Cim-based data visualization display method, system, device and storage medium

Abstract

This invention relates to the field of urban information modeling (CIM) technology, specifically providing a data visualization method, system, device, and storage medium based on CIM. The method includes: acquiring and preprocessing multi-source urban data of spatial objects, business attributes, and planning indicators; establishing association rules based on a semantic mapping library and fusing them to generate a CIM structured dataset; extracting spatial structure and multi-element association features; dividing the data into three visualization levels—overall area, district, and plot—and configuring differentiated rendering parameters and dynamic linkage rules; matching and binding multi-source data with hierarchical units to construct a cross-scale linked 3D model; and building a visualization platform through a 3D rendering engine, integrating a planning analysis toolset, and establishing a dynamic linkage mechanism to achieve real-time calculation of planning indicators and threshold early warning. This invention achieves deep fusion of multi-source data and cross-scale linked display, improving visualization accuracy and decision support capabilities, and adapting to the needs of various urban planning and governance scenarios.

Description

Technical Field

[0001] This invention relates to the field of urban information modeling technology, specifically to a data visualization method, system, device, and storage medium based on CIM. Background Technology

[0002] City Information Modeling (CIM), as the core digital foundation for the construction of new smart cities, integrates multi-dimensional data resources such as urban space, business, and indicators, providing a unified digital support carrier for the entire lifecycle management of urban planning, construction, and governance. As my country's new urbanization enters a high-quality development stage, the reliance of urban planning and governance on CIM technology continues to increase. Especially in scenarios such as existing space updates, planning scheme verification, and dynamic monitoring of indicators, there is an urgent need to transform massive amounts of heterogeneous CIM data into intuitive and interactive decision-making data through data visualization technology, achieving precise "data-driven" governance. Currently, existing CIM data visualization methods have initially achieved the integration and three-dimensional presentation of multi-source urban data, but significant technical bottlenecks still exist in practical applications, making it difficult to meet the refined and dynamic needs of urban planning and governance. First, the depth of multi-source data integration is insufficient. Urban spatial object data, business attribute data, and planning indicator data often originate from different management departments, with heterogeneous data formats and inconsistent semantic standards. Existing technologies mostly use simple overlay or key field matching for integration, lacking a unified semantic mapping system and association rules. This results in a disconnect between "space-attribute-indicator," making it difficult to uncover the inherent logical connections between data. Visualization is merely at the level of "data piling up," failing to provide in-depth data support for decision-making.

[0003] Secondly, there is a lack of cross-scale hierarchical display and linkage capabilities. Urban planning and governance need to take into account the management needs of different scales, such as overall planning, regional coordination, and plot implementation. However, existing CIM visualizations mostly adopt a single-scale display mode or only achieve simple zoom browsing. They have not established standardized multi-scale hierarchical unit division rules, and the information transmission between different scales is not coherent. It is difficult to achieve cross-scale linkage analysis of "overall macro-control - regional meso-level coordination - plot micro-level implementation", and cannot adapt to the accuracy requirements of different management scenarios.

[0004] Third, the dynamic interaction and intelligent analysis functions are weak. Traditional CIM visualization is mainly static display. Although it can present the current urban data, it lacks dynamic analysis tools that are deeply integrated with planning business, making it difficult to realize the real-time calculation and verification of core planning indicators such as plot ratio and green space ratio. At the same time, no indicator threshold early warning mechanism has been established. When the planning scheme or urban status exceeds the control threshold, it is impossible to trigger an early warning and locate the problem area in time, causing the visualization platform to become a "display tool" rather than a "decision support tool".

[0005] Fourth, there is a lack of mechanisms for handling data association conflicts. During the fusion of multi-source data, semantic conflicts such as field matching contradictions and attribute and spatial misalignment often occur. Existing technologies lack clear rules for determining conflict priorities and often resort to random elimination or manual intervention, resulting in insufficient accuracy and reliability of data fusion, which in turn affects the credibility of visualization results and their value for decision-making applications.

[0006] In summary, existing CIM data visualization methods have significant limitations in terms of data fusion depth, cross-scale linkage, dynamic analysis and early warning, and conflict resolution, making it difficult to support the intelligent and precise upgrading of urban planning and governance. Therefore, developing a CIM data visualization method that can achieve deep semantic fusion of multi-source data, cross-scale hierarchical linkage, dynamic calculation of planning indicators, and threshold early warning has become an urgent need for the current industry development. Summary of the Invention

[0007] To overcome the shortcomings of existing technologies, this invention provides a data visualization method and system based on CIM to solve the problems in existing technologies.

[0008] One embodiment of the present invention provides a data visualization method based on CIM, comprising the following steps: S10. Obtain multi-source urban data including spatial object data, business attribute data, and planning indicator data, and preprocess the multi-source urban data to obtain a preprocessed dataset. S20. Based on the pre-built semantic mapping library, establish semantic association rules between spatial object data, business attribute data and planning indicator data, and use the semantic association rules to perform semantic association fusion on the preprocessed dataset to generate a CIM structured dataset. S30. Perform scene element analysis and feature extraction on the CIM structured dataset to obtain a spatial association feature set, which includes spatial structure features and multi-element association features. S40. Based on spatial structural features and multi-element correlation features, the system divides the data into three levels of visualization units, including the global scale, the area scale, and the plot scale. Differentiated display rendering parameters are configured for each level of unit, and dynamic linkage rules between levels are defined. S50. Based on semantic association rules, spatial structure features, multi-element association features and dynamic linkage rules between levels, spatial object data, business attribute data and planning indicator data are matched and bound with three-level visualization hierarchical units to obtain a multi-source fusion urban 3D model that supports cross-scale linkage. S60. Render a multi-source fusion urban 3D model based on a 3D rendering engine to construct an interactive 3D urban visualization platform; integrate a planning analysis toolset into the 3D urban visualization platform and establish a dynamic linkage mechanism between the tool analysis results and planning indicators to perform real-time calculation of planning indicators and threshold warnings, and generate cross-scale information urban visualization display results.

[0009] This application also relates to a data visualization system based on CIM, including: The data processing module is used to acquire multi-source urban data, including spatial object data, business attribute data, and planning indicator data, and to preprocess the multi-source urban data to obtain a preprocessed dataset. The association and fusion module is used to establish semantic association rules between spatial object data, business attribute data and planning indicator data based on a pre-built semantic mapping library, and to perform semantic association and fusion on the pre-processed dataset using the semantic association rules to generate a CIM structured dataset. The feature extraction module is used to analyze scene elements and extract features from the CIM structured dataset to obtain a spatial association feature set, which includes spatial structure features and multi-element association features. The hierarchy division module is used to divide the visualization hierarchy into three levels: global scale, area scale, and plot scale, based on spatial structure features and multi-element correlation features. It also configures differentiated display rendering parameters for each level and defines dynamic linkage rules between levels. The association matching module is used to match and bind spatial object data, business attribute data, and planning indicator data with three-level visualization hierarchical units based on semantic association rules, spatial structure features, multi-element association features, and dynamic linkage rules between levels, so as to obtain a multi-source fusion urban 3D model that supports cross-scale linkage. The visualization module is used to render multi-source fused 3D urban models based on a 3D rendering engine, and to build an interactive 3D urban visualization platform. The 3D urban visualization platform integrates a planning analysis toolset and establishes a dynamic linkage mechanism between the tool analysis results and planning indicators, so as to perform real-time calculation of planning indicators and threshold warnings, and generate cross-scale information urban visualization display results.

[0010] This application also relates to a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described CIM-based data visualization method.

[0011] This application also relates to a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned CIM-based data visualization method.

[0012] The data visualization method, system, device, and storage medium based on CIM provided in the above embodiments have the following beneficial effects: This invention achieves deep semantic fusion of urban data by acquiring and preprocessing multi-source data and establishing association rules using a semantic mapping library. This effectively solves the problems of heterogeneous multi-source data, semantic conflicts, and fragmented associations, generating a structured CIM dataset. By extracting spatial structure and multi-element association features, it divides the visualization into three levels: overall area, district, and plot, and configures differentiated parameters and dynamic linkage rules to achieve cross-scale hierarchical linkage display, adapting to the diverse planning and governance needs of macro-level coordination and micro-level implementation. By constructing a cross-scale linkage 3D model, integrating a planning analysis toolset, and establishing a dynamic linkage mechanism, it achieves real-time calculation of planning indicators and threshold early warning, breaking through the limitations of traditional static CIM visualization. This upgrades the visualization platform from a "data display tool" to an "intelligent decision-making support carrier," significantly improving the accuracy, interactivity, and efficiency of urban planning and governance, and providing strong technical support for the construction of new smart cities. Attached Figure Description

[0013] Figure 1 A flowchart illustrating a CIM-based data visualization method provided in this embodiment of the invention; Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions in the embodiments of the present invention will now be clearly and completely described in conjunction with the accompanying drawings.

[0015] Reference Figure 1 One embodiment of the present invention provides a data visualization method based on CIM, comprising the following steps: S10. Obtain multi-source urban data including spatial object data, business attribute data, and planning indicator data, and preprocess the multi-source urban data to obtain a preprocessed dataset. S20. Based on the pre-built semantic mapping library, establish semantic association rules between spatial object data, business attribute data and planning indicator data, and use the semantic association rules to perform semantic association fusion on the preprocessed dataset to generate a CIM structured dataset. S30. Perform scene element analysis and feature extraction on the CIM structured dataset to obtain a spatial association feature set, which includes spatial structure features and multi-element association features. S40. Based on spatial structural features and multi-element correlation features, the system divides the data into three levels of visualization units, including the global scale, the area scale, and the plot scale. Differentiated display rendering parameters are configured for each level of unit, and dynamic linkage rules between levels are defined. S50. Based on semantic association rules, spatial structure features, multi-element association features and dynamic linkage rules between levels, spatial object data, business attribute data and planning indicator data are matched and bound with three-level visualization hierarchical units to obtain a multi-source fusion urban 3D model that supports cross-scale linkage. S60. Render a multi-source fusion urban 3D model based on a 3D rendering engine to construct an interactive 3D urban visualization platform; integrate a planning analysis toolset into the 3D urban visualization platform and establish a dynamic linkage mechanism between the tool analysis results and planning indicators to perform real-time calculation of planning indicators and threshold warnings, and generate cross-scale information urban visualization display results.

[0016] In this embodiment, as described in steps S10-S60 above, a full-process technical system is implemented, encompassing multi-source data standardization preprocessing, deep semantic association fusion, hierarchical spatial feature extraction, three-level visualization unit construction, cross-scale data model binding, dynamic rendering, and intelligent analysis. This system integrates the CIM digital foundation with urban planning visualization logic, overcoming the systemic bottlenecks in existing technologies such as insufficient multi-source data association fusion, lack of cross-scale hierarchical linkage, weak dynamic interaction and intelligent analysis, and missing data association conflict handling. A hierarchical, interconnected, and intelligent CIM data visualization framework adapted to the entire lifecycle management of urban planning is constructed, providing full-cycle visualized decision support for urban planning, scheme verification, dynamic supervision, and precise governance, as detailed below: Step S10 is the urban multi-source data collection and standardized preprocessing stage. The core is to comprehensively collect heterogeneous data from all dimensions of space, business, and indicators required for urban planning and governance. Through systematic preprocessing, the differences in data format, quality, and spatiotemporal dimensions are eliminated, forming a regular and unified preprocessed dataset that can be directly used for semantic fusion. This solves the fundamental pain points of scattered sources, heterogeneous formats, and inconsistent quality of urban multi-source data in existing technologies, and lays a stable data foundation for subsequent semantic association fusion and CIM structured data construction.

[0017] Specifically, this includes two core operations: targeted acquisition of multi-source urban data and multi-dimensional preprocessing. The multi-source urban data acquisition focuses on the core data requirements of the CIM platform construction, comprehensively collecting spatial object data (such as urban topography data, building vector data, road and pipeline network data, 3D model data of structures, site boundary data, etc., which can be obtained through legitimate channels such as public geographic information service platforms, public urban spatial information databases, public 3D urban model sharing platforms, and industry public spatial data sharing portals), and business attribute data (such as land ownership data, population statistics data, construction approval data, municipal operation and maintenance data, environmental monitoring data, etc.). The data can be obtained through legal channels such as public urban governance data sharing platforms, industry public statistical data portals, public environmental monitoring data service platforms, and compliant and anonymized public urban operation and maintenance datasets. The data covers three core categories: urban physical space, management business, and planning constraints. This ensures that the data sources are comprehensive and the dimensions are complete.

[0018] Standardized preprocessing addresses issues such as inconsistent formats, spatiotemporal misalignment, redundancy, and noise interference in multi-source data. It involves a standard preprocessing workflow including data cleaning (e.g., removing duplicate spatial vector data and monitoring values ​​that deviate abnormally from the normal range), format conversion (e.g., converting CAD drawing data, Shapefile vector data, and Excel attribute data to CIM platform-compatible GeoJSON and IFC standard formats), spatiotemporal calibration (e.g., unifying the coordinate system to the CGCS2000 geodetic coordinate system and aligning to the standard municipal spatiotemporal reference), redundancy removal, and missing data completion (e.g., using neighborhood interpolation to complete locally missing parcel attribute data). This process unifies the spatiotemporal reference and storage format of the data, removes invalid and erroneous data, and completes key missing information, ultimately resulting in a preprocessed dataset that is compliant in quality, format, and spatiotemporally aligned.

[0019] It should be noted that all the multi-source data collected in this step are publicly available data that can be legally and compliantly obtained by those skilled in the art through public channels, and there are no non-public confidential internal documents.

[0020] Step S20 is the multi-source data semantic association fusion and CIM structured data generation stage. The core is to rely on the pre-built semantic mapping system to establish standardized semantic association logic between the three types of core data. By fusion at the semantic level, the semantic barriers between data are broken down, and the scattered pre-processed datasets are integrated into a hierarchical and clearly related CIM structured dataset. This solves the pain points of existing technologies, such as simple superposition of multi-source data, fragmented semantic association, and insufficient fusion depth. It provides a structured and highly related data carrier for subsequent scene element analysis and feature extraction. Specifically, based on a pre-built semantic mapping library, a general semantic association rule is established to adapt to the three core data types: spatial object data, business attribute data, and planning indicator data. This clarifies the semantic association logic and path of the three types of data. Based on this semantic association rule, a holistic semantic association fusion operation is performed on the pre-processed dataset to establish inherent semantic associations between the data and eliminate the data fragmentation problem caused by inconsistent semantic standards. Then, in accordance with the data organization specifications of the CIM platform, the fused data is structured and hierarchically organized to finally generate a CIM structured dataset that meets the CIM visualization requirements.

[0021] Step S30 involves the CIM structured data scenario analysis and spatial association feature extraction. Its core is to deconstruct the semantically fused CIM structured dataset into a scenario-based framework and mine its core features. This extracts a set of core features that characterize the urban spatial layout and the relationships between multiple elements, forming a spatial association feature set that supports hierarchical division and model construction. This addresses the pain points of insufficient data feature mining and unclear spatial and business indicator relationships in existing technologies, providing crucial feature basis for subsequent three-level visualization hierarchical unit division. Specifically, the CIM structured dataset is first comprehensively deconstructed into scenario elements, distinguishing and extracting spatial entity elements, attribute control elements, and indicator constraint elements. Then, spatial and association-level features are extracted from the deconstructed elements, yielding spatial structural features reflecting the urban spatial layout and topological relationships, as well as multi-element association features reflecting the relationships between space, attributes, and indicators. Finally, these two types of core features are integrated to form a complete spatial association feature set, providing standardized feature support for subsequent hierarchical division and model binding.

[0022] Step S40 is the three-level visualization hierarchy unit construction and visualization configuration stage. The core is based on the inherent logic of spatial correlation characteristics. According to the scale requirements of urban planning and management, it divides the city into three levels of standardized visualization hierarchy units: whole area, area, and plot. At the same time, it configures the appropriate display parameters for different level units and establishes the linkage logic between levels to form a complete hierarchical visualization configuration system. This solves the pain points of insufficient cross-scale display capabilities, lack of standard hierarchical division, and inability to link information between levels in existing technologies. It lays the hierarchical framework foundation for subsequent cross-scale 3D model construction and visualization display. Specifically, based on the distribution patterns of spatial structural characteristics and multi-element correlation characteristics, and combined with the actual needs of urban planning and management, a three-level visualization hierarchy unit is defined at the overall scale, area scale, and plot scale, and the coverage and management boundaries of each level are clearly defined. Different display rendering parameters are configured for the display needs and characteristic attributes of different level units to adapt to the visualization accuracy and display emphasis of each level. At the same time, combined with the business logic of planning management, dynamic linkage rules between the three-level visualization hierarchy units are defined, and the basic logic of information transmission, view switching, and data synchronization between levels is clarified, ultimately completing the overall construction and configuration of the three-level visualization hierarchy unit.

[0023] Step S50 involves matching and binding multi-source data with hierarchical units and constructing a 3D model. Its core is to rely on established semantic association rules, spatial association feature sets, and dynamic linkage rules between levels to accurately match and compliantly bind three types of core urban multi-source data with three levels of visualization hierarchical units. This constructs a multi-source fusion urban 3D model capable of cross-scale information linkage, addressing the pain points of data and display hierarchy mismatch and the inability to collaboratively display cross-scale data in existing technologies. It provides a core model carrier for subsequent 3D rendering and platform construction. Specifically, based on semantic association rules as the underlying association basis, spatial structural features and multi-element association features in the spatial association feature set as the matching guide, and dynamic linkage rules between levels as constraints, a matching and binding logic for urban multi-source data and three levels of visualization hierarchical units is established. Following this logic, spatial object data, business attribute data, and planning indicator data are respectively matched and effectively bound to the corresponding level of visualization units, eliminating invalid binding relationships that do not conform to the association rules and hierarchical constraints. Finally, all valid binding relationships are integrated to construct a multi-source fusion urban 3D model with cross-scale data association and support for dynamic linkage between levels.

[0024] Step S60 is the 3D model rendering, visualization platform construction, and intelligent analysis output stage. The core is to complete the hierarchical rendering of the model through a 3D rendering engine, integrate planning analysis tools, establish dynamic linkage and early warning mechanisms for indicators, build an interactive 3D city visualization platform, and finally output cross-scale visualization display results that meet the needs of real-time planning calculation and threshold early warning. This solves the pain points of existing visualization platforms that are mainly static displays, lack intelligent analysis tools, and lack indicator early warning mechanisms, and realizes the upgrade of the CIM visualization platform from a simple display tool to an intelligent decision-making support carrier. Specifically, the system comprises three core operations: hierarchical model rendering, platform integration and construction, and dynamic linkage and early warning implementation. It employs a professional 3D rendering engine, using differentiated rendering parameters at each level to perform hierarchical adaptation rendering of multi-source fused 3D urban models, ensuring the accuracy and aesthetics of the display at each scale. Based on the rendered model, an interactive 3D urban visualization platform is built, integrating an analysis toolset adapted to planning business needs. A dynamic linkage mechanism is established between the tool analysis results and planning indicators, enabling real-time calculation and data synchronization of planning indicators. Corresponding early warning thresholds are set for each level of planning indicators; when an indicator value triggers a threshold, an early warning operation is automatically executed. Finally, by integrating the rendering model, analysis tools, and linkage early warning mechanism, a complete cross-scale information urban visualization display result is output, providing intuitive, dynamic, and intelligent decision support for urban planning and governance.

[0025] In one embodiment, step S20 specifically includes the following steps: S21. Construct a semantic mapping library, which includes a mapping table of spatial identifiers and spatial dimension labels for spatial object data, a mapping table of attribute fields and control dimension labels for business attribute data, a mapping table of indicator fields and constraint dimension labels for planning indicator data, and an association weight table between each dimension label configured with preset weight ratios. S22. Based on the semantic mapping library, construct a three-dimensional bidirectional semantic association rule including combined association matching, dimensional association matching, and cross-level step-by-step transfer association. The three-dimensional bidirectional semantic association rule embeds a semantic conflict priority judgment rule. When multiple fields match in conflict, the valid association is judged according to the preset weight ratio of the association weight table, and the invalid association is eliminated. S23. Using spatial units as core anchor points, three-dimensional bidirectional semantic association rules are used to perform targeted semantic fusion on the preprocessed dataset to form a preliminary association set of space-attribute-indicator; S24. Based on the semantic conflict priority judgment rule, verify the initial association set of space-attribute-indicator, retain valid associations and remove invalid associations, and obtain the fused dataset after deduplication. S25. Organize the fused dataset into a structured manner according to the preset structure of three-level visualization hierarchical units: global area, district, and plot, to generate a CIM structured dataset.

[0026] In this embodiment, as described in steps S21-S25 above, the core process involves constructing a semantic mapping library, designing three-dimensional bidirectional association rules, performing targeted semantic fusion, conflict verification and deduplication, and hierarchical structured organization to achieve deep semantic association fusion of multi-source urban data. This addresses the core pain points of existing technologies, such as inconsistent semantic standards, fragmented data associations, and lack of conflict handling, providing a standardized and reusable semantic fusion framework for the generation of CIM structured datasets. The specific steps are as follows: Step S21 is the foundational support construction stage for semantic fusion. Its core is building a semantic mapping library covering three types of data: space, attributes, and indicators. Through standardized label mapping and associated weight configuration, it establishes the underlying logic for semantic relationships between data, providing a quantitative basis for subsequent association rule design and conflict determination. Specifically, the semantic mapping library contains four core mapping tables, all stored in structured table format to ensure efficient querying and retrieval: a mapping table of spatial identifiers and spatial dimension labels for spatial object data. Spatial identifiers are unique identification information for various spatial units (such as land parcel codes, unique building IDs, road network node numbers, etc.), and spatial dimension labels are standardized semantic labels representing spatial characteristics (such as location type, spatial morphology, topological relationships, land use nature, etc.); a mapping table of attribute fields for business attribute data and management dimension labels. Attribute fields are core fields of business data (such as land use right type, construction approval status, municipal facility operation and maintenance level, etc.), and management dimension labels are semantic classification labels corresponding to management businesses (such as ownership control, approval control, operation and maintenance control, security control, etc.); and planning indicator data... The system uses a mapping table between indicator fields and constraint dimension labels. The indicator fields are core indicators for planning and control (such as plot ratio, building density, green space ratio, and area of ​​public service facilities). The constraint dimension labels are the corresponding control semantic labels (such as development intensity constraints, ecological environment constraints, public service constraints, and spatial form constraints). A correlation weight table with preset weight percentages is configured to quantify the semantic correlation strength between different dimension labels. The preset weight percentages are calibrated based on the business logic of urban planning governance (e.g., the correlation weight between spatial dimension labels and control dimension labels is set to 0.4, the correlation weight between control dimension labels and constraint dimension labels is set to 0.35, and the correlation weight between spatial dimension labels and constraint dimension labels is set to 0.25). The total weight is 1, ensuring the quantitative feasibility of conflict determination.

[0027] Step S22 is the core rule design stage of semantic association. Its core is to construct three-dimensional bidirectional semantic association rules based on a semantic mapping library, encompassing space, attributes, and indicators, and embedding semantic conflict priority judgment logic to solve the problems of unclear multi-source data association paths and lack of standardized conflict handling. Specifically, the three-dimensional bidirectional semantic association rules include three core association matching methods to achieve full-dimensional association between the three types of data: combined association matching, where spatial object data and business attribute data are combined and associated through spatial identifiers and spatial dimension labels (e.g., by using a plot code + land use nature label for dual matching, the spatial data of a plot is associated with the corresponding land ownership business data); dimensional association matching, where business attribute data and planning indicator data are directly associated through control dimension labels and constraint dimension labels (e.g., by using ownership control labels + development intensity constraint labels, the land ownership business data is associated with plot ratio planning indicator data); and cross-level step-by-step transfer. The association refers to the indirect association between spatial object data and planning indicator data through a hierarchical process of spatial dimension labels, control dimension labels, and constraint dimension labels (e.g., spatial data → land use label → control dimension label → development intensity constraint label → planning indicator data, thus completing the semantic association between space and indicators). At the same time, the rules embed semantic conflict priority judgment rules. When multiple fields match in conflict (e.g., a plot of land matches two different plot ratio indicator data at the same time), the association is determined to be valid according to the preset weight ratio of the association weight table. The association with the higher weight ratio is valid, and the rest are invalid associations and are eliminated to ensure the uniqueness and rationality of the association results.

[0028] Step S23 is the implementation stage of targeted semantic fusion. Its core is to use spatial units as the core anchor point and rely on three-dimensional bidirectional semantic association rules to target and integrate scattered data in the preprocessed dataset, forming a preliminary spatial-attribute-indicator association set to solve the problem of scattered and unrelated data. Specifically, firstly, spatial units are determined as the core anchor point, and the smallest analytical unit of existing space (such as plots, blocks, grid units, etc.) is selected to ensure spatial consistency in the association fusion. Then, the three-dimensional bidirectional semantic association rules designed in step S22 are invoked to target and match spatial object data, business attribute data, and planning indicator data in the preprocessed dataset: using the spatial identifier of a single spatial unit as the search keyword, the corresponding business attribute data is found through combined association matching; the corresponding planning indicator data is found through cross-level hierarchical transfer association; and the three types of data are bound according to the unique identifier of the spatial unit, forming an association pattern of "one spatial unit corresponding to a set of attribute data and indicator data". Finally, the association results of all spatial units are integrated to form a preliminary spatial-attribute-indicator association set containing semantic associations of spatial, attribute, and indicator data, ensuring semantic connectivity of multi-source data for each spatial unit.

[0029] Step S24 is the optimization and verification step of the association results. The core is to verify the initial association set using semantic conflict priority judgment rules, eliminating invalid associations and duplicate data to improve the accuracy and purity of the fused data. Specifically, the verification is performed using a "full verification + key verification" approach: Full verification checks all association records in the initial association set one by one for semantic conflicts (such as mismatches between the land use nature label of spatial units and the ownership type label of business attribute data, or logical contradictions between the constraint dimension label of planning indicator data and the spatial dimension label). For records with conflicts, the valid associations with the highest weight are retained and invalid associations are eliminated according to the semantic conflict priority judgment rules set in step S22. Key verification focuses on high-frequency conflicting data types (such as conflicts between construction approval data and planning indicator data, conflicts between municipal operation and maintenance data and spatial topology data), using a combination of manual review and machine verification to ensure the accuracy of conflict handling. After verification, the association set is deduplicated, eliminating duplicate association records (such as multiple identical business data corresponding to the same attribute field of the same spatial unit), ultimately resulting in a conflict-free, duplicate-free, and semantically consistent fused dataset.

[0030] Step S25 is the hierarchical organization of the fused data. The core is to organize and arrange the fused dataset according to the preset structure of three-level visualization hierarchical units: global area, district, and plot, to generate a CIM structured dataset that meets the CIM visualization requirements. Specifically, the pre-defined structure of the three-tiered visualization unit is first clarified: the overall scale is based on the city's administrative boundaries (e.g., the entire prefecture-level city, the entire municipal district, etc.); the area scale is based on functional clusters or control zones (e.g., central business districts, old residential area renewal areas, ecological protection zones, etc.); and the plot scale is based on a single land parcel or the smallest construction unit (e.g., state-owned construction land use right parcels, independent construction plots, etc.). Then, following the hierarchical logic of "overall scale - area - plot," the fused dataset is hierarchically categorized: all spatial units are divided according to their respective levels, and spatial, attribute, and indicator data of units at the same level are grouped together, establishing a hierarchical index; simultaneously, corresponding metadata information (e.g., data source, update time, associated weight version, verification status, etc.) is configured for each level of data to ensure data traceability; ultimately, a clearly hierarchical, structurally sound, and closely related CIM structured dataset is formed, providing a high-quality data carrier for subsequent scene element analysis and feature extraction.

[0031] In one embodiment, the combined association matching, dimensional association matching, and cross-level hierarchical transfer association specifically refer to: Spatial object data and business attribute data are combined, associated, and matched using spatial identifiers and spatial dimension labels; Business attribute data and planning indicator data are matched and associated through control dimension labels and constraint dimension labels; Spatial object data and planning indicator data are linked across levels through spatial dimension labels, control dimension labels, and constraint dimension labels.

[0032] In this embodiment, as described above, the core is to concretize and standardize the three core association matching methods of the three-dimensional bidirectional semantic association rules, clarify the applicable data types, association basis, and operation logic of each matching method, and solve the practical pain points of ambiguous multi-source data association paths and unclear matching logic in the existing technology. It provides a standardized operation specification that can be directly implemented for the full-dimensional semantic association of spatial, attribute, and indicator data, ensuring the accuracy and consistency of semantic fusion, as detailed below: (1) Combined association matching (adapting spatial object data and business attribute data): The core is to establish a precise association between spatial object data and business attribute data through a dual verification mechanism of "unique spatial identifier + standardized spatial dimension label," ensuring that the association results have spatial uniqueness and semantic consistency, and avoiding association misalignment problems caused by matching a single identifier. Specifically, the spatial identifier of the spatial object data serves as a rigid matching basis (ensuring accurate spatial positioning), while the spatial dimension label serves as a semantic supplementary basis (ensuring business attribute adaptation). The combination of the two forms a dual association condition: first, the target spatial unit is located through the spatial identifier (such as the land parcel code JZ2024-001, the building unique ID: JZ-0035, the road network node number ND-1289, etc.); then, the business attribute data that matches the spatial characteristics is filtered through the spatial dimension label (such as land use nature - residential land, spatial form - high-rise residential area, topological relationship - adjacent to main road, etc.), ultimately achieving "one spatial unit corresponds to a set of precisely matched business attribute data." For example, a plot of land is spatially identified as “Plot JZ2024-001”, and its spatial dimension label is “Residential Land + Suburban Area + Adjacent to Park Green Space”. Through combination and matching, the business attribute data of the plot can be accurately associated with the land use right type (state-owned land granted by the state), construction approval status (completed and filed), property service level (first-class property) and other business attributes, avoiding confusion with the business data of other plots.

[0033] (2) Dimensional correlation matching (adapting business attribute data and planning indicator data): The core principle is to establish a direct link between business management logic and planning control requirements by relying on the semantic fit of "control dimension labels + constraint dimension labels," reflecting the inherent control logic of "business execution - planning constraints," and solving the problem of semantic disconnect between business data and planning data. Specifically, control dimension labels for business attribute data represent the core scope of business management, while constraint dimension labels for planning indicator data represent the core direction of planning control. The two are directly linked based on semantic relevance: control dimension labels focus on the core demands of business management (such as ownership control, approval control, operation and maintenance control, and security control), while constraint dimension labels focus on the core objectives of planning control (such as development intensity constraints, ecological environment constraints, public service constraints, and spatial form constraints). When the two types of labels are highly semantically compatible, the corresponding business attribute data and planning indicator data are automatically linked. For example, the control dimension label for business attribute data is "ownership control - state-owned residential land", and the constraint dimension label for planning indicator data is "development intensity constraint - residential land plot ratio". The two are semantically directly matched. Through dimension association matching, the business data can be accurately associated with the planning indicator data of "residential land plot ratio ≤ 2.5". Another example is the dimension association between "operation and maintenance control - municipal water supply facilities" and "public service constraint - water supply service coverage radius", which realizes the accurate matching between municipal operation and maintenance business data and water supply planning indicators.

[0034] (3) Cross-level hierarchical transfer and association (adapting spatial object data and planning indicator data): The core issue is the lack of direct semantic connection between spatial object data and planning indicator data. It uses three types of standardized tags as intermediaries to build a hierarchical association link of "space → business → planning," breaking down the semantic barriers between the two types of data and achieving indirect but precise semantic association. Specifically, relying on the tag association logic in the semantic mapping library, a complete association path is constructed through a three-level tag hierarchical transfer: First, spatial object data is associated with its own spatial dimension tag (e.g., land use nature - commercial service facility land) to the corresponding control dimension tag (e.g., approval control - commercial project approval); second, this control dimension tag is associated with the semantically compatible constraint dimension tag (e.g., development intensity constraint - commercial land plot ratio); third, the constraint dimension tag is finally associated with the corresponding planning indicator data, forming a complete transfer link of "spatial object data → spatial dimension tag → control dimension tag → constraint dimension tag → planning indicator data." For example, the spatial object data of a commercial plot (spatial identifier: SY2024-015, spatial dimension label: commercial service facility land + urban core area) is first transferred to the "approval and control - commercial project approval" label through the "commercial service facility land" label, and then transferred to the "development intensity constraint - commercial land plot ratio" label through this label, and finally associated with the planning indicator data of "commercial land plot ratio ≤ 4.0", thus completing the indirect semantic association between space and planning indicators.

[0035] In one embodiment, step S30 specifically includes the following steps: S31. Based on the preset structure of the three-level visualization hierarchy unit of the whole area-region-plot, the CIM structured dataset is deconstructed into hierarchical scene elements to obtain the spatial entity elements, attribute control elements and indicator constraint elements corresponding to each level. S32. Perform cross-level topological relationship analysis on spatial entity elements at each level, and extract spatial structural features including hierarchical nesting relationships, spatial adjacency relationships and topological connectivity; S33. Perform multi-dimensional correlation mining on the spatial structure features and the corresponding attribute control elements and indicator constraint elements at each level to extract multi-element correlation features including spatial-attribute mapping features, attribute-indicator constraint features and spatial-indicator transit correlation features. S34. Spatial structural features and multi-element correlation features are hierarchically integrated according to three-level visualization hierarchical units to obtain spatial correlation feature sets corresponding to the whole-area scale, area scale and plot scale.

[0036] In this embodiment, as described in steps S31-S34 above, the core is to accurately extract spatial correlation feature sets from the CIM structured dataset through a process of hierarchical scene element deconstruction, cross-level topological relationship analysis, multi-dimensional association mining, and hierarchical feature integration. This addresses the pain points of fragmented data feature mining, ambiguous spatial-attribute-indicator association logic, and lack of systematic cross-level features in existing technologies. It provides standardized and hierarchical feature support for subsequent three-level visualization hierarchical unit division and multi-source data matching and binding. The specific steps are as follows: Step S31 is the preliminary element decomposition step for feature extraction. The core is to decompose the CIM structured dataset into layers and scenarios based on the preset structure of three-level visual hierarchical units, and separate three core elements: space, attributes, and indicators, laying the element foundation for subsequent feature extraction. Specifically, using a pre-defined three-tiered visualization structure of "overall area - district - plot" as the decomposition framework (overall scale: such as a prefecture-level city administrative area; district scale: such as a science and technology innovation park; plot scale: such as state-owned construction land parcel numbered D-08), a hierarchical deconstruction operation is performed on the semantically fused CIM structured dataset: spatial entity elements, attribute control elements, and indicator constraint elements are separated into three core elements according to the hierarchy. Among them, spatial entity elements are the core elements representing the physical spatial form of the city (such as topography, buildings, road networks, green spaces, and municipal facilities); attribute control elements are the management business elements corresponding to the spatial unit (such as land ownership type, construction approval status, facility operation and maintenance level, and safety control standards); and indicator constraint elements are the planning control elements guiding spatial development and construction (such as plot ratio, building density, green space ratio, height limit, and public service facility allocation indicators). Finally, the three sets of elements corresponding to each level are obtained, ensuring that the elements correspond accurately to the levels and avoiding confusion between elements across levels.

[0037] Step S32 is the spatial structure feature extraction step. The core is to conduct cross-level topological relationship analysis on spatial entity elements at each level, and to mine the core structural features that can represent the spatial layout and related forms, thus solving the problem of the lack of systematic extraction of spatial features. Specifically, for the spatial entity elements obtained from the deconstruction at each level, a topological relationship analysis algorithm is used to perform topological analysis across and within levels: First, the hierarchical nesting relationship across levels is analyzed (such as the inclusion relationship of a global scale containing 3 area-scale units, a certain area scale containing 12 plot-scale units, and the connection relationship of unit boundaries); then, the spatial adjacency relationship within a level is analyzed (such as the boundary adjacency relationship of adjacent parcels at the plot scale, the adjacent distribution relationship of roads and buildings at the area scale, and the spatial adjacency relationship between ecological protection zones and urban built-up areas at the global scale); finally, the topological connectivity across units is analyzed (such as the connectivity relationship between road network nodes, the pipeline connectivity path of municipal pipe networks, and the connectivity coverage relationship of the service area of ​​public service facilities); the results of the three types of topological relationships are integrated to extract spatial structural features that include hierarchical nesting relationships, spatial adjacency relationships, and topological connectivity, forming a subset of spatial structural features corresponding to each level.

[0038] Step S33 is the multi-element association feature extraction step. The core is to conduct multi-dimensional association mining of spatial structure features with attributes and indicator elements at each level, and extract features that can represent the inherent association logic of the three elements of "space-attribute-indicator" to solve the problem of fragmented association of the three types of elements. Specifically, taking the spatial structural features extracted in step S32 as the core, correlation analysis is conducted with the corresponding attribute control elements and indicator constraint elements at each level: First, spatial-attribute mapping features are mined, that is, the correspondence between spatial structural features and attribute control elements is analyzed (e.g., spatial adjacency with main roads corresponds to commercial land ownership attributes, and the spatial form of high-rise buildings corresponds to Class I high-rise fire control attributes, etc.); Second, attribute-indicator constraint features are mined, that is, the constraint relationship between attribute control elements and indicator constraint elements is analyzed (e.g., state-owned residential land attributes correspond to the constraint indicator of plot ratio ≤ 2.2, and municipal park attributes correspond to the constraint indicator of green space ratio ≥ 80%, etc.); Third, spatial-indicator transit correlation features are mined, that is, by using attribute control elements as transit points, indirect correlations are established between spatial structural features and indicator constraint elements (e.g., spatial adjacency between a plot and a school → educational supporting service attributes → constraint indicator of public service facility construction area ≥ 500 square meters); Finally, the results of the three types of correlation analysis are integrated to extract multi-element correlation features that include spatial-attribute mapping features, attribute-indicator constraint features, and spatial-indicator transit correlation features.

[0039] Step S34 is the feature hierarchical integration step. The core is to organize the spatial structure features and multi-element association features hierarchically according to the logic of three-level visualization hierarchical units, forming a complete and suitable spatial association feature set, and solving the problems of scattered feature distribution and mismatch with hierarchical requirements. Specifically, using a three-tiered visualization unit structure of global area, district, and plot as the integration framework, the hierarchical classification and integration of features are performed: the subsets of spatial structure features extracted in step S32 are matched and bound with the corresponding multi-element association features extracted in step S33, ensuring a one-to-one correspondence between spatial structure features and multi-element association features at the same level; hierarchical identifiers and feature metadata (such as feature extraction algorithms, data sources, association weight versions, etc.) are configured for each level of feature set; optimization is performed for the feature emphasis of different levels: the global scale feature set focuses on macroscopic spatial layout, cross-district topological connectivity, and global indicator constraint association features; the district scale feature set focuses on mesoscopic spatial adjacency relationships and functional group attribute-indicator association features; and the plot scale feature set focuses on microscopic spatial morphology and precise mapping features of individual plot attributes-indicators; finally, a complete spatial association feature set corresponding to the global scale, district scale, and plot scale is formed, providing accurate feature support for subsequent three-tiered visualization unit division and multi-source data matching and binding.

[0040] In one embodiment, step S40 specifically includes the following steps: S41. Based on the cross-level topological relationships and multi-element relationship characteristics in the spatial association feature set, the hierarchical boundary thresholds of the whole area scale, the area scale and the plot scale are delineated, and the division results of the three-level visualization hierarchical units are determined in combination with the planning management attributes of the urban planning control unit. S42. For each level of unit, configure differentiated display rendering parameters based on its corresponding spatial structure features and multi-element association features; S43. Based on the real-time calculation requirements of planning indicators and the threshold early warning requirements, define dynamic linkage rules between the three-level visualization hierarchical units. S44. Integrate the division results of the three-level visualization hierarchy units, differentiated display rendering parameters, and dynamic linkage rules between levels to generate a hierarchical visualization configuration system.

[0041] In this embodiment, as described in steps S41-S44 above, the core is to construct a standardized and highly adaptable hierarchical visualization configuration system through a process of defining hierarchical boundary thresholds, configuring differentiated rendering parameters, defining dynamic linkage rules, and integrating the configuration system. This addresses the pain points of existing technologies, such as the lack of standards for cross-scale hierarchical division, homogenization of display parameters, and lack of logical hierarchical linkage. It provides a directly implementable hierarchical configuration basis for the construction and visualization of multi-source fusion urban 3D models. The specific steps are as follows: Step S41 is the three-level visualization hierarchical unit division stage. Its core is to define hierarchical boundary thresholds and determine the final division result based on spatial correlation characteristics and urban planning and control attributes, addressing the problems of lacking quantitative basis for hierarchical division and its disconnect from planning and management needs. Specifically, it uses cross-level topological relationships (hierarchical nesting, spatial adjacency, topological connectivity) and multi-element correlation characteristics (space-attribute-indicator correlation logic) concentrated in spatial correlation characteristics as the core quantitative basis to define hierarchical boundary thresholds at three scales: overall area, district, and plot. The overall area scale boundary threshold is based on the city's macro-control scope (e.g., the administrative boundaries of prefecture-level cities, the planning scope of metropolitan areas, with the corresponding topological connectivity threshold being "cross-regional road network connectivity rate ≥ 90%"). The district scale boundary threshold is guided by functional clusters or control zones (e.g., central business districts, old residential area renewal areas, ecological protection zones, with the corresponding spatial adjacency relationship threshold being "the proportion of adjacent spatial units with consistent functional attributes ≥ 8%"). 5%); The boundary threshold for the plot scale is based on the smallest construction control unit (such as state-owned construction land parcels and independently developed plots, with the corresponding attribute-indicator association threshold being "100% matching rate of single plot attributes and indicator constraints"); On this basis, the boundary threshold is fine-tuned and optimized in conjunction with the planning management attributes of the urban planning control unit (such as the type of control unit, land use functional zoning, development intensity control level, etc.), and finally the specific division results of the three-level visualization hierarchical units are determined (such as dividing the whole area into 1 municipal unit, dividing the area into 8 functional group units, and dividing the plot scale into 326 parcel units), ensuring that the division results not only conform to the spatial characteristic logic, but also adapt to the actual needs of planning management.

[0042] Step S42 is the differentiated display rendering parameter configuration stage. Its core is to configure exclusive rendering parameters tailored to the display needs of each level based on the spatial structural features and multi-element relationship characteristics, thus solving the problem of homogenized display parameters and the inability to highlight core information at each level. Specifically, differentiated display rendering parameters are configured for different feature emphases and display needs at the global, regional, and plot levels. Parameter types cover core dimensions such as geometric precision, texture resolution, color mapping, layer display priority, and transparency: The global scale focuses on macroscopic layout display, configuring rendering parameters with low geometric precision (e.g., buildings simplified to block models), low texture resolution (e.g., 1024×1024 pixels), and high transparency (e.g., terrain layer transparency 60%). Color mapping is guided by functional zoning (e.g., green for ecological land, gray for urban built-up areas); the regional scale focuses on mesoscopic functional synergy display, configuring medium geometric precision (e.g., preserving the main outline details of buildings) and medium texture resolution (e.g., 2048×2048 pixels). The rendering parameters are set to medium transparency (e.g., 40% transparency for facility layers), with color mapping guided by attribute control (e.g., blue for approved plots and yellow for undeveloped plots). For plot-scale rendering, the focus is on showcasing micro-level details, with high geometric precision (e.g., preserving building doors, windows, and facade details), high texture resolution (e.g., 4096×4096 pixels), and low transparency (e.g., 10% transparency for building layers). Color mapping is guided by indicator constraints (e.g., green for plots meeting floor area ratio standards and red for plots exceeding standards). All parameters are precisely adapted to the spatial structural characteristics of the corresponding level (e.g., micro-spatial morphology at the plot scale) and multi-element correlation characteristics (e.g., functional group attributes-indicator correlation at the area scale) to ensure that the display effect matches the level requirements.

[0043] Step S43 is the definition of dynamic linkage rules between levels. Its core is to clarify the linkage logic of the three-level hierarchical units based on the business requirements of real-time calculation of planning indicators and threshold early warning, thus solving the problems of information fragmentation and lack of coordinated response between levels. Specifically, based on the planning management logic of "macro-level control - meso-level coordination - micro-level implementation," three types of core dynamic linkage rules are defined: First, view switching linkage rules, clarifying the triggering conditions and response methods for view scaling, panning, and jumping between levels (e.g., when the mouse wheel zooms to a 1:5000 scale, it automatically switches from the global scale to the area scale; when clicking on an area unit, it automatically locates and zooms to the corresponding plot scale view); Second, data synchronization linkage rules, ensuring the consistency and continuity of data information during level switching (e.g., the "average green space ratio of the area" displayed at the global scale is synchronized after switching to the area scale). The system displays detailed green space ratio data for each plot within the area; after a plot-level indicator is updated, the aggregated indicator data for the entire area and the whole region are automatically updated synchronously; thirdly, it defines the early warning response linkage rules, defining the cross-level linkage logic when an indicator threshold early warning is triggered (e.g., after an early warning is triggered at the plot level for "exceeding the floor area ratio standard", the corresponding area unit is automatically highlighted upwards and located downwards to the specific plot exceeding the standard, while the location of the early warning area is marked at the whole region scale); all linkage rules are deeply bound to the real-time calculation needs of planning indicators (such as cross-level indicator aggregate calculation) and threshold early warning needs (such as cross-level early warning location), ensuring accurate and efficient linkage response.

[0044] Step S44 is the integration of the hierarchical visual configuration system. The core is to systematically integrate the hierarchical division results, differentiated rendering parameters, and dynamic linkage rules to form a structured and reusable configuration system, solving the problem of scattered configuration information and the inability to call it in a unified manner. Specifically, standardized data formats (such as XML and JSON) are used to integrate and encapsulate three types of core configuration information: First, a unique level ID is assigned to each level unit (such as global ID: G-001, area ID: P-003, plot ID: L-128), establishing an association index between levels; second, the differentiated rendering parameters of each level are organized into parameter sets according to the structure of "parameter type-parameter value-applicable scenario" and bound to the corresponding level ID; then, the dynamic linkage rules are organized into rule scripts according to the structure of "trigger condition-linkage action-response priority" and associated with the corresponding level switching, data synchronization, and early warning response scenarios; finally, metadata information (such as configuration version, update time, compatible CIM platform version, planning control standard basis, etc.) is added to the configuration system to ensure the traceability and compatibility of the configuration system; finally, a complete hierarchical visualization configuration system is generated, which can be directly used for subsequent matching and binding of multi-source data and hierarchical units, and hierarchical rendering of 3D models, providing standardized configuration support for the construction of the CIM visualization platform.

[0045] In one embodiment, step S50 specifically includes the following steps: S51. Based on the hierarchical visualization configuration system, three-dimensional bidirectional semantic association rules, spatial structural features and multi-element association features, establish feature label mapping and matching rules between urban multi-source data and three-level visualization hierarchical units; S52. According to the feature label mapping matching rules, spatial object data, business attribute data and planning indicator data are respectively matched and bound to the hierarchical units corresponding to the whole area scale, area scale and plot scale. S53. Verify the hierarchical directional matching and binding results based on semantic conflict priority judgment rules, eliminate invalid binding relationships with data mismatch and hierarchical out-of-bounds, and retain valid binding relationships that meet the requirements of the hierarchical visual configuration system; S54. Integrate the effective binding relationships to construct a multi-source fusion urban 3D model that supports cross-scale linkage.

[0046] In this embodiment, as described in steps S51-S54 above, the core is to achieve accurate adaptation and binding of multi-source urban data with three-level visualization hierarchical units through a process of establishing matching rules, hierarchical targeted binding, conflict verification and purification, and effective relationship integration. This solves the pain points of data and hierarchy mismatch, invalid binding relationships, and insufficient cross-scale linkage support in existing technologies, and constructs a multi-source fusion urban 3D model with hierarchical, associative, and linkage characteristics. The specific steps are as follows: Step S51 is the rule construction stage for matching data with hierarchical units. The core is to integrate the technical achievements mentioned above, establish standardized feature label mapping and matching rules, provide a unified and reusable operational basis for hierarchical targeted binding, and avoid binding deviations caused by chaotic matching logic. Specifically, based on four core technical elements, a feature label mapping and matching rule applicable to the entire domain is constructed: First, relying on the hierarchical IDs (such as global ID: G-001, area ID: P-005, and plot ID: L-216) and hierarchical attributes in the hierarchical visualization configuration system, the hierarchical belonging constraints of data matching are clarified; Second, the label association logic in the three-dimensional bidirectional semantic association rule is used to ensure that data matching conforms to the semantic association path of "space-attribute-indicator"; Third, spatial structural features (such as hierarchical nesting relationships and spatial adjacency relationships) are combined to limit the spatial scope boundary of data matching; Fourth, multi-element association features (such as spatial-attribute mapping features and attribute-indicator constraint features) are referenced to clarify the feature adaptation requirements of different types of data and hierarchical units; The final matching rule includes four-dimensional constraints of "hierarchical belonging-semantic association-spatial scope-feature adaptation", such as the specific rule that "plot-scale data must match plot ID, conform to spatial-attribute semantic association, be limited to the spatial scope of the corresponding plot, and adapt to micro-spatial morphological features".

[0047] Step S52 is the hierarchical targeted matching and binding implementation step. Its core is to accurately bind the three types of core urban multi-source data to corresponding hierarchical units according to preset matching rules, achieving a one-to-one correspondence between data and levels, and solving the problems of data dispersion and disconnection from visualization levels. Specifically, the targeted matching and binding operation is performed in the hierarchical order of "overall area - district - plot": For the overall area-level hierarchical units, macro-level spatial object data (such as city topography, cross-district road network framework, and overall ecological patches), business attribute data (such as total city population, overall status of construction approvals, and cross-regional municipal operation and maintenance coordination data), and planning indicator data (such as average city development intensity, target value for overall green space ratio, and total amount of public service facilities allocated across the entire area) are bound; for the district-level hierarchical units, meso-level spatial object data (such as building clusters within the district, district-level road network, and district green space squares) and business attribute data (such as district land rights) are bound. This includes data on zoning statistics, detailed construction approvals for different areas, and the operational and maintenance levels of facilities within those areas, as well as planning indicator data (such as the area's plot ratio range, the upper limit of building density for the area, and the standards for the allocation of public service facilities in the area). For plot-scale hierarchical units, it binds micro-level spatial object data (such as 3D models of buildings on individual plots, pipe network interfaces within plots, and topographical details of plot boundaries), business attribute data (such as ownership certificates for individual plots, data on the entire construction approval process for plots, and operational and maintenance records of facilities within plots), and planning indicator data (such as precise plot ratio values, measured building density values, and green space ratio compliance values ​​for individual plots). This ensures that each type of data is specifically matched to its corresponding hierarchical unit, without cross-level mismatches or omissions.

[0048] Step S53 is the matching and binding result verification and purification stage. The core is to eliminate invalid binding relationships through semantic conflict priority judgment rules, improve the accuracy and effectiveness of binding results, and solve problems such as data mismatch, hierarchical out-of-bounds, and semantic conflicts that may occur during the binding process. Specifically, the verification operation adopts a "full verification + key verification" approach: Full verification checks all matched and bound records one by one to check for data mismatches (such as binding industrial land attribute data to residential land plot units), hierarchical boundary violations (such as binding micro-scale building data at the plot scale to a global scale unit), semantic conflicts (such as semantic contradictions between the plot ratio index and land use attribute bound to the same plot); For bound records with problems, the valid association relationship is determined according to the semantic conflict priority judgment rules set in step S22 and the preset weights of the association weight table. For example, "when data from a certain area matches two adjacent area units at the same time, the binding relationship is retained according to the area unit with the higher spatial adjacency weight ratio"; Key verification checks high-frequency mismatched data types (such as cross-level indicator data, boundary area spatial data, multi-ownership cross-business data, etc.), using a combination of machine verification and manual review to ensure verification accuracy; After verification, all invalid binding relationships are removed, and finally, valid binding relationships that conform to the matching rules, are semantically consistent, and hierarchically adapted are retained.

[0049] Step S54 is the effective binding relationship integration and 3D model construction stage. The core is to systematically integrate the purified effective binding relationships to build a multi-source fusion urban 3D model that supports cross-scale linkage, providing a core carrier for subsequent 3D rendering and platform construction. Specifically, firstly, the effective binding relationships at each level are hierarchically categorized, and an association index is established according to the structure of "Level ID - Data Type - Binding Feature," such as "Plot ID: L-216 - Spatial Object Data - Building 3D Model - Binding Feature: Microscopic Spatial Morphology." Secondly, based on hierarchical nesting relationships and dynamic linkage rules, cross-level data association links are established, such as the linkage link of "Plot-scale Single Plot Floor Area Ratio Data → Area-scale Summary Floor Area Ratio Data → Global-scale Average Floor Area Ratio Data," ensuring that cross-level data is traceable and synchronized. Finally, 3D model construction technologies (such as BIM and GIS fusion modeling technology, oblique photogrammetry 3D real-scene modeling technology, GIS topology association modeling technology, parametric batch modeling technology, etc.) are used to deeply integrate and encapsulate the spatial object data, business attribute data, planning indicator data, and corresponding hierarchical unit 3D frameworks at each level. Specifically, this is achieved through model lightweighting (such as simplifying non-core details of LOD hierarchical modeling, using LOD1 block models at the global scale, and LOD2 at the area scale). The model employs a contour model and LOD3 scale for land parcels, data attribute mounting (linking business attribute data and planning indicator data to component-level nodes of the 3D model in key-value pairs, such as binding building components to corresponding plot ratio and ownership information), and topological relationship embedding (pre-setting hierarchical nesting and spatial adjacency topological association logic in the model to support cross-level linkage), forming a multi-source fusion urban 3D model that includes data association, hierarchical linkage, and semantic consistency. This model has three core capabilities: first, loading data on demand by level (supporting loading all data of a single level or cross-level combined loading to adapt to different viewing needs); second, seamless switching of views across levels (based on preset topological association logic, maintaining data context continuity during switching without information gaps); and third, precise querying of data details based on binding relationships (clicking on any component of the model allows real-time retrieval of associated spatial, attribute, and indicator data across all dimensions, such as clicking on a building to view its land use, approval status, and plot ratio compliance status), fully adapting to the visualization needs of subsequent hierarchical rendering and dynamic interactive analysis.

[0050] In one embodiment, step S60 specifically includes the following steps: S61. Based on differentiated display rendering parameters, a 3D rendering engine is used to perform hierarchical adaptation rendering on the multi-source fusion urban 3D model to obtain a hierarchical adaptation rendering model. S62. Establish a planning analysis toolset based on the planning indicator types. The planning analysis toolset includes, but is not limited to, a plot ratio calculation tool, a green space ratio calculation tool, a spatial conflict detection tool, and an indicator threshold comparison tool. Each tool has built-in calculation logic corresponding to the three-level visualization unit. S63. Establish a dynamic linkage mechanism between tool analysis results and planning indicators: Establish a real-time call association between the planning analysis toolset and the planning indicator library, which is constructed according to the three-level visualization hierarchical unit classification. The planning indicator library stores planning indicator data after hierarchical unit matching and binding. When the tool completes the planning indicator analysis of the target hierarchical unit, the analysis results are synchronized to the planning indicator library of that level in real time, triggering the automatic update of the corresponding indicator values ​​in the library. When the planning indicator library completes the indicator value update, the system automatically links the 3D model rendering status of the corresponding hierarchical unit and synchronously refreshes the visualization display content of that level. S64. For the planning indicators corresponding to each level unit, set threshold ranges according to the level differences. When the planning indicator value of any level unit triggers the corresponding threshold range, the system automatically locates the level unit and highlights it to realize the planning indicator threshold warning. S65. Integrate hierarchical adaptive rendering models, planning analysis toolsets, and dynamic linkage mechanisms to build an interactive 3D city visualization platform, outputting cross-scale information city visualization display results for real-time calculation of planning indicators and threshold early warning.

[0051] In this embodiment, as described in steps S61-S65 above, the core is to construct a 3D city visualization platform with dynamic interaction, intelligent analysis, and threshold early warning capabilities through a process of hierarchical adaptation rendering, planning and analysis toolset construction, dynamic linkage mechanism establishment, threshold early warning setting, and visualization platform integration. This addresses the pain points of static visualization, weak analysis functions, and lack of real-time early warning in existing technologies, achieving a core upgrade of CIM visualization from data display to intelligent decision support. The specific steps are as follows: Step S61 is the multi-source fusion urban 3D model hierarchical adaptation rendering stage. Its core is to rely on differentiated display rendering parameters and a professional 3D rendering engine to achieve accurate rendering of models at each level. This ensures that the display effects at different scales both meet the requirements and maintain performance, resolving issues such as mismatch between rendering effects and hierarchical requirements, and loading stuttering. Specifically, it uses mature 3D rendering engines (such as Unreal Engine 5, Unity 3D, Cesium, SuperMap iClient3D, ArcGIS Pro 3D rendering module, etc.) and strictly follows the differentiated display rendering parameters configured in step S42 to perform hierarchical adaptation rendering operations: the global scale model adopts a low-precision rendering strategy, with geometric precision at LOD1 (Level of The detail-oriented model is simplified (e.g., only the main body of buildings is retained, and micro-topographical undulations of less than 5 meters are ignored). The texture resolution is 1024×1024 pixels, and colors are mapped using functional zone color blocks (e.g., dark green for ecological reserves and light gray for urban built-up areas) to ensure clear macro-scale layout and efficient loading. The area-scale model uses a medium-precision rendering strategy, with geometric precision built to the LOD2 standard (e.g., retaining the main outlines of buildings and simplifying the representation of door and window openings). The texture resolution is increased to 2048×2048 pixels, and colors are controlled by attributes. The land parcel scale model employs a high-precision rendering strategy, with geometric precision refined to LOD3 standards (e.g., restoring building facade details, door and window styles, and site paving textures). The texture resolution reaches 4096×4096 pixels, and colors are mapped according to the indicators that meet the standards (e.g., green for meeting the floor area ratio standard and bright red for exceeding the standard), accurately presenting micro-level details. The final result is a hierarchical adaptive rendering model that adapts to different scale requirements, loads smoothly, and displays accurately.

[0052] Step S62 is the construction phase of the planning analysis toolset. Its core is to establish a professional toolset covering key indicator calculations and conflict detection, addressing the lack of deep business integration in existing visualization platforms. Specifically, based on the types of urban planning indicators (development intensity, ecological environment, public services, spatial form, etc.), a targeted planning analysis toolset is constructed. Each tool incorporates differentiated calculation logic corresponding to the three-level visualization units, ensuring adaptability to analysis needs at different scales: the plot ratio calculation tool incorporates hierarchical calculation logic at the "plot scale (single plot building area / land area), zone scale (zone total building area / zone total land area), and overall scale (overall zone total building area / overall zone urban construction land area)," supporting automatic extraction of land area and building area data bound to the model for real-time calculation; the green space ratio calculation tool uses logic at the "plot scale (plot green space area / plot total area), zone scale (zone total green space area / zone total area)"... The system includes a design tool that can identify green space entities in the model and automatically calculate their area; a spatial conflict detection tool with built-in detection logic for spatial topological conflicts (such as building spacing being less than the standard limit, or buildings encroaching on road red lines) and indicator conflicts (such as actual development intensity exceeding the planning limit), adapting to different levels of control standards (such as implementing detailed planning standards at the plot scale and implementing zoning planning standards at the area scale); an indicator threshold comparison tool that supports custom indicator comparison rules, enabling quick verification of the deviation between the actual values ​​of indicators at each level and the planning thresholds; in addition, the toolset can be expanded to include building density calculation tools, public service facility coverage analysis tools, and solar shading simulation tools, all of which support one-click access and real-time result display, fully meeting the actual business needs of planning preparation and scheme verification.

[0053] Step S63 establishes a dynamic linkage mechanism between tool analysis results and planning indicators. The core of this mechanism is to build a closed-loop linkage logic of "tool calculation - indicator library update - model rendering synchronization," resolving the issues of disconnect between analysis results and visualization, and untimely data updates. Specifically, the linkage mechanism consists of three core operations: First, a planning indicator library is constructed according to three levels of visualization hierarchy (e.g., a comprehensive planning indicator library, a regional planning indicator library, and a plot planning indicator library). This library stores planning indicator data (e.g., plot ratio, green space ratio, building density, etc. at each level) matched and bound in step S52, and each indicator is assigned a unique identifier and update trigger interface. Second, a real-time call association is established between the planning analysis toolset and the planning indicator libraries at each level. When the tool is called, it automatically reads the basic data (e.g., land area, planning thresholds) of the corresponding level indicator library. After analysis, the update trigger interface is used to complete the process. The results are synchronized to the indicator library in real time (e.g., after the plot-scale floor area ratio calculation tool completes the analysis of a plot, it automatically updates the actual floor area ratio value of that plot in the plot indicator library); finally, a linkage mapping between the indicator library and the rendering status of the 3D model is established. When any indicator value in the indicator library is updated, the system automatically triggers the refresh of the rendering status of the corresponding level unit of the 3D model (e.g., if the floor area ratio of a plot is updated from 2.1 to 2.3, it will remain green if it does not exceed the standard, and automatically switch to red if it exceeds the planning limit of 2.2), achieving millisecond-level synchronization of "analysis-update-display" to ensure that the visualized content is completely consistent with the data status.

[0054] Step S64 is the setting and execution of planning indicator threshold early warnings. The core is to set threshold ranges for indicators based on hierarchical differentiation, establish an automatic early warning response mechanism, and solve the problems of untimely detection of exceeding planning indicators and difficulty in locating problem areas. Specifically, threshold ranges are set according to the principle of "hierarchical adaptation and indicator classification," and then the early warning response logic is configured: Regarding threshold range setting, for whole-area scale indicators, range thresholds are set according to macro-control requirements (e.g., whole-area green space ratio early warning threshold: qualified range ≥35%, warning range 30%-35%, exceeding range <30%); for area-scale indicators, range thresholds are set according to functional zoning requirements (e.g., central business district plot ratio early warning threshold: qualified range 2.5-4.0, warning range 4.0-4.5, exceeding range >4.5); for plot-scale indicators, precise thresholds are set according to detailed planning requirements (e.g., plot ratio early warning threshold for a residential plot: upper limit 2.2, warning line 2.1, qualified line ≤2.2). Thresholds can be manually adjusted and versions can be saved; regarding the early warning response logic, when any level unit... When the planned indicator values ​​trigger the corresponding threshold range, the system automatically executes a "triple response": First, a location response, quickly locking the position of the unit in the 3D view and centering it (if a plot exceeds the limit, it automatically zooms to the view of that plot); second, a highlighting annotation, using high-contrast colors (such as bright red and bright yellow) with a flashing effect to mark the target unit (e.g., plots exceeding the limit are marked with a red flashing border, and plots under warning are marked with a yellow static border); third, an information pop-up, automatically displaying warning details including "indicator name, current value, planning threshold, deviation range, and problem type" (e.g., "plot ratio: 2.3, planning upper limit: 2.2, deviation +0.1, status: exceeding the limit"); it also supports exporting warning information (such as Excel reports and PDF analysis reports) for convenient follow-up and rectification.

[0055] Step S65 is the integration and output stage of the 3D city visualization platform. Its core is to systematically integrate rendering models, analysis tools, linkage mechanisms, and early warning functions to build an interactive comprehensive platform that outputs cross-scale visualization results that meet the needs of the entire planning process. Specifically, the platform integration adopts a modular architecture design: First, a view interaction module supports basic operations such as zooming, panning, rotating, and layer switching (e.g., one-click jump from the entire area to the target plot), and view saving (e.g., quick retrieval of commonly used analysis views); second, a tool calling module integrates planning analysis toolsets in an icon-based format (e.g., floor area ratio calculation tool icon, spatial conflict detection tool icon), supporting click-to-call, parameter customization (e.g., adjusting the statistical caliber of green space ratio calculation), and batch analysis (e.g., selecting multiple areas to calculate green space ratio simultaneously); third, a data query module supports real-time retrieval of associated spatial object data, business attribute data, and planning indicator data by clicking on model components (e.g., buildings, plot boundaries) (e.g., clicking on a building allows viewing its ownership). The system includes: (1) certification, (2) approval processes, and (3) plot ratio data binding; (4) an early warning management module, which centrally displays early warning information at all levels, supports filtering by "early warning level, indicator type, and spatial range," and allows one-click location of the early warning unit to view rectification suggestions; the final 3D urban visualization platform outputs cross-scale information urban visualization results in three core forms: (1) a real-time interactive 3D visualization interface (supporting comparison of planning schemes and real-time viewing of dynamic supervision); (2) exportable indicator analysis reports (such as statistics on the compliance rate of indicators at each level and a list of early warning issues); and (3) visualization analysis reports (including 3D screenshots, indicator trend charts, and conflict detection results), comprehensively meeting the full-process decision-making needs of urban planning, scheme verification, dynamic supervision, and precise governance.

[0056] In one embodiment, a CIM-based data visualization system is provided, which corresponds to the CIM-based data visualization method described in the previous embodiment. The CIM-based data visualization system includes: The data processing module is used to acquire multi-source urban data, including spatial object data, business attribute data, and planning indicator data, and to preprocess the multi-source urban data to obtain a preprocessed dataset. The association and fusion module is used to establish semantic association rules between spatial object data, business attribute data and planning indicator data based on a pre-built semantic mapping library, and to perform semantic association and fusion on the pre-processed dataset using the semantic association rules to generate a CIM structured dataset. The feature extraction module is used to analyze scene elements and extract features from the CIM structured dataset to obtain a spatial association feature set, which includes spatial structure features and multi-element association features. The hierarchy division module is used to divide the visualization hierarchy into three levels: global scale, area scale, and plot scale, based on spatial structure features and multi-element correlation features. It also configures differentiated display rendering parameters for each level and defines dynamic linkage rules between levels. The association matching module is used to match and bind spatial object data, business attribute data, and planning indicator data with three-level visualization hierarchical units based on semantic association rules, spatial structure features, multi-element association features, and dynamic linkage rules between levels, so as to obtain a multi-source fusion urban 3D model that supports cross-scale linkage. The visualization module is used to render multi-source fused 3D urban models based on a 3D rendering engine, and to build an interactive 3D urban visualization platform. The 3D urban visualization platform integrates a planning analysis toolset and establishes a dynamic linkage mechanism between the tool analysis results and planning indicators, so as to perform real-time calculation of planning indicators and threshold warnings, and generate cross-scale information urban visualization display results.

[0057] For specific limitations regarding a CIM-based data visualization system, please refer to the limitations of a CIM-based data visualization method described above, which will not be repeated here. The modules in the aforementioned CIM-based data visualization system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0058] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows. Figure 2 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database is used for data storage, data processing, and data analysis. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a CIM-based data visualization method.

[0059] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a CIM-based data visualization method.

[0060] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a CIM-based data visualization method.

[0061] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0062] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0063] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A data visualization method based on CIM, characterized in that, Includes the following steps: S10. Obtain multi-source urban data including spatial object data, business attribute data, and planning indicator data, and preprocess the multi-source urban data to obtain a preprocessed dataset. S20. Based on a pre-built semantic mapping library, establish semantic association rules between spatial object data, business attribute data, and planning indicator data. Use the semantic association rules to perform semantic association fusion on the pre-processed dataset to generate a CIM structured dataset. The semantic association rules include combined association matching, dimensional association matching, and cross-level step-by-step association. The semantic association rules embed semantic conflict priority judgment rules. When multiple fields match in conflict, the effective association is determined according to the preset weight ratio of the association weight table, and the invalid association is eliminated. S30. Perform scene element analysis and feature extraction on the CIM structured dataset to obtain a spatial association feature set. The spatial association feature set includes spatial structure features and multi-element association features. The multi-element features include spatial entity elements, attribute control elements and indicator constraint elements. S40. Based on spatial structural features and multi-element correlation features, a three-level visualization hierarchy unit is divided, including the global scale, the area scale and the plot scale. Differentiated display rendering parameters are configured for each level unit, and dynamic linkage rules between the levels of each level unit are defined. The dynamic linkage rules include view switching linkage rules, data synchronization linkage rules and early warning response linkage rules. S50. Based on semantic association rules, spatial structure features, multi-element association features and dynamic linkage rules between levels, spatial object data, business attribute data and planning indicator data are matched and bound with three-level visualization hierarchical units to obtain a multi-source fusion urban 3D model that supports cross-scale linkage. S60: Renders multi-source fused 3D city models based on a 3D rendering engine, and builds an interactive 3D city visualization platform; The three-dimensional city visualization platform integrates a planning analysis toolset and establishes a dynamic linkage mechanism between the tool analysis results and planning indicators. This mechanism is used to perform real-time calculation of planning indicators and threshold early warning, and to generate city visualization results with cross-scale information.

2. The data visualization method based on CIM as described in claim 1, characterized in that, Step S20 specifically includes the following steps: S21. Construct a semantic mapping library, which includes a mapping table of spatial identifiers and spatial dimension labels for spatial object data, a mapping table of attribute fields and control dimension labels for business attribute data, a mapping table of indicator fields and constraint dimension labels for planning indicator data, and an association weight table between each dimension label configured with preset weight ratios. S22. Construct semantic association rules based on the semantic mapping library; S23. Using spatial units as core anchor points, semantic association rules are used to perform targeted semantic fusion on the preprocessed dataset to form a preliminary association set of space-attribute-indicator; S24. Based on the semantic conflict priority judgment rule, verify the initial association set of space-attribute-indicator, retain valid associations and remove invalid associations, and obtain the fused dataset after deduplication. S25. Organize the fused dataset into a structured manner according to the preset structure of three-level visualization hierarchical units: global area, district, and plot, to generate a CIM structured dataset.

3. The data visualization method based on CIM as described in claim 2, characterized in that, The combined association matching, dimensional association matching, and cross-level hierarchical transfer association are specifically as follows: Spatial object data and business attribute data are combined, associated, and matched using spatial identifiers and spatial dimension labels; Business attribute data and planning indicator data are matched and associated through control dimension labels and constraint dimension labels; Spatial object data and planning indicator data are linked across levels through spatial dimension labels, control dimension labels, and constraint dimension labels.

4. The data visualization method based on CIM as described in claim 2, characterized in that, Step S30 specifically includes the following steps: S31. Based on the preset structure of the three-level visualization hierarchy unit of the whole area-region-plot, the CIM structured dataset is parsed for hierarchical scene elements. S32. Perform cross-level topological relationship analysis on spatial entity elements at each level, and extract spatial structural features including hierarchical nesting relationships, spatial adjacency relationships and topological connectivity; S33. Perform multi-dimensional correlation mining on the spatial structure features and the corresponding attribute control elements and indicator constraint elements at each level to extract multi-element correlation features including spatial-attribute mapping features, attribute-indicator constraint features and spatial-indicator transit correlation features. S34. Spatial structural features and multi-element correlation features are hierarchically integrated according to three-level visualization hierarchical units to obtain spatial correlation feature sets corresponding to the whole-area scale, area scale and plot scale.

5. The data visualization method based on CIM as described in claim 4, characterized in that, Step S40 specifically includes the following steps: S41. Based on the cross-level topological relationships and multi-element relationship characteristics in the spatial association feature set, the hierarchical boundary thresholds of the whole area scale, the area scale and the plot scale are delineated, and the division results of the three-level visualization hierarchical units are determined in combination with the planning management attributes of the urban planning control unit. S42. For each level of unit, configure differentiated display rendering parameters based on its corresponding spatial structure features and multi-element association features; S43. Based on the real-time calculation requirements of planning indicators and the threshold early warning requirements, define dynamic linkage rules between the three-level visualization hierarchical units. S44. Integrate the division results of the three-level visualization hierarchy units, differentiated display rendering parameters, and dynamic linkage rules between levels to generate a hierarchical visualization configuration system.

6. The data visualization method based on CIM as described in claim 5, characterized in that, Step S50 specifically includes the following steps: S51. Based on the hierarchical visualization configuration system, semantic association rules, spatial structure features and multi-element association features, establish feature label mapping and matching rules between urban multi-source data and three-level visualization hierarchical units; S52. According to the feature label mapping matching rules, spatial object data, business attribute data and planning indicator data are respectively matched and bound to the hierarchical units corresponding to the whole area scale, area scale and plot scale. S53. Verify the hierarchical directional matching and binding results based on semantic conflict priority judgment rules, eliminate invalid binding relationships with data mismatch and hierarchical out-of-bounds, and retain valid binding relationships that meet the requirements of the hierarchical visual configuration system; S54. Integrate the effective binding relationships to construct a multi-source fusion urban 3D model that supports cross-scale linkage.

7. The data visualization method based on CIM as described in claim 6, characterized in that, Step S60 specifically includes the following steps: S61. Based on differentiated display rendering parameters, a 3D rendering engine is used to perform hierarchical adaptation rendering on the multi-source fusion urban 3D model to obtain a hierarchical adaptation rendering model. S62. Establish a planning analysis toolset based on the types of planning indicators. The planning analysis toolset includes a plot ratio calculation tool, a green space ratio calculation tool, a spatial conflict detection tool, and an indicator threshold comparison tool. Each tool has built-in calculation logic corresponding to the three-level visualization unit. S63. Establish a dynamic linkage mechanism between tool analysis results and planning indicators: Establish a real-time call association between the planning analysis toolset and the planning indicator library constructed according to the three-level visualization hierarchical unit classification. The planning indicator library stores planning indicator data after hierarchical unit matching and binding. When the tool completes the planning indicator analysis of the target hierarchical unit, the analysis results are synchronized to the planning indicator library of that level in real time, triggering the automatic update of the corresponding indicator values ​​in the library. Once the planning indicator library has completed the indicator value update, the system will automatically link the rendering status of the 3D model of the corresponding level unit and refresh the visualization display content of that level simultaneously. S64. For the planning indicators corresponding to each level unit, set threshold ranges according to the level differences. When the planning indicator value of any level unit triggers the corresponding threshold range, the system automatically locates the level unit and highlights it to realize the planning indicator threshold warning. S65. Integrate hierarchical adaptive rendering models, planning analysis toolsets, and dynamic linkage mechanisms to build an interactive 3D city visualization platform, outputting cross-scale information city visualization display results for real-time calculation of planning indicators and threshold early warning.

8. A CIM-based data visualization system, used to implement the steps of the CIM-based data visualization method as described in any one of claims 1-7, characterized in that, include: The data processing module is used to acquire multi-source urban data, including spatial object data, business attribute data, and planning indicator data, and to preprocess the multi-source urban data to obtain a preprocessed dataset. The association and fusion module is used to establish semantic association rules between spatial object data, business attribute data and planning indicator data based on a pre-built semantic mapping library, and to perform semantic association and fusion on the pre-processed dataset using the semantic association rules to generate a CIM structured dataset. The feature extraction module is used to analyze scene elements and extract features from the CIM structured dataset to obtain a spatial association feature set. The spatial association feature set includes spatial structure features and multi-element association features. The multi-element features include spatial entity elements, attribute control elements, and indicator constraint elements. The hierarchical division module is used to divide the visualization into three levels of units based on spatial structure features and multi-element correlation features, including global scale, area scale and plot scale. Differentiated display rendering parameters are configured for each level of unit, and dynamic linkage rules between the levels of units are defined. The dynamic linkage rules include view switching linkage rules, data synchronization linkage rules and early warning response linkage rules. The association matching module is used to match and bind spatial object data, business attribute data, and planning indicator data with three-level visualization hierarchical units based on semantic association rules, spatial structure features, multi-element association features, and dynamic linkage rules between levels, so as to obtain a multi-source fusion urban 3D model that supports cross-scale linkage. The visualization module is used to render multi-source fused 3D city models based on a 3D rendering engine, and to build an interactive 3D city visualization platform. The three-dimensional city visualization platform integrates a planning analysis toolset and establishes a dynamic linkage mechanism between the tool analysis results and planning indicators. This mechanism is used to perform real-time calculation of planning indicators and threshold early warning, and to generate city visualization results with cross-scale information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a CIM-based data visualization method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a CIM-based data visualization method as described in any one of claims 1-7.

Images