A site selection planning method and device, electronic equipment and storage medium

Abstract

The application provides a site selection planning method and device, electronic equipment and a storage medium, and relates to the technical field of space planning. The method comprises the following steps: constructing a space-time knowledge graph according to planning information of a target area; extracting a retrieval condition from received site selection planning question information, and retrieving each entity combination matching the retrieval condition from the space-time knowledge graph; taking an entity combination satisfying a space constraint condition pre-associated with the target area as a target entity combination, and determining each planning project aggregation area according to a space entity in the target entity combination; determining a support capacity level corresponding to the planning project aggregation area according to area supporting information corresponding to the planning project aggregation area, and generating a site selection planning result based on the support capacity level. The application not only considers space and project factors, but also fully combines the actual support capacity of the area, thereby ensuring the efficiency and reliability of site selection planning.

Description

Technical Field

[0001] This invention relates to the field of spatial planning technology, and more specifically, to a site selection planning method, apparatus, electronic device, and storage medium. Background Technology

[0002] In the process of urban development, site selection for various urban development planning projects is a crucial step, profoundly impacting the city's long-term development, rational allocation of resources, and improvement of residents' quality of life. The reliability, rationality, and scientific nature of site selection directly affect the project's operational efficiency, economic benefits, and overall environmental and social benefits after completion.

[0003] However, existing site selection planning methods largely rely on manual operation. Planners need to collect a large amount of data and rely on their experience and professional knowledge to analyze, judge, and make decisions, which has obvious limitations, is inefficient, and can easily lead to unreasonable site selection. Summary of the Invention

[0004] The problem addressed by this invention is how to improve the efficiency and reliability of site selection planning.

[0005] To address the above problems, this invention provides a site selection planning method, comprising: A spatiotemporal knowledge graph is constructed based on the planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and space entities that are associated with the planning project entities; The system extracts search criteria from the received site selection planning query information and retrieves entity combinations that match the search criteria from the spatiotemporal knowledge graph; wherein, the search criteria include at least one of project conditions, time conditions and spatial conditions; and each entity combination includes a planning project entity and the time entity and the spatial entity that are connected to the planning project entity. The entity combination that satisfies the pre-associated spatial constraints of the target area is taken as the target entity combination, and each planning project cluster area is determined based on the spatial entities in the target entity combination; wherein, each planning project cluster area covers at least the area where one of the spatial entities is located; Based on the regional supporting information corresponding to the planned project cluster area, the supporting capacity level corresponding to the planned project cluster area is determined, and a site selection planning result is generated based on the supporting capacity level.

[0006] Optionally, the step of constructing a spatiotemporal knowledge graph based on the acquired planning information of the target area includes: The planning information is subjected to entity extraction, relation extraction, and attribute extraction to obtain each target entity and its corresponding attribute information and entity relations; wherein, the target entity includes the planning project entity, the time entity, and the spatial entity; the attribute information corresponding to the planning project entity includes at least one of the following: planning project name, domain type, location description, and responsible entity; the attribute information corresponding to the time entity includes time type and time boundary; the attribute information corresponding to the spatial entity includes the area coordinate range; The coordinate range of the region is mapped to the gridded map corresponding to the target region to obtain the grid region, and the preset grid code corresponding to the grid region is used as the attribute information of the spatial entity. The attribute information is associated with the corresponding target entity, and the relationship between the target entities is determined according to the relationship between the entities to construct the spatiotemporal knowledge graph.

[0007] Optionally, retrieving combinations of entities from the spatiotemporal knowledge graph that match the search criteria includes: For each of the search conditions, each of the target entities that match the search condition is obtained from the spatiotemporal knowledge graph, and based on the target entities and other target entities that are associated with the target entities, each combination of candidate entities corresponding to the search condition is obtained; When the logical relationship between each of the search conditions is an AND relationship, the entity combination is obtained based on the intersection of each of the candidate entity combinations corresponding to each of the search conditions; When the logical relationship between each of the search conditions is an OR relationship, the entity combination is obtained based on the union of each of the candidate entity combinations corresponding to each of the search conditions.

[0008] Optionally, before selecting the combination of entities that satisfy the pre-associated spatial constraints of the target region as the target entity combination, the method further includes: Obtain the constraint boundaries of the target area and the coordinate range of infeasible areas within the target area, and map the coordinate range of infeasible areas onto the gridded map to obtain infeasible grid areas; When the grid region is located within the constraint boundary and does not overlap with the infeasible grid region, the grid region is selected as a candidate grid region. When the shortest distance between the candidate grid region and the infeasible grid region is less than a preset distance threshold, the region type corresponding to the infeasible grid region is obtained. Determine whether the area type is a preset protected area type; wherein, the preset protected area type includes farmland areas; If so, then it is determined that the entity combination corresponding to the candidate grid region does not satisfy the spatial constraint condition; If not, then the entity combination corresponding to the candidate grid region is determined to satisfy the spatial constraint condition.

[0009] Optionally, determining the clusters of each planned project based on the spatial entities in the target entity combination includes: Based on a preset clustering radius, spatial entities in all combinations of the target entities are clustered by distance. When a cluster containing multiple spatial entities is obtained, the planned project cluster area is obtained based on the minimum convex hull corresponding to the grid region corresponding to all spatial entities in the cluster. When the area of ​​the grid region corresponding to any spatial entity other than the cluster is greater than a preset area threshold, the planned project cluster area is obtained based on the grid region.

[0010] Optionally, the regional supporting information includes road network data, infrastructure data, and public service supporting data; determining the support capacity level corresponding to the planned project cluster area based on the acquired regional supporting information includes: Based on the road network data, the traffic accessibility index corresponding to the planned project cluster area is determined, and based on the comparison result of the traffic accessibility index and the preset accessibility index threshold, the first support capacity level corresponding to the road network data is determined; wherein, the traffic accessibility index includes road network density; Based on the infrastructure data, the infrastructure carrying capacity index corresponding to the planned project cluster area is determined, and based on the comparison result between the infrastructure carrying capacity index and the preset carrying capacity index threshold, the second support capacity level corresponding to the infrastructure data is determined; wherein, the infrastructure carrying capacity index includes at least one of infrastructure coverage rate and capacity margin; Based on the public service supporting data, the service supporting indicators corresponding to the planned project cluster area are determined, and based on the comparison results between the service supporting indicators and the preset service supporting indicator thresholds, the third support capability level corresponding to the public service supporting data is determined; wherein, the service supporting indicators include supporting coverage rate; The support capability level corresponding to the planned project cluster area is determined based on the lowest level among the first support capability level, the second support capability level, and the third support capability level.

[0011] Optionally, the site selection planning result includes the planning type corresponding to the planned project cluster area; generating the site selection planning result based on the support capacity level includes: When the support capacity level is greater than the preset level threshold, the planning type corresponding to the planned project cluster area is a priority construction area; When the support capacity level is less than or equal to the preset level threshold, the planning type corresponding to the planned project cluster area is a gap-filling area.

[0012] In this invention, a spatiotemporal knowledge graph is constructed based on the planning information of the target area. This graph integrates planning project entities with temporal and spatial entities through association relationships. This facilitates the discovery of the distribution and connections of different planning projects across time and space dimensions, providing a comprehensive and structured data foundation for subsequent site selection planning analysis. This improves the reliability of site selection planning and avoids analytical omissions due to information dispersion. Furthermore, at least one retrieval condition from the site selection planning query information—project conditions, temporal conditions, and spatial conditions—is extracted. Matching entity combinations are then retrieved from the spatiotemporal knowledge graph. This allows for precise location of relevant entity information based on different dimensions of site selection requirements, providing more targeted foundational data for subsequent site selection planning and further enhancing its reliability. After obtaining the entity combinations, combinations that satisfy the pre-associated spatial constraints of the target area are selected as target entity combinations. This ensures that the site selection planning complies with the spatial planning requirements of the target area, avoiding conflicts between site selection and existing spatial constraints and ensuring the feasibility and compliance of the site selection. Furthermore, this invention determines the support capacity level of the planned project cluster area based on the regional supporting information, and generates site selection planning results based on the support capacity level. This ensures that the site selection planning not only considers spatial and project-specific factors, but also fully integrates the actual support capacity of the region. Thus, while ensuring the efficiency and reliability of the site selection planning, it also ensures that the site selection planning results are more practically instructive.

[0013] The present invention also provides a site selection planning device, comprising: A construction module is used to construct a spatiotemporal knowledge graph based on the obtained planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and spatial entities that are associated with the planning project entities; The retrieval module is used to extract retrieval conditions from the received site selection planning query information and retrieve the entity combinations that match the retrieval conditions from the spatiotemporal knowledge graph; wherein, the retrieval conditions include at least one of project conditions, time conditions and spatial conditions; and an entity combination includes a planning project entity and the time entity and the spatial entity that are connected to the planning project entity. A combination module is used to take the combination of entities that satisfy the pre-associated spatial constraints of the target area as a target entity combination, and determine each planning project cluster area based on the spatial entities in the target entity combination; wherein, a planning project cluster area covers at least the area where one of the spatial entities is located; The generation module is used to determine the support capacity level corresponding to the planned project cluster area based on the obtained regional supporting information, and generate site selection planning results based on the support capacity level.

[0014] The site selection planning device and the site selection planning method provided by this invention have essentially the same advantages as the prior art, and will not be repeated here.

[0015] The present invention also provides an electronic device, including a memory and a processor; The memory is used to store computer programs; The processor is used to implement the location planning method as described above when executing the computer program.

[0016] The electronic device provided by this invention has essentially the same advantages as the site selection and planning method compared to the prior art, and will not be elaborated further here.

[0017] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the address planning method described above.

[0018] The advantages of the computer-readable storage medium and the site selection planning method provided by this invention compared to the prior art are basically the same, and will not be repeated here. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the site selection planning method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the site selection and planning device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0021] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0022] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0023] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0024] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties. The collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0025] like Figure 1 As shown in the figure, an embodiment of the present invention provides a site selection planning method, which includes the following steps: S1: Construct a spatiotemporal knowledge graph based on the planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and spatial entities that are related to the planning project entities.

[0026] Specifically, in this embodiment, the target area refers to a specific geographical area for site selection planning, such as a specific urban area of ​​a city or a development zone, and subsequent site selection planning work revolves around this area. The planning information referred to in this embodiment refers to various planning-related data concerning the target area, such as policy information on urban construction planning, industrial development planning, and infrastructure planning. The spatiotemporal knowledge graph referred to in this embodiment includes planning project entities, time entities, and spatial entities. Among them, planning project entities identify various planned construction projects within the target area, such as newly built industrial parks, schools, and commercial complexes. Time entities represent time information related to the planning project, such as project commencement and completion dates. Spatial entities represent geographical location information related to the planning project, such as specific blocks and areas, which can be represented based on electronic fences on an electronic map.

[0027] In one embodiment, planning information for the target area can be collected through multiple channels, and information extraction (such as triple extraction) can be performed on the collected planning information to obtain the entities and the relationships between them. Based on this, each entity can be treated as a node, and the relationships between entities can be treated as edges, thereby constructing a spatiotemporal knowledge graph.

[0028] S2: Extract search criteria from the received site selection planning query information, and retrieve the entity combinations that match the search criteria from the spatiotemporal knowledge graph; wherein, the search criteria include at least one of project conditions, time conditions and spatial conditions; an entity combination includes a planning project entity and a time entity and a spatial entity that are connected to the planning project entity.

[0029] Specifically, in this embodiment, the site selection planning query information refers to the user-inputted questions or requirements related to site selection planning, used to extract retrieval conditions for searching within the spatiotemporal knowledge graph. The retrieval conditions in this embodiment include at least one of the following: project conditions (such as project type, scale, etc.), time conditions (such as the expected timeframe for project completion), and spatial conditions (such as the approximate geographical location of the desired site, surrounding environmental requirements, etc.), used to filter out entity combinations that meet the requirements from the spatiotemporal knowledge graph. In this embodiment, the entity combination refers to a planned project entity and a set of associated time and spatial entities. For example, for a new shopping mall project entity, the associated time entity could be the planned opening date, and the associated spatial entity could be the specific map area where the project is planned to be built.

[0030] In one embodiment, a large language model can be used to parse the received site selection planning query information to accurately extract the retrieval conditions, such as project conditions, time conditions, and spatial conditions. Based on this, the extracted retrieval conditions can be matched with a spatiotemporal knowledge graph. For example, a serial retrieval method can be used to first retrieve planning project entities that match project conditions (such as project type). Then, the time entities associated with each retrieved planning project entity are obtained, and time entities matching the time conditions are selected. Further, the spatial entities corresponding to the planned project entities associated with the selected time entities are obtained, and spatial entities matching the spatial conditions are selected. Finally, based on the planned project entities corresponding to each selected spatial entity and their corresponding time entities, combinations of entities are obtained.

[0031] S3: Take the combination of entities that meet the pre-associated spatial constraints of the target area as the target entity combination, and determine the clusters of each planning project based on the spatial entities in the target entity combination; wherein, a planning project cluster covers at least the area where a spatial entity is located.

[0032] Specifically, the spatial constraints referred to in this embodiment refer to pre-set spatial restrictions for the target area, such as land use restrictions corresponding to different areas, environmental protection restrictions within the target area, etc., used to filter out entity combinations that meet the spatial requirements, thereby obtaining the target entity combination. The planned project cluster area referred to in this embodiment covers at least the area where one spatial entity is located, and there are one or more planned projects within this area, such as an industrial park containing multiple new factories and supporting facilities.

[0033] In one embodiment, after obtaining the entity combinations, it can be determined whether each entity combination meets the pre-associated spatial constraints of the target area. For example, for a certain entity combination, it can be determined whether the area corresponding to its spatial entity overlaps with a restricted planning area (such as farmland, ecological protection area, etc.). If they overlap, it indicates that the spatial constraints are not met. The entity combinations that meet the spatial constraints are selected as target entity combinations, and the clusters of planning projects are determined based on the spatial entities in the target entity combinations. For example, when the shortest distance between the areas corresponding to different spatial entities is less than a preset distance threshold (such as 500m), the corresponding planning project clusters can be determined based on the minimum convex hulls of the different spatial entities. As another example, suppose the area corresponding to a certain spatial entity is greater than a preset area threshold (such as 50000m²). 2 If so, the area corresponding to the spatial entity can be regarded as the cluster area for planned projects.

[0034] S4: Based on the regional supporting information corresponding to the planned project cluster area, determine the support capacity level corresponding to the planned project cluster area, and generate the site selection planning results based on the support capacity level.

[0035] Specifically, in this embodiment, the regional supporting information refers to the various supporting conditions of the planned project cluster area, including information on infrastructure (such as water and electricity supply, road facilities), public services (such as medical and educational resources), etc., used to assess the area's support capacity. The support capacity level in this embodiment refers to the level assigned after assessing the support capacity of the planned project cluster area based on the regional supporting information, for example, divided into high, medium, and low levels. A high level means that the area has relatively complete supporting facilities and strong support capacity for the planned projects. The site selection planning result in this embodiment refers to the final recommendation or plan for site selection planning based on the support capacity level of the planned project cluster area, such as recommending which planned project cluster areas to select sites (e.g., prioritizing the highest support capacity level).

[0036] In this embodiment, a spatiotemporal knowledge graph is constructed based on the planning information of the target area. This integrates planning project entities with temporal and spatial entities through association relationships, facilitating the discovery of the distribution and connections of different planning projects across time and space dimensions. This provides a comprehensive and structured data foundation for subsequent site selection planning analysis, thereby improving the reliability of site selection planning and avoiding analytical omissions due to information dispersion. Furthermore, at least one retrieval condition from the project conditions, temporal conditions, and spatial conditions is extracted from the site selection planning query information, and matching entity combinations are retrieved from the spatiotemporal knowledge graph. This allows for precise location of relevant entity information based on different dimensions of site selection requirements, providing more targeted foundational data for subsequent site selection planning and further improving its reliability. After obtaining the entity combinations, the entity combinations that satisfy the pre-associated spatial constraints of the target area are selected as the target entity combinations. This ensures that the site selection planning complies with the spatial planning requirements of the target area, avoiding conflicts between the site selection and existing spatial constraints, and ensuring the feasibility and compliance of the site selection. Furthermore, this embodiment determines the support capacity level of the planned project cluster area based on the regional supporting information, and generates site selection planning results based on the support capacity level. This ensures that the site selection planning not only considers spatial and project-specific factors, but also fully integrates the actual support capacity of the region. Thus, while ensuring the efficiency and reliability of the site selection planning, it also ensures that the site selection planning results are more practically instructive.

[0037] Optionally, a spatiotemporal knowledge graph is constructed based on the obtained planning information of the target area, including: The planning information is subjected to entity extraction, relation extraction, and attribute extraction to obtain each target entity and its corresponding attribute information and entity relations. Among them, the target entities include planning project entities, time entities, and spatial entities. The attribute information corresponding to the planning project entity includes at least one of the following: planning project name, field type, description of the area, and responsible entity. The attribute information corresponding to the time entity includes time type and time boundary. The attribute information corresponding to the spatial entity includes the area coordinate range. The coordinate range of the region is mapped to the gridded map corresponding to the target region to obtain the grid region, and the preset grid code corresponding to the grid region is used as the attribute information of the spatial entity. Each attribute information is associated with its corresponding target entity, and the relationships between the target entities are determined based on the relationships between the entities, thus constructing a spatiotemporal knowledge graph.

[0038] Specifically, in this embodiment, entity extraction refers to the operation of identifying and extracting meaningful objects such as planning project entities, time entities, and spatial entities from planning information. Relationship extraction refers to the operation of mining the connections between target entities in planning information, such as the relationships between planning project entities and time and spatial entities. Attribute extraction refers to the operation of extracting descriptive attribute information for planning project entities, time entities, and spatial entities. Target entities include planning project entities, time entities, and spatial entities. The attribute information corresponding to a planning project entity includes at least one of the following: planning project name, industry type, location description, and responsible entity. The planning project name represents a specific identifier for the planning project entity, used to clearly distinguish different planning projects, such as "XX City Cultural and Art Center Construction Project"; the industry type represents the industry category to which the planning project belongs, such as education, industry, commerce, etc.; the location description represents a textual description of the geographical location of the planning project, such as "located at the intersection of XX Road and XX Road in XX District"; and the responsible entity represents the unit or organization responsible for implementing the planning project. In this embodiment, the attribute information corresponding to a time entity includes time type and time boundary. The time type distinguishes the type of time represented by the time entity, such as start date, completion date, and construction period. The time boundary represents the start and end points of the time range represented by the time entity, such as from 2026-01-01 (start point) to 2028-12-31 (end point). In this embodiment, the attribute information corresponding to a spatial entity includes regional coordinate range, such as representing the area of ​​a plot of land using latitude and longitude coordinates.

[0039] In this embodiment, entity extraction, relation extraction, and attribute extraction can be performed on planning information based on preset prompts, thereby obtaining each target entity and its corresponding attribute information and entity relations (e.g., using named entity recognition methods from natural language processing, combined with a planning domain-specific dictionary and rules). After obtaining the attribute information of the planning project entities and the time entities, the attribute information can be structured. For example, it can be stored in JSON format according to a preset template. Further, for the extracted regional coordinate range, a gridded geographic map corresponding to the target region can be obtained, and it can be divided into grids and encoded to generate a preset grid code dataset. On this basis, the regional coordinate range can be mapped to the gridded geographic map to obtain grid regions, and the preset grid codes corresponding to the grid regions can be used as the attribute information corresponding to spatial entities, which is beneficial to further improve the positioning accuracy and management efficiency of spatial entities in the knowledge graph. In this way, this embodiment can associate each target entity with its corresponding attribute information, determine the association between each target entity according to the entity relations, and construct a spatiotemporal knowledge graph. By extracting entities, relationships, and attributes from planning information, complex planning information is integrated and stored in a structured manner, facilitating the querying, analysis, and utilization of planning information, thereby improving the efficiency of planning information management and understanding.

[0040] Optionally, combinations of entities matching the search criteria can be retrieved from the spatiotemporal knowledge graph, including: For each retrieval condition, each target entity matching the retrieval condition is obtained from the spatiotemporal knowledge graph, and based on the target entity and other target entities that are related to the target entity, each combination of candidate entities corresponding to the retrieval condition is obtained. When the logical relationship between each search condition is an AND relationship, the entity combination is obtained based on the intersection of each candidate entity combination corresponding to each search condition; When the logical relationship between the search criteria is an OR relationship, the entity combination is obtained based on the union of the candidate entity combinations corresponding to each search criterion.

[0041] In this embodiment, each search condition corresponds to a type of target entity. Based on the type of search condition, target entities matching the search condition can be obtained from the spatiotemporal knowledge graph. Then, based on the target entities and other target entities that are related to them, candidate entity combinations corresponding to the search condition are obtained. In this embodiment, the candidate entity combination is essentially the same as the entity combination. Accordingly, in this embodiment, a candidate entity combination includes a planning project entity and time and space entities that are connected to the planning project entity.

[0042] In one embodiment, while extracting search criteria from the site selection planning query information using a large language model, the logical relationships between the search criteria can also be extracted. These logical relationships include AND (i.e., simultaneously satisfying different search criteria) and OR (i.e., satisfying any one search criterion is sufficient). It should be understood that in this embodiment, when there are more than three search criteria, both AND and OR relationships may exist simultaneously. Correspondingly, multiple entity combinations satisfying these logical relationships can be generated. When the logical relationship between the search criteria is AND, this embodiment obtains the entity combination based on the intersection of the candidate entity combinations corresponding to each search criterion; when the logical relationship between the search criteria is OR, the entity combination is obtained based on the union of the candidate entity combinations corresponding to each search criterion. While performing parallel searches based on different search criteria, it also facilitates flexible adjustment of the entity combination generation method according to the logical relationship requirements of different search criteria in the actual site selection planning query information, thereby ensuring the efficiency and accuracy of the search.

[0043] Optionally, before using the combination of entities that satisfy the pre-associated spatial constraints of the target region as the target entity combination, the method further includes: Obtain the constraint boundaries of the target area and the coordinate range of infeasible areas within the target area, and map the coordinate range of infeasible areas onto a gridded map to obtain infeasible gridded areas; When the grid region is located within the constraint boundary and does not overlap with the infeasible grid region, the grid region is used as a candidate grid region. When the shortest distance between the candidate grid region and the infeasible grid region is less than a preset distance threshold, obtain the region type corresponding to the infeasible grid region. Determine whether the area type is a preset protected area type; among which, the preset protected area type includes farmland areas; If so, then the entity combination corresponding to the candidate grid region is determined to not meet the spatial constraint conditions; If not, then the entity combination corresponding to the candidate grid region is determined to satisfy the spatial constraint conditions.

[0044] Specifically, in this embodiment, the constraint boundary refers to the boundary range corresponding to the target area, used to define the geographical limits within which site selection activities can be carried out, and to limit the spatial scope of site selection planning. The infeasible area coordinate range in this embodiment refers to the coordinate range of areas within the target area that are unsuitable for planning project construction due to certain reasons (such as existing special-purpose land, ecological protection zones, etc.), and can be obtained through a vector map. The gridded map in this embodiment refers to a map formed by dividing the target area into multiple regular grid units, each grid unit having a unique identifier, and different granularities can be set for different areas.

[0045] In one embodiment, after obtaining the coordinate range of the infeasible area, it can be mapped to a gridded map to obtain the infeasible grid area (e.g., it can be represented by a preset area code corresponding to the infeasible grid area). When the grid area is within the constraint boundary and the grid area does not overlap with the infeasible grid area, it indicates that the implementation of the planned project entity corresponding to the grid area will not affect the existing regional construction, that is, the implementation of the planned project entity is relatively feasible. At this time, the grid area is used as a candidate grid area, and it is further determined whether the shortest distance between the candidate grid area and the infeasible grid area is less than a preset distance threshold (e.g., 200m). If not, it is determined that the entity combination corresponding to the candidate grid area meets the spatial constraint conditions. If yes, it is further determined whether the area type is a preset protected area type (e.g., farmland area, ecological protection area, etc., which can be set in advance according to actual needs). If yes, it indicates that the implementation of the planned project entity corresponding to the candidate grid area may have an adverse effect on the infeasible grid area (e.g., pollution, etc.). At this time, it is determined that the entity combination corresponding to the candidate grid area does not meet the spatial constraint conditions, which is conducive to fully considering the impact of special areas on site selection and eliminating the adverse effects that may be caused to preset protected area types such as farmland areas in advance. If not, the entity combination corresponding to the candidate grid area is determined to meet the spatial constraints, thus fully ensuring the compliance and reliability of the site selection plan.

[0046] Optionally, based on the spatial entities in the target entity combination, the clusters of each planned project can be determined, including: Based on the preset clustering radius, spatial entities in all target entity combinations are clustered by distance; When a cluster containing multiple spatial entities is obtained, the planned project cluster area is obtained based on the minimum convex hull corresponding to the grid region corresponding to all spatial entities in the cluster. When the area of ​​the grid region corresponding to any spatial entity outside the cluster is greater than the preset area threshold, the planned project cluster area is obtained based on the grid region.

[0047] Specifically, in this embodiment, the preset clustering radius refers to a pre-defined numerical value used to measure the distance relationship between spatial entities. During distance clustering, if the minimum distance between two spatial entities is less than this radius, they can be grouped into the same class, thus forming a cluster. In this embodiment, a cluster refers to a set of multiple spatial entities that are close to each other during distance clustering. The spatial entities within each cluster are relatively concentrated in space, representing a potential area for planned projects.

[0048] In one embodiment, after performing distance clustering on spatial entities in all target entity combinations according to a preset clustering radius, when a cluster containing multiple spatial entities is obtained, a planning project cluster area can be obtained based on the minimum convex hull corresponding to the grid area of ​​all spatial entities in the cluster. For example, based on the minimum convex hull, the boundary of the minimum convex hull is equidistantly expanded by a preset distance (e.g., 300m) to form a ring region, thereby obtaining the planning project cluster area. In this way, this embodiment rationally divides spatially proximate planning projects into the same planning project cluster area, which is conducive to the coordinated development and resource sharing between different planning projects. However, when the area of ​​the grid area corresponding to any spatial entity outside the cluster is greater than a preset area threshold (e.g., 50000m), the cluster area is determined. 2 When the grid area corresponding to an independent spatial entity is large, the planned project cluster area can be obtained based on this grid area. For example, a circular or rectangular buffer zone can be generated with the geometric center of the grid area as the center, thereby obtaining the planned project cluster area, avoiding the neglect of large independent spatial entities, and further improving the comprehensiveness and rationality of site selection planning.

[0049] Optionally, regional supporting information includes road network data, infrastructure data, and public service supporting data; based on the obtained regional supporting information corresponding to the planned project cluster area, the supporting capacity level corresponding to the planned project cluster area is determined, including: Based on road network data, the traffic accessibility index corresponding to the planned project cluster area is determined, and based on the comparison results between the traffic accessibility index and the preset accessibility index threshold, the first support capacity level corresponding to the road network data is determined; among which, the traffic accessibility index includes road network density; Based on infrastructure data, determine the infrastructure carrying capacity index corresponding to the planned project cluster area, and determine the second support capacity level corresponding to the infrastructure data based on the comparison results between the infrastructure carrying capacity index and the preset carrying capacity index threshold; wherein, the infrastructure carrying capacity index includes at least one of infrastructure coverage rate and capacity margin. Based on the public service support data, the service support indicators corresponding to the planned project cluster area are determined, and based on the comparison results between the service support indicators and the preset service support indicator thresholds, the third support capability level corresponding to the public service support data is determined; among them, the service support indicators include the support coverage rate. The support capacity level corresponding to the planned project cluster area is determined based on the lowest level among the first, second, and third support capacity levels.

[0050] Specifically, in this embodiment, the regional supporting information includes road network data, infrastructure data, and public service supporting data, which can be obtained through an information disclosure platform. Road network data represents information about the road network within the planned project cluster area, such as the layout, length, and width of roads; infrastructure data represents data related to various types of infrastructure within the planned project cluster area, such as the distribution and capacity of water and electricity supply facilities, communication facilities, etc.; public service supporting data represents information related to public service facilities within the planned project cluster area, such as the number, scale, and service scope of public service facilities such as schools, hospitals, and parks.

[0051] In one embodiment, the traffic accessibility index represents a parameter used to measure the traffic convenience of a planned project cluster area, which includes road network density, i.e., the length of roads per unit area. For example, the road network density value can be obtained by calculating the total length of roads within the planned project cluster area and dividing it by the total area of ​​the area. Based on this, the first support capability level corresponding to the road network data is determined according to the comparison result between the traffic accessibility index and a preset accessibility index threshold. The preset accessibility index threshold can be set in advance according to needs. In this embodiment, when the traffic accessibility index is greater than twice the preset accessibility index threshold, the corresponding first support capability level can be high; when the traffic accessibility index is less than twice the preset accessibility index threshold but greater than the preset accessibility index threshold, the corresponding first support capability level can be medium; and when the traffic accessibility index is less than the preset accessibility index threshold, the corresponding first support capability level can be low.

[0052] In one embodiment, the infrastructure carrying capacity index represents a parameter used to measure the infrastructure carrying capacity within a planned project cluster area. It includes at least one of infrastructure coverage (e.g., the proportion of the area covered by hydropower facilities to the total area of ​​the planned project cluster area) and capacity margin (e.g., the remaining power supply capacity of electrical facilities). For example, in this embodiment, the infrastructure coverage can be obtained by statistically analyzing the area covered by various types of infrastructure (e.g., hydropower, communication facilities) and then dividing it by the total area of ​​the planned project cluster area. And / or, the capacity margin can be obtained based on the design capacity and current usage of the infrastructure. Based on this, in this embodiment, a second support capacity level corresponding to the infrastructure data can be determined according to the comparison result between the infrastructure carrying capacity index and a preset carrying capacity index threshold. The preset carrying capacity index threshold can be set in advance according to demand (e.g., infrastructure coverage greater than 80%, and / or capacity margin greater than 35%). The method for determining the second support capacity level in this embodiment is basically the same in principle as the method for determining the first support capacity level described above, and will not be repeated here.

[0053] In one embodiment, the service support index represents a parameter used to assess the completeness of public service facilities in a planned project cluster area. This includes the coverage rate of facilities, such as the proportion of the population served by schools to the total population of the planned project cluster area, reflecting the service capacity of public service facilities. Based on this, this embodiment can determine the third support capability level corresponding to the public service support data based on the comparison result between the service support index and a preset service support index threshold. The preset service support index threshold can be set in advance according to demand (e.g., a coverage rate greater than 40%). The method for determining the third support capability level in this embodiment is basically the same in principle as the method for determining the first support capability level described above, and will not be repeated here.

[0054] In this embodiment, determining the first, second, and third support capability levels facilitates a reliable quantitative assessment of the road network, infrastructure, and public service facilities within the planned project cluster area. Based on this, this embodiment determines the corresponding support capability level for the planned project cluster area based on the lowest of the aforementioned levels. This helps to identify current shortcomings in the supporting infrastructure of the planned project cluster area, providing a scientific and objective basis for subsequent site selection planning results.

[0055] Optionally, the site selection planning results include the planning type corresponding to the planned project cluster area; the site selection planning results are generated based on the support capacity level, including: When the support capacity level is greater than the preset level threshold, the planning type corresponding to the planned project cluster area is a priority construction area; When the support capacity level is less than or equal to the preset level threshold, the planning type corresponding to the planned project cluster area is the gap-filling area.

[0056] Specifically, in this embodiment, the site selection planning results include the planning type corresponding to the planned project cluster area. The planning type includes priority construction areas and areas to address shortcomings, etc., which are used to guide the development and construction strategies of different areas. In this embodiment, the preset level threshold refers to a pre-set limit value of the support capacity level used to classify the planning type of the planned project cluster area, thereby determining the planning type corresponding to the planned project cluster area.

[0057] In one embodiment, when the support capability level is greater than a preset threshold (e.g., intermediate level), the planning type corresponding to the planned project cluster area is a priority construction area. This indicates that the planned project entities within the project cluster area not only meet the compliance requirements for implementation, but also that the supporting information within the current project cluster area is conducive to supporting the efficient construction of each planned project. Conversely, when the support capability level is less than or equal to the preset threshold, it indicates that although the planned project entities within the project cluster area currently meet the compliance requirements for implementation, the supporting information corresponding to the current planned project cluster area is not yet complete, and it is necessary to address the corresponding shortcomings. The planning type corresponding to this planned project cluster area is designated as a shortcoming-filling area, which helps ensure the reliability and reference value of the site selection planning results.

[0058] Optionally, after obtaining the planning type corresponding to the planned project cluster area, a site selection planning strategy can be generated using a large language model based on the pre-associated prompt templates for different planning types. For example, the prompt template for a priority construction area could be: "The XX planned project cluster area is a priority construction area, with a transportation accessibility index of XX, an infrastructure carrying capacity index of XX, and a service support index of XX. It is recommended to expedite land supply and project approval." Similarly, the prompt template for a gap-filling area could be: "The XX planned project cluster area is a gap-filling area, with a transportation accessibility index of XX, an infrastructure carrying capacity index of XX, and a service support index of XX. Currently, the XX indicator (i.e., the index corresponding to the lowest support capacity level) is insufficient, and it is recommended to prioritize its corresponding construction direction."

[0059] like Figure 2 As shown, an embodiment of the present invention provides a site selection planning device 200, comprising: The construction module 210 is used to construct a spatiotemporal knowledge graph based on the planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and space entities that are associated with the planning project entities; The retrieval module 220 is used to extract retrieval conditions from the received site selection planning query information and retrieve the entity combinations that match the retrieval conditions from the spatiotemporal knowledge graph; wherein, the retrieval conditions include at least one of project conditions, time conditions and spatial conditions; and one entity combination includes a planning project entity and the time entity and the spatial entity that are connected to the planning project entity. The combination module 230 is used to take the combination of entities that satisfy the pre-associated spatial constraints of the target area as the target entity combination, and determine each planning project cluster area based on the spatial entities in the target entity combination; wherein, each planning project cluster area covers at least the area where one of the spatial entities is located. The generation module 240 is used to determine the support capacity level corresponding to the planned project cluster area based on the obtained regional supporting information, and generate site selection planning results based on the support capacity level.

[0060] The site selection planning device and the site selection planning method provided in this embodiment can produce basically the same technical effects, and will not be described again here.

[0061] like Figure 3 As shown, an electronic device 300 provided in this embodiment of the invention includes a memory 310 and a processor 320; the memory 310 is used to store a computer program; the processor 320 is used to implement the address planning method as described above when the computer program is executed.

[0062] Alternatively, an electronic device 300 includes a memory 310 and a processor 320 coupled to the memory 310; the memory 310 is configured to store a computer program; and the processor 320 is configured to perform the following operations when the computer program is executed: A spatiotemporal knowledge graph is constructed based on the planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and space entities that are associated with the planning project entities; The system extracts search criteria from the received site selection planning query information and retrieves entity combinations that match the search criteria from the spatiotemporal knowledge graph; wherein, the search criteria include at least one of project conditions, time conditions and spatial conditions; and each entity combination includes a planning project entity and the time entity and the spatial entity that are connected to the planning project entity. The entity combination that satisfies the pre-associated spatial constraints of the target area is taken as the target entity combination, and each planning project cluster area is determined based on the spatial entities in the target entity combination; wherein, each planning project cluster area covers at least the area where one of the spatial entities is located; Based on the regional supporting information corresponding to the planned project cluster area, the supporting capacity level corresponding to the planned project cluster area is determined, and a site selection planning result is generated based on the supporting capacity level.

[0063] The electronic device and the site selection planning method provided in this embodiment can produce basically the same technical effects, and will not be described again here.

[0064] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the address planning method described above.

[0065] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations: A spatiotemporal knowledge graph is constructed based on the planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and space entities that are associated with the planning project entities; The system extracts search criteria from the received site selection planning query information and retrieves entity combinations that match the search criteria from the spatiotemporal knowledge graph; wherein, the search criteria include at least one of project conditions, time conditions and spatial conditions; and each entity combination includes a planning project entity and the time entity and the spatial entity that are connected to the planning project entity. The entity combination that satisfies the pre-associated spatial constraints of the target area is taken as the target entity combination, and each planning project cluster area is determined based on the spatial entities in the target entity combination; wherein, each planning project cluster area covers at least the area where one of the spatial entities is located; Based on the regional supporting information corresponding to the planned project cluster area, the supporting capacity level corresponding to the planned project cluster area is determined, and a site selection planning result is generated based on the supporting capacity level.

[0066] The computer-readable storage medium and the site selection planning method provided in this embodiment can produce essentially the same technical effects, and will not be described again here.

[0067] The present invention will now be described an electronic device 300 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic device 300 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 300 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0068] Electronic device 300 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0069] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0070] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A site selection planning method, characterized in that, include: A spatiotemporal knowledge graph is constructed based on the planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and space entities that are associated with the planning project entities; The system extracts search criteria from the received site selection planning query information and retrieves entity combinations that match the search criteria from the spatiotemporal knowledge graph; wherein, the search criteria include at least one of project conditions, time conditions and spatial conditions; and each entity combination includes a planning project entity and the time entity and the spatial entity that are connected to the planning project entity. The entity combination that satisfies the pre-associated spatial constraints of the target area is taken as the target entity combination, and each planning project cluster area is determined based on the spatial entities in the target entity combination; wherein, each planning project cluster area covers at least the area where one of the spatial entities is located; Based on the regional supporting information corresponding to the planned project cluster area, the supporting capacity level corresponding to the planned project cluster area is determined, and a site selection planning result is generated based on the supporting capacity level.

2. The site selection planning method according to claim 1, characterized in that, The step of constructing a spatiotemporal knowledge graph based on the acquired planning information of the target area includes: The planning information is subjected to entity extraction, relation extraction, and attribute extraction to obtain each target entity and its corresponding attribute information and entity relations; wherein, the target entity includes the planning project entity, the time entity, and the spatial entity; the attribute information corresponding to the planning project entity includes at least one of the following: planning project name, domain type, location description, and responsible entity; the attribute information corresponding to the time entity includes time type and time boundary; the attribute information corresponding to the spatial entity includes the area coordinate range; The coordinate range of the region is mapped to the gridded map corresponding to the target region to obtain the grid region, and the preset grid code corresponding to the grid region is used as the attribute information of the spatial entity. The attribute information is associated with the corresponding target entity, and the relationship between the target entities is determined according to the relationship between the entities to construct the spatiotemporal knowledge graph.

3. The site selection planning method according to claim 2, characterized in that, The step of retrieving combinations of entities from the spatiotemporal knowledge graph that match the search criteria includes: For each of the search conditions, each of the target entities that match the search condition is obtained from the spatiotemporal knowledge graph, and based on the target entities and other target entities that are associated with the target entities, each combination of candidate entities corresponding to the search condition is obtained; When the logical relationship between each of the search conditions is an AND relationship, the entity combination is obtained based on the intersection of each of the candidate entity combinations corresponding to each of the search conditions; When the logical relationship between each of the search conditions is an OR relationship, the entity combination is obtained based on the union of each of the candidate entity combinations corresponding to each of the search conditions.

4. The site selection planning method according to claim 3, characterized in that, Before selecting the combination of entities that satisfy the pre-associated spatial constraints of the target region as the target entity combination, the method further includes: Obtain the constraint boundaries of the target area and the coordinate range of infeasible areas within the target area, and map the coordinate range of infeasible areas onto the gridded map to obtain infeasible grid areas; When the grid region is located within the constraint boundary and does not overlap with the infeasible grid region, the grid region is selected as a candidate grid region. When the shortest distance between the candidate grid region and the infeasible grid region is less than a preset distance threshold, the region type corresponding to the infeasible grid region is obtained. Determine whether the area type is a preset protected area type; wherein, the preset protected area type includes farmland areas; If so, then it is determined that the entity combination corresponding to the candidate grid region does not satisfy the spatial constraint condition; If not, then the entity combination corresponding to the candidate grid region is determined to satisfy the spatial constraint condition.

5. The site selection planning method according to claim 2, characterized in that, The step of determining the cluster areas of each planned project based on the spatial entities in the target entity combination includes: Based on a preset clustering radius, spatial entities in all combinations of the target entities are clustered by distance. When a cluster containing multiple spatial entities is obtained, the planned project cluster area is obtained based on the minimum convex hull corresponding to the grid region corresponding to all spatial entities in the cluster. When the area of ​​the grid region corresponding to any spatial entity other than the cluster is greater than a preset area threshold, the planned project cluster area is obtained based on the grid region.

6. The site selection planning method according to claim 1, characterized in that, The regional supporting information includes road network data, infrastructure data, and public service supporting data; The step of determining the support capacity level corresponding to the planned project cluster area based on the obtained regional supporting information includes: Based on the road network data, the traffic accessibility index corresponding to the planned project cluster area is determined, and based on the comparison result of the traffic accessibility index and the preset accessibility index threshold, the first support capacity level corresponding to the road network data is determined; wherein, the traffic accessibility index includes road network density; Based on the infrastructure data, the infrastructure carrying capacity index corresponding to the planned project cluster area is determined, and based on the comparison result between the infrastructure carrying capacity index and the preset carrying capacity index threshold, the second support capacity level corresponding to the infrastructure data is determined; wherein, the infrastructure carrying capacity index includes at least one of infrastructure coverage rate and capacity margin; Based on the public service supporting data, the service supporting indicators corresponding to the planned project cluster area are determined, and based on the comparison results between the service supporting indicators and the preset service supporting indicator thresholds, the third support capability level corresponding to the public service supporting data is determined; wherein, the service supporting indicators include supporting coverage rate; The support capability level corresponding to the planned project cluster area is determined based on the lowest level among the first support capability level, the second support capability level, and the third support capability level.

7. The site selection planning method according to claim 6, characterized in that, The site selection planning results include the planning type corresponding to the planned project cluster area; the generation of site selection planning results based on the support capability level includes: When the support capacity level is greater than the preset level threshold, the planning type corresponding to the planned project cluster area is a priority construction area; When the support capacity level is less than or equal to the preset level threshold, the planning type corresponding to the planned project cluster area is a gap-filling area.

8. A site selection and planning device, characterized in that, include: A construction module is used to construct a spatiotemporal knowledge graph based on the obtained planning information of the target area; wherein, the spatiotemporal knowledge graph includes planning project entities and time entities and spatial entities that are associated with the planning project entities; The retrieval module is used to extract retrieval conditions from the received site selection planning query information and retrieve the entity combinations that match the retrieval conditions from the spatiotemporal knowledge graph; wherein, the retrieval conditions include at least one of project conditions, time conditions and spatial conditions; and an entity combination includes a planning project entity and the time entity and the spatial entity that are connected to the planning project entity. A combination module is used to take the combination of entities that satisfy the pre-associated spatial constraints of the target area as a target entity combination, and determine each planning project cluster area based on the spatial entities in the target entity combination; wherein, a planning project cluster area covers at least the area where one of the spatial entities is located; The generation module is used to determine the support capacity level corresponding to the planned project cluster area based on the obtained regional supporting information, and generate site selection planning results based on the support capacity level.

9. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the location planning method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the location planning method as described in any one of claims 1 to 7.

Images