BIM and GIS fusion of building information model and geographic information integration management system

Abstract

The application relates to a building information model and geographic information integrated management system based on BIM and GIS fusion, comprising the following modules: a multi-source heterogeneous data acquisition module, which is used for respectively acquiring original BIM data and original GIS data from BIM modeling software and a GIS database, the original BIM data containing geometric and attribute information of building components, the original GIS data containing spatial and attribute information of geographic elements, and performing unified preprocessing of coordinate systems and semantics; and a space-time state base element construction module connected with the multi-source heterogeneous data acquisition module, which is used for extracting space-time state base elements with geometric shapes, semantic attributes, effective time intervals and space occupation information as basic units of fusion data based on the unified preprocessing of the BIM data and the GIS data; the application can realize deep fusion of BIM and GIS and more efficient management.

Description

Technical Field

[0001] This invention relates to the field of management systems, specifically to a building information model and geographic information integrated management system that integrates BIM and GIS. Background Technology

[0002] In the current field of architectural engineering, BIM technology focuses on the detailed modeling and internal attribute management of building components, while GIS technology focuses on the expression of macro-geospatial information and regional planning and control. In practical applications, both commonly suffer from data heterogeneity. Original BIM data uses a local engineering coordinate system, while GIS data uses a geographic coordinate system, resulting in inconsistent spatial benchmarks. Furthermore, the semantics of BIM components and the classification of GIS elements lack standardized mapping relationships, making direct fusion of multi-source data difficult. Simultaneously, traditional fusion methods lack unified spatiotemporal data units, failing to achieve integrated four-dimensional expression of geometry, attributes, time, and space. Multi-scale data matching relies on manual methods or simple rules, resulting in low accuracy and efficiency, and challenges in rendering scheduling, conflict detection, and other aspects. The lack of intelligent and quantitative technical support in key aspects such as full lifecycle data management, environmental adaptive control, predictive maintenance of components, and cross-scale data consistency verification results in problems such as large data matching errors, high system rendering load, difficulty in automatically verifying planning compliance, slow spatiotemporal data retrieval, extensive operation and maintenance management, and easy location drift and spatial misalignment of cross-scale models. It cannot meet the integrated management needs of building projects from design, construction, operation and maintenance to demolition, which are characterized by high precision and high efficiency. This restricts the large-scale implementation and application efficiency improvement of the deep integration of BIM and GIS in the field of building engineering. Therefore, a building information model and geographic information integrated management system that integrates BIM and GIS is proposed. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a building information modeling and geographic information integration management system that integrates BIM and GIS, comprising the following modules:

[0004] The multi-source heterogeneous data acquisition module is used to acquire raw BIM data and raw GIS data from BIM modeling software and GIS database respectively. The raw BIM data contains geometric and attribute information of building components, and the raw GIS data contains spatial and attribute information of geographic features. It also performs unified preprocessing of coordinate system and semantics.

[0005] The spatiotemporal state primitive construction module is connected to the multi-source heterogeneous data acquisition module. It is used to extract spatiotemporal state primitives with geometric shape, semantic attributes, effective time interval and spatial occupancy information as the basic unit of fused data based on unified preprocessed BIM data and GIS data.

[0006] The multi-scale dynamic fusion engine, connected to the spatiotemporal state primitive construction module, is used to adaptively select spatiotemporal state primitives of different levels and granularities for fusion according to the dynamic changes of the application scenario, so as to generate a scene-adaptive heterogeneous data association view.

[0007] The full lifecycle data asset management module, connected to the multi-scale dynamic fusion engine, is used to create data shadows with spatiotemporal constraints for building projects in the design, construction, operation and maintenance and demolition stages based on heterogeneous data association views, and to record the status change trajectory of the data shadows.

[0008] The conflict detection and early warning module is connected to the multi-scale dynamic fusion engine and the full lifecycle data asset management module, respectively. It is used to monitor the logical consistency and spatial compliance between spatiotemporal state primitives from different sources in real time, and generate early warning information with spatial location markers when a conflict is detected.

[0009] Furthermore, the multi-source heterogeneous data acquisition module includes a coordinate system transformation unit and a semantic mapping unit. The coordinate system transformation unit converts the local engineering coordinate system of the original BIM data into a geographic coordinate system consistent with the original GIS data through spatial similarity transformation. The semantic mapping unit constructs a bidirectional mapping dictionary between the BIM semantic ontology and the GIS feature ontology to semantically align the building component types with the geographic feature classifications.

[0010] Furthermore, the spatiotemporal state primitives include:

[0011] The geometry field is used to describe the three-dimensional shape of building components or geographic features. The geometry field supports three different precision storage formats: voxels, boundary representations, and parametric surfaces under multi-scale expression.

[0012] A semantic tag set used to store non-geometric attributes of building components or geographic features. The semantic tag set adopts a dynamically expandable key-value pair structure.

[0013] The effective time interval used to record the effective start and end times of the entities corresponding to the spatiotemporal state primitives;

[0014] Position and orientation fields used to describe the precise occupancy information of spatiotemporal state primitives in continuous space.

[0015] Furthermore, the multi-scale dynamic fusion engine includes a fusion matching degree calculation module. This module calculates the fusion matching degree between the spatiotemporal state primitives of the building components and the spatiotemporal state primitives of the geographic elements to be matched. It obtains a spatial proximity metric by performing a negative exponential decay calculation on the Euclidean distance between the three-dimensional spatial center points of the building component and geographic element spatiotemporal state primitives. Simultaneously, it obtains a semantic similarity metric by performing a cosine similarity calculation based on the ontology dictionary on the semantic label vector of the building component spatiotemporal state primitive and the classification attribute vector of the geographic element spatiotemporal state primitive. Finally, the fusion matching degree is obtained by weighted summation of the spatial proximity metric and the semantic similarity metric. Specifically:

[0016] ;

[0017] in, To achieve a high degree of compatibility, The Euclidean distance is the center point in three-dimensional space. The cosine similarity is based on the ontology dictionary, and α, β, and γ are the preset spatial weight coefficient, distance decay coefficient, and semantic weight coefficient, respectively. When the fusion matching degree exceeds the preset dynamic threshold, the multi-scale dynamic fusion engine establishes a permanent association pair between the spatiotemporal state primitives of the corresponding building components and the spatiotemporal state primitives of the geographic elements.

[0018] Furthermore, the multi-scale dynamic fusion engine also includes a dynamic LOD scheduling module. This module calculates the rendering urgency of each spatiotemporal state primitive to be rendered. It obtains the first urgency component by calculating the ratio of the projected area of ​​the primitive in the current rendering viewport to the total screen pixel area; the second urgency component by calculating the ratio of the current viewpoint distance to the preset nearest effective rendering distance; and the third urgency component by querying a predefined task priority mapping table. Finally, it linearly sums the first, second, and third urgency components, assigning preset weights, to obtain the rendering urgency. Specifically:

[0019] ;

[0020] Where U represents the rendering urgency. For the projected area, Total screen pixel area The nearest effective rendering distance, Distance from the current viewpoint The priority value is obtained from the task priority mapping table. These are the viewpoint weight coefficient, distance weight coefficient, and task weight coefficient, respectively. The dynamic LOD scheduling module selects a corresponding level from multiple predefined detail levels based on the numerical range of rendering urgency, performs geometric simplification and texture reduction on the spatiotemporal state primitives, and then sends them into the rendering pipeline.

[0021] Furthermore, the conflict detection and early warning module includes a spatial compliance analysis unit. This unit calculates the intrusion potential energy between the spatiotemporal state primitives of the building component to be detected and the spatiotemporal state primitives of the GIS planning control area. It obtains the two-dimensional intrusion area by projecting the geometry of the building component's spatiotemporal state primitives onto a horizontal plane, calculates the overlap area between the two-dimensional intrusion area and the control boundary defined by the GIS planning control area's spatiotemporal state primitives, and then queries a preset intrusion unit cost coefficient based on the semantic attributes of the building component's spatiotemporal state primitives. Finally, it multiplies the overlap area and the intrusion unit cost coefficient to obtain the intrusion potential energy. Specifically:

[0022] ;

[0023] in, For intrusion potential energy, The overlapping area is... This is the unit cost coefficient for intrusion;

[0024] The spatial compliance analysis unit also calculates the elevation difference between the top elevation of the spatiotemporal state element of the building component and the preset maximum allowable elevation within the spatiotemporal state element of the GIS planning control area. The elevation difference is then divided by the preset characteristic elevation step size and rounded to obtain the over-level index. When the intrusion potential energy is greater than the preset potential energy threshold or the over-level index is greater than the preset over-level threshold, the conflict detection and early warning module generates early warning information including the coordinates of the intrusion location and the type of violation.

[0025] Furthermore, the full lifecycle data asset management module integrates a spatiotemporal four-dimensional index structure. This structure employs a hybrid indexing approach that combines a three-dimensional spatial R-tree with an ordered list of time dimensions. Each data shadow is assigned a four-dimensional key value composed of a longitude interval, a latitude interval, an elevation interval, and a valid time interval. When a retrieval request carrying a query spatiotemporal range is received, the full lifecycle data asset management module first uses a three-dimensional spatial R-tree to filter out candidate data shadows that intersect with the spatial range in the query spatiotemporal range. Then, it obtains the final retrieval result by comparing whether the valid time interval overlaps with the time range in the query spatiotemporal range among the candidate data shadows.

[0026] Furthermore, the full lifecycle data asset management module also includes an environmental impact-driven correction unit. This unit continuously acquires outdoor meteorological GIS spatiotemporal state primitives related to the building operation and maintenance phase from the multi-scale dynamic fusion engine, and extracts outdoor dry-bulb temperature and relative humidity parameters from them. The environmental impact-driven correction unit inputs these outdoor dry-bulb temperature and relative humidity parameters into a building energy consumption impact calculation model. This model adopts a multiple linear regression form that includes temperature and humidity cross terms, and its coefficients are obtained by fitting historical energy consumption data. The model calculates the HVAC system setpoint temperature correction amount under the current environment, and then adds the HVAC system setpoint temperature correction amount to the preset baseline setpoint temperature to obtain the corrected target setpoint temperature. The mathematical expression of the building energy consumption impact calculation model is as follows:

[0027] ;

[0028] in, Set temperature correction values ​​for HVAC systems. Outdoor dry-bulb temperature, Relative humidity, The preset reference dry-bulb temperature, The preset reference relative humidity, These are the primary temperature influence coefficient, the primary humidity influence coefficient, and the temperature-humidity coupling influence coefficient, respectively.

[0029] The environmental impact-driven correction unit feeds back the corrected target set temperature to the operating parameters of the corresponding spatiotemporal state primitives of the building HVAC components through a multi-scale dynamic fusion engine.

[0030] Furthermore, the full lifecycle data asset management module also includes a predictive maintenance analysis unit based on spatiotemporal correlation rules. This predictive maintenance analysis unit retrieves historical fault records of specified building component spatiotemporal state primitives from the spatiotemporal four-dimensional index structure, along with the GIS environmental parameter time series at the time of synchronous occurrence. It then calculates the degradation acceleration coefficient of the building component. This is achieved by weighting and summing the deviation of each environmental parameter from its preset baseline value in the GIS environmental parameter time series with preset environmental parameter influence factors to obtain the comprehensive environmental incentive index. Finally, it uses the comprehensive environmental incentive index as a power exponent to power the natural constant, and multiplies this by the baseline degradation rate of the building component under standard conditions to obtain the degradation acceleration coefficient. Specifically:

[0031] ;

[0032] in, This is the degradation acceleration factor. As a baseline degradation rate, For the i-th environmental parameter, the influencing factor is... This represents the current value of the i-th environmental parameter in the GIS environmental parameter time series. is the preset baseline value for the i-th environmental parameter, and n is the total number of environmental parameter types involved in the calculation; the predictive maintenance analysis unit uses the degradation acceleration coefficient to update the remaining lifespan of the spatiotemporal state primitives of building components in real time. When the remaining lifespan is lower than the preset maintenance warning threshold, a predictive maintenance work order containing component identification and recommended maintenance time is automatically generated.

[0033] Furthermore, the system also includes a cross-scale consistency verification module. This module periodically extracts macro-GIS topographic spatiotemporal state primitives and building component spatiotemporal state primitives that are within the same geographical range and whose effective time intervals overlap from the multi-scale dynamic fusion engine. It calculates the consistency deviation between the average surface elevation in the macro-GIS topographic spatiotemporal state primitives and the bottom absolute elevation in the building component spatiotemporal state primitives. Simultaneously, it calculates the vector deviation magnitude between the surface horizontal displacement field extracted from the macro-GIS topographic spatiotemporal state primitives and the monitoring displacement vector of the same location point extracted from the building component spatiotemporal state primitives. When the consistency deviation or vector deviation magnitude exceeds a preset deviation tolerance threshold, the cross-scale consistency verification module initiates a data coordination process. Using the spatial benchmark of the macro-GIS topographic spatiotemporal state primitives as a reference, it performs an overall adjustment correction on the absolute position of the building component spatiotemporal state primitives using the least squares adjustment method.

[0034] The beneficial effects of this invention are reflected in:

[0035] By employing a multi-source heterogeneous data acquisition module, BIM and GIS data achieve coordinate system unification and semantic bidirectional alignment, eliminating differences in spatial benchmarks and semantic expressions. A standardized fusion unit is formed based on a spatiotemporal state primitive construction module. A multi-scale dynamic fusion engine adaptively completes data association and matching at different levels, improving fusion accuracy and scenario adaptability. A full lifecycle data asset management module establishes spatiotemporally constrained data shadows and records state trajectories, enabling traceability and efficient management of building process data. A conflict detection and early warning module monitors logical consistency and spatial compliance in real time, generating timely early warning information with location markers to reduce project compliance risks and safety hazards. The overall system achieves deep and stable integration of BIM and GIS, significantly improving the efficiency of building project data management, the accuracy of spatial decision-making, and the level of intelligent operation and maintenance, effectively reducing data conflicts, preprocessing costs, and subsequent coordination workload. Attached Figure Description

[0036] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0037] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0038] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0039] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0040] like Figure 1 As shown, the Building Information Modeling and Geographic Information Integration Management System, which integrates BIM and GIS, includes the following modules:

[0041] The multi-source heterogeneous data acquisition module is used to acquire raw BIM data and raw GIS data from BIM modeling software and GIS database respectively. The raw BIM data contains geometric and attribute information of building components, and the raw GIS data contains spatial and attribute information of geographic features. It also performs unified preprocessing of coordinate system and semantics.

[0042] The spatiotemporal state primitive construction module is connected to the multi-source heterogeneous data acquisition module. It is used to extract spatiotemporal state primitives with geometric shape, semantic attributes, effective time interval and spatial occupancy information as the basic unit of fused data based on unified preprocessed BIM data and GIS data.

[0043] The multi-scale dynamic fusion engine, connected to the spatiotemporal state primitive construction module, is used to adaptively select spatiotemporal state primitives of different levels and granularities for fusion according to the dynamic changes of the application scenario, so as to generate a scene-adaptive heterogeneous data association view.

[0044] The full lifecycle data asset management module, connected to the multi-scale dynamic fusion engine, is used to create data shadows with spatiotemporal constraints for building projects in the design, construction, operation and maintenance and demolition stages based on heterogeneous data association views, and to record the status change trajectory of the data shadows.

[0045] The conflict detection and early warning module is connected to the multi-scale dynamic fusion engine and the full lifecycle data asset management module, respectively. It is used to monitor the logical consistency and spatial compliance between spatiotemporal state primitives from different sources in real time, and generate early warning information with spatial location markers when a conflict is detected.

[0046] The multi-source heterogeneous data acquisition module includes a coordinate system transformation unit and a semantic mapping unit. The coordinate system transformation unit transforms the local engineering coordinate system of the original BIM data into a geographic coordinate system consistent with the original GIS data through spatial similarity transformation. The semantic mapping unit constructs a bidirectional mapping dictionary between the BIM semantic ontology and the GIS feature ontology to semantically align the building component type with the geographic feature classification.

[0047] By setting coordinate system transformation units and semantic mapping units in the multi-source heterogeneous data acquisition module, the BIM local engineering coordinate system can be unified to the GIS geographic coordinate system through spatial similarity transformation, eliminating the problem of inconsistent spatial benchmarks. At the same time, the bidirectional mapping dictionary enables accurate alignment between the BIM semantic ontology and the GIS feature ontology, solving the semantic heterogeneity problem and significantly improving the accuracy of data fusion, matching efficiency and system compatibility. This provides a stable and reliable data foundation for subsequent spatiotemporal state primitive construction and multi-scale dynamic fusion.

[0048] If a project's BIM model uses an independent local engineering coordinate system, and the GIS data uses the WGS84 geographic coordinate system, the coordinate system transformation unit performs a spatial similarity transformation to achieve coordinate unification. The transformation formula is:

[0049] ;

[0050] Take the scale factor Translation parameters BIM component point coordinates Convert to geographic coordinates This achieves complete consistency between BIM and GIS spatial benchmarks; the semantic mapping unit uses a two-way dictionary to map the BIM component types "beam" and "slab" to the GIS element categories "horizontal building components" and "building covering structure" respectively, achieving precise semantic alignment and ensuring unambiguous data matching.

[0051] The spatiotemporal state primitives include:

[0052] The geometry field is used to describe the three-dimensional shape of building components or geographic features. The geometry field supports three different precision storage formats: voxels, boundary representations, and parametric surfaces under multi-scale expression.

[0053] A semantic tag set used to store non-geometric attributes of building components or geographic features. The semantic tag set adopts a dynamically expandable key-value pair structure.

[0054] The effective time interval used to record the effective start and end times of the entities corresponding to the spatiotemporal state primitives;

[0055] The position and orientation fields used to describe the precise occupancy information of spatiotemporal state primitives in continuous space;

[0056] By defining spatiotemporal state primitives that include geometric fields, semantic tag sets, effective time intervals, and position and attitude fields, and by supporting three storage formats for geometric objects—voxels, boundary representations, and parametric surfaces—and by adopting a dynamically expandable key-value pair structure for semantic tag sets, it is possible to achieve a standardized four-dimensional integrated expression of geometric, attribute, temporal, and spatial dimensions for building components and geographic elements. This improves the consistency, scalability, and retrieval efficiency of multi-source data fusion, adapts to multi-scale expression and dynamic rendering requirements, and provides a unified, stable, and high-precision data unit foundation for subsequent fusion calculations, conflict detection, and full lifecycle management.

[0057] Taking the spatiotemporal state primitive of a building frame column as an example, the geometry field uses the boundary representation B-Rep format to store the three-dimensional shape data of the column. The semantic tag set records the structure of key-value pairs {"component type":"column","material":"C30 concrete","floor":"3rd floor","function":"vertical load-bearing"}, with the effective time interval set from 2025-07-01 to 2075-07-01. The position and attitude fields record the coordinates of the three-dimensional center point (X,Y,Z)=(116.403,39.932,12.50) and the attitude angle (α,β,γ)=(0°,0°,0°).

[0058] When representing the same component at multiple scales, the geometry field can be switched to voxel format, and the voxel resolution is determined by the formula. Calculate and take the maximum size of the component. With a voxel count of N=20, the voxel size is obtained. It meets the needs of long-distance rendering and fast retrieval; the semantic tag set can dynamically add key-value pairs {"maintenance level":"level 2","design service life":"50 years"} to realize flexible attribute expansion and fully constitute standardized spatiotemporal state primitives.

[0059] The multi-scale dynamic fusion engine includes a fusion matching degree calculation module. This module calculates the fusion matching degree between the spatiotemporal state primitives of building components and geographic elements to be matched. It obtains a spatial proximity metric by performing a negative exponential decay calculation on the Euclidean distance between the three-dimensional spatial center points of the building component and geographic element spatiotemporal state primitives. Simultaneously, it obtains a semantic similarity metric by performing a cosine similarity calculation based on an ontology dictionary on the semantic label vectors of the building component spatiotemporal state primitives and the classification attribute vectors of the geographic element spatiotemporal state primitives. Finally, it obtains the fusion matching degree by weighted summation of the spatial proximity metric and the semantic similarity metric. Specifically:

[0060] ;

[0061] in, To achieve a high degree of compatibility, The Euclidean distance is the center point in three-dimensional space. The cosine similarity is based on the ontology dictionary, and α, β, and γ are the preset spatial weight coefficient, distance decay coefficient, and semantic weight coefficient, respectively. When the fusion matching degree exceeds the preset dynamic threshold, the multi-scale dynamic fusion engine establishes a permanent association pair between the spatiotemporal state primitives of the corresponding building components and the spatiotemporal state primitives of the geographic elements.

[0062] By setting up a fusion matching degree calculation module within the multi-scale dynamic fusion engine, spatial proximity is calculated using the negative exponential decay of Euclidean distance at the center point in three-dimensional space, and semantic similarity is calculated by combining the cosine similarity of the ontology dictionary and weighted summation to obtain the fusion matching degree. This enables accurate and automatic association matching between spatiotemporal state primitives of BIM components and spatiotemporal state primitives of GIS geographic elements, improving the objectivity, accuracy, and robustness of fusion matching, avoiding errors from manual matching, improving data association efficiency, and providing a reliable association basis for the subsequent generation of dynamic fusion views.

[0063] The preset coefficients are set to α=0.6, β=0.1, γ=0.4, and the dynamic threshold is set to 0.75; the coordinates of the three-dimensional center point of the spatiotemporal state primitive of the building component to be matched are... The coordinates of the three-dimensional center point of the spatiotemporal state primitive element of GIS geographic features are: The Euclidean distance between the center points of the two in three-dimensional space is calculated using the formula... Calculation yields ;

[0064] Spatial proximity metric value ;

[0065] The semantic tag vector is obtained by calculating the cosine similarity of the ontology dictionary. ;

[0066] Fusion matching degree according to formula Calculation yields If the value is greater than the preset dynamic threshold of 0.75, the system will automatically establish a permanent association pair between the spatiotemporal state primitive of the building component and the spatiotemporal state primitive of the geographic element.

[0067] The multi-scale dynamic fusion engine also includes a dynamic LOD scheduling module. This module calculates the rendering urgency of each spatiotemporal state primitive to be rendered. It obtains the first urgency component by calculating the ratio of the projected area of ​​the primitive in the current rendering viewport to the total screen pixel area; the second urgency component by calculating the ratio of the current viewpoint distance to the preset nearest effective rendering distance; and the third urgency component by querying a predefined task priority mapping table. Finally, it assigns preset weights to the first, second, and third urgency components and then linearly sums them to obtain the rendering urgency. Specifically:

[0068] ;

[0069] Where U represents the rendering urgency. For the projected area, Total screen pixel area The nearest effective rendering distance, The distance to the current viewpoint. The priority value is obtained from the task priority mapping table. These are the view weight coefficient, distance weight coefficient, and task weight coefficient, respectively. The dynamic LOD scheduling module selects a corresponding level from multiple predefined detail levels based on the numerical range of rendering urgency, performs geometric simplification and texture reduction on the spatiotemporal state primitives, and then sends them into the rendering pipeline.

[0070] By setting a dynamic LOD scheduling module within the multi-scale dynamic fusion engine, the rendering urgency is calculated based on the projection area ratio, viewpoint distance ratio, and task priority weighting. The corresponding detail level is automatically selected for geometric simplification and texture reduction according to the rendering urgency. This can significantly reduce the system rendering load, improve scene loading and interaction smoothness, and achieve adaptive and efficient rendering of multi-scale spatiotemporal state primitives, balancing visual accuracy and operational efficiency.

[0071] Set weight coefficients Total screen area Pixel, component projected area Pixels, nearest effective rendering distance Current viewpoint distance Task priority ;

[0072] The first urgent component is ;

[0073] The second urgent component is ;

[0074] Rendering urgency according to formula The calculated value is U = 0.5 × 0.025 + 0.3 × 0.25 + 0.2 × 0.9 = 0.0125 + 0.075 + 0.18 = 0.2675;

[0075] This value corresponds to a medium level of detail. The dynamic LOD scheduling module automatically performs a moderate degree of geometric simplification and texture reduction on this spatiotemporal state primitive before sending it into the rendering pipeline, thereby reducing rendering pressure while ensuring visual effects.

[0076] The conflict detection and early warning module includes a spatial compliance analysis unit. This unit calculates the intrusion potential energy between the spatiotemporal state primitives of the building component to be detected and the spatiotemporal state primitives of the GIS planning control area. It obtains the two-dimensional intrusion area by projecting the geometry of the building component's spatiotemporal state primitives onto a horizontal plane, calculates the overlap area between the two-dimensional intrusion area and the control boundary defined by the GIS planning control area's spatiotemporal state primitives, and then queries a preset intrusion unit cost coefficient based on the semantic attributes of the building component's spatiotemporal state primitives. Finally, it multiplies the overlap area and the intrusion unit cost coefficient to obtain the intrusion potential energy. Specifically:

[0077] ;

[0078] in, For intrusion potential energy, The overlapping area, This is the unit cost coefficient for intrusion;

[0079] The spatial compliance analysis unit also calculates the elevation difference between the top elevation of the spatiotemporal state element of the building component and the preset maximum allowable elevation within the spatiotemporal state element of the GIS planning control area. The elevation difference is then divided by the preset characteristic elevation step size and rounded to obtain the over-level index. When the intrusion potential energy is greater than the preset potential energy threshold or the over-level index is greater than the preset over-level threshold, the conflict detection and early warning module generates early warning information including the coordinates of the intrusion location and the type of violation.

[0080] The spatial compliance analysis unit also calculates the elevation difference between the top elevation of the spatiotemporal state element of the building component and the preset maximum allowable elevation within the spatiotemporal state element of the GIS planning control area. The elevation difference is then divided by the preset characteristic elevation step size and rounded to obtain the over-level index. When the intrusion potential energy is greater than the preset potential energy threshold or the over-level index is greater than the preset over-level threshold, the conflict detection and early warning module generates early warning information including the coordinates of the intrusion location and the type of violation.

[0081] By using the spatial compliance analysis unit in the conflict detection and early warning module, and employing a dual detection mechanism of intrusion potential energy and overstepping index, the degree of spatial violation between building components and GIS planning control areas can be accurately quantified. This enables automated and quantitative judgment of building spatial compliance, timely generation of early warning information with coordinates and types, effectively avoiding violations such as planning over-limit and spatial intrusion, and improving project planning compliance and risk prevention capabilities.

[0082] Set the unit cost coefficient for intrusion The potential energy threshold is 5000, the preset maximum allowable elevation is 80m, the characteristic elevation step size is 5m, and the threshold for exceeding the limit is 2; the overlap area between the spatiotemporal state primitives of building components and the GIS planning control area. The intrusion potential energy is calculated according to the formula. Calculated =5×1200=6000; The top elevation of this component is 92m, the elevation difference is 92-80=12m, and the skipping index is... ;

[0083] Since the intrusion potential energy 6000 > 5000 and the over-level index reaches the threshold 2, the conflict detection and early warning module immediately generates early warning information containing the intrusion location coordinates and violation type.

[0084] The full lifecycle data asset management module integrates a spatiotemporal four-dimensional index structure. This structure employs a hybrid indexing approach that combines a three-dimensional spatial R-tree with an ordered list of time dimensions. Each data shadow is assigned a four-dimensional key value composed of a longitude interval, a latitude interval, an elevation interval, and a valid time interval. When a retrieval request carrying a query for the spatiotemporal range is received, the full lifecycle data asset management module first uses a three-dimensional spatial R-tree to filter out candidate data shadows that intersect with the spatial range in the query spatiotemporal range. Then, it compares the valid time intervals of the candidate data shadows with the time ranges in the query spatiotemporal range to obtain the final retrieval result.

[0085] By integrating a spatiotemporal four-dimensional index structure that combines a three-dimensional spatial R-tree with a time-ordered list into the full lifecycle data asset management module, and assigning a four-dimensional key value consisting of longitude, latitude, elevation, and effective time interval to each data shadow, the retrieval speed and filtering accuracy of spatiotemporal data throughout the building's lifecycle can be significantly improved, the computational overhead of multi-dimensional queries can be reduced, and rapid location, efficient traversal, and stable management of data under spatiotemporal constraints can be achieved, ensuring the real-time response of data queries and the efficiency of system operation.

[0086] The four-dimensional key values ​​of a building project's data shadow are set as longitude interval [116.400, 116.406], latitude interval [39.930, 39.936], elevation interval [0, 50], and effective time interval [2025-06-01, 2026-06-01]. When a retrieval request is received with a query spatiotemporal range of longitude [116.402, 116.405], latitude [39.931, 39.935], elevation [10, 40], and time [2025-09-01, 2026-03-01], the system first filters out candidate data shadows that intersect with the query spatial range using a three-dimensional spatial R-tree. Then, it compares the overlap between the effective time interval and the query time range in the candidate set, and finally accurately outputs the data shadows that meet the conditions. The retrieval process does not require a full traversal, significantly improving the efficiency of spatiotemporal matching, and the query results are complete and without omissions.

[0087] The full lifecycle data asset management module also includes an environmental impact-driven correction unit. This unit continuously acquires outdoor meteorological GIS spatiotemporal state primitives related to the building operation and maintenance phase from the multi-scale dynamic fusion engine, and extracts outdoor dry-bulb temperature and relative humidity parameters. The unit then inputs these parameters into a building energy consumption impact calculation model. This model employs a multiple linear regression form including temperature and humidity cross terms, with coefficients obtained by fitting historical energy consumption data. The model calculates the HVAC system setpoint temperature correction amount under the current environment, and then adds this correction amount to the preset baseline setpoint temperature to obtain the corrected target setpoint temperature. The mathematical expression for the building energy consumption impact calculation model is as follows:

[0088] ;

[0089] in, Set temperature correction values ​​for HVAC systems. Outdoor dry-bulb temperature, Relative humidity, The preset reference dry-bulb temperature, The preset reference relative humidity, These are the primary temperature influence coefficient, the primary humidity influence coefficient, and the temperature-humidity coupling influence coefficient, respectively.

[0090] The environmental impact-driven correction unit feeds back the corrected target set temperature to the operating parameters of the corresponding spatiotemporal state primitive of the building HVAC component through a multi-scale dynamic fusion engine;

[0091] By setting up an environmental impact-driven correction unit in the full lifecycle data asset management module, extracting temperature and humidity parameters based on the spatiotemporal state primitives of outdoor meteorological GIS, and using a multiple linear regression model including temperature and humidity cross terms to calculate the HVAC set temperature correction amount, it is possible to achieve dynamic adaptive correction of HVAC operating parameters with the outdoor environment during the building operation and maintenance phase. This effectively reduces building energy consumption, improves indoor comfort and the level of intelligent operation and maintenance, and realizes refined energy consumption management under the integration of BIM and GIS.

[0092] Set reference dry bulb temperature =25℃, reference relative humidity =50%, primary temperature influence coefficient =0.3, primary humidity influence coefficient =0.1, temperature and humidity coupling influence coefficient =0.005, the reference set temperature is 26℃;

[0093] Current outdoor dry bulb temperature =32℃, relative humidity =70%;

[0094] Temperature correction amount calculation, substituting into the formula yields... =0.3×(32−25)+0.1×(70−50)+0.005×(32−25)×(70−50)=0.3×7+0.1×20+0.005×7×20=2.1+2+0.7=4.8℃;

[0095] The corrected target temperature is 26℃ + 4.8℃ = 30.8℃. The system automatically feeds back this target temperature to the operating parameters of the corresponding HVAC component's spatiotemporal state element to complete intelligent control.

[0096] The full lifecycle data asset management module also includes a predictive maintenance analysis unit based on spatiotemporal correlation rules. This unit retrieves historical fault records of specified building component spatiotemporal state primitives from a spatiotemporal four-dimensional index structure, along with the GIS environmental parameter time series at the time of synchronous occurrence. It then calculates the degradation acceleration coefficient of the building component. This is achieved by weighting and summing the deviation of each environmental parameter from its preset baseline value in the GIS environmental parameter time series with preset environmental parameter influence factors to obtain an environmental comprehensive incentive index. Finally, it exponentiates this index with a natural constant and multiplies it by the baseline degradation rate of the building component under standard conditions to obtain the degradation acceleration coefficient. Specifically:

[0097] ;

[0098] in, This is the degradation acceleration factor. As a baseline degradation rate, For the i-th environmental parameter, the influencing factor is... This represents the current value of the i-th environmental parameter in the GIS environmental parameter time series. is the preset baseline value for the i-th environmental parameter, and n is the total number of environmental parameter types involved in the calculation; the predictive maintenance analysis unit uses the degradation acceleration coefficient to update the remaining life of the spatiotemporal state primitives of building components in real time. When the remaining life is lower than the preset maintenance warning threshold, a predictive maintenance work order containing component identification and recommended maintenance time is automatically generated.

[0099] By setting up a predictive maintenance analysis unit based on spatiotemporal correlation rules in the full lifecycle data asset management module, and combining the time series of GIS environmental parameters to calculate the degradation acceleration coefficient and update the remaining lifespan of components in real time, it is possible to achieve intelligent assessment of the health status of building components and early warning of faults, automatically generate accurate maintenance work orders, improve operation and maintenance safety, reduce the risk of sudden failures, extend the service life of components, and optimize maintenance costs.

[0100] Set the baseline degradation rate =0.002 / day, n=2 types of environmental parameters involved in the calculation, the first type of environmental parameter is temperature, and the influencing factor is... =0.005, current value =35℃, reference value =25℃, the second environmental parameter is humidity, influencing factors =0.003, current value =75%, benchmark value =50%;

[0101] First, calculate the comprehensive environmental incentive index:

[0102] ;

[0103] The degradation acceleration factor is calculated using the formula. Calculated ;

[0104] The system updates the remaining lifespan of the component in real time based on the degradation acceleration coefficient. When the remaining lifespan is lower than the preset maintenance warning threshold, a predictive maintenance work order containing the component's unique identifier and recommended maintenance time is automatically generated.

[0105] The system also includes a cross-scale consistency verification module. This module periodically extracts macro-GIS topographic spatiotemporal state primitives and building component spatiotemporal state primitives that are within the same geographical range and whose effective time intervals overlap from the multi-scale dynamic fusion engine. It calculates the consistency deviation between the average surface elevation in the macro-GIS topographic spatiotemporal state primitives and the absolute bottom elevation in the building component spatiotemporal state primitives. Simultaneously, it calculates the vector deviation magnitude between the surface horizontal displacement field extracted from the macro-GIS topographic spatiotemporal state primitives and the monitoring displacement vector of the same location point extracted from the building component spatiotemporal state primitives. When the consistency deviation or vector deviation magnitude exceeds a preset deviation tolerance threshold, the cross-scale consistency verification module initiates a data coordination process. Using the spatial benchmark of the macro-GIS topographic spatiotemporal state primitives as a reference, it performs an overall adjustment correction on the absolute position of the building component spatiotemporal state primitives using the least squares adjustment method.

[0106] By setting up a cross-scale consistency verification module, the elevation consistency and displacement vector deviation of macroscopic GIS topographic spatiotemporal state primitives and building component spatiotemporal state primitives within the same geographical range and overlapping time intervals are periodically verified. The least squares adjustment is used to make overall corrections with GIS spatial benchmarks as a reference. This can effectively eliminate cross-scale data spatial benchmark deviations, model drift and position misalignment problems, ensure the spatial consistency, topological correctness and long-term stability of BIM and GIS fusion data, and improve the data accuracy and decision reliability of the entire system.

[0107] Setting the deviation tolerance threshold to 0.05m, the average surface elevation of the macro-GIS topographic spatiotemporal state primitives for a certain building area is 45.32m, while the absolute bottom elevation of the spatiotemporal state primitives of the corresponding building components is 45.38m. The consistency deviation between the two is |45.38−45.32|=0.06m, which is greater than 0.05m. Simultaneously, the horizontal displacement vector of the surface at the same location is extracted as (0.03,0.04)m, and the building monitoring displacement vector is (0.06,0.08)m. The vector deviation magnitude is calculated using the formula... Calculated The tolerance threshold is reached; the cross-scale consistency verification module starts the data coordination process, using the macro GIS topographic spatiotemporal state primitive as the benchmark, and adopts the least squares adjustment method to perform overall adjustment correction on the absolute position of the spatiotemporal state primitive of building components, so that the elevation deviation and vector deviation both fall back to the threshold range.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A building information modeling and geographic information integrated management system that integrates BIM and GIS, characterized in that, Includes the following modules: The multi-source heterogeneous data acquisition module is used to acquire raw BIM data and raw GIS data from BIM modeling software and GIS database respectively. The raw BIM data contains geometric and attribute information of building components, and the raw GIS data contains spatial and attribute information of geographic features. It also performs unified preprocessing of coordinate system and semantics. The spatiotemporal state primitive construction module is used to extract spatiotemporal state primitives with geometric shape, semantic attributes, effective time intervals and spatial occupancy information as basic units of fused data based on unified preprocessed BIM data and GIS data. The multi-scale dynamic fusion engine, connected to the spatiotemporal state primitive construction module, is used to adaptively select spatiotemporal state primitives of different levels and granularities for fusion according to the dynamic changes of the application scenario, so as to generate a scene-adaptive heterogeneous data association view. The full lifecycle data asset management module is used to create data shadows with spatiotemporal constraints for building projects in the design, construction, operation and maintenance and demolition stages based on heterogeneous data association views, and record the status change trajectory of the data shadows. The conflict detection and early warning module is connected to the multi-scale dynamic fusion engine and the full lifecycle data asset management module, respectively. It is used to monitor the logical consistency and spatial compliance between spatiotemporal state primitives from different sources in real time, and generate early warning information with spatial location markers when a conflict is detected.

2. The BIM and GIS integrated building information model and geographic information management system according to claim 1, characterized in that: The multi-source heterogeneous data acquisition module includes a coordinate system transformation unit and a semantic mapping unit. The coordinate system transformation unit converts the local engineering coordinate system of the original BIM data into a geographic coordinate system consistent with the original GIS data through spatial similarity transformation. The semantic mapping unit constructs a bidirectional mapping dictionary between the BIM semantic ontology and the GIS feature ontology to semantically align the building component types with the geographic feature classifications.

3. The BIM and GIS integrated building information model and geographic information management system according to claim 2, characterized in that: The spatiotemporal state primitives include: The geometry field is used to describe the three-dimensional shape of building components or geographic features. The geometry field supports three different precision storage formats: voxels, boundary representations, and parametric surfaces under multi-scale expression. A semantic tag set used to store non-geometric attributes of building components or geographic features. The semantic tag set adopts a dynamically expandable key-value pair structure. The effective time interval used to record the effective start and end times of the entities corresponding to the spatiotemporal state primitives; Position and orientation fields used to describe the precise occupancy information of spatiotemporal state primitives in continuous space.

4. The BIM and GIS integrated building information model and geographic information management system according to claim 3, characterized in that: The multi-scale dynamic fusion engine includes a fusion matching degree calculation module. This module calculates the fusion matching degree between the spatiotemporal state primitives of building components and the spatiotemporal state primitives of geographic elements. It obtains the spatial proximity metric by performing a negative exponential decay calculation on the Euclidean distance between the three-dimensional spatial center points of the building component spatiotemporal state primitives and the geographic element spatiotemporal state primitives. At the same time, it obtains the semantic similarity metric by performing a cosine similarity calculation based on the ontology dictionary on the semantic label vector of the building component spatiotemporal state primitives and the classification attribute vector of the geographic element spatiotemporal state primitives. Finally, the fusion matching degree is obtained by weighted summing of the spatial proximity metric and the semantic similarity metric. When the fusion matching degree exceeds the preset dynamic threshold, the multi-scale dynamic fusion engine establishes a permanent association pair between the spatiotemporal state primitives of the corresponding building components and the spatiotemporal state primitives of the geographic elements.

5. The BIM and GIS integrated building information model and geographic information management system according to claim 4, characterized in that: The multi-scale dynamic fusion engine also includes a dynamic LOD scheduling module. The dynamic LOD scheduling module calculates the rendering urgency of each spatiotemporal state primitive to be rendered. It obtains the first urgency component by calculating the ratio of the projection area of ​​the spatiotemporal state primitive to be rendered in the current rendering field to the total screen pixel area, obtains the second urgency component by calculating the ratio of the current viewpoint distance to the preset nearest effective rendering distance, obtains the third urgency component by querying a predefined task priority mapping table, and obtains the rendering urgency by linearly summing the first urgency component, the second urgency component and the third urgency component after assigning preset weights. The dynamic LOD scheduling module selects a corresponding level from multiple predefined detail levels based on the numerical range of rendering urgency, performs geometric simplification and texture reduction on the spatiotemporal state primitives, and then sends them into the rendering pipeline.

6. The BIM and GIS integrated building information model and geographic information management system according to claim 5, characterized in that: The conflict detection and early warning module includes a spatial compliance analysis unit. The spatial compliance analysis unit calculates the intrusion potential energy between the spatiotemporal state primitives of the building component to be detected and the spatiotemporal state primitives of the GIS planning control area. It obtains the two-dimensional intrusion area by projecting the geometry of the spatiotemporal state primitives of the building component onto the horizontal plane, calculates the overlap area between the two-dimensional intrusion area and the control boundary defined by the spatiotemporal state primitives of the GIS planning control area, and queries the preset intrusion unit cost coefficient according to the semantic attributes of the spatiotemporal state primitives of the building component. Then, it performs a product operation on the overlap area and the intrusion unit cost coefficient to obtain the intrusion potential energy. The spatial compliance analysis unit also calculates the elevation difference between the top elevation of the spatiotemporal state element of the building component and the preset maximum allowable elevation within the spatiotemporal state element of the GIS planning control area. The elevation difference is then divided by the preset characteristic elevation step size and rounded to obtain the over-level index. When the intrusion potential energy is greater than the preset potential energy threshold or the over-level index is greater than the preset over-level threshold, the conflict detection and early warning module generates early warning information including the coordinates of the intrusion location and the type of violation.

7. The BIM and GIS integrated building information model and geographic information management system according to claim 6, characterized in that: The full lifecycle data asset management module integrates a spatiotemporal four-dimensional index structure. This structure employs a hybrid indexing approach that combines a three-dimensional spatial R-tree with an ordered list of time dimensions. Each data shadow is assigned a four-dimensional key value consisting of a longitude interval, a latitude interval, an elevation interval, and a valid time interval. When a retrieval request carrying a query spatiotemporal range is received, the full lifecycle data asset management module first uses a three-dimensional spatial R-tree to filter out candidate data shadows that intersect with the spatial range in the query spatiotemporal range. Then, it compares the valid time intervals of the candidate data shadows with the time ranges in the query spatiotemporal range to obtain the final retrieval result.

8. The BIM and GIS integrated building information model and geographic information management system according to claim 7, characterized in that: The full lifecycle data asset management module also includes an environmental impact-driven correction unit. This unit continuously obtains outdoor meteorological GIS spatiotemporal state primitives related to the building operation and maintenance phase from the multi-scale dynamic fusion engine, and extracts outdoor dry-bulb temperature and relative humidity parameters from them. The environmental impact-driven correction unit inputs the outdoor dry-bulb temperature and relative humidity parameters into a building energy consumption impact calculation model. The building energy consumption impact calculation model adopts a multiple linear regression form that includes temperature and humidity cross terms. The building energy consumption impact calculation model obtains the HVAC system set temperature correction amount under the current environment, and then adds the HVAC system set temperature correction amount to the preset baseline set temperature to obtain the corrected target set temperature. The environmental impact-driven correction unit feeds back the corrected target set temperature to the operating parameters of the corresponding spatiotemporal state primitives of the building HVAC components through a multi-scale dynamic fusion engine.

9. The BIM and GIS integrated building information model and geographic information management system according to claim 8, characterized in that: The full lifecycle data asset management module also includes a predictive maintenance analysis unit based on spatiotemporal correlation rules. The predictive maintenance analysis unit retrieves historical fault records of the spatiotemporal state primitives of specified building components from the spatiotemporal four-dimensional index structure, as well as the time series of GIS environmental parameters at the time of synchronous occurrence, and calculates the degradation acceleration coefficient of the building components. It obtains the comprehensive environmental incentive index by weighting and summing the deviation of each environmental parameter in the GIS environmental parameter time series from its preset benchmark value with the preset environmental parameter influence factor. Then, it uses the comprehensive environmental incentive index as the power exponent to perform a power operation on the natural constant, and multiplies it by the benchmark degradation rate of the building component in the standard environment to obtain the degradation acceleration coefficient. The predictive maintenance analysis unit uses the degradation acceleration coefficient to update the remaining lifespan of the spatiotemporal state primitives of building components in real time. When the remaining lifespan is lower than the preset maintenance warning threshold, it automatically generates a predictive maintenance work order that includes the component identification and recommended maintenance time.

10. The BIM and GIS integrated building information model and geographic information management system according to claim 9, characterized in that: The system also includes a cross-scale consistency verification module. The cross-scale consistency verification module periodically extracts macro-GIS terrain spatiotemporal state primitives and building component spatiotemporal state primitives that are in the same geographical range and whose effective time intervals overlap from the multi-scale dynamic fusion engine. It calculates the consistency deviation between the average elevation of the land surface in the macro-GIS terrain spatiotemporal state primitives and the absolute elevation of the bottom in the building component spatiotemporal state primitives. At the same time, it calculates the vector deviation magnitude between the horizontal displacement field of the land surface extracted from the macro-GIS terrain spatiotemporal state primitives and the monitoring displacement vector of the same location point extracted from the building component spatiotemporal state primitives. When the consistency deviation or vector deviation magnitude exceeds the preset deviation tolerance threshold, the cross-scale consistency verification module starts the data coordination process. Taking the spatial benchmark of the macro GIS terrain spatiotemporal state primitive as a reference, the absolute position of the spatiotemporal state primitive of the building component is adjusted and corrected by the least squares adjustment method.

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