Method and device for displaying three-dimensional model of large building and computer equipment

Abstract

The invention relates to a three-dimensional model display method and device of a large building and computer equipment. The method comprises the following steps: acquiring laser scanning point cloud data and building structure drawing data of a large building; performing preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data which can be used for three-dimensional modeling; constructing a building internal structure model of the large building based on the building structure drawing data; and fusing the building internal structure model and the point cloud model data to obtain a three-dimensional building model of the large building, and displaying the three-dimensional building model. By adopting the method, the display integrity of the large building model can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method, device, computer equipment, computer-readable storage medium, and computer program product for displaying a three-dimensional model of a large building. Background Art

[0002] Currently, the method for displaying the three-dimensional model of large buildings has been widely used in many fields such as urban planning, smart campuses, and architectural design. However, due to the complex structure and rich spatial levels of large buildings themselves, there are still obvious limitations in the existing display methods in expressing the overall spatial relationship and detailed structure. The display integrity of the three-dimensional model of large buildings is poor, resulting in a poor overall display effect of the model, and it is easy for users to have deviations in understanding the building structure during the observation, analysis, or interaction process. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for displaying a three-dimensional model of a large building that can improve the display integrity of the large building model.

[0004] In a first aspect, this application provides a method for displaying a three-dimensional model of a large building, including:

[0005] Obtain the laser scanning point cloud data and building structure drawing data of the large building;

[0006] Perform preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling;

[0007] Based on the building structure drawing data, construct the internal building structure model of the large building;

[0008] Fuse the internal building structure model with the point cloud model data to obtain the three-dimensional building model of the large building, and display the three-dimensional building model.

[0009] In one embodiment, the performing preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling includes:

[0010] Perform noise point removal processing on the laser scanning point cloud data to obtain denoised point cloud data;

[0011] Perform precision compression processing on the denoised point cloud data to obtain simplified point cloud data;

[0012] Convert the simplified point cloud data into a data format compatible with the three-dimensional engine of the display page of the three-dimensional building model to obtain point cloud model data.

[0013] In one embodiment, the noise point removal process for the laser scanning point cloud data to obtain the denoised point cloud data includes:

[0014] Determine the average spatial distance between any point in the laser scanning point cloud data and other points in the preset domain;

[0015] In the case where the average spatial distance is greater than the preset distance threshold, determine the any point as a noise point;

[0016] Remove the noise point from the laser scanning point cloud data to obtain the denoised point cloud data.

[0017] In one embodiment, the precision compression process for the denoised point cloud data to obtain the simplified point cloud data includes:

[0018] Based on the preset precision parameter, divide the denoised point cloud data into multiple spatial grids;

[0019] Calculate the coordinate average value of the original points in each spatial grid to obtain the representative point of each spatial grid;

[0020] Retain the representative points in each spatial grid and remove the original points in each spatial grid to obtain the simplified point cloud data.

[0021] In one embodiment, after displaying the three-dimensional building model, it further includes:

[0022] Obtain the power data and environmental data of the large building;

[0023] Associate the power data with the corresponding three-dimensional building model to obtain an association result, and determine the internal spatial position of the environmental data in the large building to obtain a matching result;

[0024] According to the association result and the matching result, display the power data and the environmental data on the display page of the three-dimensional building model.

[0025] In one embodiment, the step of displaying the power data and the environmental data on the display page of the three-dimensional building model according to the association result and the matching result includes:

[0026] Take the statistical information of the power data, the statistical information of the environmental data, the power data and the environmental data as cache data and cache them in the buffer area;

[0027] In response to an operation instruction for the display page, call the cached data from the buffer area, and adjust the position, style, and display range of the cached data in real time to synchronously display the cached data and the three-dimensional building model.

[0028] In a second aspect, the present application further provides a three-dimensional model display device for a large building, including:

[0029] A data acquisition module, configured to acquire laser scanning point cloud data and building structure drawing data of a large building;

[0030] A data processing module, configured to perform preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling;

[0031] A model construction module, configured to construct an internal building structure model of the large building based on the building structure drawing data;

[0032] A model display module, configured to fuse the internal building structure model and the point cloud model data to obtain a three-dimensional building model of the large building, and display the three-dimensional building model.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] Acquire laser scanning point cloud data and building structure drawing data of a large building;

[0035] Perform preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling;

[0036] Construct an internal building structure model of the large building based on the building structure drawing data;

[0037] Fuse the internal building structure model and the point cloud model data to obtain a three-dimensional building model of the large building, and display the three-dimensional building model.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0039] Acquire laser scanning point cloud data and building structure drawing data of a large building;

[0040] Perform preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling;

[0041] Construct an internal building structure model of the large building based on the building structure drawing data;

[0042] Fuse the internal building structure model with the point cloud model data to obtain a three-dimensional building model of the large building, and display the three-dimensional building model.

[0043] In a fifth aspect, the present application also provides a computer program product, including a computer program which, when executed by a processor, implements the following steps:

[0044] Obtain the laser scanning point cloud data and building structure drawing data of the large building;

[0045] Perform preprocessing and format conversion on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling;

[0046] Construct an internal building structure model of the large building based on the building structure drawing data;

[0047] Fuse the internal building structure model with the point cloud model data to obtain a three-dimensional building model of the large building, and display the three-dimensional building model.

[0048] The above three-dimensional model display method, device, computer device, computer-readable storage medium and computer program product for large buildings. First, obtain the laser scanning point cloud data and building structure drawing data of the large building, which can respectively obtain the information sources of the external form and internal structure of the building. The point cloud data reflects the spatial geometric characteristics of the building entity, while the drawing data provides the structural information of the building structure, providing a multi-dimensional raw data basis for subsequent three-dimensional model construction and helping to improve the richness and diversity of the model source data. Then, preprocess and perform format conversion on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling. By preprocessing the laser scanning point cloud data, noise can be effectively removed, the data volume can be compressed, and the geometric accuracy can be improved. At the same time, format conversion transforms the original point cloud data into a data structure recognizable by the three-dimensional modeling system, thereby enhancing the usability and loading efficiency of the point cloud data in the three-dimensional modeling process and improving the model processing and rendering performance. Then, based on the building structure drawing data, construct the internal structure model of the large building. Constructing the internal structure model based on the building drawings can accurately express the spatial distribution relationship of building internal components such as floors, walls, doors and windows, making up for the structural detail problems that are difficult to cover by laser scanning, thereby enhancing the performance ability of the building three-dimensional model to express structural information and improving the spatial logic and structural integrity of the model. Finally, fuse the internal structure model of the building with the point cloud model data to obtain the three-dimensional building model of the large building and display the three-dimensional building model. By fusing the two types of model data, the external form and internal structure of the building can be presented simultaneously in a unified three-dimensional scene, realizing the complementary enhancement of structural information and spatial information. The fusion display method improves the expression integrity of the three-dimensional building model, enabling users to obtain a more comprehensive and real building visualization experience during observation and analysis. In the above method, by obtaining multi-source building data, performing specialized data preprocessing and model construction, and fusing different structural information, a large building three-dimensional model display effect with high integrity and high precision is finally generated, thereby significantly improving the structural restoration ability and visual expression effect of building three-dimensional display, improving the understanding accuracy of users during the interaction process, and being applicable to digital visualization scenarios with high requirements for building structural integrity. Brief Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0050] Figure 1Schematic flowchart of a method for displaying a 3D model of a large building in an embodiment;

[0051] Figure 2 Schematic flowchart of the point cloud model data generation step in an embodiment;

[0052] Figure 3 Schematic flowchart of the power data and environmental data display step in an embodiment;

[0053] Figure 4 Block diagram of the structure of a 3D model display device for a large building in an embodiment;

[0054] Figure 5 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0055] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application.

[0056] In an embodiment, as Figure 1 shown, a method for displaying a 3D model of a large building is provided. In this embodiment, an example is given where the method is applied to a terminal. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, etc. The server can be an independent physical server, can also be a server cluster or distributed system composed of multiple physical servers, and can also be a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0057] Step S101, obtaining the laser scanning point cloud data and building structure drawing data of the large building.

[0058] Among them, the laser scanning point cloud data refers to a set of discrete point sets with spatial coordinate information obtained after spatially measuring a building entity by a 3D laser scanning device, and is used to express the geometric feature information of the external and partially visible internal structures of the building; the building structure drawing data refers to two-dimensional or three-dimensional digital drawings formed in the building design stage, including but not limited to floor plans, elevation views, sectional views, and construction structure drawings, and is used to express information such as the internal structure, component dimensions, and spatial distribution of the building.

[0059] Exemplarily, the terminal can communicate with the data interface of the three-dimensional laser scanning device, receive the original point cloud data file output by the scanning system, and store the data in the local or cloud data storage module. At the same time, the terminal can also access the building information management system or the archive database to obtain the structural drawing data corresponding to the target building, and support the reading and parsing of multiple formats, including CAD (Computer-Aided Design) format, PDF (Portable Document Format) format, or IFC (Industry Foundation Classes) format, etc. After the terminal completes data reception, it can establish preliminary identification and classification for the point cloud data and the drawing data for preprocessing and model construction operations in subsequent steps.

[0060] Step S102: Preprocess and perform format conversion on the laser scanning point cloud data to generate point cloud model data that can be used for 3D modeling.

[0061] Among them, preprocessing refers to operations such as noise removal, data regularization, and accuracy optimization on the laser scanning point cloud data to improve data quality and modeling efficiency; format conversion processing refers to converting the original point cloud data into a data format that can be parsed and used by a 3D modeling system or a graphics rendering engine; point cloud model data refers to the structured data of a point set that can be used for 3D modeling after processing and can accurately represent the geometric information of the building space.

[0062] Exemplarily, after receiving the original point cloud data, the terminal first performs a noise removal operation. Specifically, it can identify and delete outlier points that deviate too far from the neighborhood mean based on the Euclidean distance distribution between points, density estimation, or statistical anomaly detection algorithms. Subsequently, the terminal can perform voxelization processing on the point cloud data, that is, divide the entire point cloud space into equal-volume cubic grids (voxels), and only retain one representative point in each voxel to effectively compress the data scale while maximizing the retention of geometric features and improving subsequent processing efficiency.

[0063] After the preprocessing is completed, the terminal can call the point cloud format conversion module to convert the processed data from the original device format into a data structure recognizable by the Web side. For example, the terminal can convert the point cloud data into a JSON format supported by the rendering engine or a custom binary format, and structurally organize attributes such as the coordinates, colors, and normal vectors of the points to generate point cloud model data including vertex arrays, color arrays, index arrays, etc. To improve the loading efficiency, the terminal can also perform segmented compression or slicing management on the converted data to support the streaming loading and local rendering of subsequent models. The terminal can process the original and complex laser scanning data into point cloud model data with a standardized structure, strong compatibility, and directly applicable to modeling and rendering, laying the foundation for the subsequent construction of three-dimensional building models.

[0064] Step S103, based on the building structure drawing data, construct the internal structure model of the large building.

[0065] Among them, the internal structure model of the building refers to a three-dimensional geometric model that digitally expresses the internal space structure, functional zoning, and structural elements (such as walls, columns, doors, windows, floors, corridors, etc.) of a large building, and this model can be used for subsequent visualization display, equipment layout, parameter annotation, and other operations.

[0066] Exemplarily, after the terminal completes the reading and parsing of the building structure drawing data, it first performs structural recognition processing on the drawing file, and uses the drawing parsing module to automatically extract key structural information, including but not limited to building floor plans, room numbers, wall positions, door opening directions, stairs, elevators, pipeline and other structural information. If the input drawing is in CAD format, the terminal can extract the primitive data representing building components through geometric element layer recognition methods (such as recognizing line segments, blocks, layers); if it is in PDF or scanned image format, the terminal can combine image recognition technologies (such as OCR or deep learning) for vectorization processing and component contour recognition.

[0067] After the recognition is completed, the terminal calls the 3D reconstruction engine according to the extracted geometric and structural information to convert the spatial layout in the 2D drawing into a 3D structure model. Specifically, the terminal can set a unified elevation logic and component parameter library, stretch the extracted 2D wall line segments to generate a wall solid model according to the set height; replace the door and window primitives with preset standard models and accurately locate their spatial positions; for multi-story buildings, the terminal can read the floor identification and section information in the drawing, stack the structures of each layer to complete the multi-layer structure modeling of the entire building.

[0068] In addition, in order to achieve fusion and matching with subsequent point cloud models, the terminal will unify the spatial reference system (coordinate origin, height reference, etc.) and mark the spatial positioning information of each component (such as world coordinates, hierarchical information, functional areas, etc.) during the construction of the three-dimensional structural model, thereby ensuring that the internal structural model of the building has a complete data structure that can be integrated, operated, and rendered.

[0069] Step S104: fuse the building internal structure model with the point cloud model data to obtain a three-dimensional building model of the large building, and display the three-dimensional building model.

[0070] Among them, the three-dimensional architectural model refers to a complete three-dimensional digital model that integrates the external form information and internal structure information of the building. It has the characteristics of high geometric accuracy, clear structural hierarchy, and accurate spatial relationship; fusion refers to the alignment, integration and unification of data models from different sources (i.e., the internal structure model of the building and the point cloud model data) in the same spatial reference system, thereby forming an integrated three-dimensional representation; display refers to the process of loading and displaying the fused three-dimensional architectural model in a visual interface through a three-dimensional graphics rendering engine, and supporting user interactive operations.

[0071] Exemplarily, after constructing the internal structure model of the building and obtaining the processed point cloud model data, the terminal first unifies the spatial coordinate system of the two. Specifically, the terminal uses rigid transformation (including translation, rotation, and scaling) to align the coordinate systems of the two based on the reference points (such as floor origins, coordinate annotations, reference lines, etc.) extracted from the building structure drawings and the spatial positioning information of the point cloud model. In order to improve the fusion accuracy, the terminal can also execute an automatic registration algorithm, such as point-to-surface or point-to-edge alignment optimization based on the ICP (Iterative Closest Point) algorithm to ensure that the internal structure and the external contour strictly coincide in space.

[0072] After the registration is completed, the terminal integrates the two types of model data in a unified scene and constructs a unified 3D scene graph structure. In this structure, point cloud data can be used to render the building's appearance and texture features, and the structural model is used to express the relationship between floors, walls, doors, windows and other components. The two complement each other, thereby achieving a high degree of restoration of the overall appearance and structural levels of the building.

[0073] After the model fusion is completed, the terminal calls the 3D graphics rendering engine (such as Three.js) to load the fused building model into the front-end interface, supporting interactive operations such as zooming, rotating, roaming, component clicking, and layer opening and closing. At the same time, the terminal can superimpose environmental data (such as temperature and humidity, energy consumption status, etc.) or additional attribute information (such as component name, size, number, etc.) according to user needs to achieve the combination of visualization and informationization.

[0074] In the above method for displaying the three-dimensional model of a large building, first, obtain the laser scanning point cloud data and building structure drawing data of the large building, which can respectively obtain the information sources of the external form and internal structure of the building. The point cloud data reflects the spatial geometric characteristics of the building entity, while the drawing data provides the structural information of the building structure, providing a multi-dimensional raw data basis for subsequent three-dimensional model construction and helping to improve the richness and diversity of the model source data. Next, preprocess and perform format conversion on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling. By preprocessing the laser scanning point cloud data, noise can be effectively removed, the data volume can be compressed, and the geometric accuracy can be improved. At the same time, format conversion transforms the original point cloud data into a data structure recognizable by the three-dimensional modeling system, thereby enhancing the usability and loading efficiency of the point cloud data in the three-dimensional modeling process and improving the model processing and rendering performance. Then, based on the building structure drawing data, construct the internal structure model of the large building. Constructing the internal structure model based on the building drawings can accurately express the spatial distribution relationship of internal building components such as floors, walls, doors, and windows, making up for the structural detail problems that are difficult to cover by laser scanning, thereby enhancing the performance of the building three-dimensional model in expressing structural information and improving the spatial logic and structural integrity of the model. Finally, fuse the internal structure model of the building with the point cloud model data to obtain the three-dimensional building model of the large building and display the three-dimensional building model. By fusing the two types of model data, the external form and internal structure of the building can be presented simultaneously in a unified three-dimensional scene, realizing the complementary enhancement of structural information and spatial information. The fusion display method improves the expression integrity of the three-dimensional building model, enabling users to obtain a more comprehensive and real building visualization experience during observation and analysis. In the above method, by obtaining multi-source building data, performing specialized data preprocessing and model construction, and fusing different structural information, a large building three-dimensional model display effect with high integrity and high precision is finally generated, thereby significantly improving the structural restoration ability and visual expression effect of building three-dimensional display, improving the understanding accuracy of users during the interaction process, and being applicable to digital visualization scenarios with high requirements for building structural integrity.

[0075] In an exemplary embodiment, as Figure 2 shown, the above step S102 of preprocessing and performing format conversion on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling can also be implemented through the following steps:

[0076] Step S201, perform noise point removal processing on the laser scanning point cloud data to obtain denoised point cloud data;

[0077] Step S202, perform precision compression processing on the denoised point cloud data to obtain simplified point cloud data;

[0078] Step S203: Convert the simplified point cloud data into a data format compatible with the 3D engine of the display page of the 3D building model to obtain point cloud model data.

[0079] Among them, the laser scanning point cloud data is usually spatial point set data collected by a 3D laser scanning device, including attributes such as the 3D coordinates (X, Y, Z), intensity value, and color value of each point. The data volume is huge and the primitiveness is strong.

[0080] Exemplarily, the terminal can process based on the "statistical outlier filtering algorithm": set a fixed search radius or K nearest neighbor parameters, calculate the average distance between each point and its neighboring points, and calculate the global standard deviation; if the average distance of a certain point from the neighborhood is much higher than the global standard deviation threshold, it is determined as a noise point and removed. In addition, methods such as point density analysis algorithm or normal vector consistency detection can also be combined to improve the removal accuracy. The accuracy compression process refers to compressing the point cloud data volume on the premise of retaining the building geometric features to reduce the subsequent processing and rendering costs. The terminal can adopt the "voxel grid downsampling" method to divide the point cloud space into three-dimensional cube units (voxels) of the same size, retain a representative point (such as the centroid point or a random point) in each voxel, and discard the remaining points. This operation can not only greatly reduce the number of points, but also avoid visual defects caused by uneven point density, thereby maintaining the coherence and visualization clarity of the 3D structure. The data format conversion process refers to the terminal converting the above simplified point cloud data into a standard data structure that can be parsed by the 3D visualization engine. The terminal can package the point cloud data into.json,.glTF or.bin binary formats supported by Three.js, and the content includes the point coordinate array, color array, vertex index, normal vector information, etc. At the same time, to improve the Web-side loading performance, the terminal can perform chunked compression on the data file during the conversion process, and mark the segment boundaries and metadata to achieve streaming transmission and asynchronous loading. After the conversion is completed, the terminal uploads the point cloud model data to the display service platform or caches it in the front-end resource pool for direct invocation by the subsequent 3D modeling module or rendering module.

[0081] In this embodiment, by sequentially performing data preprocessing processes such as noise removal, accuracy compression, and format conversion, the spatial expression accuracy, data processing efficiency, and rendering compatibility of the laser scanning point cloud data are significantly improved, effectively reducing the data redundancy and error accumulation risks in the subsequent modeling process, thus laying a solid data foundation for constructing a high-quality large-scale building 3D model.

[0082] In an exemplary embodiment, the above step S201 performs noise point removal processing on the laser scan point cloud data to obtain denoised point cloud data, and further includes: determining the average spatial distance between any point in the laser scan point cloud data and other points in the preset neighborhood; in the case where the average spatial distance is greater than the preset distance threshold, determining any point as a noise point; removing the noise point from the laser scan point cloud data to obtain denoised point cloud data.

[0083] Wherein, the preset neighborhood refers to a neighborhood range set in three-dimensional space centered on the target point, usually defined based on Euclidean distance or a fixed number of neighboring points; the average spatial distance refers to the average value of the distances between the target point and all other points in its neighborhood, used to reflect whether the point falls within a high-density area; the distance threshold refers to a distance boundary set based on the overall point cloud density experience, and exceeding this boundary may indicate that the point is an isolated or invalid point.

[0084] Exemplarily, after receiving the original laser scan point cloud data, the terminal first performs neighborhood analysis on each point in the dataset. Specifically, the terminal retrieves the K nearest neighboring points (e.g., K = 20) for each point and calculates the Euclidean distance between the point and all neighboring points.

[0085] Then, the terminal performs an averaging operation on the above distance values to obtain the average spatial distance of the current target point. If the average distance is greater than the preset distance threshold (e.g., set to 1.5 times the global mean based on the global point density experience), it is determined that the area where the point is located is a sparse distribution area, and there is a high possibility that it is an isolated point caused by scanning occlusion, reflection anomaly, or edge breakage. Furthermore, the point is marked as a noise point.

[0086] After completing the judgment of all points, the terminal removes all the data marked as noise points from the point cloud set to form a denoised point cloud data set. To improve efficiency, the terminal can adopt a fast neighborhood retrieval algorithm based on KD-tree (k-dimensional tree) to accelerate the processing speed of large-scale point clouds. At the same time, during the removal process, the terminal can set additional filtering conditions, such as restricting the maximum removal ratio, to avoid misdeleting the edge area.

[0087] In this embodiment, by calculating the average spatial distance between the laser scan point and its neighborhood and performing noise identification and removal operations based on the set distance threshold, isolated points, outliers, and other abnormal noises in the point cloud data can be effectively identified, thereby improving the geometric continuity and structural stability of the point cloud data and providing a high-quality geometric basis for subsequent modeling and rendering.

[0088] In an exemplary embodiment, the above step S202 performs precision compression processing on the denoised point cloud data to obtain simplified point cloud data, and further includes: dividing the denoised point cloud data into multiple spatial grids based on a preset precision parameter; calculating the coordinate average value of the original points in each spatial grid to obtain a representative point for each spatial grid; retaining the representative points in each spatial grid and removing the original points in each spatial grid to obtain the simplified point cloud data.

[0089] Among them, precision compression processing refers to reducing the total number of points by controlling the spatial sampling density of the point cloud data to achieve the purpose of optimizing storage and rendering efficiency; the preset precision parameter refers to the spatial unit size set by the system for controlling the compression granularity, usually represented by the side length of a cubic voxel, such as 0.1 meter, 0.5 meter, etc.; the spatial grid refers to a regular three-dimensional unit obtained by dividing the three-dimensional space where the point cloud is located according to the preset precision, and each unit is used to classify the points falling within its range; the representative point refers to the central point calculated based on the coordinate mean value of all points within the grid, which is used to replace the spatial information of all original points within the grid.

[0090] Exemplarily, after the terminal completes the denoising process of the point cloud data, according to the set precision control requirements, such as "generate a sampling unit every 1 cm", the three-dimensional space where the point cloud data is located is divided along the X, Y, and Z coordinate axes to form a uniform cubic voxel grid structure. This structure can accelerate the division process by constructing a three-dimensional hash table or a spatial index.

[0091] For each spatial grid unit, the terminal performs an average coordinate operation on all the original points contained therein, that is, calculates the average coordinate values of all points in the X, Y, and Z axis directions respectively to form the representative point of the voxel unit. This representative point can be regarded as the spatial geometric center of the point group within the voxel range and is used to replace all the original points in this area.

[0092] Subsequently, the terminal collects the representative points in all voxel grid units to form a new point set data structure and removes all the original points, thereby obtaining the simplified point cloud data with a simple structure and a significantly reduced quantity. To further enhance the visual continuity, the terminal can retain the average values of attributes such as color, intensity, or normal vector during the processing, so that the compressed point set still has good visual expressiveness.

[0093] In addition, the terminal can flexibly control the compression intensity by adjusting the voxel side length parameter to achieve the optimal balance between the data volume and the spatial precision. For example, using a 0.1-meter voxel can achieve rich details but a relatively large data volume, while using a 0.5-meter voxel is more suitable for rapid overall shape modeling and browsing.

[0094] In this embodiment, through a compression strategy based on spatial grid division and representative point extraction, while retaining the building form features as much as possible, the volume of point cloud data is significantly reduced, effectively reducing the processing burden of subsequent 3D modeling and graphics rendering, and improving the data transmission efficiency and response speed.

[0095] In an exemplary embodiment, as Figure 3 shown, after the 3D building model is displayed in step S104, it further includes:

[0096] Step S301, obtaining the power data and environmental data of a large building;

[0097] Step S302, associating the power data with the corresponding 3D building model to obtain an association result, and determining the internal space position of the environmental data in the large building to obtain a matching result;

[0098] Step S303, according to the association result and the matching result, display the power data and environmental data on the display page of the 3D building model.

[0099] Among them, the power data refers to data information such as the operating status, power consumption, voltage and current generated by various power equipment (such as air conditioners, lighting, elevators, distribution boxes, etc.) in the building; the environmental data refers to environmental monitoring data such as temperature, humidity, air quality, and noise collected in different areas inside the building; the association result refers to the data mapping relationship after binding the power data to specific components or equipment in the 3D building model; the matching result refers to the spatial positioning relationship where the environmental data is accurately corresponding to the actual monitoring position in the 3D model space.

[0100] Exemplarily, the terminal can access the building management system, smart meter gateway, environmental monitoring equipment or cloud Internet of Things platform, etc., to obtain the power data and environmental data in the large building in real time or periodically, and store them in the data interface module to form a structured data record, including fields such as equipment number, data value, timestamp, and spatial position identifier.

[0101] After the data acquisition is completed, the terminal matches according to the equipment number or installation position code with the preset component or equipment ID in the 3D building model, and binds the power data to the corresponding equipment component node in the 3D model, so as to obtain the association result of the power data. For example, the number of a certain air conditioner equipment is "AC-01", and there is a built-in ID "Model_AC01" in the 3D model. The terminal establishes a binding relationship accordingly and attaches parameters such as the current power value and operating status of the air conditioner to this node.

[0102] For environmental data, the terminal spatially matches the data with the spatial regions in the 3D model according to the coordinate information of its collection location (such as floor number, room number, or precise coordinates). For example, a certain temperature sensor is set in the "2F - meeting room", and the terminal binds its temperature value to the center position of the corresponding room component in the 3D model to form the matching result of environmental data.

[0103] After completing data association and matching, the terminal dynamically updates the visualization effect of the 3D building model on the display page. Specifically, the terminal calls the rendering engine interface to intuitively display the bound power and environmental data in various forms such as floating labels, heat maps, color coding, and value panels in the model interface. For example, a label of "Current power: 3.2kW" appears above the air conditioning equipment model, or a temperature color distribution map is rendered in a certain area, indicating "Room temperature: 26.5°C".

[0104] To enhance the user interaction experience, the terminal also supports the user to click on the model equipment or area to pop up the detailed power and environmental historical data curves at that position, or to implement data alarm linkage. For example, when the power is overloaded or the temperature is abnormal, the model node changes color and gives a prompt.

[0105] In this embodiment, by obtaining the power and environmental data of a large building and precisely binding them to the corresponding components and spatial positions in the 3D building model, the integrated interaction of 3D display and real - time data monitoring is achieved, improving the functionality and management value of the 3D model, and providing visual support for the operation monitoring, energy efficiency analysis, and environmental adjustment of the building.

[0106] In an exemplary embodiment, the above - mentioned step S303 of displaying power data and environmental data in the display page of the 3D building model according to the association result and the matching result further includes: caching the statistical information of power data, the statistical information of environmental data, power data, and environmental data as cache data into the buffer area; in response to an operation instruction for the display page, calling the cache data from the buffer area and dynamically adjusting the position, style, and display range of the cache data display to synchronously display the cache data and the 3D building model.

[0107] Among them, statistical information refers to characteristic values obtained by performing aggregation analysis on power data or environmental data collected over a period of time, including maximum value, minimum value, average value, change trend, etc.; cached data refers to data copies pre-stored in the front-end or intermediate-layer system to improve display response speed and reduce backend call frequency; cache area refers to a high-frequency memory area or high-speed data container maintained by the terminal for storing cached data, which supports fast reading, writing, and updating; operation instructions for the display page include user behaviors such as zooming the model, rotating the view, clicking on components, and dragging the window; synchronous display means that the cached data is kept in real-time consistency with the 3D building model in terms of spatial position, visual style, and model state.

[0108] Exemplarily, after the terminal completes the binding of power data and model nodes and the matching of environmental data and spatial positions, it will perform automatic statistical processing on this type of data. The terminal can set the granularity by hour, day, week, etc., to generate statistical indicators for power consumption or environmental change data, such as the power consumption trend chart of equipment, the temperature and humidity fluctuation range of a certain room, etc., and construct them together with the original data into a cached data structure.

[0109] The terminal then stores all the original data and statistical results in the front-end cache area in the form of key-value pairs or structures. For example, using the component ID as the key to cache the corresponding real-time data value and statistical analysis result; or using the spatial position coordinates as the key to cache multiple environmental indicators in this area to ensure quick response when the user's view changes.

[0110] When the user executes an operation instruction on the display page, such as rotating the model, switching floors, or clicking on a component node, the terminal immediately reads the corresponding cached data from the cache area and dynamically adjusts the display position, style, and display range of the data according to the current model state. For example, if the user zooms the model to a larger view, the terminal can automatically adjust the font size and offset position of the floating label; if the user moves to another floor, the terminal will automatically hide the data display of the current floor and load the data labels of the new floor to ensure visual clarity and accurate positioning. To further improve the display fluency, the terminal can also implement an incremental update mechanism for cached data, such as only updating the component data that has changed, avoiding overall re-rendering, and improving performance.

[0111] In this embodiment, through structured caching of power and environmental data and dynamic display scheduling combined with user interaction operations, efficient synchronous display of multi-source data in the 3D building model is achieved, which not only significantly reduces data call latency, but also improves the interaction response speed and visual adaptability, providing users with a stable, flexible, and highly real-time data visualization experience.

[0112] In an exemplary embodiment, after presenting the three-dimensional building model in step S104 above, the following steps are further included: constructing a corresponding three-dimensional device model based on the size information of the target device in the large building, and determining the deployment information of the three-dimensional device model in the three-dimensional building model; according to the deployment information, loading the three-dimensional device model into the three-dimensional scene where the three-dimensional building model is located, and presenting the operation status data of the target device in real time on the display page.

[0113] Among them, the target device refers to the functional facility devices deployed inside the building, including but not limited to air conditioning systems, photovoltaic panels, charging piles, distribution boxes, energy storage devices, sensor nodes, etc.; the size information refers to the geometric size parameters corresponding to the device in reality, including length, width, height, installation direction, etc., which are used to construct a three-dimensional model that accurately restores the real object; the three-dimensional device model refers to a digital three-dimensional geometric representation generated by a modeling tool based on the true physical shape of the target device, which can be rendered and interacted with in a virtual scene; the deployment information refers to the spatial positioning parameters such as the specific position, orientation, and hierarchical relationship of the three-dimensional device model in the building model; the operation status data refers to the dynamic parameters collected or uploaded during the actual operation of the device, including the operation status (on / off), current power, temperature, voltage, fault code, etc.

[0114] Exemplarily, after the terminal presents the three-dimensional building model, according to the building design drawings, device installation plans, or the data interface of the building management system, it extracts the size specification parameters corresponding to each type of target device, such as the length, width, and height of the device, the shape of the housing, the position of the interface, etc. The terminal can call a three-dimensional modeling tool (such as based on Three.js or a modeling library) to automatically generate a device geometry model, or load a pre-built model of the corresponding device type from a standard device library, and then scale and adjust according to the size information to generate a three-dimensional device model that is close to the actual physical proportion.

[0115] Subsequently, based on the installation position information in the deployment plan or building drawings, the terminal determines the spatial deployment parameters of each device model in the building model, including the floor number, room location, absolute position (X, Y, Z) in the coordinate system, and orientation (rotation angle), etc., and structurally binds this deployment information to the device model.

[0116] After the deployment information is determined, the terminal loads the three-dimensional device model into the three-dimensional scene where the three-dimensional building model is located, inserts it into the correct spatial position, and sets an appropriate hierarchical relationship to ensure that there is no conflict in the rendering and interaction logic between the device model and the building components. This process supports dynamic loading, that is, lazy loading and rendering are performed when the user's perspective enters the area where the device is located to save performance resources.

[0117] To achieve the visualization of the device operation status, the terminal obtains the current operation status data of the target device through real-time data interfaces such as WebSocket (a full-duplex communication protocol), MQTT (Message Queuing Telemetry Transport), HTTP (Hypertext Transfer Protocol) polling, etc., and dynamically binds it to the 3D device model. For example, "Running" or "Fault" labels are displayed on the top of the air conditioner model, or its status is reflected by color changes (green / red). When the user clicks on the device model, a detailed operation data (power, energy consumption, temperature, etc.) information panel can also pop up, realizing the deep integration of the 3D model and real-time data.

[0118] In this embodiment, by constructing a highly restored 3D device model and loading it into the building scene according to the deployment information, not only the expression integrity and detail level of the 3D building model are improved, but also the visualization display of the operation status of key devices in the building is realized, providing intuitive and efficient 3D visualization support for operation and maintenance management, energy consumption monitoring, and device positioning.

[0119] In another exemplary embodiment, the present application provides a 3D large building flexible adjustment management system based on Three.js, which is used to achieve the 3D reconstruction of the building appearance and internal structure, the real-time visualization of environmental and power data, and the model integration and operation status display of key devices. The system processing process includes: data collection and preprocessing of laser scanning point cloud data and building structure drawing data; converting the point cloud data into a 3D modeling format supported by Three.js; constructing a complete 3D building model; spatially binding the building model with real-time operation data such as temperature, humidity, and power; and displaying an interactive 3D building management interface on the browser side to realize model roaming, data viewing, and device linkage control.

[0120] The processing process of this embodiment specifically includes the following steps:

[0121] 1. Building data collection and fusion: The terminal collects the laser scanning point cloud data of the large building to obtain the geometric shape of the building's external structure, and at the same time reads the building structure drawing data to accurately represent the internal space layout, such as floor structure, wall orientation, door and window positions, etc. The terminal performs format cleaning and standardization processing on the above heterogeneous data and establishes a spatial coordinate mapping relationship to ensure the consistency and integrity of subsequent modeling.

[0122] 2. Point Cloud Data Preprocessing and Conversion: The terminal performs noise removal and precision compression on the original point cloud data. For noise removal, an inter-point average distance screening mechanism is adopted to filter out isolated abnormal points; for precision compression, representative points are retained through voxel grid resampling to reduce data redundancy. After data simplification, the terminal converts the point cloud data into a data format recognizable by Three.js, such as JSON or binary format, and constructs necessary information structures such as vertex arrays, color arrays, and index structures to ensure its correct parsing in the 3D rendering engine.

[0123] 3. Reconstruction of 3D Building Structure Model: Based on the cleaned drawing data, the terminal converts the 2D structure information into a 3D spatial structure through a geometric modeling module, including structural units such as floors, walls, doors, and windows, and establishes a hierarchical relationship and a coordinate unified model. Subsequently, the terminal fuses the structure model with the point cloud model to generate a complete 3D building model.

[0124] 4. Equipment Model Construction and Integration: The terminal constructs high-precision 3D equipment models according to the actual dimensions, shapes, and deployment information of target equipment such as air conditioners, photovoltaic panels, charging piles, and energy storage devices. The equipment models are bound to the specified positions of the building model through coordinate conversion, and appropriate hierarchical relationships and interaction attributes are set to ensure the accurate positioning, correct rendering, and independent response capabilities of the equipment in the 3D scene.

[0125] 5. Real-time Operation Data Docking and Binding: The terminal accesses the operation data of the building management system in real time through WebSocket, MQTT, or other interface protocols, including power load, temperature and humidity, equipment status, energy consumption indicators, etc. The above data establishes a binding relationship with the 3D model components according to the equipment ID or spatial location to form an associative structure that can be dynamically rendered.

[0126] 6. Multi-level Caching and Dynamic Display Mechanism: To optimize performance, the terminal caches the equipment operation data and environmental monitoring data, and establishes a cache pool with the component ID or spatial block as the key. In response to user operation instructions on the display page (such as zooming, rotating, clicking), the terminal extracts and dynamically adjusts the data display content from the cache area, including position offset, display style (color, font, label), display range, etc., to achieve real-time synchronous display of data and models.

[0127] 7. 3D Scene Interaction and Information Display: Finally, the terminal loads the building model and equipment model in the browser-side 3D scene built based on Three.js, supporting users to perform operations such as rotation, zooming, roaming, and clicking. Users can view the real-time operation status, historical data curves, and alarm information of any component or equipment through the visualization panel. The model supports advanced interaction methods such as area switching, floor expansion, and data heat map overlay to achieve visual integration of building-level, equipment-level, and area-level data.

[0128] For example, a visualization example is as follows: In the center of the page, the spatial layout of all buildings and the road system are presented in the form of a 3D overview map of the park. 3D models of photovoltaic panels are arranged on the rooftops, and several large building bodies are presented on both sides of the main road. Columns such as equipment information, energy statistics, environmental overview, and area analysis are set at the top. The left panel shows information such as the total number of equipment, the number of buildings, and the area load status in the park. The right side shows the building zoning structure list and the unit energy consumption ranking. When clicking on any building in the model, an interactive label appears at the top, showing multiple real-time data such as its current power, equipment status, temperature, and humidity. It supports users to jump to the detailed 3D scene of the building, realizing the scene switching from macro park management to micro building analysis. Various industrial buildings are visually arranged on both sides of the central main road, and the interactive label shows parameters such as its current load value, current and voltage, and environmental status in real time. The scene details accurately restore the park terrain, building density, and road distribution, and overall achieve the digital presentation of the trinity of "building - equipment - data".

[0129] Another visualization example is as follows: Focus on regional-level energy dispatching control management. In the interface, the regional map is the main view, and the location distribution of each building or site is marked by a heat map. The left module of the page shows core indicators such as the total energy consumption, the number of active devices, and the average room temperature in the current area. The right side shows the area list and the comparison of energy consumption rankings. Below, bar charts and line charts are used to show the historical energy consumption, operation duration, temperature and humidity change trends of different devices or areas. This interface can quickly locate the target building through map interaction and switch to the 3D scene, realizing the seamless switching from macro dispatching to micro scenes. The terminal presets the binding relationship between the building identifier represented by each red dot and the 3D model in the background. After clicking on the area, it automatically jumps to the corresponding 3D display page, realizing spatial linkage and information coherence.

[0130] In this embodiment, through the integrated modeling of laser point cloud and drawing structure, equipment model integration, and data dynamic binding mechanism, a 3D visualization management platform for large buildings is realized, which has the characteristics of high structural restoration degree, strong real-time data display, and flexible interaction response. It is applicable to complex scenarios such as park-level building management, intelligent factory dispatching, and smart building operation and maintenance.

[0131] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0132] Based on the same inventive concept, an embodiment of the present application also provides a three-dimensional model display device for a large building for implementing the three-dimensional model display method of the large building involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the three-dimensional model display device for a large building provided below can refer to the limitations on the three-dimensional model display method of the large building in the above text, and will not be repeated here.

[0133] In an exemplary embodiment, as Figure 4 shown, a three-dimensional model display device for a large building is provided, including: a data acquisition module 401, a data processing module 402, a model construction module 403, and a model display module 404, where:

[0134] The data acquisition module 401 is used to acquire the laser scanning point cloud data and building structure drawing data of the large building;

[0135] The data processing module 402 is used to preprocess and perform format conversion on the laser scanning point cloud data to generate point cloud model data that can be used for three-dimensional modeling;

[0136] The model construction module 403 is used to construct the internal building structure model of the large building based on the building structure drawing data;

[0137] The model display module 404 is used to fuse the internal building structure model with the point cloud model data to obtain a three-dimensional building model of the large building and display the three-dimensional building model.

[0138] In one embodiment, the above data processing module 402 is further configured to perform noise point removal processing on the laser scanning point cloud data to obtain denoised point cloud data; perform precision compression processing on the denoised point cloud data to obtain simplified point cloud data; and convert the simplified point cloud data into a data format compatible with the 3D engine of the display page of the 3D building model to obtain point cloud model data.

[0139] In one embodiment, the above data processing module 402 is further configured to determine the average spatial distance between any point in the laser scanning point cloud data and other points in the preset domain; in the case where the average spatial distance is greater than the preset distance threshold, determine any point as a noise point; and remove the noise point from the laser scanning point cloud data to obtain denoised point cloud data.

[0140] In one embodiment, the above data processing module 402 is further configured to divide the denoised point cloud data into multiple spatial grids based on preset precision parameters; calculate the coordinate average value of the original points in each spatial grid to obtain the representative point of each spatial grid; retain the representative points in each spatial grid, and remove the original points in each spatial grid to obtain simplified point cloud data.

[0141] In one embodiment, the above 3D model display device for large buildings further includes a data display module, configured to obtain the power data and environmental data of the large building; associate the power data with the corresponding 3D building model to obtain an association result, and determine the internal spatial position of the environmental data in the large building to obtain a matching result; and display the power data and environmental data on the display page of the 3D building model according to the association result and the matching result.

[0142] In one embodiment, the above data display module is further configured to cache the statistical information of the power data, the statistical information of the environmental data, the power data and the environmental data as cache data in a buffer; in response to an operation instruction for the display page, call the cache data from the buffer, and adjust the position, style and display range of the cache data display in real time to synchronously display the cache data and the 3D building model.

[0143] Each module in the above 3D model display device for large buildings can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 5As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it realizes a method for displaying a three-dimensional model of a large building. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0145] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0146] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are realized.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are realized.

[0148] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are realized.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0150] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0151] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0152] The above-described embodiments merely represent several implementation manners of this application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A method for displaying a three-dimensional model of a large building, characterized in that, The method includes: Obtaining the laser scanning point cloud data and building structure drawing data of a large building; Performing preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for 3D modeling; Based on the building structure drawing data, constructing an internal building structure model of the large building; Fusing the internal building structure model with the point cloud model data to obtain a 3D building model of the large building, and displaying the 3D building model.

2. The method according to claim 1, wherein The performing preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for 3D modeling includes: Performing noise point removal processing on the laser scanning point cloud data to obtain denoised point cloud data; Performing precision compression processing on the denoised point cloud data to obtain simplified point cloud data; Converting the simplified point cloud data into a data format compatible with the 3D engine of the display page of the 3D building model to obtain point cloud model data.

3. The method according to claim 2, wherein The performing noise point removal processing on the laser scanning point cloud data to obtain denoised point cloud data includes: Determining the average spatial distance between any point in the laser scanning point cloud data and other points in a preset domain; In the case where the average spatial distance is greater than a preset distance threshold, determining the any point as a noise point; Removing the noise point from the laser scanning point cloud data to obtain denoised point cloud data.

4. The method according to claim 2, characterized in that, The performing precision compression processing on the denoised point cloud data to obtain simplified point cloud data includes: Based on a preset precision parameter, dividing the denoised point cloud data into multiple spatial grids; Calculating the coordinate average value of the original points in each spatial grid to obtain a representative point of each spatial grid; Retaining the representative points in each spatial grid and removing the original points in each spatial grid to obtain simplified point cloud data.

5. The method according to claim 1, wherein After displaying the 3D building model, it further includes: Obtaining the power data and environmental data of the large building; Associating the power data with the corresponding 3D building model to obtain an association result, and determining the internal spatial position of the environmental data in the large building to obtain a matching result; According to the association result and the matching result, displaying the power data and the environmental data on the display page of the 3D building model.

6. The method according to claim 5, wherein The displaying the power data and the environmental data on the display page of the 3D building model according to the association result and the matching result includes: Taking the statistical information of the power data, the statistical information of the environmental data, the power data, and the environmental data as cache data and caching them in a cache area; In response to an operation instruction for the display page, calling the cache data from the cache area, and real-time adjusting the position, style, and display range of the cache data displayed to synchronously display the cache data and the 3D building model.

7. A three-dimensional model display device for a large building, characterized in that, The device includes: A data acquisition module for obtaining the laser scanning point cloud data and building structure drawing data of a large building; A data processing module, configured to perform preprocessing and format conversion processing on the laser scanning point cloud data to generate point cloud model data that can be used for 3D modeling; A model construction module, configured to construct an internal building structure model of the large building based on the building structure drawing data; A model display module, configured to fuse the internal building structure model with the point cloud model data to obtain a 3D building model of the large building, and display the 3D building model.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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