Digital asset library construction method for urban multi-professional facilities

Abstract

The invention is suitable for the technical field of data processing, and provides an urban multi-professional facility digital asset library construction method, which comprises the following steps: collecting multi-source heterogeneous data of facilities in different professional fields; screening data from the multi-source heterogeneous data based on a facility type special precision threshold rule and a cross-professional space coupling weight factor to obtain an initial asset data set; performing format conversion and semantic mapping on data in the initial asset data set to generate standardized asset data; fusing the standardized asset data by adopting an entity association method based on a spatial index and a graph model; detecting whether the proportion of the number of feature points with spatial position conflicts in the fusion process is lower than a preset conflict threshold value, and if yes, generating target fusion data; and constructing a digital asset library of the urban multi-professional facility according to the target fusion data. The accurate association quality and the collaborative management reliability of the multi-professional facility data are improved, and efficient decision making of the smart city is effectively supported.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for constructing a digital asset database for multiple urban facilities. Background Technology

[0002] With the deepening of smart city construction, the demand for digital and refined management of facilities in various professional fields such as urban underground pipelines, transportation facilities, and municipal facilities is increasing. Currently, various professional departments generally use technologies such as building information modeling, geographic information systems, and the Internet of Things to independently collect and store multi-source heterogeneous data such as the spatial location, geometric shape, and operational status of their professional facilities, thus initially forming their own digital information databases.

[0003] However, the accuracy requirements for data from different specialized facilities vary significantly, and the lack of a unified and effective quality screening mechanism leads to errors in the integrated asset database. Furthermore, the database fails to consider the complex three-dimensional spatial dependencies and cross-domain business logic between facilities, requiring users to manually query related information across multiple disparate systems. This impacts the efficiency of emergency response and collaborative management. The low quality, poor correlation, and insufficient consistency of the asset database data easily result in inefficient applications and high decision-making risks, failing to support the precise management and efficient collaboration of smart cities. Summary of the Invention

[0004] This invention provides a method for constructing a digital asset database for multiple urban facilities, which addresses the problems of low data quality, poor correlation, and insufficient consistency in existing asset databases.

[0005] This invention provides a method for constructing a digital asset database for multiple urban facilities, including:

[0006] Collect multi-source heterogeneous data of facilities in different professional fields, including spatial location, geometric shape and operational status data;

[0007] Based on a predefined urban multi-professional facility classification system, facility type-specific precision threshold rules are determined, and data is filtered from the multi-source heterogeneous data through the facility type-specific precision threshold rules and cross-professional spatial coupling weight factors to obtain an initial asset dataset;

[0008] The data in the initial asset dataset is subjected to format conversion and semantic mapping to generate standardized asset data;

[0009] An entity association method based on spatial indexing and graph models is adopted to fuse the standardized asset data according to the three-dimensional spatial topological relationship of urban facilities and cross-domain business logic relationship;

[0010] If the proportion of feature points with spatial location conflicts during the fusion process is lower than a preset conflict threshold, then the target fused data is generated.

[0011] A digital asset database of urban multi-disciplinary facilities is constructed based on the target fusion data.

[0012] Furthermore, the data filtering through facility type-specific precision threshold rules and cross-professional spatial coupling weighting factors includes:

[0013] Obtain the preset spatial precision threshold, attribute precision threshold, and data timeliness threshold in the facility type-specific precision threshold rules;

[0014] When the spatial location error of the collected data is less than the spatial accuracy threshold for the corresponding facility type, the attribute field missing rate is lower than the attribute accuracy threshold, and the interval between the collection time and the current time is less than the data timeliness threshold, the spatial conflict risk is verified by combining the cross-professional spatial coupling weight factor, and the data is included in the initial asset dataset.

[0015] Furthermore, the cross-professional spatial coupling weighting factor is a quantification function based on the spatial distance between facilities and the intensity of business dependence.

[0016] Furthermore, when the facility to be collected belongs to multiple facility types in multiple classification systems, this includes:

[0017] The most stringent spatial precision threshold and attribute precision threshold among the multiple facility types are selected as the screening criteria. The most stringent spatial precision threshold is the threshold with the smallest value, and the most stringent attribute precision threshold is the threshold with the largest value.

[0018] The shortest data timeliness threshold among the multiple facility types is selected as the screening criterion; the cross-professional spatial coupling weight factor is set as the superposition value of the coupling weights of multiple facility types.

[0019] Furthermore, the step of performing format conversion and semantic mapping on the data in the initial asset dataset to generate standardized asset data includes:

[0020] The data in the initial asset dataset is decomposed into semantic basic units and semantic association units. The semantic basic units represent the core attribute information of the facilities, and the semantic association units represent the association information between the facilities.

[0021] The first semantic information of the semantic basic unit is analyzed, and the first semantic information includes at least facility identification, key geometric parameters and the professional field to which it belongs;

[0022] The second semantic information of the semantic association unit is parsed, and the second semantic information includes at least the associated facility identifier and the association type;

[0023] Generate pointing edges based on the spatial topological relationship or cross-domain business logic relationship between the first semantic information and the second semantic information;

[0024] The standardized asset data is generated by combining the first semantic information, the second semantic information, and the pointing edge.

[0025] Furthermore, the generation of pointing edges includes:

[0026] When there is a physical connection between the facilities represented by the first semantic information and the second semantic information, a pointing edge representing the physical connection is generated based on the three-dimensional spatial topological relationship;

[0027] When there is a business dependency between the facilities represented by the first semantic information and the second semantic information, a pointing edge representing the business dependency is generated based on predefined cross-domain business logic rules.

[0028] Furthermore, the spatial index and entity association method of the graph model includes:

[0029] Establish a spatial relationship engine based on a spatial index structure to retrieve spatial proximity and topological relationships between facilities;

[0030] Construct a business logic model based on a graph structure, where entity nodes in the graph structure represent facility instances in the standardized asset data, and relational edges in the graph structure represent cross-domain business logic dependencies between facilities.

[0031] Furthermore, the entity association method based on spatial indexing and graph models integrates the standardized asset data according to the three-dimensional spatial topological relationships of urban facilities and cross-domain business logic relationships, including:

[0032] Using the spatial relationship engine, spatial location matching and three-dimensional spatial topology calculation are performed on facility instances in the standardized asset data to establish spatial associations between facilities;

[0033] Using the business logic model, the entity nodes and relationship edges in the standardized asset data are mapped to the graph structure to establish cross-domain business logic associations between facilities;

[0034] Based on the spatial association and the business logic association, entity association and attribute fusion are performed on the standardized asset data.

[0035] Furthermore, the entity association and attribute fusion of the standardized asset data includes:

[0036] When the spatial association and the business logic association point to the same physical facility, the entity nodes corresponding to the same facility in the standardized asset data from different sources will have their attributes merged and conflicts resolved.

[0037] When the spatial association and the business logic association point to different facilities but have an interactive relationship, a corresponding relationship edge is established in the graph structure and the association attribute is assigned.

[0038] Furthermore, whether the proportion of feature points with spatial location conflicts during the detection and fusion process is lower than a preset conflict threshold includes:

[0039] Obtain the spatial feature point set of the facility instance to be associated during the fusion process. The spatial feature point set includes geometric endpoints, key turning points, and connection points.

[0040] Based on predefined spatial location tolerance rules, it is determined whether there are any conflict points in the set of spatial feature points that overlap in location or exceed a preset distance threshold;

[0041] Calculate the ratio of the number of conflict points to the total number of points in the spatial feature point set, and determine whether the ratio is lower than the preset conflict threshold.

[0042] As can be seen from the above technical solutions, the present invention has the following advantages:

[0043] This invention employs facility-type-specific precision threshold rules and cross-professional spatial coupling weighting factors to collaboratively screen multi-source heterogeneous data. It sets differentiated standards for spatial precision, attribute completeness, and data timeliness based on the functional importance of different professional facilities, quantifying spatial dependencies between facilities to avoid high-risk conflicts. Secondly, it performs two-dimensional fusion of standardized asset data, accurately calculating three-dimensional positional relationships based on a spatial index structure and constructing a cross-professional business dependency network using a graph model. Finally, it generates target fused data that meets the requirements by detecting the conflict ratio of spatial feature points during the fusion process and controlling it within a preset threshold. The digital asset database constructed by this invention effectively improves the accuracy of multi-professional facility data association and the reliability of collaborative management, effectively supporting efficient decision-making in smart cities. Attached Figure Description

[0044] Figure 1 This is a schematic flowchart of an embodiment of a method for constructing a digital asset database for multiple urban facilities according to the present invention;

[0045] Figure 2 This is a schematic flowchart of another embodiment of a method for constructing a digital asset database for multiple urban facilities according to the present invention;

[0046] Figure 3This is a schematic flowchart of another embodiment of a method for constructing a digital asset database for multiple urban facilities according to the present invention;

[0047] Figure 4 This is a schematic flowchart of another embodiment of a method for constructing a digital asset database for multiple urban facilities according to the present invention;

[0048] Figure 5 This is a schematic flowchart of another embodiment of a method for constructing a digital asset database for multiple urban facilities according to the present invention. Detailed Implementation

[0049] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0050] Example 1

[0051] The implementation method in this embodiment can be implemented in a system, on a server, or on a terminal; no specific limitation is made. The following section will describe the method for constructing a digital asset database for multi-disciplinary urban facilities in this application from a system implementation perspective. Please refer to... Figures 1 to 5 The method provided in this application includes the following steps:

[0052] S11. Collect multi-source heterogeneous data of facilities in different professional fields. The multi-source heterogeneous data includes spatial location, geometric shape and operational status data.

[0053] In the process of digitizing smart city infrastructure, network devices such as edge computing gateways and central server clusters acquire facility information in real time or periodically from the following three types of heterogeneous data sources through pre-deployed multi-source data acquisition adapters: Spatial location data source: acquiring the absolute geographic coordinates and offset relative to the reference point of the facility through GNSS positioning terminals and laser scanning point cloud equipment; Geometric shape data source: extracting the geometric structural parameters of the facility from the BIM design model library and oblique photogrammetry 3D model; Operational status data source: accessing real-time telemetry data from 10T sensors and SCADA systems deployed on the facility.

[0054] The spatial location data is a floating-point coordinate sequence, the geometric data includes vector graphics and 3D mesh data, and the operational status data is a time-series numerical stream. Different professional facilities require different data collection parameters. For example, underground pipeline professionals need to collect data on pipe type (string), burial depth (floating-point number), and internal wall corrosion coefficient (floating-point number); traffic facility professionals need to collect data on the number of lanes (integer), road surface smoothness index (floating-point number), and real-time traffic flow (integer); and municipal lighting professionals need to collect data on lamp power (integer), illuminance sensor values ​​(integer), and energy consumption statistics (floating-point number).

[0055] S12. Based on the predefined urban multi-professional facility classification system, determine the facility type-specific precision threshold rules, and filter data from multi-source heterogeneous data through the facility type-specific precision threshold rules and cross-professional spatial coupling weight factors to obtain the initial asset dataset;

[0056] In this embodiment, the predefined urban multi-professional facility classification system is a cross-domain classification framework that includes urban infrastructure. It encompasses five major professional domains: power facilities (e.g., substations, distribution cabinets), water supply and drainage facilities (e.g., pumping stations, sewage pipes), traffic facilities (e.g., traffic lights, road markings), municipal lighting (e.g., streetlights, landscape lights), and communication facilities (e.g., 5G base stations, fiber optic junction boxes). Each professional domain has three levels of classification (e.g., power facilities → transmission equipment → high-voltage cables). The facility type-specific precision threshold rule is a pre-set quality control standard based on the facility's functional importance, safety level, and data application scenario. The cross-professional spatial coupling weight factor is a function that quantifies the spatial proximity and business dependence between facilities: w = α·e ―βd +γI dep Where d is the three-dimensional Euclidean distance between facilities, α and β are distance attenuation coefficients, with the default α = 0.7 and β = 0.02. dep The business dependency intensity index, ranging from 0 to 1, is defined by the domain knowledge base. γ is the business weight coefficient, with a default value of γ = 0.3. Based on this, the process of filtering data from multi-source heterogeneous data using facility type-specific precision threshold rules and cross-professional spatial coupling weight factors is as follows:

[0057] S121. Obtain the preset spatial precision threshold, attribute precision threshold, and data timeliness threshold in the facility type-specific precision threshold rules;

[0058] Spatial accuracy thresholds: high-voltage power cables (0.05 meters), traffic road markings (0.2 meters), municipal streetlights (0.1 meters); attribute accuracy thresholds: missing rate of voltage level field for power equipment ≤1%, missing rate of pipe diameter field for water supply network ≤3%; data timeliness thresholds: operational status data (≤5 minutes), geometric shape data (≤30 days).

[0059] S122. When the spatial location error of the collected data is less than the spatial accuracy threshold of the corresponding facility type, the attribute field missing rate is lower than the attribute accuracy threshold, and the interval between the collection time and the current time is less than the data timeliness threshold, the data is included in the initial asset dataset by combining the cross-professional spatial coupling weight factor to verify the spatial conflict risk.

[0060] Implementation process: Data that does not meet the three basic thresholds is discarded (e.g., if the GNSS positioning error of a streetlight is 0.15 meters, it is discarded if it exceeds the threshold of 0.1 meters). For data that passes the initial screening, the coupling weight between the data and other professional facilities within a 50-meter radius is calculated. If the weight is greater than the set value and the spatial distance is less than the safety distance (e.g., the safety distance between an oil pipeline and a cable trench is 1 meter), a conflict warning is triggered. A manual review process is initiated for conflicting data to confirm whether it is a genuine spatial overlap (e.g., the multi-layered structure of an overpass) or a collection error. Data is only included in the dataset if it is confirmed to be a non-genuine conflict.

[0061] Specifically, when the facility to be collected belongs to multiple facility types in multiple classification systems, the following are included:

[0062] 1. Select the most stringent spatial precision threshold and attribute precision threshold among multiple facility types as the screening criteria. The most stringent spatial precision threshold is the threshold with the smallest value, and the most stringent attribute precision threshold is the threshold with the largest value.

[0063] 2. Select the shortest data timeliness threshold among multiple facility types as the screening criterion; set the cross-professional spatial coupling weight factor as the superposition value of the coupling weight of multiple facility types.

[0064] The most stringent spatial accuracy threshold and attribute accuracy threshold among multiple facility types were selected as the screening criteria:

[0065] Spatial accuracy: Take the minimum threshold (e.g., if a smart street light with communication function is both a lighting facility (threshold 0.1 meters) and a communication facility (threshold 0.05 meters), then 0.05 meters will be used);

[0066] Attribute precision: Take the maximum threshold (e.g., for the missing rate threshold of the lighting power field: the requirement is ≤2% in the lighting field and ≤1% in the communication field, so ≤1% is used).

[0067] The shortest data timeliness threshold among multiple facility types was selected as the screening criterion:

[0068] Data timeliness: Use the shortest timeliness (e.g., for substation temperature data: the power sector requires ≤10 minutes, and the security sector requires ≤2 minutes, so use ≤2 minutes).

[0069] Set the cross-professional spatial coupling weight factor as the sum of the coupling weights of multiple facility types: Where wk is the weight coefficient for the k-th professional type, and n is the number of professional types described in the facility.

[0070] S13. Perform format conversion and semantic mapping on the data in the initial asset dataset to generate standardized asset data;

[0071] In this embodiment, format conversion is used to unify the geometric representation and coordinate system of data from different sources, eliminating differences in data structure; semantic mapping is used to establish the correspondence between facility attributes and domain knowledge systems, achieving cross-disciplinary semantic understanding. The specific implementation process is as follows:

[0072] S131. Decompose the data in the initial asset dataset into semantic basic units and semantic association units. Semantic basic units represent the core attribute information of the facilities, and semantic association units represent the association information between the facilities.

[0073] The semantic foundation unit extracts the core attributes of facility entities and identifies key fields through an attribute parsing engine. For example, it separates inherent attributes such as pipe material, pipe diameter, and pressure rating from water supply network data. The semantic association unit identifies the relationship characteristics between facilities and detects explicit / implicit connections through an association analysis module. For example, it parses the membership relationship between ring main units and cables from a power system topology diagram.

[0074] S132. Analyze the first semantic information of the semantic basic unit. The first semantic information includes at least the facility identifier, key geometric parameters, and the professional field to which it belongs.

[0075] The core elements of the analysis process are: Facility identification: A globally unique ID is generated according to the rule of "professional code-spatial grid-serial number", such as "PW_15A_1024" representing facility number 1024 in grid number 15 of the water supply profession; Key geometric parameters: Linear facilities are converted into a sequence of coordinates of the start point, end point, and key control points; Volumetric facilities have their outer envelope box size and spatial attitude parameters extracted; Complex structural facilities retain a simplified LOD2 model; Professional domain: Matching a pre-set ontology classification tree, such as "110kV oil-immersed transformer → power transmission and transformation equipment → power facilities".

[0076] S133. Parse the second semantic information of the semantic association unit, the second semantic information including at least the association facility identifier and association type;

[0077] The associated facility identifier is a global ID for locating the target facility, ensuring cross-professional traceability; the association type is a type label matched from a predefined relational database, mainly divided into physical connection type and business dependency type.

[0078] S134. Generate pointing edges based on the spatial topological relationship or cross-domain business logic relationship between the first semantic information and the second semantic information;

[0079] The generation of pointing edges includes the following:

[0080] 1. When there is a physical connection between the facilities represented by the first semantic information and the second semantic information, a pointing edge representing the physical connection is generated based on the three-dimensional spatial topological relationship;

[0081] 2. When there is a business dependency between the facilities represented by the first semantic information and the second semantic information, a pointing edge representing the business dependency is generated based on predefined cross-domain business logic rules.

[0082] Specifically, the generation logic distinguishes between two scenarios: 1. Physical connection pointing edges: spatial relationship calculation based on the facility's 3D model; detection of facility contact surfaces via a geometry engine, generating physical connection edges when the contact area ratio exceeds 95%; additional connection attributes (such as interface type, sealing level). 2. Business dependency pointing edges: execution of predefined cross-domain business rules; calling the rule engine to determine business relationships between facilities, such as "traffic lights and distribution boxes within 10 meters generate a power supply relationship"; quantifying dependency strength (0-1 value) and incorporating it into edge attributes.

[0083] S135. Based on the first semantic information, the second semantic information, and the pointing edge, generate standardized asset data.

[0084] The first semantic information is encapsulated into facility objects containing metadata, geometric parameters, and professional tags; the second semantic information is combined with the pointing edges to form a relational subgraph; and structured data containing geometric models and relationships is generated according to the CityGML standard.

[0085] S14. Employ an entity association method based on spatial indexing and graph models to integrate standardized asset data according to the three-dimensional spatial topological relationships of urban facilities and cross-domain business logic relationships;

[0086] In this embodiment, the spatial index and graph model entity association method includes:

[0087] 1. Establish a spatial relationship engine based on a spatial index structure to retrieve spatial proximity and topological relationships between facilities;

[0088] 2. Construct a business logic model based on a graph structure. Entity nodes in the graph structure represent facility instances in the standardized asset data, and relational edges in the graph structure represent cross-domain business logic dependencies between facilities.

[0089] The spatial relationship engine is a three-dimensional spatial retrieval structure based on R-tree index. It accelerates the proximity query and topological relationship calculation (containment, intersection, adjacency, etc.) between facilities through spatial partitioning algorithms. The attribute graph structure represents the entity nodes of the facility instances in the standardized asset data (such as a transformer or a section of pipeline). The relationship edges represent cross-domain business logic dependencies (such as "power supply" and "data transmission"), with attached attributes such as weight and direction.

[0090] Based on this, the process of integrating standardized asset data is as follows:

[0091] S141. Using a spatial relationship engine, spatial location matching and three-dimensional spatial topology calculation are performed on facility instances in standardized asset data to establish spatial associations between facilities;

[0092] S142. Using a business logic model, the entity nodes and relationship edges in the standardized asset data are mapped to the graph structure to establish cross-domain business logic relationships between facilities;

[0093] S143. Based on spatial association and the aforementioned business logic association, perform entity association and attribute fusion on standardized asset data.

[0094] In addition, when the spatial association and the business logic association point to the same physical facility, the entity nodes corresponding to the same facility in the standardized asset data from different sources are merged and conflict resolved; when the spatial association and the business logic association point to different facilities but have an interaction relationship, the corresponding relationship edge is established in the graph structure and the association attribute is assigned.

[0095] Specifically, firstly, spatial location matching is performed on all facility instances in the standardized asset data using a pre-built 3D R-tree index; the Euclidean distance between facilities is calculated, and if it is less than a professional threshold (e.g., 0.5 meters for underground pipelines), a proximity relationship is marked; simultaneously, a geometric kernel is invoked to perform 3D topology calculations, generating spatial associations such as containment and adjacency. Secondly, facility instances are mapped to graph nodes, such as a distribution box being mapped to a power facility node, and business relationship generation is triggered based on a pre-defined rule base; when node attributes meet rule conditions, such as a power node existing within 10 meters of a traffic light node, weighted business dependency edges are automatically created. When spatial and business associations both point to the same physical facility, multi-source attributes are merged and conflicts are resolved; when they point to different facilities but have interactions, relationship edges are created in the graph structure and interaction parameters are attached, ultimately outputting a unified, merged facility network.

[0096] S15. Detect whether the proportion of feature points with spatial location conflicts during the fusion process is lower than the preset conflict threshold. If so, generate the target fusion data.

[0097] S151. Obtain the spatial feature point set of the facility instances to be associated during the fusion process. The spatial feature point set includes geometric endpoints, key turning points, and connection points.

[0098] S152. Based on predefined spatial location tolerance rules, determine whether there are overlapping or conflict points exceeding a preset distance threshold in the set of spatial feature points;

[0099] S153. Calculate the ratio of the number of conflict points to the total number of points in the spatial feature point set, and determine whether the ratio is lower than the preset conflict threshold.

[0100] Specifically, key spatial feature point sets are obtained from the geometric model of the facility instances to be associated, including geometric endpoints (such as pipeline start and end points), key turning points (such as road centerline inflection points), and connection points (such as valve interface center points). Secondly, based on predefined spatial location tolerance rules, an iterative nearest-point algorithm is used to detect overlapping or out-of-range points between feature point sets. When the difference in three-dimensional coordinates between two points is less than the tolerance, it is marked as an overlapping conflict; when the distance between adjacent facility points is greater than a safety threshold, it is marked as a spacing conflict. Finally, the proportion of conflict points to the total number of feature points is calculated. If the proportion is lower than a preset conflict threshold (default 5%), the fusion is considered valid; otherwise, a conflict handling mechanism is triggered, such as automatic coordinate correction or manual review.

[0101] For example, when merging a water supply pipe (12 feature points) and a cable trench (10 feature points), three coordinate overlap conflict points were detected. The distance between the turning point of the water supply pipe and the corner point of the cable trench was 0.08 meters, which is less than the 0.1-meter tolerance. The conflict ratio = 3 / (12+10) = 13.6% > 5%. After activating the automatic avoidance algorithm and moving the water supply pipe down by 0.15 meters, the conflict ratio was reduced to 1.8%, and the detection was passed.

[0102] S16. Construct a digital asset database of urban multi-professional facilities based on target fusion data.

[0103] Based on validated target fusion data, a multimodal urban infrastructure asset repository is constructed. The storage architecture includes: a spatial layer using the Geomesa spatiotemporal database to store the 3D coordinates and topological relationships of the facilities; a business layer using the Neo4j graph database to manage cross-domain dependency networks; and an attribute layer using a MongoDB document library to record dynamic operational parameters. This asset repository effectively addresses decision-making risks caused by low data quality, poor correlation, and insufficient consistency.

[0104] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a digital asset database for multiple urban facilities, characterized in that, include: Collect multi-source heterogeneous data of facilities in different professional fields, including spatial location, geometric shape and operational status data; Based on a predefined urban multi-professional facility classification system, facility type-specific precision threshold rules are determined, and data is filtered from the multi-source heterogeneous data through the facility type-specific precision threshold rules and cross-professional spatial coupling weight factors to obtain an initial asset dataset; The data in the initial asset dataset is subjected to format conversion and semantic mapping to generate standardized asset data; An entity association method based on spatial indexing and graph models is adopted to fuse the standardized asset data according to the three-dimensional spatial topological relationship of urban facilities and cross-domain business logic relationship; If the proportion of feature points with spatial location conflicts during the fusion process is lower than a preset conflict threshold, then the target fused data is generated. A digital asset database of urban multi-disciplinary facilities is constructed based on the target fusion data.

2. The method for constructing a digital asset database for urban multi-professional facilities according to claim 1, characterized in that, The data screening process, which combines facility type-specific precision threshold rules with cross-professional spatial coupling weighting factors, includes: Obtain the preset spatial precision threshold, attribute precision threshold, and data timeliness threshold in the facility type-specific precision threshold rules; When the spatial location error of the collected data is less than the spatial accuracy threshold for the corresponding facility type, the attribute field missing rate is lower than the attribute accuracy threshold, and the interval between the collection time and the current time is less than the data timeliness threshold, the spatial conflict risk is verified by combining the cross-professional spatial coupling weight factor, and the data is included in the initial asset dataset.

3. The method for constructing a digital asset database for urban multi-professional facilities according to claim 2, characterized in that, The cross-disciplinary spatial coupling weighting factor is a quantification function based on the spatial distance between facilities and the intensity of business dependence.

4. The method for constructing a digital asset database of urban multi-professional facilities according to any one of claims 1-3, characterized in that, When the facility to be collected belongs to the facility type in multiple classification systems, including: The most stringent spatial precision threshold and attribute precision threshold among the multiple facility types are selected as the screening criteria. The most stringent spatial precision threshold is the threshold with the smallest value, and the most stringent attribute precision threshold is the threshold with the largest value. The shortest data timeliness threshold among the multiple facility types is selected as the screening criterion; the cross-professional spatial coupling weight factor is set as the superposition value of the coupling weights of multiple facility types.

5. The method for constructing a digital asset database for urban multi-professional facilities according to claim 1, characterized in that, The step of performing format conversion and semantic mapping on the data in the initial asset dataset to generate standardized asset data includes: The data in the initial asset dataset is decomposed into semantic basic units and semantic association units. The semantic basic units represent the core attribute information of the facilities, and the semantic association units represent the association information between the facilities. The first semantic information of the semantic basic unit is analyzed, and the first semantic information includes at least facility identification, key geometric parameters and the professional field to which it belongs; The second semantic information of the semantic association unit is parsed, and the second semantic information includes at least the associated facility identifier and the association type; Generate pointing edges based on the spatial topological relationship or cross-domain business logic relationship between the first semantic information and the second semantic information; The standardized asset data is generated by combining the first semantic information, the second semantic information, and the pointing edge.

6. The method for constructing a digital asset database for urban multi-professional facilities according to claim 5, characterized in that, The generation of pointing edges includes: When there is a physical connection between the facilities represented by the first semantic information and the second semantic information, a pointing edge representing the physical connection is generated based on the three-dimensional spatial topological relationship; When there is a business dependency between the facilities represented by the first semantic information and the second semantic information, a pointing edge representing the business dependency is generated based on predefined cross-domain business logic rules.

7. The method for constructing a digital asset database for urban multi-professional facilities according to claim 1, characterized in that, The spatial index and graph model entity association method includes: Establish a spatial relationship engine based on a spatial index structure to retrieve spatial proximity and topological relationships between facilities; Construct a business logic model based on a graph structure, where entity nodes in the graph structure represent facility instances in the standardized asset data, and relational edges in the graph structure represent cross-domain business logic dependencies between facilities.

8. The method for constructing a digital asset database for urban multi-professional facilities according to claim 7, characterized in that, The entity association method, based on spatial indexing and graph models, integrates the standardized asset data according to the three-dimensional spatial topological relationships of urban facilities and cross-domain business logic relationships, including: Using the spatial relationship engine, spatial location matching and three-dimensional spatial topology calculation are performed on facility instances in the standardized asset data to establish spatial associations between facilities; Using the business logic model, the entity nodes and relationship edges in the standardized asset data are mapped to the graph structure to establish cross-domain business logic associations between facilities; Based on the spatial association and the business logic association, entity association and attribute fusion are performed on the standardized asset data.

9. The method for constructing a digital asset database for urban multi-professional facilities according to claim 8, characterized in that, The entity association and attribute fusion of the standardized asset data includes: When the spatial association and the business logic association point to the same physical facility, the entity nodes corresponding to the same facility in the standardized asset data from different sources will have their attributes merged and conflicts resolved. When the spatial association and the business logic association point to different facilities but have an interactive relationship, a corresponding relationship edge is established in the graph structure and the association attribute is assigned.

10. The method for constructing a digital asset database for urban multi-professional facilities according to claim 1, characterized in that, Whether the proportion of feature points with spatial location conflicts during the detection and fusion process is lower than a preset conflict threshold includes: Obtain the spatial feature point set of the facility instance to be associated during the fusion process. The spatial feature point set includes geometric endpoints, key turning points, and connection points. Based on predefined spatial location tolerance rules, it is determined whether there are any conflict points in the set of spatial feature points that overlap in location or exceed a preset distance threshold; Calculate the ratio of the number of conflict points to the total number of points in the spatial feature point set, and determine whether the ratio is lower than the preset conflict threshold.

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