Multi-element and multi-scale data management method and system based on CIM basic platform

Abstract

The invention relates to the technical field of CIM (common information model) platforms, in particular to a multi-element and multi-scale data management method and system based on a CIM basic platform, and relates to data acquisition, access and classification in a service interface, incremental acquisition and offline copying mode. Data needs to be translated to conform to spatial, elevation and temporal references of a CIM platform, unify geocoding and spatial expression, and semantic-based place name address association. And the BIM and the CIM are fused through a knowledge graph. And performing three-dimensional data grading processing, and constructing CIM1 to CIM5 grade data. Government affair data are subjected to graph falling through a unified address library. Overall data governance covers data aggregation, classified governance, two-dimensional and three-dimensional fusion, BIM and CIM fusion technology and government affair data association, classification and level-to-level management of data is realized, different data combination application is supported, and CIM business application and government affair process auxiliary approval and decision making are assisted.

Description

Technical Field

[0001] The present invention relates to the technical field of CIM platform, and in particular to a multivariate and multi-scale data governance method and system based on a CIM basic platform. Background Art

[0002] In the process of building the CIM (city information model) platform, the data types involved are extremely rich, covering basic geographic data, three-dimensional model data, BIM (building information model) data, Internet of Things data, and various unstructured data. These data are not only in various formats, but also have great differences in geometric accuracy. In addition, the span of data is wide, and the uncertainty of data is also prominent. Therefore, data governance tasks such as how to standardize the standards for data construction, how to unify the storage and expression logic of data, and how to establish associations between data have become the key to CIM construction. Through effective data governance, the value of digital assets can be significantly magnified, the effectiveness of data elements can be released, and the in-depth application of CIM+ applications can be supported.

[0003] In application scenarios such as smart cities, existing data governance technologies mainly focus on data integration, classification, and classification governance. At the horizontal level, data is managed in a standardized manner by combing data, performing classification, classification, and unifying precision and format. However, existing technologies have obvious defects at the vertical level. Specifically, existing technologies fail to build entity fusion data of space and government data based on three-dimensional data, thereby achieving vertical integration of data of different types and levels. In addition, existing technologies fail to achieve a deep integration of technology and business, resulting in the inability to fully tap the deep application potential of CIM+ applications. Therefore, in order to better promote the construction of the CIM platform, it is necessary to improve and innovate on the basis of existing technologies to solve these key problems. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention discloses a multivariate and multi-scale data governance method and system based on a CIM basic platform to solve the above problems.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a multivariate and multi-scale data governance method based on a CIM basic platform, comprising the following steps:

[0007] Acquire data, and access and classify data through service interface mode, incremental collection mode, and offline copy mode;

[0008] According to the requirements of CIM basic platform construction, unified data conversion is carried out for the spatial benchmark, elevation benchmark and time benchmark of each type of data;

[0009] The unified geographic entity coding adopts Geohash, the spatial unit system is unified with multi-granularity expression, the place name address is unified based on semantic association, and the integration of BIM and CIM relies on knowledge graph;

[0010] Conduct hierarchical processing on the gathered 3D data and construct CIM1-CIM5 graded result data;

[0011] Obtain place name addresses or spatial coordinate information of government data, and map non-spatial data through a unified address database.

[0012] Furthermore, the unified data conversion includes the following steps:

[0013] Use data conversion software to convert the collected geojson, shp, Tif, and CAD formats into a geographic database;

[0014] Through geographic information editing software, the GCJ-02 coordinate system of urban physical examination data, QR code place name address data and the WGS84 coordinate system of some smart fire protection data are converted into the national 2000 coordinate system by applying the two-dimensional polynomial conversion method, thus realizing the unification of data coordinate system.

[0015] Use geographic information data editing software to merge the data and arrange the layers in the order from top to bottom as annotations, point features, line features, and surface features.

[0016] Furthermore, in the method, the spatial code of the building is based on Geohash coding. When the coding is divided n times, the lengths of the left and right intervals of latitude / longitude are d B (n), d L (n), the formula is:

[0017]

[0018]

[0019] It is easy to get from the above formula that when the longitude and latitude are divided and coded for the same number of times, the interval length relationship is: L (n) = 2d B (n), d L (n+1)=d B (n);

[0020] Extract primitive elements from the geographic database, and process them through attributes and coding to form entity data in the Geodatabase format. Reorganize these elements, expand their attributes and unify their coding to create a geographic entity database that meets the requirements. Primitives are composed of geometry and attributes, and there is a one-to-one or many-to-one relationship between them and entities; entities are represented by relationship tables and attribute tables. The relationship table connects primitives and entities, and the attribute table contains entity attribute information; primitives of the same entity are associated through primitive identification codes to establish a connection between primitives and entities; primitives are associated with attribute tables through entity identification codes to achieve the association between entities and primitives.

[0021] Furthermore, in the method, the government data association organization is performed, including the following steps:

[0022] Through the unified address database, the spatial geographic location coordinate information of the standard place name address is obtained, and the spatial information attached to the full life cycle data of the project land planning permit, construction project planning, construction permit and completion acceptance is matched and spatially located on the map, so as to realize the association between the project data and the CIM base plate data;

[0023] Connect with the relevant housing business data of the Municipal Housing and Construction Bureau, including housing property information, housing property information and housing safety census information, and associate housing business data with CIM baseboard data based on unified spatial benchmark and place name address data;

[0024] Access existing IoT sensing device data, based on unified spatial benchmarks and place name and address data, to achieve spatial positioning of IoT sensing information such as building monitoring data and municipal facility monitoring data and their association with CIM baseboard data;

[0025] Realize the association and connection of special application data such as CIM+smart construction site data, CIM+city lifeline, CIM+intelligent networking, CIM+smart fire protection and CIM+city physical examination with CIM models.

[0026] Furthermore, in the method, the classification of CIM1 includes the following steps:

[0027] Using digital elevation model (DEM) and remote sensing image (DOM), a CIM1 level terrain model is generated through data superposition and calculation;

[0028] Integrate city-level, county-level, town-level, and village-level administrative division data to generate the CIM1 administrative division model;

[0029] Based on the water system distribution data and combined with the terrain model, the CIM1 water system model is generated;

[0030] Traffic model processing

[0031] Integrate transportation infrastructure data such as roads, railways, bridges, etc. to generate the CIM1 transportation model;

[0032] Using vegetation distribution data and terrain model, the CIM1 vegetation model is generated;

[0033] Integrate city component data;

[0034] Classify and encode CIM1 level model objects;

[0035] The CIM1 level model is composed of the fusion of the above multiple types of models and is stored in a universal three-dimensional data format.

[0036] Furthermore, in the method, the classification of CIM2 includes the following steps:

[0037] The 1:500 digital elevation model data DEM and the 0.2-meter precision remote sensing image DOM are superimposed and calculated to produce the CIM2 terrain model;

[0038] Symbolize more detailed administrative division data such as streets and communities to generate CIM2 administrative district model, and correct the model elevation value by combining digital elevation model data;

[0039] Use 3D artificial fine model data to convert and generate CIM2 building appearance model;

[0040] Using the 3D artificial fine model, according to the house property list, the floor plan and elevation in the house construction engineering CAD drawing, the 3D artificial fine model data within the 5 square kilometers of access is processed and converted into a CIM2 building internal model with different layers and households;

[0041] Based on the CIM1 water system model, combined with the water system model in the three-dimensional artificial fine model data, the CIM2 water system model is converted;

[0042] Based on the CIM1 traffic model, combined with the traffic model in the 3D artificial fine model data, the CIM2 traffic model is converted;

[0043] Based on the CIM1 vegetation model, combined with the vegetation model in the three-dimensional artificial fine model data, the CIM2 vegetation model is converted;

[0044] The site model in the three-dimensional artificial fine model data is converted into a CIM2 site model;

[0045] Convert the pipeline and gallery 3D model into CIM2 pipeline and gallery model;

[0046] Use geological body 3D model data to convert and generate CIM2 geological model;

[0047] Extract the sketch model data of the oblique photography model, combine it with the municipal facilities census data, replace the key urban component models with artificial precision models, and convert them into CIM2 urban component models;

[0048] According to the building standard address, check whether the CIM2 building appearance model object and the CIM2 building interior model object have corresponding identification codes. If yes, directly assign the value, if no, perform new identification codes, and record the corresponding relationship between the standard address and the identification code;

[0049] The CIM2 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

[0050] Furthermore, in the method, the classification of CIM3 includes the following steps:

[0051] Using the CIM2 level building appearance model, unnecessary details were removed through further refinement and optimization;

[0052] Based on the CIM2 level building interior model, the model is refined and summarized, and the floor, wall, corridor, staircase, elevator, door and window elements are accurately modeled, while other auxiliary structures are simplified;

[0053] Taking the CIM2 traffic model as the basis, the geometric accuracy and attribute information of roads, bridges and tunnels are further refined to ensure that the model can accurately reflect the key elements of traffic flow lines, traffic signs and signal lights;

[0054] Based on the CIM2 level site model, further refine and optimize the vegetation, terrain, water bodies and other elements within the site to ensure that the site model can accurately reflect the site's use function and spatial layout;

[0055] Using the CIM2-level pipeline gallery model, further refine and classify underground pipelines, cables, pipes and other facilities to ensure that the model can accurately reflect the distribution, direction and connection relationship of the pipelines;

[0056] CIM2-level geological model data is used to further refine and classify the geological bodies to ensure that the model can accurately reflect the geological structure, rock layer distribution and geological disaster risk areas.

[0057] Combined with the CIM2-level urban component model, further refine and optimize key urban components to ensure that the model can accurately reflect the appearance characteristics, functional attributes and spatial location of urban components;

[0058] According to the building standard address, check whether the CIM3 building appearance model object and CIM3 building interior model object have corresponding identification codes. If yes, directly assign the value; if no, perform new identification codes, and record the corresponding relationship between the standard address and the identification code;

[0059] The CIM3 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

[0060] Furthermore, in the method, the classification of CIM4 includes the following steps:

[0061] Use BIM building model data, delete the details of the building internal model to generate a CIM4 building appearance model, or use 3D artificial fine model data to convert and generate a CIM4 building appearance model;

[0062] Based on the BIM building model, extract the building's internal floor, internal and external walls, corridors, stairs, elevators, doors and windows, summarize and synthesize other elements to generate the CIM4 building internal model;

[0063] Generate CIM4 traffic model based on CIM3 traffic model conversion, and generate CIM4 site model based on CIM3 site model conversion;

[0064] Based on the 3D model of pipeline corridor, 3D model of subway station and underground shop model, the CIM4 underground space model is converted and generated;

[0065] According to the building standard address, check whether the CIM4 building appearance model object and the CIM4 building interior model object have corresponding identification codes. If yes, directly assign the value, if no, perform new identification coding, and record the corresponding relationship between the standard address and the identification code;

[0066] The CIM4 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

[0067] Furthermore, in the method, the classification of CIM5 includes the following steps:

[0068] Using high-precision remote sensing image data and ground-measured vegetation information, the CIM4 vegetation model is refined to generate a CIM5 vegetation model.

[0069] Using high-precision point cloud data obtained by laser scanning technology, the CIM4-level pipeline and corridor model is refined to generate the CIM5-level pipeline and corridor model;

[0070] Combine geological exploration data with high-resolution 3D geological models to deepen the CIM4 geological model and generate a CIM5 geological model.

[0071] Through field investigation and data collection, the CIM4-level urban component model is further refined to generate the CIM5-level urban component model;

[0072] According to the building standard address, check whether the CIM5-level building appearance model object and the CIM5-level building interior model object have corresponding identification codes. If so, directly assign values ​​and codes. If not, perform new identification codes and record the corresponding relationship between the standard address and the identification code.

[0073] The CIM5 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

[0074] In a second aspect, the present invention provides a multivariate and multi-scale data governance system based on a CIM basic platform, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, the multivariate and multi-scale data governance method based on the CIM basic platform described in the first aspect is implemented.

[0075] The beneficial effects of the present invention are:

[0076] The present invention is committed to realizing an overall data governance solution from data aggregation, data classification management, two-dimensional and three-dimensional data fusion, BIM and CIM fusion technology, to government data association organization. Through this series of technical means, the present invention can effectively classify and manage massive, multi-dimensional and multi-scale data horizontally, ensuring the orderliness and accessibility of data.

[0077] At the same time, in the vertical dimension, the present invention supports the combined use of different data, so that data can be integrated and analyzed in a more flexible way. This overall data governance solution not only supports the efficient operation of CIM (city information model) business applications, but also provides strong data support for auxiliary approval and decision-making of government affairs processes, thereby improving government work efficiency and the scientific nature of decision-making.

[0078] Through the application of the present invention, it is possible to achieve optimal configuration of data resources, enhance the value of data utilization, and provide a solid technical foundation for the construction of smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 This is a principle block diagram of the multivariate and multi-scale data governance method based on the CIM basic platform. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In one embodiment, referring to Figure 1 As shown, a multivariate and multi-scale data governance method based on the CIM basic platform is provided, including the following steps:

[0083] Acquire data, and access and classify data through service interface mode, incremental collection mode, and offline copy mode;

[0084] According to the requirements of CIM basic platform construction, unified data conversion is carried out for the spatial benchmark, elevation benchmark and time benchmark of each type of data;

[0085] The unified geographic entity coding adopts Geohash, the spatial unit system is unified with multi-granularity expression, the place name address is unified based on semantic association, and the integration of BIM and CIM relies on knowledge graph;

[0086] Conduct hierarchical processing on the gathered 3D data and construct CIM1-CIM5 graded result data;

[0087] Obtain place name addresses or spatial coordinate information of government data, and map non-spatial data through a unified address database.

[0088] In one embodiment, data access and classification, this embodiment is based on the data construction requirements specified in the "Technical Guidelines for the Basic Platform of Urban Information Model (CIM) (Revised Edition)" and the "Technical Standards for the Basic Platform of Urban Information Model" (CJJT315-2022). This embodiment carefully sorts out the current status of Changsha's urban spatial data, and uniformly accesses and aggregates relevant data that meet the requirements. The data types cover spatiotemporal basic data, resource survey data, planning and control data, engineering construction project data, public thematic data, IoT perception data, and thematic application data, a total of seven categories. The main sources of data include the Changsha Natural Resources and Planning Bureau, the Changsha Housing and Urban-Rural Development Bureau, and the Changsha City "Super Brain". For these six categories of data, according to the differences in their sources and the specific requirements for data docking, this study adopts the service interface mode, incremental collection mode, and offline copy mode to achieve data access and classification.

[0089] In one embodiment, data preprocessing, this embodiment follows the CIM basic platform construction specification, and performs unified spatial reference, elevation reference and time reference conversion processing on various data in the project. The spatial reference uniformly adopts the National 2000 Coordinate System, and the elevation system uniformly adopts the 1985 National Elevation Reference.

[0090] This embodiment mainly focuses on the time-space reference conversion of urban physical examination data, QR code place name address data, smart fire protection data and intelligent network connection data. Among them, the spatial reference of urban physical examination data, QR code place name address data, intelligent network connection data and some smart fire protection data was originally the GCJ-02 coordinate system, and the algorithm configuration is required to realize the conversion from the GCJ-02 coordinate system to the national 2000 coordinate system; in addition, the spatial reference of some smart fire protection data was originally the WGS84 coordinate system, and the algorithm configuration is also required to realize the conversion from the WGS84 coordinate system to the national 2000 coordinate system.

[0091] The specific operation steps of this embodiment are as follows:

[0092] 1) Format conversion

[0093] Data conversion software is used to convert the collected data in geojson, shp, Tif, CAD and other formats into a unified geographic database format.

[0094] 2) Coordinate transformation

[0095] Using geographic information data editing software and the two-dimensional polynomial method for coordinate system conversion, the GCJ-02 coordinate system of urban physical examination data, QR code place name and address data, and the WGS84 coordinate system of some smart fire protection data are converted into the national 2000 coordinate system to achieve the unification of data coordinate systems.

[0096] 3) Data collation

[0097] In view of the large amount and variety of CIM-related data, coordinate conversion needs to be carried out in batches. Therefore, geographic information data editing software is used to merge the data and sort out the layer order after merging. According to relevant national standards, the layer order should be annotation, point element, line element, and surface element from top to bottom.

[0098] In one embodiment, the geographic entity coding based on Geohash is unified. This embodiment performs unified spatial coding on the geographic spatial objects in Changsha City, including water systems, roads, buildings, pipelines, etc., based on the "Hunan Province Urban Information Model Basic Data Standard" (DBJ43 / T531-2022) and the CIM basic platform data storage requirements. For different types of three-dimensional geographic entities, based on the administrative division number as the benchmark, combined with the data classification of CIM1-CIM5, according to GB / T25529-2010 geographic information classification and coding rules, and combined with the coding of professional application fields, a unified coding system is constructed. The coding system consists of a model level code (2 bits), a classification code (4 bits), a district and county code (2 bits), a township / street code (3 bits), a village / community code (3 bits), a time code (6 bits) and a serial number (10 bits).

[0099] In the classification of buildings, the serial number adopts the GeoHash 20-digit coding method, which integrates the spatial location, architectural form, construction time and other spatiotemporal information of the building, and effectively solves the problems of complex structure, single information and poor stability of traditional building geographic entity coding. The spatial code of the building is based on Geohash coding. Compared with traditional coordinate floating-point calculation, it has significant advantages in regional division, spatial indexing and neighboring point query, and its speed performance is also excellent even when processing massive data.

[0100] According to the above algorithm, when the code is divided and encoded n times, the length of the left (right) interval of latitude / longitude is d B (n), d L (n), the formula is:

[0101]

[0102]

[0103] It is easy to get from the above formula that when the longitude and latitude are divided and coded for the same number of times, the interval length relationship is: L (n) = 2d B (n), d L (n+1)=d B (n). When n = 25, d B =5.36442E-06,dL =1.07288E-05, therefore, if Geohash coding achieves meter-level positioning accuracy, it needs to be segmented 25 times, and the number of Geohash coding bits is 10. If conditions permit, it can be expanded to three-dimensional spatial position coding.

[0104] Extract entity primitive elements from the basic geographic database, which include various basic graphics in geographic space, such as points, lines, and surfaces. Through further attribute expansion and entity encoding, we can convert these primitive elements into entity data that conforms to the Geodatabase format. In this process, we not only focus on the geometric features of the primitives, but also on their related attribute information to ensure the integrity and accuracy of the data.

[0105] Next, the extracted elements are reorganized and their attributes are expanded to meet specific application requirements. This stage includes classifying, integrating and optimizing the data to ensure that the data is organized more reasonably and convenient for subsequent processing and application. On this basis, unified coding and object-oriented processing are carried out to form a geographic entity database that meets specific requirements.

[0106] In a geographic entity database, primitives are represented by geometric features and related attributes. There is usually a one-to-one or many-to-one association between primitives and entities. For example, a road entity is usually composed of multiple primitives, which together describe the shape, location, and attributes of the road. A house entity may be composed of one or more primitives, each of which represents a part of the house, such as a roof, a wall, etc.

[0107] Entities are represented in the database by relationship tables and attribute tables. The relationship table acts as a "bridge" between primitives and entities. It associates primitives belonging to the same entity through primitive identification codes, thereby establishing an association relationship between primitive elements and entities. The attribute table is used to connect the attribute information of each entity type, and establishes an association with the entity attribute table through the entity identification code, thereby realizing the association relationship between entities and primitives.

[0108] In practical applications, entity models can be established through these relationship tables and attribute tables according to specific needs. Entity models can be simple point, line, and surface models, or complex three-dimensional models, or even four-dimensional models that include time dimensions. Through entity models, we can better understand and analyze various entities in geographic space, and provide strong data support for applications such as urban planning, traffic management, and environmental monitoring.

[0109] In one embodiment, this embodiment adopts a unified multi-granularity expression method to construct a spatial unit system, and the geographic entities exhibit multimodal characteristics. In the geographic information database, these entities can be described and represented in a variety of different ways. For example, the multimodal characteristics of the building model cover different dimensions (two-dimensional, three-dimensional), different levels of structure, and different graphic expression states. These multimodal features make the expressed geographic entity information richer and more comprehensive. Granularity, as a key control indicator of multimodal expression, not only reflects the structural level of the geographic entity itself, but also reflects the depth and breadth of the user's cognition of geographic objects and spatial phenomena.

[0110] This embodiment divides the granularity into two types: structural granularity and expression granularity. Structural granularity is the granularity that describes the structural hierarchy of geographic entities based on the "part-of" principle. It reveals the inherent hierarchical structure of geographic entities and is subdivided into structural analysis method and reduction and decomposition method. The structural analysis method focuses on using the "part-of" relationship to disassemble the building entity step by step into relatively independent components. The reduction and decomposition law draws on the reduction theory of physics and decomposes through the "part-of" relationship based on the principle of relative independence at the functional level. For buildings, according to specific granularity standards, they can be reduced and decomposed into different structural unit entities. This method is mainly applied to three-dimensional entities.

[0111] The second concept of granularity, expression granularity, is based on the "kind-of" type organization principle to describe the level of cognition of geographic entities. Expression granularity corresponds to the map scale. It covers the depth and accuracy of the visualization of geographic phenomena, including the granularity of spatial data expression, the level of abstraction of geographic laws, the level of user information perception, the depth of field of geographic scene expression, and the level of digital twin element derivation. This granularity is mainly used for two-dimensional entities.

[0112] This embodiment is based on Changsha's electronic map, real estate registration, topographic map and other data resources, and comprehensively organizes the two-dimensional data of administrative divisions, plots, parcels, roads, buildings, POIs, surveying and remote sensing within the city. Through spatial association technology, the relevant point, line and surface spatial units are sequentially connected and integrated to achieve the fusion of spatial data of different scales and sources, mainly organized according to the expression granularity.

[0113] When expressing three-dimensional entities in spatial units, the building is taken as the core and the construction granularity concept is introduced for modeling to realize the multi-granularity expression of the building based on the construction granularity. According to the relationship between the building entity and other building entities and the size of the physical space, the building is divided into four levels from high to low, from L0 to L3.

[0114] Joint level: At this construction granularity level, a building complex is composed of multiple buildings, forming a joint building with special significance, which is defined as L0. Building level: At this construction granularity expression level, the building is an independent individual with independent physical space and practical significance, which is defined as L1. Floor level: At this construction granularity expression level, the physical meaning of the building is to exist as a floor in a building, occupying less space than the building entity at the building level, which is defined as L2. Room level: At this construction granularity expression level, the building being expressed is only the smallest component of the building, but still has the attributes and functions of an independent building, which is defined as L3.

[0115] According to the current view scale, the display content of spatial data at different levels is associated to achieve non-level display and automatic switching of multi-scale spatial data.

[0116] In one embodiment, this embodiment is committed to building a unified place name and address system based on semantic association. On this basis, the integrated place name and address data are deeply cleaned and standardized, so as to build a comprehensive space management system covering multiple levels such as cities, districts and counties (cities), streets, communities, grids, buildings, houses, etc., and in this process, a unified Changsha urban place name and address database is formed. In the data cleaning process, we carefully divided eight stages: preprocessing, missing value cleaning, format content cleaning, logical error cleaning, non-demand data cleaning, correlation verification, data quality inspection and data storage. The place name and address coding rules are mainly formulated based on the GB / T 23705-2009 standard and the structured place name and address description rules described in the previous section. At the same time, based on the existing data, we have determined the basic regional restrictions, including community neighborhood committees, administrative villages and government-enterprise integration units.

[0117] Based on the two-dimensional place name address, this embodiment constructs a three-dimensional place name address library, which is compatible with the unified address library of the Political and Legal Committee, the public security standard address library, the regulatory place name address library and other standard address libraries. By performing standard conversion, conflict detection, error and omission troubleshooting and information correction, we have established a training library, a CIM three-dimensional place name address benchmark mother library and a series of semantic dictionaries to construct a three-dimensional place name address library. For the data describing the three-dimensional entity address information, we use natural language processing technology to extract the spatial semantic information, identify the spatial entity objects, and extract the spatial orientation and topological relationship to form a standard address with a complete structure.

[0118] In this embodiment, spatial semantic association refers to converting the text address into the spatial geographic coordinates corresponding to the place name address code by parsing the spatial location information implicit in the text address description. This process realizes the spatial association between the address description subject and the existing city entity data, and establishes the mapping relationship between the unified address and the CIM model.

[0119] In one embodiment, the knowledge graph is used to realize the integration of BIM and CIM. This technology optimizes the original data of CIM three-dimensional model and BIM model through a series of methods, including structural optimization, texture construction, spatial grouping, matrix smoothing and triangle surface balance. These technologies are pre-designed in the data production stage to improve the quality of the model. In addition, by simplifying the model, compressing the model data, separating the BIM digital model and extracting different levels of detail (LOD), this embodiment performs lightweight processing on the model data.

[0120] This embodiment uses a multi-level of detail (LOD) model structure to create multiple display levels for the models in the scene. These levels perform different degrees of triangle and texture simplification according to different display requirements to construct a pyramid model. During the rendering process, the corresponding data level is selected for display according to the distance between the observer's perspective and the object, or the currently loaded and displayed data level is controlled according to the pixel size occupied by the model node. In this way, the memory loading and rendering of the contour model and the fine entity model are dynamically managed, which achieves the retention of professional structure and information while compressing the amount of data.

[0121] This embodiment aims to create an efficient knowledge graph-based spatial index and build a BIM calculation engine.

[0122] 1) First, we classify building components. This proposal will briefly sort out the categories of building components without going into details.

[0123] 2) Secondly, we established a coding system. This scheme refers to the Omniclass classification standard to encode components. The coding structure is as follows: component system type code + component category code + building number + building name + component code.

[0124] 3) Next, build a BIM-based architectural knowledge graph:

[0125] BIM ontology construction. It is mainly divided into shared ontology and production ontology. Shared ontology is constructed based on the basic attributes of the basic units of the BIM model, that is, the basic attributes of the zero components, including identity, size, location and material. The production ontology mainly includes component information, part information and accessory information. Component information is the main component element of the model, mainly including columns, plates, beams, doors, windows, etc. Part information is the main element of construction, including concrete, steel bars, embedded parts, pipes, junction boxes, etc.

[0126] Construction of architectural knowledge graph. We use Protégé-5.5.0 software to structure the assembly knowledge information and create an architectural knowledge graph by establishing a class hierarchy, defining the relationship between classes, and visually expressing the classes.

[0127] 4) Then, the CIM model knowledge graph is constructed:

[0128] BIM mainly corresponds to CIM building geographic entities, and the mapping and matching relationship between the two needs to be manually marked. The semantic relationship of basic geographic entities is summarized into three types of relationships: topological relationship, subordinate relationship, and temporal relationship. These three types of semantic relationships involve the basic geographic entity category, semantic description, and triple structure expression. In the construction of the relationship between CIM model and BIM, the topological relationship is the most important, and the spatial analysis algorithm is used to construct the topological relationship between the entity and BIM.

[0129] 5) Finally, knowledge graph storage:

[0130] We use the graph database Apache Jena to store RDF data. Apache Jena provides components such as TDB and Fuseki. TDB is a component for storing RDF type data, and Fuseki is a SPARQL server provided by Jena. It supports SPARQL language retrieval and can run efficiently on a single machine and on the server side. In this way, we have achieved the storage of knowledge graphs.

[0131] In addition, we have established a BIM database, converted the BIM format in the CIM platform, and stored it in the database by discipline, block, building and structure. Based on the structural characteristics of the BIM model, we encapsulate parameterized model components and implement component-based object management. The building is associated with related engineering construction projects, including project drawings, project information, photos, documents and other thematic attribute information.

[0132] In one embodiment, by integrating the sketch model data of the shadow model with the municipal facilities census data, we successfully converted and generated the CIM3 city component model.

[0133] (10) Object encoding

[0134] We have classified and coded CIM3 model objects in detail. Based on the building standard address, we checked whether the CIM3 building exterior model object and CIM3 building interior model object already have corresponding identification codes. If they exist, we directly use these values ​​for encoding; if not, we create new identification codes and record the corresponding relationship between the standard address and the identification code.

[0135] (11) Output

[0136] The CIM3 level model is a fusion of multiple models. It is stored in a universal 3D data format and includes a model description file of the spatial projection and file directory structure.

[0137] The processing of CIM4 level model is as follows:

[0138] CIM4-level models should show a solid 3D framework and a fine model of the internal and external surface details. For the parts with a side length of convex and concave structures greater than or equal to 0.2 meters, we carried out detailed modeling to ensure that the model matches the actual texture. We used a combination of LiDAR and oblique photography to model.

[0139] (1) Terrain model processing

[0140] We superimposed the 1:2000 digital elevation model data (DEM) with the 0.5-meter precision remote sensing image (DOM) to produce the CIM4 terrain model.

[0141] (2) Administrative district model processing

[0142] We symbolized the administrative division data at the city, county, town, and village levels, generated the CIM4 administrative district model, and corrected the elevation values ​​of the model based on the digital elevation model data.

[0143] (3) Building appearance model processing

[0144] We used the 3D artificial fine model data to convert and generate the CIM4 building appearance model.

[0145] (4) Building interior model processing

[0146] Based on the house property list, floor plans and elevations in the house construction project CAD drawings, we processed the three-dimensional artificial fine model data within the accessed 25 square kilometers, and converted it into a layered and household-based CIM4 building interior model.

[0147] (5) Water system model processing

[0148] Based on the CIM3 water system model and combined with the water system model in the three-dimensional artificial fine model data, we converted and generated the CIM4 water system model.

[0149] (6) Traffic model processing

[0150] Based on the CIM3 traffic model and combined with the traffic model in the three-dimensional artificial fine model data, we converted and generated the CIM4 traffic model.

[0151] (7) Vegetation model processing

[0152] Based on the CIM3 vegetation model and combined with the vegetation model in the three-dimensional artificial fine model data, we converted and generated the CIM4 vegetation model.

[0153] (8) Site model processing

[0154] We converted the site model in the three-dimensional artificial fine model data into the CIM4 site model.

[0155] (9) Pipeline and corridor model processing

[0156] We converted the pipeline and gallery 3D model into the CIM4 pipeline and gallery model.

[0157] (10) Geological model processing

[0158] We used the geological body three-dimensional model data to convert and generate the CIM4 geological model.

[0159] (11) Fine model processing of urban components

[0160] We extracted the sketch model data of the oblique photography model, combined it with the municipal facilities census data, replaced the key urban component models with artificial precision models, and converted them into CIM4 urban component models.

[0161] (12) Object encoding

[0162] We classified and coded the CIM4 level model objects. Based on the building standard address, we checked whether the CIM4 building appearance model object and CIM4 building interior model object already had corresponding identification codes. If they did, we directly used these values ​​for coding; if they did not, we created new identification codes and recorded the corresponding relationship between the standard address and the identification code.

[0163] (13) Output

[0164] The CIM4 level model is a fusion of multiple models. It is stored in a universal 3D data format and includes a model description file of the spatial projection and file directory structure.

[0165] The processing of CIM5 level model is as follows:

[0166] The CIM5 model should meet the geometric accuracy (functional level) of the model's main content space occupancy, functional zoning and other requirements, corresponding to the building information model geometric accuracy G1-G2 level, attribute depth N1-N2 level. It can express the building's stratification and household division ("house set"), and the part of the surface convex and concave structure with a side length greater than or equal to 0.05 meters should be modeled in detail. We can use BIM, laser point cloud and other methods to combine modeling.

[0167] (1) Building appearance model processing

[0168] We use BIM building model data, delete the details of the building's internal model, and generate a CIM building appearance model. Alternatively, we use 3D artificial fine model data and convert it into a CIM5 building appearance model.

[0169] (2) Building interior model processing

[0170] Based on the BIM building model, we extracted the building's internal floor slabs, internal and external walls, corridors, stairs, elevators, doors and windows, and summarized and integrated other elements to generate the CIM5 building interior model.

[0171] (3) Traffic model processing

[0172] We generated the CIM5 traffic model based on the CIM4 traffic model conversion.

[0173] (4) Site model processing

[0174] We generated the CIM5 site model based on the conversion of the CIM4 site model.

[0175] (5) Underground space model processing

[0176] Based on the 3D model of pipeline corridors, subway stations and underground shops, we converted and generated the CIM5 underground space model.

[0177] (6) Object encoding

[0178] We classified and coded CIM5-level model objects. Based on the building standard address, we checked whether the CIM5 building exterior model object and CIM5 building interior model object already had corresponding identification codes. If they did, we directly used these values ​​for encoding; if they did not, we created new identification codes and recorded the corresponding relationship between the standard address and the identification code.

[0179] (7) Output

[0180] The CIM5 level model is a fusion of multiple models. It is stored in a universal 3D data format and includes a model description file of the structure.

[0181] In one embodiment, the association and organization of government data is carried out to obtain place name addresses or spatial coordinate information, and then the non-spatial data is processed into geographic information using a unified address database. A cleaning procedure is performed on government data, covering aspects such as correcting data errors, checking data consistency, improving data content, and processing invalid data, so as to improve the overall quality of government data.

[0182] This embodiment accurately matches government data with entities at a specific address scale and maps the data to the corresponding spatial entity. The address scale involves different levels such as administrative divisions, residential communities, units, medical institutions, educational institutions, parks, green spaces, buildings, units, floors, and houses. At the same time, batch matching operations are supported. According to the address description information field in the structured data, batch entity matching tasks are performed to achieve association with the city information model (CIM) baseboard data.

[0183] This embodiment builds a thematic data base associated with the CIM model according to different business application requirements. In addition, the matching accuracy is counted and tested, and the missing or wrong address information is reported and tested based on the matching algorithm and description of various task results to improve the matching accuracy. In the subsequent operation process, the various elements of the government data can be dynamically loaded and updated, and the comprehensive update of the government thematic data base can also be supported.

[0184] This embodiment obtains the spatial geographic location coordinate information of the standard place name address through a unified address database, matches the spatial information attached to the full life cycle data of the project construction project land planning permit, construction project planning, construction permit and completion acceptance, and spatially locates it on the map, thereby realizing the association between the process construction project data and the CIM baseboard data.

[0185] This embodiment connects to the relevant housing business data of the Municipal Housing and Urban-Rural Development Bureau, including house property information, house property information, house safety census information, etc., and associates the housing business data with the CIM baseboard data based on a unified spatial benchmark and place name and address data.

[0186] This embodiment accesses the existing IoT sensing device data in Changsha city, and based on the unified spatial reference and place name address data, realizes the spatial positioning of IoT sensing information such as building monitoring data and municipal facility monitoring data and its association with CIM baseboard data.

[0187] This embodiment realizes the association and connection of special application data such as CIM+smart construction site data, CIM+city lifeline, CIM+intelligent networking, CIM+smart fire protection and CIM+city physical examination with the CIM model.

[0188] In one embodiment, a multivariate and multi-scale data governance system based on a CIM basic platform is provided, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, a multivariate and multi-scale data governance method based on the CIM basic platform is implemented.

[0189] In summary, the present invention is committed to realizing an overall data governance solution from data aggregation, data classification management, two-dimensional and three-dimensional data fusion, BIM and CIM fusion technology, to government data association organization. Through this series of technical means, the present invention can effectively classify and manage massive, multi-dimensional, and multi-scale data horizontally, ensuring the orderliness and accessibility of data.

[0190] At the same time, in the vertical dimension, the present invention supports the combined use of different data, so that data can be integrated and analyzed in a more flexible way. This overall data governance solution not only supports the efficient operation of CIM (city information model) business applications, but also provides strong data support for auxiliary approval and decision-making of government affairs processes, thereby improving government work efficiency and the scientific nature of decision-making.

[0191] Through the application of the present invention, it is possible to achieve optimal configuration of data resources, enhance the value of data utilization, and provide a solid technical foundation for the construction of smart cities.

[0192] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multivariate and multi-scale data governance method based on the CIM basic platform, characterized in that: The following steps are involved: Acquire data, and access and classify data through service interface mode, incremental collection mode, and offline copy mode; According to the requirements of CIM basic platform construction, unified data conversion is carried out for the spatial benchmark, elevation benchmark and time benchmark of each type of data; The unified geographic entity coding adopts Geohash, the spatial unit system is unified with multi-granularity expression, the place name address is unified based on semantic association, and the integration of BIM and CIM relies on knowledge graph; Conduct hierarchical processing on the gathered 3D data and construct CIM1-CIM5 graded result data; Obtain place name addresses or spatial coordinate information of government data, and map non-spatial data through a unified address database.

2. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 1 is characterized in that: In the method, when performing unified data conversion, the following steps are included: Use data conversion software to convert the collected geojson, shp, Tif, and CAD formats into a geographic database; Through geographic information editing software, the GCJ-02 coordinate system of urban physical examination data, QR code place name address data and the WGS84 coordinate system of some smart fire protection data are converted into the national 2000 coordinate system by applying the two-dimensional polynomial conversion method, thus realizing the unification of data coordinate system. Use geographic information data editing software to merge the data and arrange the layers in the order from top to bottom as annotations, point features, line features, and surface features.

3. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 1 is characterized in that: In the method, the spatial code of the building is based on Geohash coding. When the coding is divided n times, the lengths of the left and right intervals of latitude / longitude are d and d respectively. B (n), d L (n), the formula is: It is easy to get from the above formula that when the longitude and latitude are divided and coded for the same number of times, the interval length relationship is: L (n) = 2d B (n), d L (n+1)=d B (n); Extract primitive elements from the geographic database, and process them through attributes and coding to form entity data in the Geodatabase format. Reorganize these elements, expand their attributes and unify their coding to create a geographic entity database that meets the requirements. Primitives are composed of geometry and attributes, and there is a one-to-one or many-to-one relationship between them and entities; entities are represented by relationship tables and attribute tables. The relationship table connects primitives and entities, and the attribute table contains entity attribute information; primitives of the same entity are associated through primitive identification codes to establish a connection between primitives and entities; primitives are associated with attribute tables through entity identification codes to achieve the association between entities and primitives.

4. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 1 is characterized in that: In the method, the government data association organization is performed, including the following steps: Through the unified address database, the spatial geographic location coordinate information of the standard place name address is obtained, and the spatial information attached to the full life cycle data of the project land planning permit, construction project planning, construction permit and completion acceptance is matched and spatially located on the map, so as to realize the association between the project data and the CIM base plate data; Connect with the relevant housing business data of the Municipal Housing and Construction Bureau, including housing property information, housing property information and housing safety census information, and associate housing business data with CIM baseboard data based on unified spatial benchmark and place name address data; Access existing IoT sensing device data, based on unified spatial benchmarks and place name and address data, to achieve spatial positioning of IoT sensing information such as building monitoring data and municipal facility monitoring data and their association with CIM baseboard data; Realize the association and connection of special application data such as CIM+smart construction site data, CIM+city lifeline, CIM+intelligent networking, CIM+smart fire protection and CIM+city physical examination with CIM models.

5. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 1 is characterized in that: In the method, the classification of CIM1 includes the following steps: Using digital elevation model (DEM) and remote sensing image (DOM), a CIM1 level terrain model is generated through data superposition and calculation; Integrate city-level, county-level, town-level, and village-level administrative division data to generate the CIM1 administrative division model; Based on the water system distribution data and combined with the terrain model, the CIM1 water system model is generated; Traffic model processing Integrate transportation infrastructure data such as roads, railways, bridges, etc. to generate the CIM1 transportation model; Using vegetation distribution data and terrain model, the CIM1 vegetation model is generated; Integrate city component data; Classify and encode CIM1 level model objects; The CIM1 level model is composed of the fusion of the above multiple types of models and is stored in a universal three-dimensional data format.

6. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 5 is characterized in that: In the method, the classification of CIM2 includes the following steps: The 1:500 digital elevation model data DEM and the 0.2-meter precision remote sensing image DOM are superimposed and calculated to produce the CIM2 terrain model; Symbolize more detailed administrative division data such as streets and communities to generate CIM2 administrative district model, and correct the model elevation value by combining digital elevation model data; Use 3D artificial fine model data to convert and generate CIM2 building appearance model; Using the 3D artificial fine model, according to the house property list, the floor plan and elevation in the house construction engineering CAD drawing, the 3D artificial fine model data within the 5 square kilometers of access is processed and converted into a CIM2 building internal model with different layers and households; Based on the CIM1 water system model, combined with the water system model in the three-dimensional artificial fine model data, the CIM2 water system model is converted; Based on the CIM1 traffic model, combined with the traffic model in the 3D artificial fine model data, the CIM2 traffic model is converted; Based on the CIM1 vegetation model, combined with the vegetation model in the three-dimensional artificial fine model data, the CIM2 vegetation model is converted; The site model in the three-dimensional artificial fine model data is converted into a CIM2 site model; Convert the pipeline and gallery 3D model into CIM2 pipeline and gallery model; Use geological body 3D model data to convert and generate CIM2 geological model; Extract the sketch model data of the oblique photography model, combine it with the municipal facilities census data, replace the key urban component models with artificial precision models, and convert them into CIM2 urban component models; According to the building standard address, check whether the CIM2 building appearance model object and the CIM2 building interior model object have corresponding identification codes. If yes, directly assign the value, if no, perform new identification codes, and record the corresponding relationship between the standard address and the identification code; The CIM2 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

7. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 6 is characterized in that: In the method, the classification of CIM3 includes the following steps: Using the CIM2 level building appearance model, unnecessary details were removed through further refinement and optimization; Based on the CIM2 level building interior model, the model is refined and summarized, and the floor, wall, corridor, staircase, elevator, door and window elements are accurately modeled, while other auxiliary structures are simplified; Taking the CIM2 traffic model as the basis, the geometric accuracy and attribute information of roads, bridges and tunnels are further refined to ensure that the model can accurately reflect the key elements of traffic flow lines, traffic signs and signal lights; Based on the CIM2 level site model, further refine and optimize the vegetation, terrain, water bodies and other elements within the site to ensure that the site model can accurately reflect the site's use function and spatial layout; Using the CIM2-level pipeline gallery model, further refine and classify underground pipelines, cables, pipes and other facilities to ensure that the model can accurately reflect the distribution, direction and connection relationship of the pipelines; CIM2-level geological model data is used to further refine and classify the geological bodies to ensure that the model can accurately reflect the geological structure, rock layer distribution and geological disaster risk areas. Combined with the CIM2-level urban component model, further refine and optimize key urban components to ensure that the model can accurately reflect the appearance characteristics, functional attributes and spatial location of urban components; According to the building standard address, check whether the CIM3 building appearance model object and CIM3 building interior model object have corresponding identification codes. If yes, directly assign the value; if no, perform new identification codes, and record the corresponding relationship between the standard address and the identification code; The CIM3 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

8. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 7 is characterized in that: In the method, the classification of CIM4 includes the following steps: Use BIM building model data, delete the details of the building internal model to generate a CIM4 building appearance model, or use 3D artificial fine model data to convert and generate a CIM4 building appearance model; Based on the BIM building model, extract the building's internal floor, internal and external walls, corridors, stairs, elevators, doors and windows, summarize and synthesize other elements to generate the CIM4 building internal model; Generate CIM4 traffic model based on CIM3 traffic model conversion, and generate CIM4 site model based on CIM3 site model conversion; Based on the 3D model of pipeline corridor, 3D model of subway station and underground shop model, the CIM4 underground space model is converted and generated; According to the building standard address, check whether the CIM4 building appearance model object and the CIM4 building interior model object have corresponding identification codes. If yes, directly assign the value, if no, perform new identification coding, and record the corresponding relationship between the standard address and the identification code; The CIM4 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

9. The multivariate and multi-scale data governance method based on the CIM basic platform according to claim 8 is characterized in that: In the method, the classification of CIM5 includes the following steps: Using high-precision remote sensing image data and ground-measured vegetation information, the CIM4 vegetation model is refined to generate a CIM5 vegetation model. Using high-precision point cloud data obtained by laser scanning technology, the CIM4-level pipeline and corridor model is refined to generate the CIM5-level pipeline and corridor model; Combine geological exploration data with high-resolution 3D geological models to deepen the CIM4 geological model and generate a CIM5 geological model. Through field investigation and data collection, the CIM4-level urban component model is further refined to generate the CIM5-level urban component model; According to the building standard address, check whether the CIM5-level building appearance model object and the CIM5-level building interior model object have corresponding identification codes. If so, directly assign values ​​and codes. If not, perform new identification codes and record the corresponding relationship between the standard address and the identification code. The CIM5 level model is composed of the fusion of the above multiple types of models, stored in a universal three-dimensional data format, and contains model description files of spatial projection and file directory structure.

10. A multivariate and multi-scale data governance system based on the CIM basic platform, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. The memory is coupled to the processor, and when the processor executes the computer program, it implements the multivariate and multi-scale data governance method based on the CIM basic platform as described in any one of claims 1 to 9.

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