CIM (common information model) data coding method and system based on earth subdivision grid

Abstract

The invention relates to video coding, more particularly belongs to the technical field of data coding, and particularly relates to a CIM data coding method and system based on an earth subdivision grid, and the method comprises the steps: dividing a target city into a plurality of grid units through employing a preset earth profile grid, generating CIM elements of the target city, and carrying out the coding of the CIM data according to the geometric complexity of the CIM elements, setting an adaptive grid hierarchy selection system of the CIM elements to construct a mapping relationship between the CIM elements and a plurality of grid units, constructing a two-pole index architecture of the CIM elements in a space-time association storage unit by utilizing space-time composite grid codes of the CIM elements, and creating a hierarchy retrieval mechanism of the CIM elements, and in combination with a self-adaptive grid hierarchy selection system, space-time composite grid coding, a space-time associated storage unit and a hierarchy retrieval mechanism, executing data coding processing of the heterogeneous CIM data. According to the method, the retrieval efficiency and the storage quality of the CIM data can be optimized, and the use efficiency of the CIM data is improved.

Description

Technical Field

[0001] This invention relates to a CIM data encoding method and system based on Earth grid subdivision, belonging to the field of data encoding technology. Background Technology

[0002] CIM data coding refers to the process of systematically classifying, identifying, and coding various elements in a digital urban management model, such as buildings, roads, pipelines, and green spaces. Its core lies in assigning a unique identifier to each data object and associating it with its spatial location, attribute characteristics, and business logic, thereby constructing a digital framework for urban information. With the advancement of new urbanization, urban data is experiencing explosive growth, which brings new challenges and opportunities to urban management. CIM data coding can build standardized "digital ID cards" for urban data, achieving a leap from fragmented management to full-domain collaboration. However, dynamically updated urban elements require the coding system to have real-time adaptability, while the existing coding system lacks flexibility, which may affect the construction effectiveness of smart cities. Therefore, building an independent and controllable CIM data coding system is particularly important.

[0003] Traditional CIM data encoding mainly utilizes a general spatial data model to abstract urban spatial elements into standardized geometric objects for unified management. While this method facilitates the transmission and management of CIM data, it cannot adapt to the accuracy requirements of different scenarios, leading to a surge in data volume and affecting the efficiency of CIM data usage.

[0004] Therefore, there is an urgent need for a solution that can optimize the retrieval efficiency and storage quality of CIM data and improve the utilization efficiency of CIM data. Summary of the Invention

[0005] This invention provides a CIM data encoding method and system based on Earth grid, the main purpose of which is to optimize the retrieval efficiency and storage quality of CIM data and improve the utilization efficiency of CIM data.

[0006] To achieve the above objectives, this invention provides a CIM data encoding method based on Earth mesh, comprising:

[0007] Collect heterogeneous CIM data of the target city, wherein the heterogeneous CIM data includes BIM data, IoT data and GIS data, and perform coordinate system standardization processing on the heterogeneous CIM data to obtain standardized coordinate data;

[0008] The target city is divided into several grid cells using a preset Earth profile grid. The standardized coordinate data is mapped to several of the grid cells to generate CIM elements of the target city. The geometric complexity of the CIM elements is identified, and an adaptive grid hierarchy selection system for the CIM elements is constructed based on the geometric complexity.

[0009] Based on the adaptive grid hierarchy selection system, a mapping relationship between the CIM elements and several grid cells is constructed. Based on the mapping relationship, cross-grid elements in the CIM elements are identified. Based on the cross-grid elements, a cross-grid optimization processing layer for the CIM elements is created.

[0010] Extract the timestamps from the standardized coordinate data, and set the spatiotemporal composite grid code of the CIM element according to the timestamps and the mapping relationship. Based on the spatiotemporal composite grid code, construct the spatiotemporal associated storage unit of the CIM element.

[0011] Based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit is generated. According to the two-level index architecture and the adaptive grid hierarchy selection system, a hierarchical retrieval mechanism for the CIM elements is created.

[0012] By combining the adaptive grid hierarchy selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit, and the hierarchy retrieval mechanism, the data encoding processing of the heterogeneous CIM data is performed to obtain the data encoding result.

[0013] Optionally, the coordinate system standardization process of the heterogeneous CIM data to obtain standardized coordinate data includes:

[0014] The data type of the heterogeneous CIM data is identified using the file fingerprint features of the heterogeneous CIM data;

[0015] Based on the data type, analyze the coordinate system characteristics of the heterogeneous CIM data;

[0016] Based on the coordinate system characteristics, a coordinate system metadata extraction unit for the heterogeneous CIM data is created;

[0017] Based on the coordinate system metadata extraction unit, semantic tags for the heterogeneous CIM data are generated;

[0018] Based on the semantic tags and coordinate system features, an adaptive coordinate transformation method for the heterogeneous CIM data is set;

[0019] Collect historical coordinate transformation data of the heterogeneous CIM data, and use the historical coordinate transformation data to identify the coordinate deviation pattern of the heterogeneous CIM data;

[0020] Based on the coordinate deviation mode, a coordinate transformation compensation mechanism for the heterogeneous CIM data is set;

[0021] Based on the coordinate transformation compensation mechanism and the adaptive coordinate transformation method, a coordinate transformation quality monitoring module for the heterogeneous CIM data is created.

[0022] By combining the coordinate system metadata extraction unit, the adaptive coordinate transformation method, and the coordinate transformation quality monitoring module, coordinate system standardization processing of the heterogeneous CIM data is performed to obtain standardized coordinate data.

[0023] Optionally, mapping the standardized coordinate data to a plurality of grid cells to generate CIM elements of the target city includes:

[0024] Identify adjacent data sources in the standardized coordinate data and calculate the coordinate overlap coefficient of the adjacent data sources;

[0025] Based on the coordinate overlap coefficient, perform a coordinate consistency check on the standardized coordinate data to obtain the coordinate consistency check result.

[0026] After the coordinate consistency verification result meets the preset effect, the standardized coordinate data is mapped to several of the grid cells to obtain the mapped data;

[0027] Extract the entity geometric features from the mapping data;

[0028] The CIM raw data corresponding to the standardized coordinate data is located using the spatial coordinates in the geometric features of the entity.

[0029] The semantic labels and non-geometric attributes of the CIM raw data were parsed.

[0030] By combining the semantic tags and the non-geometric attributes, the entity semantics of the target city are generated;

[0031] The binding process between the geometric features and the entity semantics is performed to generate the CIM elements of the target city.

[0032] Optionally, constructing the adaptive grid hierarchy selection system for the CIM elements based on the geometric complexity includes:

[0033] Based on the geometric complexity, identify the semantic feature control points of the CIM elements;

[0034] Based on the geometric complexity and the semantic feature control points, the initial grid level of the CIM element is determined;

[0035] Establish dynamic adjustment rules for the initial grid hierarchy at the semantic feature control points;

[0036] Identify the environmental response factors in the dynamic adjustment rules;

[0037] Based on the environmental response factor and the dynamic adjustment rule, generate grid-level control instructions for the CIM elements;

[0038] Based on the grid level control instructions, an adaptive grid level selection system for the CIM elements is constructed.

[0039] Optionally, constructing the mapping relationship between the CIM elements and several grid cells according to the adaptive grid hierarchy selection system includes:

[0040] Based on the adaptive grid hierarchy selection system, extract the hierarchical grid corresponding to the CIM element;

[0041] Obtain the spatial partitioning parameters of the hierarchical mesh to generate the geometric representation of the hierarchical mesh;

[0042] The spatial containment relationships between the hierarchical meshes are constructed using the geometric representation;

[0043] Identify the geometric objects corresponding to the CIM elements and extract the bounding boxes of the geometric objects in a preset global coordinate system;

[0044] Based on the bounding box, determine the spatial intersection range between the CIM element and the hierarchical grid;

[0045] Based on the spatial inclusion relationship and the spatial intersection range, determine the target grid level set of the CIM element;

[0046] Calculate the matching coefficients between the plurality of said mesh cells and the target mesh hierarchy set;

[0047] Based on the matching coefficient, extract the effective grid cells of the CIM elements from the target grid hierarchy set;

[0048] Based on the effective grid cells, create a positive mapping list from the CIM features to the grid cells;

[0049] Use the forward mapping list to set the reverse index of the CIM element;

[0050] Based on the forward mapping list and the reverse index, a mapping relationship between the CIM elements and several of the grid cells is constructed.

[0051] Optionally, creating a cross-grid optimization processing layer for the CIM features based on the cross-grid features includes:

[0052] Identify the number of grids and the hierarchical difference corresponding to the cross-grid features;

[0053] Based on the number of grids and the hierarchical difference, identify the cross-grid type of the cross-grid feature;

[0054] Extract the geometric segmentation features corresponding to the cross-mesh features in the cross-mesh type;

[0055] Based on the geometric segmentation features, a cross-mesh geometric reconstruction mechanism for the CIM features is constructed;

[0056] The topological relationships of the cross-grid features are identified through the cross-grid geometry reconstruction mechanism.

[0057] Extract the anomalous topological factors of the geometric segmentation elements from the topological relationships;

[0058] Based on the abnormal topology factor, a topology error detection unit for the CIM element is created;

[0059] Based on the topology error detection unit, a hierarchical topology conflict repair system for the CIM elements is set up.

[0060] By combining the cross-mesh geometric reconstruction mechanism, the topology error detection unit, and the hierarchical topology conflict repair system, a cross-mesh optimization processing layer for the CIM elements is created.

[0061] Optionally, the step of constructing the spatiotemporal associated storage unit of the CIM element based on the spatiotemporal composite grid encoding includes:

[0062] Analyze the spatiotemporal distribution characteristics and computational load characteristics of the CIM elements;

[0063] Based on the aforementioned spatiotemporal distribution characteristics, grid storage sharding rules for the CIM elements are generated;

[0064] The data constraints of the CIM elements are defined based on the computational load characteristics.

[0065] Based on the grid storage sharding rules, a hierarchical storage architecture for the CIM elements is constructed, and the data indexing method of the hierarchical storage architecture is defined;

[0066] Based on the data constraints, configure the parallel computing interface for the CIM elements;

[0067] Based on the data indexing method and the parallel computing interface, a load balancing scheduling engine for the CIM elements is created.

[0068] Based on the load balancing scheduling engine, a spatiotemporal correlation storage unit for the CIM elements is constructed.

[0069] Optionally, the step of generating a two-level index architecture for the CIM features in the spatiotemporal associated storage unit based on the cross-mesh optimization processing layer and the spatiotemporal composite mesh encoding includes:

[0070] Extract the high-level spatial features of the cross-grid optimization processing layer and the temporal data of the spatiotemporal composite grid encoding;

[0071] Based on the high-level spatial features and the time-series data, the CIM elements are subjected to multi-dimensional partitioning to obtain a combined partitioning key;

[0072] The storage nodes in the spatiotemporal associated storage unit are determined, and the combined partition key is mapped to the storage node using a consistent hashing algorithm to generate a global partition index table for the CIM elements.

[0073] Add a coarse-grained cross-grid association field for the cross-grid optimization processing layer to the global partition index table;

[0074] Query the storage node address of the combined partition key in the global partition index table;

[0075] Based on the storage node address, construct a multidimensional local index of the combined partition key;

[0076] Based on the coarse-grained cross-grid association field, set the cross-feature association partition of the CIM feature;

[0077] Create a cross-feature index identifier for the cross-feature association partition in the multi-dimensional local index;

[0078] Construct a distributed coordination protocol between the global partition index table and the multidimensional local index;

[0079] By combining the global partition index table, the multidimensional local index, the distributed collaboration protocol, and the cross-element index identifier, a two-level index architecture for the CIM elements in the spatiotemporal related storage unit is generated.

[0080] Optionally, the step of creating a hierarchical retrieval mechanism for the CIM elements based on the two-level index architecture and the adaptive grid hierarchy selection system includes:

[0081] Based on the two-level index architecture and the adaptive grid hierarchy selection system, a cross-level query channel for the CIM elements is set up;

[0082] Based on the cross-level query channel, the query scope of the CIM element is identified;

[0083] Based on the query scope, create the optimal retrieval path for the CIM element;

[0084] Extract the feature types and associated grid levels from the adaptive grid hierarchy selection system;

[0085] Based on the element type and the associated grid level, construct the element-grid semantic map of the CIM element;

[0086] Based on the element-mesh semantic map, define the grid access priority of the CIM element;

[0087] By combining the cross-level query channel, the optimal retrieval path, and the grid access priority, a hierarchical retrieval mechanism for the CIM elements is created.

[0088] To address the aforementioned problems, the present invention also provides a CIM data encoding system based on an Earth subdivision grid, the system comprising:

[0089] The coordinate unification module is used to collect heterogeneous CIM data of the target city, wherein the heterogeneous CIM data includes BIM data, IoT data and GIS data, and performs coordinate system standardization processing on the heterogeneous CIM data to obtain standardized coordinate data.

[0090] The grid subdivision module is used to divide the target city into several grid cells using a preset Earth profile grid, map the standardized coordinate data into several grid cells, generate CIM elements of the target city, identify the geometric complexity of the CIM elements, and construct an adaptive grid hierarchy selection system for the CIM elements based on the geometric complexity.

[0091] The mapping optimization module is used to construct the mapping relationship between the CIM elements and several grid units according to the adaptive grid hierarchy selection system, identify cross-grid elements in the CIM elements based on the mapping relationship, and create a cross-grid optimization processing layer for the CIM elements based on the cross-grid elements.

[0092] The encoding and storage module is used to extract the timestamp of the standardized coordinate data, and set the spatiotemporal composite grid encoding of the CIM element according to the timestamp and the mapping relationship, and construct the spatiotemporal associated storage unit of the CIM element based on the spatiotemporal composite grid encoding.

[0093] The data retrieval module is used to generate a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, and to create a hierarchical retrieval mechanism for the CIM elements based on the two-level index architecture and the adaptive grid hierarchy selection system.

[0094] The data encoding module is used to combine the adaptive grid hierarchy selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit, and the hierarchy retrieval mechanism to perform data encoding processing on the heterogeneous CIM data and obtain the data encoding result.

[0095] Compared to the problems described in the background art, the embodiments of the present invention, by performing coordinate system standardization processing on the heterogeneous CIM data, obtain standardized coordinate data, which can ensure accurate spatial alignment and seamless integration of data from different sources, and achieve real-time synchronization of multi-source heterogeneous data. Furthermore, by mapping the standardized coordinate data to several grid cells to generate CIM elements of the target city, the embodiments of the present invention can achieve accurate classification of city elements, which helps to assign a unique grid code to each element. The embodiments of the present invention, by identifying the geometric complexity of the CIM elements and constructing an adaptive grid hierarchy selection for the CIM elements based on the geometric complexity, further demonstrate that the present invention can achieve accurate classification of city elements, which helps to assign a unique grid code to each element. The adaptive grid selection system can choose appropriate grid levels for CIM elements of varying complexity, improving the efficiency of data processing and analysis. This embodiment of the invention constructs a mapping relationship between the CIM elements and several grid units based on the adaptive grid level selection system, creating a unique and accurate spatial index for each element, significantly improving data retrieval efficiency. Furthermore, this embodiment of the invention identifies cross-grid elements within the CIM elements based on the mapping relationship, improving the accuracy and completeness of spatial data organization and optimizing data loading efficiency. This embodiment of the invention also creates a cross-grid optimization processing layer for the CIM elements based on these cross-grid elements. This invention can filter out redundant data in CIM data, improving data encoding efficiency. In this embodiment, by constructing a spatiotemporal associated storage unit for CIM elements based on the spatiotemporal composite grid encoding, efficient management, real-time analysis, and collaborative computing of large-scale grid data can be achieved, improving data reliability and access efficiency. Furthermore, this invention, by generating a two-level index architecture for CIM elements in the spatiotemporal associated storage unit based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, can improve CIM data query efficiency and optimize CIM data storage and management. This invention, by using the two-level index architecture and the adaptive... By adopting a grid-level selection system and creating a hierarchical retrieval mechanism for CIM elements, efficient data location and partitioning can be achieved, improving data retrieval efficiency while reducing data encoding storage space. This embodiment of the invention combines the adaptive grid-level selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit, and the hierarchical retrieval mechanism to perform data encoding processing on heterogeneous CIM data, obtaining data encoding results. This avoids the waste of computational resources or loss of detail caused by fixed grid resolution, effectively improving data processing efficiency and accuracy. Simultaneously, it solves the problem of limited capacity of a single storage device, improving data storage reliability and scalability. Therefore, the CIM data encoding method and system based on Earth meshing provided by this embodiment of the invention can optimize the retrieval efficiency and storage quality of CIM data, improving the utilization efficiency of CIM data. Attached Figure Description

[0096] Figure 1 A flowchart illustrating a CIM data encoding method based on Earth grid subdivision provided in an embodiment of the present invention;

[0097] Figure 2 A schematic diagram of grid partitioning for implementing the CIM data encoding method based on Earth grid partitioning, provided as an embodiment of the present invention;

[0098] Figure 3 This is a schematic diagram of a module for implementing a CIM data encoding method based on an Earth subdivision grid, as provided in an embodiment of the present invention.

[0099] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0100] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0101] This application provides a CIM data encoding method based on a gridded Earth. The executing entity of this gridded Earth CIM data encoding method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the gridded Earth CIM data encoding method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0102] Example 1:

[0103] Reference Figure 1 As shown, the data encoding of this invention belongs to video encoding, and provides a CIM data encoding method based on Earth grid partitioning. The method includes:

[0104] S1. Collect heterogeneous CIM data of the target city, wherein the heterogeneous CIM data includes BIM data, IoT data and GIS data, and perform coordinate system standardization processing on the heterogeneous CIM data to obtain standardized coordinate data.

[0105] This invention provides data support for accurate subsequent queries of urban elements by collecting heterogeneous CIM data of the target city. The heterogeneous CIM data refers to data from different sources with different formats and structures in the City Information Model (CIM), including BIM data, IoT data, and GIS data. GIS data is used to represent geospatial information, including geographic coordinates, topography, land use, and transportation networks. IoT data is used to dynamically reflect the current operating status of the city, such as traffic flow and environmental monitoring data. BIM data is used to display the detailed three-dimensional digital structure of buildings or infrastructure, including the geometric information, spatial relationships, geographic information, and attributes of building components.

[0106] Furthermore, by performing coordinate system standardization processing on the heterogeneous CIM data, the embodiments of the present invention obtain standardized coordinate data, which can ensure accurate spatial alignment and seamless integration of data from different sources, realize real-time synchronization of multi-source heterogeneous data, and avoid spatial position deviations caused by coordinate system differences. The standardized coordinate data refers to the heterogeneous data set with unified spatial reference formed by converting CIM data from different sources, formats, and coordinate systems into a unified coordinate system.

[0107] As an embodiment of the present invention, the coordinate system standardization process of the heterogeneous CIM data to obtain standardized coordinate data includes:

[0108] The data type of the heterogeneous CIM data is identified using the file fingerprint features of the heterogeneous CIM data;

[0109] Based on the data type, analyze the coordinate system characteristics of the heterogeneous CIM data;

[0110] Based on the coordinate system characteristics, a coordinate system metadata extraction unit for the heterogeneous CIM data is created;

[0111] Based on the coordinate system metadata extraction unit, semantic tags for the heterogeneous CIM data are generated;

[0112] Based on the semantic tags and coordinate system features, an adaptive coordinate transformation method for the heterogeneous CIM data is set;

[0113] Collect historical coordinate transformation data of the heterogeneous CIM data, and use the historical coordinate transformation data to identify the coordinate deviation pattern of the heterogeneous CIM data;

[0114] Based on the coordinate deviation mode, a coordinate transformation compensation mechanism for the heterogeneous CIM data is set;

[0115] Based on the coordinate transformation compensation mechanism and the adaptive coordinate transformation method, a coordinate transformation quality monitoring module for the heterogeneous CIM data is created.

[0116] By combining the coordinate system metadata extraction unit, the adaptive coordinate transformation method, and the coordinate transformation quality monitoring module, coordinate system standardization processing of the heterogeneous CIM data is performed to obtain standardized coordinate data.

[0117] The file fingerprint feature refers to a unique identifier vector generated by extracting the inherent features (such as file header information, metadata structure, specific identifiers, data organization patterns, etc.) of heterogeneous CIM data files. For example, IoT data streams contain feature fields such as device ID and timestamps. The data type refers to the data classification of different sources and characteristics determined based on the text fingerprint features in heterogeneous CIM data, such as BIM data and GIS data. The coordinate system feature refers to the unique mathematical description parameters of the data source coordinate system, including but not limited to coordinate system type, origin position, axial orientation, spatial reference identifier code, and accuracy level. The coordinate system metadata extraction unit refers to an automated metadata extraction module based on the fusion of rule engines and machine learning, used to extract coordinate system-related information, such as coordinate system name, projection parameters, and units. The semantic label refers to a label with a specific meaning assigned to a data element. For example, in CIM data, buildings are assigned building labels, and roads are assigned road labels. The adaptive coordinate transformation method refers to a method of dynamically adjusting the coordinate transformation strategy according to the characteristics and needs of the data. For example, for... For BIM data requiring high precision, a seven-parameter Bursa model can be used for coordinate transformation. For IoT data with high real-time requirements, a fast three-parameter transformation method can be used. The historical coordinate transformation data refers to the data recorded during past coordinate transformations, including coordinate values ​​before and after transformation, transformation parameters, etc. The coordinate deviation pattern refers to the regularity or pattern of coordinate deviation caused by various reasons during the coordinate transformation process. For example, by analyzing historical coordinate transformation data, it is found that the coordinates of a certain area are generally offset by 5 meters in the X direction and 3 meters in the Y direction after transformation. The coordinate transformation compensation mechanism refers to the measures taken to correct the deviation during the coordinate transformation process. For example, if the coordinates of a certain area are generally offset by 5 meters in the X direction after transformation, 5 meters can be subtracted from the X coordinate after the coordinate transformation. The coordinate transformation quality monitoring module refers to the verification tool used to monitor and evaluate the accuracy and quality of coordinate transformation in real time. For example, during the coordinate transformation process, this module calculates the difference between the coordinates before and after transformation in real time and compares it with a preset accuracy threshold. If the difference exceeds the threshold, an alarm is issued and relevant information is recorded.

[0118] Optionally, the file fingerprint features of the heterogeneous CIM data can be obtained through the magic number of the file header of the heterogeneous CIM data. Based on the coordinate system features, the coordinate system metadata extraction unit of the heterogeneous CIM data can be created using a neural network model, such as the BERT network model. The identification of the coordinate deviation pattern of the heterogeneous CIM data using the historical coordinate transformation data can be achieved using the Kriging interpolation method. Based on the coordinate transformation compensation mechanism and the adaptive coordinate transformation method, the coordinate transformation quality monitoring module of the heterogeneous CIM data can be created using the least squares matching algorithm.

[0119] S2. Using a preset Earth profile grid, the target city is divided into several grid units. The standardized coordinate data is mapped to several of the grid units to generate CIM elements of the target city. The geometric complexity of the CIM elements is identified, and an adaptive grid level selection system for the CIM elements is constructed based on the geometric complexity.

[0120] This invention provides a unified spatial reference framework for the target city by using a preset Earth profile grid to divide the target city into several grid units, thereby enabling efficient retrieval and management of urban element data. The Earth profile grid refers to a spatial grid system that regularly or irregularly divides the Earth's surface and internal space based on the Earth's curvature and geographic coordinate system, such as the GeoSOT grid. The grid unit refers to the smallest spatial unit obtained by subdividing the target city space based on the Earth profile grid system.

[0121] See Figure 2 The diagram shown is a grid partitioning schematic diagram of a CIM data encoding method based on Earth grid partitioning provided in an embodiment of the present invention. The recursive partitioning process from level 0 to level 2 is shown from left to right. The target city can achieve precise partitioning from coarse-grained (level 0, large-scale coverage) to fine-grained (level 2, more refined division) through this recursive partitioning method, so as to gradually divide the urban space into appropriate grid units.

[0122] Furthermore, by mapping the standardized coordinate data to several grid cells, the present invention generates CIM elements of the target city, which can achieve accurate classification of urban elements and help assign a unique grid code to each element. The CIM elements refer to the digital representation of various physical or abstract objects in the urban information model, including physical elements and abstract elements. For example, physical elements can be buildings, roads, bridges, etc., while administrative divisions, functional zones, population density zones, etc. belong to abstract elements.

[0123] As an embodiment of the present invention, the step of mapping the standardized coordinate data onto a plurality of grid cells to generate CIM elements of the target city includes:

[0124] Identify adjacent data sources in the standardized coordinate data and calculate the coordinate overlap coefficient of the adjacent data sources;

[0125] Based on the coordinate overlap coefficient, perform a coordinate consistency check on the standardized coordinate data to obtain the coordinate consistency check result.

[0126] After the coordinate consistency verification result meets the preset effect, the standardized coordinate data is mapped to several of the grid cells to obtain the mapped data;

[0127] Extract the entity geometric features from the mapping data;

[0128] The CIM raw data corresponding to the standardized coordinate data is located using the spatial coordinates in the geometric features of the entity.

[0129] The semantic labels and non-geometric attributes of the CIM raw data were parsed.

[0130] By combining the semantic tags and the non-geometric attributes, the entity semantics of the target city are generated;

[0131] The binding process between the geometric features and the entity semantics is performed to generate the CIM elements of the target city.

[0132] The adjacent data sources refer to CIM data from different sources that are spatially adjacent. For example, the BIM model (IFC format) of a subway station and the GIS data (Shapefile) of surrounding roads may overlap in planar projection. This overlapping area can be considered an adjacent data source. The coordinate overlap coefficient is an indicator that quantifies the degree of coordinate overlap between two adjacent data sources. It can be determined by the ratio of the overlapping area of ​​the two data source coordinate regions to the area of ​​the smaller data source. The coordinate consistency verification process is the process of verifying whether the multi-source data meets the consistency requirements in spatial representation by analyzing the coordinate overlap coefficients of adjacent data sources. For example, if the overlap coefficient between the BIM building model and the GIS road data is lower than a threshold (e.g., 0.8), an error in the building location labeling is indicated. The coordinate consistency verification result refers to the conclusion obtained after verifying the standardized coordinate data. It can be divided into two conclusions: if the data is consistent, a consistency confirmation will be given, and CIM elements will continue to be generated; if they are inconsistent, the data that failed the verification will be marked and excluded when generating CIM elements. The preset effect refers to the pre-set coordinate consistency verification passing standard, which usually includes overlap. Coefficient thresholds, allowable coordinate deviation ranges, etc. For example, when the overlap coefficient is ≥0.9 and the coordinate deviation is ≤0.5 meters, the consistency check is considered passed. The entity geometric features refer to the geometric parameters describing the shape, size, and position of the spatial entity, including geometric elements such as points, lines, surfaces, and volumes, as well as topological relationships. The spatial coordinates refer to the numerical values ​​representing the entity's position in space, usually expressed as three-dimensional coordinates (X,Y,Z) or latitude and longitude + elevation (L,B,H). The CIM raw data refers to the original CIM data source that has not undergone coordinate standardization, such as the original BIM model of a bridge and the road GI. S data, the semantic tag refers to the semantic classification identifier of the spatial entity, the non-geometric attribute refers to the attribute that does not directly describe the shape or location of the entity, including the entity's physical, functional, management, etc., such as the purpose, number of floors, and year of construction of a building, the entity semantic refers to the complete semantic description formed by combining the semantic tag with the non-geometric attribute, the binding process refers to the process of establishing the correspondence between geometric features and entity semantics to form a complete CIM element, such as automatically associating geometric features (such as area, height) with entity semantics (such as building type, usage nature) through predefined semantic mapping rules.

[0133] Optionally, adjacent data sources in the standardized coordinate data can be identified by a spatial indexing algorithm. When the coordinate consistency verification result meets the preset effect, the entity geometric features of the standardized coordinate data can be extracted according to data type classification. For example, the entity geometric features of the BIM model can be extracted by an edge detection algorithm, and the point cloud data can be extracted by a clustering algorithm. The semantic labels and non-geometric attributes of the CIM raw data can be parsed using a semantic model.

[0134] This invention identifies the geometric complexity of CIM elements and constructs an adaptive grid level selection system based on that complexity. This allows for the selection of appropriate grid levels for CIM elements with different levels of complexity, improving the efficiency of data processing and analysis. The geometric complexity refers to the degree of complexity of the geometric features of urban spatial objects. For example, museums have higher geometric complexity, while ordinary residences have lower geometric complexity. The adaptive grid level selection system is a rule system that dynamically matches grid precision based on the geometric complexity of CIM elements, such as using high-precision grids for complex elements and low-precision grids for simple elements.

[0135] Optionally, the geometric complexity of the CIM element can be identified by calculating the combined feature parameters of the CIM element, such as calculating the number of polygon sides, surface fitting error, and topological node density of the CIM element.

[0136] As an embodiment of the present invention, constructing an adaptive grid hierarchy selection system for the CIM elements based on the geometric complexity includes:

[0137] Based on the geometric complexity, identify the semantic feature control points of the CIM elements;

[0138] Based on the geometric complexity and the semantic feature control points, the initial grid level of the CIM element is determined;

[0139] Establish dynamic adjustment rules for the initial grid hierarchy at the semantic feature control points;

[0140] Identify the environmental response factors in the dynamic adjustment rules;

[0141] Based on the environmental response factor and the dynamic adjustment rule, generate grid-level control instructions for the CIM elements;

[0142] Based on the grid level control instructions, an adaptive grid level selection system for the CIM elements is constructed.

[0143] The semantic feature control points refer to key locations in CIM elements that possess both geometric features and business semantics, such as building entrances / exits, road intersections, and pipeline valve nodes. These semantic feature control points can be identified through curvature extrema and topological connectivity in the geometric complexity. For example, control points for building entrances / exits can be automatically identified through the geometric abrupt change features of the doorway (curvature > 0.8), and road intersections can be determined through centerline topological connectivity (≥ 3). The initial grid level refers to the preliminary grid fineness set based on the geometric complexity and semantic feature control points when dividing CIM elements into grids. The dynamic adjustment rules refer to a set of predefined logical conditions and mathematical expressions used as the decision-making basis for automatically correcting the grid level based on the real-time state changes of CIM elements. The environmental response factor refers to a set of dynamic parameters that quantify the impact of the external environment on the grid level, acquired in real-time through IoT devices and the business system. The grid level control command refers to a command generated based on the environmental response factor and dynamic adjustment rules to control specific adjustments to the grid level of CIM elements.

[0144] In an optional embodiment of the present invention, the initial grid level of the CIM element is determined using the following formula based on the geometric complexity and the semantic feature control points:

[0145]

[0146] Where L represents the initial grid level of the CIM element, This represents the basic grid level of the CIM element, where n represents the total number of semantic feature control points, and j represents the index of the semantic feature control point. The weight represents the geometric complexity corresponding to the j-th semantic feature control point. This represents the weight of the j-th semantic feature control point. This represents the quantized value of the geometric complexity corresponding to the j-th semantic feature control point. This represents the quantization value of the j-th semantic feature control point.

[0147] S3. Based on the adaptive grid hierarchy selection system, construct the mapping relationship between the CIM elements and several grid units. Based on the mapping relationship, identify the cross-grid elements in the CIM elements. Based on the cross-grid elements, create a cross-grid optimization processing layer for the CIM elements.

[0148] This invention, through the adaptive grid hierarchy selection system, constructs a mapping relationship between the CIM elements and several grid units, which can create a unique and accurate spatial index for each element, significantly improving the efficiency of data retrieval. The mapping relationship refers to the bidirectional association rules between the CIM elements and the subdivided grid units, including spatial mapping, semantic mapping, and temporal mapping.

[0149] As an embodiment of the present invention, constructing the mapping relationship between the CIM elements and a plurality of grid cells according to the adaptive grid hierarchy selection system includes:

[0150] Based on the adaptive grid hierarchy selection system, extract the hierarchical grid corresponding to the CIM element;

[0151] Obtain the spatial partitioning parameters of the hierarchical mesh to generate the geometric representation of the hierarchical mesh;

[0152] The spatial containment relationships between the hierarchical meshes are constructed using the geometric representation;

[0153] Identify the geometric objects corresponding to the CIM elements and extract the bounding boxes of the geometric objects in a preset global coordinate system;

[0154] Based on the bounding box, determine the spatial intersection range between the CIM element and the hierarchical grid;

[0155] Based on the spatial inclusion relationship and the spatial intersection range, determine the target grid level set of the CIM element;

[0156] Calculate the matching coefficients between the plurality of said mesh cells and the target mesh hierarchy set;

[0157] Based on the matching coefficient, extract the effective grid cells of the CIM elements from the target grid hierarchy set;

[0158] Based on the effective grid cells, create a positive mapping list from the CIM features to the grid cells;

[0159] Use the forward mapping list to set the reverse index of the CIM element;

[0160] Based on the forward mapping list and the reverse index, a mapping relationship between the CIM elements and several of the grid cells is constructed.

[0161] The hierarchical grid refers to a grid system that divides CIM elements into multiple levels according to spatial precision. The spatial partitioning parameters are core parameters describing the spatial distribution of the hierarchical grid, including grid size (e.g., 100m × 100m), coordinate origin, number of rows and columns, and spatial reference system. The geometric representation refers to the geometric figure that mathematically expresses the spatial shape of the grid cells, which can be expressed using a flexible grid boundary of non-uniform rational B-splines (NURBS). The spatial inclusion relationship means that in the hierarchical grid, each level's grid element is contained within the elements of the next lower level. The grid consists of grid elements, and each grid element at a given level is also a component of the grid elements at the level above it. For example, a level 1 grid cell may contain four level 2 grid cells. The geometric object refers to a CIM element that is a physical entity with shape and position in space, including points, lines, surfaces, and volumes. For example, a building is a closed polygonal surface, a road is a series of polylines, and a streetlight is a point entity. The preset global coordinate system refers to a unified spatial coordinate reference, such as WGS84, CGCS2000, or a city-independent coordinate system. The bounding box refers to the smallest rectangular box that encloses the geometric object. The coordinates of the lower left and upper right corners are used to represent the spatial intersection range, which refers to the overlapping area between the CIM feature geometric object and the grid cell. The specific intersection area or volume can be calculated using geometric intersection operation. The target grid level set refers to a combination of several grid levels suitable for expressing the spatial characteristics of the CIM feature, selected based on the spatial inclusion relationship and the spatial intersection range. For example, building features correspond to L3-L5 levels, and roads correspond to L2-L4 levels. The matching coefficient is an index used to evaluate the similarity between the subdivided grid and the target grid level set. The effective grid cell refers to a cell that has an actual spatial association with the CIM feature. The forward mapping list refers to an index table that maps CIM features to grid cells to form a corresponding relationship, with the format {feature ID:[grid ID1, grid ID2,...]}. The reverse index refers to the index relationship from grid cell to CIM feature, with the format {grid ID:[feature ID1, feature ID2,...]}. The reverse index can be created based on a hierarchical index tree of spatial Hilbert curves.

[0162] Optionally, the spatial inclusion relationship between the hierarchical grids constructed using the geometric representation can be determined by introducing a hierarchical topology matrix using a 9-intersection model. Based on the bounding box, the spatial intersection range between the CIM feature and the hierarchical grid can be determined by ray-based spatial intersection detection with tolerance compensation. The spatial inclusion relationship between the hierarchical grids constructed using the geometric representation can be achieved using depth-first search. The matching coefficient between the grid cell and the target grid hierarchy set can be calculated using a normalized cross-correlation function.

[0163] Furthermore, by identifying cross-grid elements in the CIM elements based on the mapping relationship, the embodiments of the present invention can improve the accuracy and completeness of spatial data organization and optimize data loading efficiency. The cross-grid elements refer to entity objects in CIM whose geometric spatial range spans two or more grid cells, such as road elements in CIM that cross three adjacent grid cells.

[0164] As an embodiment of the present invention, identifying cross-grid elements in the CIM elements based on the mapping relationship includes:

[0165] Identify the element type, associated grid ID, and hierarchical identifier corresponding to each associated grid ID of the CIM element from the mapping relationship;

[0166] Calculate the number of unique values ​​for the associated grid ID;

[0167] Using the number of unique values, the hierarchical identifier, and the element type, a cross-grid association matrix of the CIM element is constructed;

[0168] Based on the cross-grid association matrix, set the cross-grid determination threshold for the CIM element;

[0169] Based on the cross-grid determination threshold, cross-grid elements in the CIM elements are identified.

[0170] The "feature type" refers to a classification label used to identify the physical attributes or functional categories of CIM features, such as specific types like roads, buildings, and pipelines. The "associated grid ID" refers to a unique identifier of a grid that has a spatial intersection or containment relationship with a CIM feature. The "level identifier" refers to the spatial level number representing the associated grid, such as L1 for city-level grids, L2 for district-level grids, and L3 for street-level grids. The "number of unique values" refers to the number of unique grid identifiers in the associated grid ID. The "cross-grid association matrix" refers to a matrix representing the multidimensional association relationship between CIM features and grid levels, feature types, and the number of cross-grids. This matrix includes: rows: grid IDs associated with the CIM feature; columns: weight factors for level identifiers, number of unique values, and feature types; matrix elements: the percentage of intersection area between grids and features. The "cross-grid judgment threshold" refers to a quantitative standard used to determine whether a CIM feature belongs to a cross-grid feature. For example, when the number of unique values ​​is greater than the relevant threshold (n≥2), it is determined to be a cross-grid feature.

[0171] Optionally, the cross-grid association matrix of the CIM element can be constructed using the number of unique values, the hierarchical identifier, and the element type, and the number of unique values ​​of the associated grid ID can be calculated using a spatial relationship filtering algorithm.

[0172] In an optional embodiment of the present invention, the cross-grid determination threshold of the CIM element is set according to the cross-grid association matrix using the following formula:

[0173]

[0174] Where A represents the cross-grid determination threshold for CIM features, k represents the global adjustment coefficient, M represents the total number of feature types in the cross-grid association matrix, m represents the feature type index, G represents the total number of grid levels in the cross-grid association matrix, and g represents the grid level index. This represents the weight coefficient of feature type m at level g. ∥∥ represents the topological feature vector of feature type m at grid level g, and ∥∥ represents the norm sign of the topological feature vector.

[0175] It should be noted that the formula couples the feature type m and the grid level g as independent dimensions through double summation, thus achieving a three-dimensional association between feature type, level, and topological features. Norm calculations, for example, can convert the cross-grid area (unit: m²) of areal features and the cross-grid intersection length (unit: m) of linear features into dimensionless values ​​through the L2 norm, which can solve the problem of unified quantification of features with different dimensions.

[0176] This invention provides an embodiment of the invention that creates a cross-grid optimization processing layer for CIM elements based on the cross-grid elements. This can filter out redundant data in CIM data and improve data encoding efficiency. The cross-grid optimization processing layer refers to a logical layer that systematically processes and optimizes the topological relationships, spatial features, and hierarchical mappings of cross-grid elements.

[0177] As an embodiment of the present invention, the step of creating a cross-grid optimization processing layer for the CIM elements based on the cross-grid elements includes:

[0178] Identify the number of grids and the hierarchical difference corresponding to the cross-grid features;

[0179] Based on the number of grids and the hierarchical difference, identify the cross-grid type of the cross-grid feature;

[0180] Extract the geometric segmentation features corresponding to the cross-mesh features in the cross-mesh type;

[0181] Based on the geometric segmentation features, a cross-mesh geometric reconstruction mechanism for the CIM features is constructed;

[0182] The topological relationships of the cross-grid features are identified through the cross-grid geometry reconstruction mechanism.

[0183] Extract the anomalous topological factors of the geometric segmentation elements from the topological relationships;

[0184] Based on the abnormal topology factor, a topology error detection unit for the CIM element is created;

[0185] Based on the topology error detection unit, a hierarchical topology conflict repair system for the CIM elements is set up.

[0186] By combining the cross-mesh geometric reconstruction mechanism, the topology error detection unit, and the hierarchical topology conflict repair system, a cross-mesh optimization processing layer for the CIM elements is created.

[0187] The term "grid number" refers to the number of polygonal grid cells spanned by a single CIM element, calculated using a spatial coordinate mapping algorithm. The "level difference" refers to the maximum difference between different grid levels involved in a cross-grid element; for example, if an element exists in both LOD2 and LOD4 grids, the level difference is 2. The "cross-grid type" refers to different cross-grid types obtained by classifying cross-grid elements based on the number of grids spanned and the differences in their levels; for example, when a bridge model spans 4 LOD3 grids and 2 LOD4 grids, the grid number is 6, the level difference is 1, and it belongs to a multi-level cross-grid type. The "geometric segmentation element" refers to the element formed by decomposing cross-grid CIM elements according to grid boundaries and topological rules using a spatial discretization algorithm, resulting in an independent... The smallest data unit for geometric representation and spatial positioning; the cross-grid geometric reconstruction mechanism refers to the mechanism of geometric reconstruction and unified expression of cross-grid elements through geometric segmentation and reorganization; the topological relationship refers to the spatial connection relationship between geometric units of CIM elements, including adjacency, containment, intersection, etc.; the abnormal topological factor refers to the topological features that do not conform to the norm or expectation in the topological relationship, such as the overlap of adjacent geometric unit boundaries <90%; the topological error detection unit refers to the tool or module used to detect and identify abnormal topological factors, and performs topological verification of CIM elements by integrating abnormal topological factor calculation, rule matching, and error location functions; the hierarchical topological conflict repair system refers to the hierarchical repair strategy constructed according to the severity and type of topological errors.

[0188] Optionally, the topological relationship of the cross-grid features through the cross-grid geometric reconstruction mechanism can be identified by a 9-Intersection model, and the topological error detection unit of the CIM feature can be created using a spatial indexing acceleration algorithm based on the abnormal topological factor.

[0189] S4. Extract the timestamp of the standardized coordinate data, and set the spatiotemporal composite grid code of the CIM element according to the timestamp and the mapping relationship. Based on the spatiotemporal composite grid code, construct the spatiotemporal associated storage unit of the CIM element.

[0190] This invention extracts the timestamps from the standardized coordinate data and sets the spatiotemporal composite grid code for the CIM elements based on the timestamps and the mapping relationship. This allows for a visual representation of the status of urban elements at different points in time, providing underlying data support for the spatiotemporal analysis of the CIM platform. The timestamp refers to a standardized time identifier used to mark the time of CIM data generation or update, typically in UTC time format. The spatiotemporal composite grid code is a multidimensional coding system that integrates spatial location and time dimensions.

[0191] For example, the specific steps for setting the spatiotemporal composite grid code of the CIM element according to the timestamp and the mapping relationship are as follows: standardizing the timestamp using a dynamic time window compression algorithm; performing spatial coordinate encoding processing of the CIM element using hierarchical perceptual 3D Geohash to obtain a spatial code; generating a time hash value of the CIM element based on the mapping relationship; and performing a bitwise XOR operation on the spatial code and the time hash value to generate a spatiotemporal composite grid code.

[0192] Furthermore, this embodiment of the invention constructs a spatiotemporal associated storage unit for CIM elements based on the spatiotemporal composite grid coding, which can realize efficient management, real-time analysis and collaborative computing of large-scale grid data, and improve data reliability and access efficiency. The spatiotemporal associated storage unit refers to a high-performance data management architecture that realizes large-scale CIM element storage and retrieval by integrating spatiotemporal coding, distributed storage and intelligent computing technologies.

[0193] As an embodiment of the present invention, the step of constructing the spatiotemporal associated storage unit of the CIM element based on the spatiotemporal composite grid encoding includes:

[0194] Based on the spatiotemporal composite grid coding, the spatiotemporal distribution characteristics and computational load characteristics of the CIM elements are analyzed;

[0195] Based on the aforementioned spatiotemporal distribution characteristics, grid storage sharding rules for the CIM elements are generated;

[0196] The data constraints of the CIM elements are defined based on the computational load characteristics.

[0197] Based on the grid storage sharding rules, a hierarchical storage architecture for the CIM elements is constructed, and the data indexing method of the hierarchical storage architecture is defined;

[0198] Based on the data constraints, configure the parallel computing interface for the CIM elements;

[0199] Based on the data indexing method and the parallel computing interface, a load balancing scheduling engine for the CIM elements is created.

[0200] Based on the load balancing scheduling engine, a spatiotemporal correlation storage unit for the CIM elements is constructed.

[0201] The spatiotemporal distribution characteristics refer to the data distribution patterns of CIM elements in the spatial and temporal dimensions, including spatial and temporal features, such as the density difference between densely built-up urban areas and suburbs; the periodicity of data updates (e.g., sensor data collection per second) or event-triggered nature. The computational load characteristics refer to the computational resource requirements of CIM elements during processing, including data volume, computational complexity (e.g., geometric calculations, attribute analysis), and access frequency. These computational load characteristics can be represented by Long Short-Term Memory (LSTM) networks. The system predicts future load trends and generates load feature vectors. The grid storage sharding rules refer to the rules for dividing spatiotemporal grid data into distributed storage units. For example, the octree sharding rule can adaptively adjust the sharding granularity based on data density. The data constraints refer to the hard limitations that CIM element storage and computation must meet, including latency constraints, communication constraints, and consistency constraints. The hierarchical storage architecture refers to a storage system graded according to the data access frequency of CIM elements, including hot data layers, warm data layers, and cold data layers. The data indexing method refers to a retrieval method for quickly locating spatiotemporal data, such as a spatiotemporal hybrid index or a hash-timestamp two-level index. The parallel computing interface refers to an interface for supporting parallel computing, allowing the system to process multiple computing tasks simultaneously. The parallel computing interface includes a parallel computing framework and a data routing protocol. The load balancing scheduling engine refers to an intelligent scheduling system that dynamically allocates computing tasks, including a real-time monitoring module, a dynamic migration module, and a fault tolerance mechanism. The real-time monitoring module tracks the CPU / memory / network utilization of each node; the dynamic migration module migrates overloaded node tasks to idle nodes; and the fault tolerance mechanism automatically restarts failed node tasks.

[0202] Optionally, the data constraints of the CIM element can be defined by the Dijkstra algorithm based on the computational load characteristics, and the load balancing scheduling engine of the CIM element can be created using a load balancing scheduling algorithm based on the data indexing method and the parallel computing interface.

[0203] S5. Based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, generate a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit, and create a hierarchical retrieval mechanism for the CIM elements according to the two-level index architecture and the adaptive grid hierarchy selection system.

[0204] This invention, through the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, generates a two-level index architecture for CIM elements in the spatiotemporal associated storage unit. This improves the query efficiency of CIM data and optimizes the storage and management of CIM data. The two-level index architecture refers to a hierarchical index architecture for distributed storage scenarios, including a global index layer, a local index layer, and a cross-feature association layer. The global index layer is responsible for locating cross-node elements; the local index layer is responsible for accelerating multi-dimensional queries within nodes; and the cross-feature association layer is responsible for enabling jump access through link pointers in the metadata header.

[0205] As an embodiment of the present invention, the step of generating a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding includes:

[0206] Extract the high-level spatial features of the cross-grid optimization processing layer and the temporal data of the spatiotemporal composite grid encoding;

[0207] Based on the high-level spatial features and the time-series data, the CIM elements are subjected to multi-dimensional partitioning to obtain a combined partitioning key;

[0208] The storage nodes in the spatiotemporal associated storage unit are determined, and the combined partition key is mapped to the storage node using a consistent hashing algorithm to generate a global partition index table for the CIM elements.

[0209] Add a coarse-grained cross-grid association field for the cross-grid optimization processing layer to the global partition index table;

[0210] Query the storage node address of the combined partition key in the global partition index table;

[0211] Based on the storage node address, construct a multidimensional local index of the combined partition key;

[0212] Based on the coarse-grained cross-grid association field, set the cross-feature association partition of the CIM feature;

[0213] Create a cross-feature index identifier for the cross-feature association partition in the multi-dimensional local index;

[0214] Construct a distributed coordination protocol between the global partition index table and the multidimensional local index;

[0215] By combining the global partition index table, the multidimensional local index, the distributed collaboration protocol, and the cross-element index identifier, a two-level index architecture for the CIM elements in the spatiotemporal related storage unit is generated.

[0216] The high-level spatial features refer to the high-level abstract features representing spatial location in spatiotemporal composite grid coding, such as regional grid coding, spatial hierarchy division identifiers, and geographic coordinate aggregation units. The time-series data refers to time-dimensional information related to CIM elements, including timestamps, time intervals, and time periods. The combined partition key is a partition identifier formed by combining high-level spatial features and time-series data according to specific rules, consisting of a spatial feature hash value and a time-series feature value. The storage node refers to an independent physical or logical storage unit in a distributed storage system. The global partition index table records the mapping relationship between the combined partition key and storage nodes, including a Geohash prefix and a load factor. The coarse-grained cross-grid association field refers to a field in the cross-grid optimization processing layer used to identify the association relationship between different grids, such as adjacent grid groups and regional aggregation relationships. The storage node address is... The storage node's unique network address in the distributed system can be quickly located using a partition key hash value. The multidimensional local index refers to a local index structure built within the storage node based on a composite partition key, such as a B+ tree index or an inverted index, supporting multidimensional queries of CIM elements, such as spatial, temporal, and attribute combination queries. The cross-element association partition refers to the same logical partitioning of related CIM elements in different grids based on coarse-grained cross-grid association fields. The cross-element index identifier is a unique identifier, such as a UUID, used to identify the logical position or association of the cross-element association partition in the multidimensional local index. The distributed collaboration protocol refers to the set of rules for data synchronization, query routing, and transaction coordination established between the global index and the local index in a two-level index architecture. The distributed collaboration protocol includes an incremental data synchronization mechanism, a dual-threshold query routing decision model, and a PACELC transaction coordinator.

[0217] Optionally, the cross-feature association partitioning of the CIM features can be set by an improved 9-Intersection model based on the coarse-grained cross-grid association field, the cross-feature index identifier for creating the cross-feature association partitioning in the multidimensional local index can be generated by a probabilistic association model, and the distributed collaboration protocol between the global partition index table and the multidimensional local index can be constructed using the improved PACELC-CINEMA model.

[0218] Furthermore, by creating a hierarchical retrieval mechanism for CIM elements based on the bipolar index architecture and the adaptive grid hierarchical selection system, this embodiment of the invention can achieve efficient data location and partitioning, improve data retrieval efficiency, and reduce the storage space of data encoding. The hierarchical retrieval mechanism refers to a retrieval system based on the bipolar index architecture and the adaptive grid hierarchical selection system that constructs a retrieval system for CIM (City Information Model) elements with multi-dimensional hierarchical, spatiotemporal correlation mapping and dynamic adaptation capabilities.

[0219] As an embodiment of the present invention, the step of creating a hierarchical retrieval mechanism for CIM elements based on the two-level index architecture and the adaptive grid hierarchy selection system includes:

[0220] Based on the two-level index architecture and the adaptive grid hierarchy selection system, a cross-level query channel for the CIM elements is set up;

[0221] Based on the cross-level query channel, the query scope of the CIM element is identified;

[0222] Based on the query scope, create the optimal retrieval path for the CIM element;

[0223] Extract the feature types and associated grid levels from the adaptive grid hierarchy selection system;

[0224] Based on the element type and the associated grid level, construct the element-grid semantic map of the CIM element;

[0225] Based on the element-mesh semantic map, define the grid access priority of the CIM element;

[0226] By combining the cross-level query channel, the optimal retrieval path, and the grid access priority, a hierarchical retrieval mechanism for the CIM elements is created.

[0227] The cross-level query channel refers to the logical path connecting different grid levels (such as city level → region level → plot level) for continuous querying across grid levels. The query scope refers to the combination of spatial range, time interval, and attribute constraints involved in the query request. The optimal retrieval path refers to the most efficient and time-saving retrieval route calculated by an algorithm based on specific query requirements and query scope during the data retrieval process. The element type refers to the classification of CIM elements, such as buildings, roads, and vegetation. The associated grid level refers to the grid level associated with a specific CIM element. The element-grid semantic graph refers to a graph that visualizes the semantic relationship between CIM elements and grid levels, where nodes represent element types or grid levels, edges represent association relationships and strengths, and grid access priority refers to the access priority assigned to different grid levels during the query process based on the importance of the element and the query requirements.

[0228] Optionally, based on the two-level index architecture and the adaptive grid level selection system, the cross-level query channel of the CIM element can be set using a Z-order curve; based on the query range, the optimal retrieval path of the CIM element can be created using a jump algorithm; and based on the element-grid semantic graph, the grid access priority of the CIM element can be defined using a semantic rule engine. For example, if the semantic rule is set to prioritize an element as an emergency facility and its grid level is ≥ L4, then the priority is increased by 3, and the access order is automatically calculated by the graph.

[0229] S6. Combining the adaptive grid hierarchy selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit, and the hierarchy retrieval mechanism, perform data encoding processing on the heterogeneous CIM data to obtain the data encoding result.

[0230] This invention, through combining the adaptive grid hierarchy selection system, the spatiotemporal composite grid coding, the spatiotemporal associated storage unit, and the hierarchical retrieval mechanism, performs data encoding processing on heterogeneous CIM data to obtain data encoding results. This avoids the waste of computational resources or loss of detail caused by fixed grid resolution, effectively improving data processing efficiency and accuracy. Simultaneously, it solves the problem of limited capacity of a single storage device, improving data storage reliability and scalability. The data encoding processing refers to the process of converting heterogeneous CIM data into a format that is easy to store and efficiently retrieved, combining the adaptive grid hierarchy selection system, the spatiotemporal composite grid coding, the spatiotemporal associated storage unit, and the hierarchical retrieval mechanism. The data encoding result refers to the standardized data unit that, after data encoding processing, multi-source heterogeneous CIM data is transformed into possessing spatiotemporal semantics, storage mapping relationships, and retrieval identifiers.

[0231] Compared to the problems described in the background art, the embodiments of the present invention, by performing coordinate system standardization processing on the heterogeneous CIM data, obtain standardized coordinate data, which can ensure accurate spatial alignment and seamless integration of data from different sources, and achieve real-time synchronization of multi-source heterogeneous data. Furthermore, by mapping the standardized coordinate data to several grid cells to generate CIM elements of the target city, the embodiments of the present invention can achieve accurate classification of city elements, which helps to assign a unique grid code to each element. The embodiments of the present invention, by identifying the geometric complexity of the CIM elements and constructing an adaptive grid hierarchy selection for the CIM elements based on the geometric complexity, further demonstrate that the present invention can achieve accurate classification of city elements, which helps to assign a unique grid code to each element. The adaptive grid selection system can choose appropriate grid levels for CIM elements of varying complexity, improving the efficiency of data processing and analysis. This embodiment of the invention constructs a mapping relationship between the CIM elements and several grid units based on the adaptive grid level selection system, creating a unique and accurate spatial index for each element, significantly improving data retrieval efficiency. Furthermore, this embodiment of the invention identifies cross-grid elements within the CIM elements based on the mapping relationship, improving the accuracy and completeness of spatial data organization and optimizing data loading efficiency. This embodiment of the invention also creates a cross-grid optimization processing layer for the CIM elements based on these cross-grid elements. This invention can filter out redundant data in CIM data, improving data encoding efficiency. In this embodiment, by constructing a spatiotemporal associated storage unit for CIM elements based on the spatiotemporal composite grid encoding, efficient management, real-time analysis, and collaborative computing of large-scale grid data can be achieved, improving data reliability and access efficiency. Furthermore, this invention, by generating a two-level index architecture for CIM elements in the spatiotemporal associated storage unit based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, can improve CIM data query efficiency and optimize CIM data storage and management. This invention, by using the two-level index architecture and the adaptive... By adopting a grid-level selection system and creating a hierarchical retrieval mechanism for CIM elements, efficient data location and partitioning can be achieved, improving data retrieval efficiency while reducing data encoding storage space. This embodiment of the invention combines the adaptive grid-level selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit, and the hierarchical retrieval mechanism to perform data encoding processing on heterogeneous CIM data, obtaining data encoding results. This avoids the waste of computational resources or loss of detail caused by fixed grid resolution, effectively improving data processing efficiency and accuracy. Simultaneously, it solves the problem of limited capacity of a single storage device, improving data storage reliability and scalability. Therefore, the CIM data encoding method and system based on Earth meshing provided by this embodiment of the invention can optimize the retrieval efficiency and storage quality of CIM data, improving the utilization efficiency of CIM data.

[0232] Example 2:

[0233] like Figure 3 The diagram shown is a functional block diagram of a CIM data encoding system based on Earth grid partitioning according to the present invention.

[0234] The CIM data encoding system 200 based on a gridded Earth, as described in this invention, can be installed in an electronic device. Depending on the functions implemented, the CIM data encoding system based on a gridded Earth may include a coordinate unification module 201, a grid partitioning module 202, a mapping optimization module 203, an encoding storage module 204, a data retrieval module 205, and a data encoding module 206. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0235] In this embodiment of the invention, the functions of each module / unit are as follows:

[0236] The coordinate unification module 201 is used to collect heterogeneous CIM data of the target city, wherein the heterogeneous CIM data includes BIM data, IoT data and GIS data, and to perform coordinate system standardization processing on the heterogeneous CIM data to obtain standardized coordinate data.

[0237] The grid subdivision module 202 is used to divide the target city into several grid units using a preset Earth profile grid, map the standardized coordinate data into several grid units, generate CIM elements of the target city, identify the geometric complexity of the CIM elements, and construct an adaptive grid hierarchy selection system for the CIM elements based on the geometric complexity.

[0238] The mapping optimization module 203 is used to construct a mapping relationship between the CIM elements and several grid units according to the adaptive grid hierarchy selection system, identify cross-grid elements in the CIM elements based on the mapping relationship, and create a cross-grid optimization processing layer for the CIM elements based on the cross-grid elements.

[0239] The encoding and storage module 204 is used to extract the timestamp of the standardized coordinate data, and set the spatiotemporal composite grid encoding of the CIM element according to the timestamp and the mapping relationship, and construct the spatiotemporal associated storage unit of the CIM element based on the spatiotemporal composite grid encoding.

[0240] The data retrieval module 205 is used to generate a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, and to create a hierarchical retrieval mechanism for the CIM elements according to the two-level index architecture and the adaptive grid hierarchy selection system.

[0241] The data encoding module 206 is used to combine the adaptive grid hierarchy selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit and the hierarchy retrieval mechanism to perform data encoding processing of the heterogeneous CIM data and obtain data encoding results.

[0242] In detail, the modules in the CIM data encoding system 200 based on Earth grid partitioning described in this embodiment of the invention employ the same methods as described above when in use. Figure 1 The method used is the same as the CIM data encoding method based on Earth grid described in the article, and can produce the same technical effect, so it will not be repeated here.

[0243] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0244] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A CIM data encoding method based on Earth grid subdivision, characterized in that, The method includes: Collect heterogeneous CIM data of the target city, wherein the heterogeneous CIM data includes BIM data, IoT data and GIS data, and perform coordinate system standardization processing on the heterogeneous CIM data to obtain standardized coordinate data; The target city is divided into several grid cells using a preset Earth profile grid. The standardized coordinate data is mapped to several of the grid cells to generate CIM elements of the target city. The geometric complexity of the CIM elements is identified, and an adaptive grid hierarchy selection system for the CIM elements is constructed based on the geometric complexity. Based on the adaptive grid hierarchy selection system, a mapping relationship between the CIM elements and several grid cells is constructed. Based on the mapping relationship, cross-grid elements in the CIM elements are identified. Based on the cross-grid elements, a cross-grid optimization processing layer for the CIM elements is created. Extract the timestamps from the standardized coordinate data, and set the spatiotemporal composite grid code of the CIM element according to the timestamps and the mapping relationship. Based on the spatiotemporal composite grid code, construct the spatiotemporal associated storage unit of the CIM element. Based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit is generated. According to the two-level index architecture and the adaptive grid hierarchy selection system, a hierarchical retrieval mechanism for the CIM elements is created. By combining the adaptive grid hierarchy selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit, and the hierarchy retrieval mechanism, the data encoding processing of the heterogeneous CIM data is performed to obtain the data encoding result.

2. The CIM data encoding method based on Earth grid as described in claim 1, characterized in that, The coordinate system standardization process of the heterogeneous CIM data is performed to obtain standardized coordinate data, including: The data type of the heterogeneous CIM data is identified using the file fingerprint features of the heterogeneous CIM data; Based on the data type, analyze the coordinate system characteristics of the heterogeneous CIM data; Based on the coordinate system characteristics, a coordinate system metadata extraction unit for the heterogeneous CIM data is created; Based on the coordinate system metadata extraction unit, semantic tags for the heterogeneous CIM data are generated; Based on the semantic tags and coordinate system features, an adaptive coordinate transformation method for the heterogeneous CIM data is set; Collect historical coordinate transformation data of the heterogeneous CIM data, and use the historical coordinate transformation data to identify the coordinate deviation pattern of the heterogeneous CIM data; Based on the coordinate deviation mode, a coordinate transformation compensation mechanism for the heterogeneous CIM data is set; Based on the coordinate transformation compensation mechanism and the adaptive coordinate transformation method, a coordinate transformation quality monitoring module for the heterogeneous CIM data is created. By combining the coordinate system metadata extraction unit, the adaptive coordinate transformation method, and the coordinate transformation quality monitoring module, coordinate system standardization processing of the heterogeneous CIM data is performed to obtain standardized coordinate data.

3. The CIM data encoding method based on Earth grid subdivision as described in claim 1, characterized in that, The step of mapping the standardized coordinate data onto a plurality of grid cells to generate CIM elements of the target city includes: Identify adjacent data sources in the standardized coordinate data and calculate the coordinate overlap coefficient of the adjacent data sources; Based on the coordinate overlap coefficient, perform a coordinate consistency check on the standardized coordinate data to obtain the coordinate consistency check result. After the coordinate consistency verification result meets the preset effect, the standardized coordinate data is mapped to several of the grid cells to obtain the mapped data; Extract the entity geometric features from the mapping data; The CIM raw data corresponding to the standardized coordinate data is located using the spatial coordinates in the geometric features of the entity. The semantic labels and non-geometric attributes of the CIM raw data were parsed. By combining the semantic tags and the non-geometric attributes, the entity semantics of the target city are generated; The binding process between the geometric features and the entity semantics is performed to generate the CIM elements of the target city.

4. The CIM data encoding method based on Earth grid as described in claim 1, characterized in that, The step of constructing an adaptive grid hierarchy selection system for CIM elements based on the geometric complexity includes: Based on the geometric complexity, identify the semantic feature control points of the CIM elements; Based on the geometric complexity and the semantic feature control points, the initial grid level of the CIM element is determined; Establish dynamic adjustment rules for the initial grid hierarchy at the semantic feature control points; Identify the environmental response factors in the dynamic adjustment rules; Based on the environmental response factor and the dynamic adjustment rule, generate grid-level control instructions for the CIM elements; Based on the grid level control instructions, an adaptive grid level selection system for the CIM elements is constructed.

5. The CIM data encoding method based on Earth grid as described in claim 1, characterized in that, The step of constructing the mapping relationship between the CIM elements and several grid cells according to the adaptive grid hierarchy selection system includes: Based on the adaptive grid hierarchy selection system, extract the hierarchical grid corresponding to the CIM element; Obtain the spatial partitioning parameters of the hierarchical mesh to generate the geometric representation of the hierarchical mesh; The spatial containment relationships between the hierarchical meshes are constructed using the geometric representation; Identify the geometric objects corresponding to the CIM elements and extract the bounding boxes of the geometric objects in a preset global coordinate system; Based on the bounding box, determine the spatial intersection range between the CIM element and the hierarchical grid; Based on the spatial inclusion relationship and the spatial intersection range, determine the target grid level set of the CIM element; Calculate the matching coefficients between the plurality of said mesh cells and the target mesh hierarchy set; Based on the matching coefficient, extract the effective grid cells of the CIM elements from the target grid hierarchy set; Based on the effective grid cells, create a positive mapping list from the CIM features to the grid cells; Use the forward mapping list to set the reverse index of the CIM element; Based on the forward mapping list and the reverse index, a mapping relationship between the CIM elements and several of the grid cells is constructed.

6. The CIM data encoding method based on Earth grid as described in claim 1, characterized in that, The step of creating a cross-grid optimization processing layer for the CIM features based on the cross-grid features includes: Identify the number of grids and the hierarchical difference corresponding to the cross-grid features; Based on the number of grids and the hierarchical difference, identify the cross-grid type of the cross-grid feature; Extract the geometric segmentation features corresponding to the cross-mesh features in the cross-mesh type; Based on the geometric segmentation features, a cross-mesh geometric reconstruction mechanism for the CIM features is constructed; The topological relationships of the cross-grid features are identified through the cross-grid geometry reconstruction mechanism. Extract the anomalous topological factors of the geometric segmentation elements from the topological relationships; Based on the abnormal topology factor, a topology error detection unit for the CIM element is created; Based on the topology error detection unit, a hierarchical topology conflict repair system for the CIM elements is set up. By combining the cross-mesh geometric reconstruction mechanism, the topology error detection unit, and the hierarchical topology conflict repair system, a cross-mesh optimization processing layer for the CIM elements is created.

7. The CIM data encoding method based on Earth grid as described in claim 1, characterized in that, The construction of the spatiotemporal associated storage unit for the CIM elements based on the spatiotemporal composite grid encoding includes: Analyze the spatiotemporal distribution characteristics and computational load characteristics of the CIM elements; Based on the aforementioned spatiotemporal distribution characteristics, grid storage sharding rules for the CIM elements are generated; The data constraints of the CIM elements are defined based on the computational load characteristics. Based on the grid storage sharding rules, a hierarchical storage architecture for the CIM elements is constructed, and the data indexing method of the hierarchical storage architecture is defined; Based on the data constraints, configure the parallel computing interface for the CIM elements; Based on the data indexing method and the parallel computing interface, a load balancing scheduling engine for the CIM elements is created. Based on the load balancing scheduling engine, a spatiotemporal correlation storage unit for the CIM elements is constructed.

8. The CIM data encoding method based on Earth grid as described in claim 1, characterized in that, The step of generating a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding includes: Extract the high-level spatial features of the cross-grid optimization processing layer and the temporal data of the spatiotemporal composite grid encoding; Based on the high-level spatial features and the time-series data, the CIM elements are subjected to multi-dimensional partitioning to obtain a combined partitioning key; The storage nodes in the spatiotemporal associated storage unit are determined, and the combined partition key is mapped to the storage node using a consistent hashing algorithm to generate a global partition index table for the CIM elements. Add a coarse-grained cross-grid association field for the cross-grid optimization processing layer to the global partition index table; Query the storage node address of the combined partition key in the global partition index table; Based on the storage node address, construct a multidimensional local index of the combined partition key; Based on the coarse-grained cross-grid association field, set the cross-feature association partition of the CIM feature; Create a cross-feature index identifier for the cross-feature association partition in the multi-dimensional local index; Construct a distributed coordination protocol between the global partition index table and the multidimensional local index; By combining the global partition index table, the multidimensional local index, the distributed collaboration protocol, and the cross-element index identifier, a two-level index architecture for the CIM elements in the spatiotemporal related storage unit is generated.

9. The CIM data encoding method based on Earth grid as described in claim 1, characterized in that, The hierarchical retrieval mechanism for CIM elements, based on the two-level index architecture and the adaptive grid hierarchy selection system, includes: Based on the two-level index architecture and the adaptive grid hierarchy selection system, a cross-level query channel for the CIM elements is set up; Based on the cross-level query channel, the query scope of the CIM element is identified; Based on the query scope, create the optimal retrieval path for the CIM element; Extract the feature types and associated grid levels from the adaptive grid hierarchy selection system; Based on the element type and the associated grid level, construct the element-grid semantic map of the CIM element; Based on the element-mesh semantic map, define the grid access priority of the CIM element; By combining the cross-level query channel, the optimal retrieval path, and the grid access priority, a hierarchical retrieval mechanism for the CIM elements is created.

10. A CIM data encoding system based on Earth grid subdivision, characterized in that, The system includes: The coordinate unification module is used to collect heterogeneous CIM data of the target city, wherein the heterogeneous CIM data includes BIM data, IoT data and GIS data, and performs coordinate system standardization processing on the heterogeneous CIM data to obtain standardized coordinate data. The grid subdivision module is used to divide the target city into several grid cells using a preset Earth profile grid, map the standardized coordinate data into several grid cells, generate CIM elements of the target city, identify the geometric complexity of the CIM elements, and construct an adaptive grid hierarchy selection system for the CIM elements based on the geometric complexity. The mapping optimization module is used to construct the mapping relationship between the CIM elements and several grid units according to the adaptive grid hierarchy selection system, identify cross-grid elements in the CIM elements based on the mapping relationship, and create a cross-grid optimization processing layer for the CIM elements based on the cross-grid elements. The encoding and storage module is used to extract the timestamp of the standardized coordinate data, and set the spatiotemporal composite grid encoding of the CIM element according to the timestamp and the mapping relationship, and construct the spatiotemporal associated storage unit of the CIM element based on the spatiotemporal composite grid encoding. The data retrieval module is used to generate a two-level index architecture for the CIM elements in the spatiotemporal associated storage unit based on the cross-grid optimization processing layer and the spatiotemporal composite grid encoding, and to create a hierarchical retrieval mechanism for the CIM elements based on the two-level index architecture and the adaptive grid hierarchy selection system. The data encoding module is used to combine the adaptive grid hierarchy selection system, the spatiotemporal composite grid encoding, the spatiotemporal associated storage unit, and the hierarchy retrieval mechanism to perform data encoding processing on the heterogeneous CIM data and obtain the data encoding result.

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