct the abnormal transmission information.

[0048] Preferably, a state analysis is performed on the transmission simulation results to extract the key state values ​​of each element node after the simulated transmission, which serve as transmission state information. This may include: indicator achievement rate (i.e., the comparison between the actual value of the lower-level element and the target value of the upper-level transmission); boundary compliance (i.e., whether the spatial relationship between the lower-level spatial boundary and the upper-level constraint boundary exceeds the boundary, buffer distance, etc.); and rule satisfaction (i.e., the degree to which the transmission process satisfies the preset rules). Based on historical cases and experience data, an acceptable ideal state range or target value is set for each type of element or rule, and a preset transmission state threshold is determined. For example, the threshold for the achievement rate of the cultivated land retention indicator is 100%, and the threshold for the buffer distance of the ecological red line boundary is not less than 0 meters. The actual transmission state information of each element is compared with the preset transmission state threshold, and the state deviation is calculated based on the comparison results to finally determine the state difference degree. For example, if the actual construction land scale exceeds the target value by 5%, the state difference degree is +5%.

[0049] Preferably, based on the transmission constraint strength parameters of the elements in the transmission simulation results, the maximum allowable deviation range for each type of element is calculated as the element transmission tolerance. For example, the tolerance of rigid constraints may be 0%, and the tolerance of elastic constraints may be ±5%. Then, based on the element transmission tolerance, a global and benchmark deviation limit for conflict determination is determined as a global threshold. The state difference degree of each element is compared with the global threshold. When the state difference degree exceeds the allowable tolerance range, a transmission conflict is determined to have occurred at that point, and the basic information of the conflict is recorded to generate transmission conflict parameters, which include a conflict node set containing all conflicting element nodes.

[0050] Preferably, association extraction is performed based on the conflict node set to determine the transmission path directly related to the conflict node set, obtaining multiple directed conflict edges. This helps to locate the source and propagation path of the conflict. Then, conflict analysis is performed based on the attributes of the conflict nodes in the conflict node set, the magnitude of the state difference, and the associated directed conflict edges, generating multiple conflict categories including rigid conflict category, flexible deviation category, and element missing category. Among them, the rigid conflict category involves conflicts in which rigid constraints are broken and must be corrected, such as construction land encroaching on permanent basic farmland; the flexible deviation category refers to large deviations within the flexible constraint range, but not exceeding the bottom line, and optimization and adjustment are recommended, such as a certain type of land use ratio deviating significantly from the planning target value, but still within the allowable fluctuation range; the element missing category is where element nodes that should be transmitted or affected in the simulation results do not appear or have missing key attributes, and need to be supplemented, such as a lower-level plan not implementing the layout of a certain public service facility required by the higher-level plan. Finally, for each conflict instance, its conflict category, involved conflict nodes, associated directed conflict edges, specific state difference, and other information are packaged and integrated to generate complete abnormal transmission information.

[0051] Step S500: Based on the abnormal transmission information, perform conflict optimization, generate planning transmission optimization suggestions for correction guidance, and construct a transmission model.

[0052] Step S500 further includes: classifying the planning based on the rigid conflict category, the elastic deviation category, and the missing element category in conjunction with an expert knowledge base to determine multiple basic priority scores for each category; weighting and adjusting the multiple basic priority scores for each category based on the state difference to generate multiple category priority scores; arranging the multiple category priority scores in descending order to generate a priority sequence; mapping the anomaly transmission information sequentially according to the priority sequence to construct a queue of optimization to be processed; constructing an optimization strategy rule base; and retrieving and querying the optimization strategy rule base according to the queue of optimization to be processed to generate the planning transmission optimization suggestions.

[0053] Preferably, the expert knowledge base is used to encode the experience and rules of domain experts, such as prioritizing protection over development and rigid constraints over flexible guidance. Then, based on the expert knowledge base, planning classifications are performed for rigid conflict categories, flexible deviation categories, and element missing categories. This involves setting basic importance scores and determining multiple basic priority scores for each category. For example, the basic priority score for the rigid conflict category is the highest, such as 90 points, and it must be addressed first. The basic priority score for the element missing category is next, such as 70 points, as the absence of key elements would hinder the integrity of the planning system. The basic priority score for the flexible deviation category is relatively low, such as 50 points, and it allows for adjustment within a certain range. The basic scores are then weighted and adjusted based on the degree of difference in the state of each specific conflict instance, generating multiple category priority scores. The greater the difference, the higher the adjusted category priority score. For example, a conflict exceeding the rigid constraint by 5% may have a higher final priority score than a similar conflict exceeding it by only 1%.

[0054] Preferably, all conflicts are sorted in descending order according to the adjusted category priority scores to form a priority sequence for processing from high to low, ensuring the scientific nature of the processing order and prioritizing the resolution of the most urgent and serious issues; then, the abnormal transmission information is mapped sequentially according to the priority sequence to form a queue of pending optimization. Each item in the queue is a conflict instance to be processed, with its complete diagnostic information attached, and the sorting reflects the order of processing. Based on historical cases and expert experience, an optimization strategy rule base is pre-constructed. This base stores specific optimization adjustment strategies for different types and scenarios of conflict. Each optimization adjustment strategy is a condition-action rule. The condition part describes the applicable conflict type, element category, and deviation range, while the action part provides specific optimization suggestions. For example, if the conflict type is rigid and involves construction land encroaching on the ecological red line, it is recommended to adjust the construction land boundary to completely remove it from the ecological red line area, and to recommend surrounding compatible unused land as a supplementary area. If the conflict type is missing element, specifically a kindergarten, it is recommended to supplement the planned residential area with a service facility land area of ​​no less than XX square meters. Then, according to the queue of pending optimizations, each conflict instance is retrieved sequentially, and its conflict type, element type, and degree of difference information are used as query conditions to search the optimization strategy rule base. This process matches each conflict in the queue and generates specific planning transmission optimization suggestions, improving the efficiency and scientific nature of planning adjustments and conflict coordination.

[0055] Furthermore, step S500 also includes: performing mutual influence analysis based on the planning transmission optimization suggestions, identifying mutual relationships, and generating suggestion groups based on the mutual relationships; performing hierarchical division and reorganization based on the suggestion groups to generate segmented planning transmission correction guidance schemes; traversing the segmented planning transmission correction guidance schemes to provide correction guidance and constructing a linkage dataset; performing logical analysis based on the linkage datasets to construct transmission simulation logical parameters; integrating the segmented planning transmission correction guidance schemes into the transmission simulation logical parameters for automatic adjustment to generate initial transmission rule parameters; encapsulating the optimization strategy rule base as a core knowledge component, and combining it with the initial transmission rule parameters to call the interface to construct the transmission model.

[0056] Preferably, an interaction analysis is conducted based on the planning transmission optimization suggestions. This involves analyzing whether there are any mutual influences or dependencies among the various planning transmission optimization suggestions, identifying and determining the interrelationships. For example, "adjusting the boundaries of industrial land" and "increasing the area of ​​supporting green space" may be mutually reinforcing, while "expanding road width" and "protecting ancient and famous trees" may be contradictory. Based on the analyzed interrelationships, suggestions that are closely related or require coordinated implementation are grouped into suggestion groups to ensure the systematicness and coordination of the corrective measures. Then, based on the suggestion groups, a hierarchical division and reorganization are carried out, that is, according to the logical order of planning implementation, such as first resolving spatial conflicts and then adjusting indicators, first implementing rigid constraints and then optimizing flexible layouts, or operational steps, a segmented planning transmission correction guidance scheme is generated, clarifying the priority of the implementation plan and the specific implementation tasks.

[0057] Preferably, the segmented planning transmission correction guidance scheme is traversed for correction guidance. This involves simulating each step of the scheme and recording the chain reaction data triggered by each correction action during the simulation to determine the linkage dataset. For example, after adjusting the boundary of a plot, the linkage affects the land use nature of adjacent plots, the coverage of related facilities, and the overall index calculation results of the region. Then, logical analysis is performed based on the linkage dataset, including analyzing the dynamic influence patterns and logical relationships between planning elements during the correction process, and abstracting them into transmission simulation logical parameters to reflect the system's inherent behavior patterns when dealing with conflicts and making adjustments. The correction strategies in the segmented planning transmission correction guidance scheme are then integrated into the transmission simulation logical parameters for automatic adjustment. The most basic rule set in the transmission model is back-tuned, and the optimized transmission rule parameter set is output, enabling the transmission simulation of the transmission model to more closely approximate the optimized ideal state. Finally, the optimization strategy rule base is encapsulated as a core knowledge component, enabling independent invocation. A clear calling interface is defined, allowing it to receive new land planning data and use optimized rules for transmission simulation. When a conflict is detected, the optimization strategy component is automatically invoked to generate optimization adjustment suggestions. Ultimately, all data inputs, network construction, simulation engine, conflict detector, optimization strategy base, and rule parameter set are integrated and encapsulated according to the defined calling interface and data flow to generate a deployable, scientific, accurate, and controllable land spatial planning element transmission model, thereby improving the scientific rigor and coordination of the land spatial planning system.

[0058] In the above text, refer to Figure 1 This paper describes in detail a method for constructing a transmission model based on the analysis of territorial spatial planning elements according to an embodiment of the present invention. Next, we will refer to... Figure 2 This invention describes a transmission model construction system based on the analysis of territorial spatial planning elements according to an embodiment of the present invention.

[0059] The transmission model construction system based on the analysis of land spatial planning elements according to embodiments of the present invention is used to solve the technical problems existing in the prior art, such as insufficient standardization of the transmission process of planning elements, difficulty in predicting dynamic conflicts, and lack of scientific and technical support for the transmission and feedback mechanism. It achieves the technical effect of constructing a scientific, accurate, and controllable intelligent transmission model, thereby improving the scientific nature and coordination of the land spatial planning system. Figure 2 As shown, the transmission model construction system based on the analysis of territorial spatial planning elements includes: territorial spatial planning module 10, element identification and classification module 20, transmission relationship network construction module 30, abnormal transmission information identification module 40, and transmission model construction module 50.

[0060] The territorial spatial planning module 10 is used to delineate target areas for multi-level territorial spatial planning and construct a standard planning element dataset; the element identification and classification module 20 is used to identify and classify elements based on the standard planning element dataset and generate multiple planning element transmission attribute parameters; the transmission relationship network construction module 30 is used to set a transmission rule base, associate the transmission rule base with the multiple planning element transmission attribute parameters, and construct a territorial spatial planning element transmission relationship network; the abnormal transmission information identification module 40 is used to traverse the territorial spatial planning element transmission relationship network to perform transmission simulation, perform conflict detection based on the transmission simulation results, and identify abnormal transmission information; the transmission model construction module 50 is used to perform conflict optimization based on the abnormal transmission information, generate planning transmission optimization suggestions for correction guidance, and construct a transmission model.

[0061] The specific configuration of the element identification and classification module 20 will be described in detail below. The element identification and classification module 20 further includes: reading and parsing planning data of the target area based on the standard planning element dataset to obtain element metadata; performing contextual analysis based on the element metadata, identifying elements based on the analysis results, and constructing multiple core element categories; performing semantic analysis based on the multiple core element categories to construct structured semantic information; parsing the transmission direction according to the structured semantic information to determine the transmission direction parameters; parsing the transmission carrier according to the structured semantic information to determine the transmission carrier type parameters; parsing the transmission constraint according to the structured semantic information to determine the transmission constraint strength parameters; and matching and binding the transmission direction parameters, the transmission carrier type parameters, and the transmission constraint strength parameters with the standard planning element dataset to construct the multiple planning element transmission attribute parameters.

[0062] The specific configuration of the element identification and classification module 20 will be described in detail below. The element identification and classification module 20 further includes: performing geometric analysis based on the element metadata to determine the element geometric type; reading the key attributes of the element metadata to determine the key attribute field values; matching and combining the element geometric type with the key attribute field values ​​to construct a geometric-attribute combination feature library; matching the element metadata with the geometric-attribute combination feature library to determine classification feature labels; performing element identification on the element metadata according to the classification feature labels, constructing an element association network for element discrimination, and generating multi-source input evidence parameters; and using a machine learning classifier to perform fusion analysis on the multi-source input evidence parameters to construct the multiple core element categories.

[0063] The specific configuration of the transmission relationship network construction module 30 will be described in detail below. The transmission relationship network construction module 30 further includes: traversing the transmission rule base according to the transmission carrier type parameter and the transmission direction parameter to perform a first-level matching and filtering, generating rule-element data pairs; performing data input format analysis based on the transmission rule base to determine the transmission carrier type parameter to be input; comparing the transmission carrier type parameter to be input with the transmission carrier type parameter based on the rule-element data pairs to perform a second-level matching and filtering, and constructing a transmission rule list based on the filtering results; performing element adaptation identification based on the transmission rule list to construct a bidirectional mapping relationship table; using the standard planning element dataset as network nodes, and the transmission relationships of the network nodes as directed edges, connecting them according to the transmission direction parameter and the bidirectional mapping relationship table to construct a land spatial planning element transmission relationship network.

[0064] The specific configuration of the transmission relationship network construction module 30 will be described in detail below. The transmission relationship network construction module 30 further includes: performing semantic association analysis based on the network nodes to determine element semantic association data; performing spatial topology analysis based on the network nodes to determine element spatial topology relationships; performing transmission identification based on the element semantic association data and the element spatial topology relationships to generate multiple element pairs; traversing the multiple element pairs according to the transmission direction parameters to perform bidirectional transmission queries, determining the bidirectional transmission direction parameters, and determining the direction according to the bidirectional mapping relationship table: when the bidirectional transmission direction parameters are determined to be top-down and the target element node is located at a lower level according to the bidirectional mapping relationship table, then a path is created from the source element node to the target element node. When the bidirectional transmission direction parameter is determined to be bottom-up according to the bidirectional mapping table, a reverse edge is created pointing from the target element node to the source element node; the directed edge and the reverse edge are automatically connected to construct an initial transmission network; the initial transmission network is traversed for verification to generate verification results, which include isolated nodes and cyclic transmission paths; missing associations are filled based on the isolated nodes to generate first compensation data, and closed-loop deconstruction is performed based on the cyclic transmission paths to generate second compensation data; the initial transmission network is updated according to the first compensation data and the second compensation data to construct the territorial spatial planning element transmission relationship network.

[0065] The specific configuration of the anomaly transmission information identification module 40 will be described in detail below. The anomaly transmission information identification module 40 further includes: constructing a starting point set by filtering from top to bottom according to the transmission direction parameters based on the transmission attribute parameters of the planning elements, wherein the starting point set contains multiple element nodes; performing a depth-first simulation search based on the multiple element nodes to traverse the transmission relationship network of the territorial spatial planning elements, generating a first simulation search result; performing a breadth-first simulation search based on the multiple element nodes to traverse the transmission relationship network of the territorial spatial planning elements, generating a second simulation search result; and performing data alignment compensation based on the first simulation search result and the second simulation search result to generate the transmission simulation result.

[0066] The specific configuration of the abnormal transmission information identification module 40 will be described in detail below. The abnormal transmission information identification module 40 further includes: performing state analysis based on the transmission simulation results to generate transmission state information; setting a preset transmission state threshold, comparing the transmission state information with the preset transmission state threshold, calculating the state deviation based on the comparison result, and generating a state difference degree; calculating the element transmission tolerance based on the transmission simulation results, and setting a global threshold based on the element transmission tolerance; when the state difference degree is greater than the global threshold, determining a transmission conflict, generating transmission conflict parameters, the transmission conflict parameters including a conflict node set; performing association extraction based on the conflict node set to determine multiple conflict directed edges, performing conflict analysis based on the conflict node set and the multiple conflict directed edges to generate multiple conflict categories, the multiple conflict categories including rigid conflict categories, elastic deviation categories, and element missing categories; and packaging the data according to the multiple conflict directed edges based on the rigid conflict categories, the elastic deviation categories, and the element missing categories to construct the abnormal transmission information.

[0067] The specific configuration of the transmission model construction module 50 will be described in detail below. The transmission model construction module 50 further includes: classifying planning based on the rigid conflict category, the elastic deviation category, and the missing element category in conjunction with an expert knowledge base, and determining multiple basic priority scores for each category; weighting and adjusting the multiple basic priority scores for each category based on the state difference degree to generate multiple category priority scores; arranging the multiple category priority scores in descending order to generate a priority sequence; mapping the abnormal transmission information sequentially according to the priority sequence to construct a queue of optimization to be processed; constructing an optimization strategy rule base; and retrieving and querying the optimization strategy rule base according to the queue of optimization to be processed to generate the planning transmission optimization suggestions.

[0068] The specific configuration of the transmission model construction module 50 will be described in detail below. The transmission model construction module 50 further includes: performing mutual influence analysis based on the planning transmission optimization suggestions, identifying interrelationships, and generating suggestion groups based on the interrelationships; performing hierarchical division and reorganization based on the suggestion groups to generate segmented planning transmission correction guidance schemes; traversing the segmented planning transmission correction guidance schemes to provide correction guidance and constructing a linkage dataset; performing logical analysis based on the linkage dataset to construct transmission simulation logical parameters; integrating the segmented planning transmission correction guidance schemes into the transmission simulation logical parameters for automatic adjustment to generate initial transmission rule parameters; encapsulating the optimization strategy rule base as a core knowledge component, and combining it with the initial transmission rule parameters to call the interface to construct the transmission model.

[0069] The transmission model construction system based on the analysis of territorial spatial planning elements provided in the embodiments of the present invention can execute the transmission model construction method based on the analysis of territorial spatial planning elements provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for constructing a transmission model based on the analysis of elements in territorial spatial planning, characterized in that, The method includes: Delineate target areas for multi-level territorial spatial planning and construct a standard planning element dataset; Based on the standard planning element dataset, element identification and classification are performed to generate multiple planning element transmission attribute parameters; A transmission rule base is set up, and the transmission rule base is associated with the transmission attribute parameters of the multiple planning elements to construct a transmission relationship network of territorial spatial planning elements; The transmission relationship network of the aforementioned land spatial planning elements is traversed to perform a transmission simulation. Conflict detection is performed based on the transmission simulation results, and abnormal transmission information is identified. Based on the abnormal transmission information, conflict optimization is performed, planning transmission optimization suggestions are generated for correction guidance, and a transmission model is constructed. The method for constructing a land spatial planning element transmission relationship network by associating the transmission rule base with the transmission attribute parameters of the multiple planning elements includes: The transmission rule base is traversed according to the transmission carrier type parameter and transmission direction parameter to perform the first-level matching and filtering, and generate rule-feature data pairs. Based on the aforementioned transmission rule base, data input format analysis is performed to determine the type parameters of the transmission carrier to be input. Based on the rule-feature data, a second-level matching and filtering process is performed to compare the input transmission carrier type parameter with the transmission carrier type parameter, and a transmission rule list is constructed based on the filtering results. Based on the list of transmission rules, element adaptation identification is performed, and a bidirectional mapping relationship table is constructed. The standard planning element dataset is used as network nodes, and the transmission relationship of the network nodes is used as directed edges. The nodes are connected according to the transmission direction parameters and the bidirectional mapping relationship table to construct a transmission relationship network of land spatial planning elements. The method involves using the standard planning element dataset as network nodes, the transmission relationships of the network nodes as directed edges, and connecting them according to the transmission direction parameters and the bidirectional mapping relationship table to construct a transmission relationship network for territorial spatial planning elements. Semantic association analysis is performed based on the network nodes to determine the semantic association data of the elements. Spatial topology analysis is performed based on the network nodes to determine the spatial topological relationships of the elements; Based on the semantic association data of the elements and the spatial topological relationship of the elements, multiple element pairs are generated through transmission recognition. According to the stated transmission direction parameters, a bidirectional transmission query is performed on the multiple element pairs to determine the bidirectional transmission direction parameters, and the direction is determined according to the bidirectional mapping table. When the bidirectional transmission direction parameter is determined to be from top to bottom and the target feature node is located at the lower level according to the bidirectional mapping relationship table, a directed edge is created from the source feature node to the target feature node. When the bidirectional transmission direction parameter is determined to be from bottom to top according to the bidirectional mapping relationship table, a reverse edge is created from the target feature node to the source feature node. An initial conduction network is constructed by automatically connecting the directed edges and the reverse edges. The initial propagation network is traversed for verification, and a verification result is generated. The verification result includes isolated nodes and loop propagation paths. Based on the isolated nodes, perform association missing completion to generate first compensation data; based on the cyclic transmission path, perform closed-loop deconstruction to generate second compensation data. The initial transmission network is updated based on the first compensation data and the second compensation data to construct the transmission relationship network of the land and space planning elements.

2. The method for constructing a transmission model based on the analysis of territorial spatial planning elements as described in claim 1, characterized in that, Based on the aforementioned standard planning element dataset, element identification and classification are performed to generate multiple planning element transmission attribute parameters. The method includes: Based on the standard planning element dataset, the planning data of the target area is read and parsed to obtain element metadata; Context analysis is performed based on the element metadata, and element identification is performed based on the analysis results to construct multiple core element categories; Semantic analysis is performed based on the aforementioned multiple core element categories to construct structured semantic information; The transmission direction is parsed according to the structured semantic information to determine the transmission direction parameters; The transmission carrier is parsed according to the structured semantic information to determine the transmission carrier type parameters; Based on the structured semantic information, the transmitted constraints are parsed to determine the transmitted constraint strength parameters; The transmission direction parameter, the transmission carrier type parameter, and the transmission constraint strength parameter are matched and bound with the standard planning element dataset to construct the multiple planning element transmission attribute parameters.

3. The method for constructing a transmission model based on the analysis of territorial spatial planning elements as described in claim 2, characterized in that, Contextual analysis is performed based on the element metadata, and element identification is performed based on the analysis results to construct multiple core element categories. The method includes: Geometric analysis is performed based on the element metadata to determine the element geometric type; Read the key attributes of the element metadata and determine the values ​​of the key attribute fields of the element; The geometric type of the feature is matched and combined with the key attribute field value of the feature to construct a geometric-attribute combination feature library; The element metadata is matched with the geometric-attribute combined feature library to determine the classification feature labels; Based on the classification feature labels, element metadata is identified, an element association network is constructed for element discrimination, and multi-source input evidence parameters are generated. A machine learning classifier is used to perform fusion analysis on the multi-source input evidence parameters to construct the multiple core element categories.

4. The method for constructing a transmission model based on the analysis of territorial spatial planning elements as described in claim 2, characterized in that, The method for traversing the transmission relationship network of the aforementioned land spatial planning elements and performing transmission simulation includes: Based on the planning element transmission attribute parameters, the starting point set is constructed by filtering from top to bottom according to the transmission direction parameters. The starting point set contains multiple element nodes. Based on the multiple element nodes, a depth-first simulation search is performed to traverse the land spatial planning element transmission relationship network and generate the first simulation search result. Based on the multiple element nodes, a breadth-first simulation search is performed to traverse the land spatial planning element transmission relationship network and generate a second simulation search result. The transmission simulation result is generated by performing data alignment compensation based on the first simulated search result and the second simulated search result.

5. The method for constructing a transmission model based on the analysis of territorial spatial planning elements as described in claim 4, characterized in that, Collision detection is performed based on the conduction simulation results to identify abnormal conduction information. Methods include: Based on the conduction simulation results, state analysis is performed to generate conduction state information; A preset conduction state threshold is set, the conduction state information is compared with the preset conduction state threshold, and the state deviation is calculated based on the comparison result to generate a state difference degree. Calculate the element transmission tolerance based on the transmission simulation results, and set a global threshold according to the element transmission tolerance; When the state difference is greater than the global threshold, a propagation conflict is determined, and a propagation conflict parameter is generated, which contains a set of conflict nodes. Based on the set of conflict nodes, association extraction is performed to identify multiple conflict directed edges. Conflict analysis is then performed based on the set of conflict nodes and the multiple conflict directed edges to generate multiple conflict categories, including rigid conflict category, elastic deviation category, and feature missing category. Based on the rigid conflict category, the elastic deviation category, and the missing element category, the data is packaged according to the multiple conflict directed edges to construct the anomaly transmission information.

6. The method for constructing a transmission model based on the analysis of territorial spatial planning elements as described in claim 5, characterized in that, Based on the aforementioned anomaly propagation information, conflict optimization is performed to generate planning propagation optimization suggestions. The method includes: Based on the rigid conflict category, the elastic deviation category, and the element missing category, combined with the expert knowledge base, planning classification is performed to determine the basic priority scores for multiple categories. Based on the state difference, the basic priority scores of multiple categories are weighted and adjusted to generate multiple category priority scores; Arrange the multiple category priority scores in descending order to generate a priority sequence; The abnormal transmission information is mapped sequentially according to a priority sequence to construct a queue of optimization to be processed; An optimization strategy rule base is constructed, and the optimization strategy rule base is searched and queried according to the optimization queue to be processed to generate the planning transmission optimization suggestions.

7. The method for constructing a transmission model based on the analysis of territorial spatial planning elements as described in claim 6, characterized in that, Generate planning transmission optimization suggestions for correction guidance, and construct a transmission model. Methods include: Based on the planning transmission optimization suggestions, an interaction analysis is performed to identify the interrelationships, and suggestion groups are generated according to the interrelationships. Based on the aforementioned recommendation group, a hierarchical division and reorganization are performed to generate a segmented planning transmission and correction guidance scheme. The segmented planning transmission correction guidance scheme is iterated through to provide correction guidance, and a linked dataset is constructed. Logical analysis is performed based on the aforementioned linked dataset to construct the conduction simulation logic parameters; The segmented planning transmission correction guidance scheme is integrated into the transmission simulation logic parameters for automatic adjustment, generating initial transmission rule parameters; The optimization strategy rule base is encapsulated as a core knowledge component, and the transmission model is constructed by combining it with the initial transmission rule parameter call interface.

8. A transmission model construction system based on the analysis of territorial spatial planning elements, characterized in that, The system is used to implement the transmission model construction method based on the analysis of territorial spatial planning elements as described in any one of claims 1 to 7, and the system includes: The territorial spatial planning module is used to delineate target areas for multi-level territorial spatial planning and to construct a standard planning element dataset. The feature identification and classification module is used to identify and classify features based on the standard planning feature dataset and generate multiple planning feature transmission attribute parameters. The transmission relationship network construction module is used to set up a transmission rule base, associate the transmission rule base with the transmission attribute parameters of the multiple planning elements, and construct a transmission relationship network of territorial spatial planning elements. The abnormal transmission information identification module is used to traverse the transmission relationship network of the land and space planning elements to perform transmission simulation, perform conflict detection based on the transmission simulation results, and identify abnormal transmission information. The transmission model construction module is used to perform conflict optimization based on the abnormal transmission information, generate planning transmission optimization suggestions for correction guidance, and construct the transmission model.

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