Building surveying and mapping method based on laser pay-off instrument

Abstract

The invention discloses a building surveying and mapping method based on a laser paying-off instrument, relates to the technical field of building surveying and mapping, and improves the visualization effect of building structure data. A multi-core acquisition mechanism is set for each laser pay-off instrument, so that the laser pay-off instruments emit laser signals to the data acquisition ranges of the laser pay-off instruments according to the multi-core acquisition mechanisms, various types of laser data are generated, and a multi-dimensional coordinate system is established; the method comprises the following steps: mapping various types of laser data of a laser paying-off instrument into a multi-dimensional coordinate system for data fusion to obtain a data block set, fusing the data block set of the laser paying-off instrument, establishing a voxel grid, inputting a fusion result of the data block set into the voxel grid, and obtaining structural attributes of a target building. And setting tree nodes and child nodes in the voxel grid according to the structure attribute of the target building to obtain a spatial association tree, and calling corresponding data in the spatial association tree to construct a local visualization graph according to a viewing area and a viewing mode uploaded by a user.

Description

Technical Field

[0001] This invention relates to the field of architectural surveying technology, specifically a method for architectural surveying based on a laser line laying instrument. Background Technology

[0002] In the field of architecture, accurate information collection, processing, and visualization of target buildings have always been important research directions. Traditional building information collection methods have many limitations. For example, manual measurement is not only inefficient but also prone to human error, making it difficult to meet the high-precision, large-scale data collection needs of modern buildings. While some existing automated collection technologies have improved collection efficiency to some extent, the types of data collected are limited and cannot fully reflect the complex spatial structure and characteristics of buildings.

[0003] In terms of data processing, traditional methods lack effective data fusion mechanisms, making it difficult to integrate data from different sources and of different types, resulting in low data utilization efficiency. Furthermore, for the visualization of building information, previous technologies often only provide simple two-dimensional views, failing to intuitively present the three-dimensional spatial structure of the building and thus unable to meet users' needs for viewing detailed information about different areas of the building. Therefore, this paper proposes a building surveying method based on a laser line laying instrument. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a building surveying method based on a laser line laying instrument.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A building surveying method based on a laser line laying instrument includes the following steps:

[0007] Step 1: Install several laser line-finding instruments on the target building. Adjust the acquisition direction and spatial position of each laser line-finding instrument according to the spatial structure of the target building to establish a building laser acquisition network.

[0008] Step 2: Set up a multi-core acquisition mechanism for each laser line-laying instrument. Then, the laser line-laying instrument emits laser signals to its data acquisition range according to the multi-core acquisition mechanism and generates various types of laser data.

[0009] Step 3: Establish a multi-dimensional coordinate system, map the various types of laser data from the laser line-finding instrument onto the multi-dimensional coordinate system, perform data fusion to obtain a set of data blocks, then fuse the set of data blocks from the laser line-finding instrument, establish a voxel grid, and then input the fusion result of the set of data blocks into the voxel grid.

[0010] Step 4: Obtain the structural attributes of the target building. Based on the structural attributes of the target building, set tree nodes and child nodes in the voxel grid to obtain a spatial association tree. Based on the viewing area and viewing method uploaded by the user, retrieve the corresponding data in the spatial association tree to construct a local visualization map.

[0011] Furthermore, the process of installing the laser line-laying device on the target building includes:

[0012] The acquisition direction and spatial position of each laser line-finding instrument are adjusted according to the spatial structure of the target building:

[0013] Install m laser line-laying devices inside the target building. Each laser line-laying device consists of a laser generator, an optical lens, and a wireless communication module, where m is an integer greater than 10.

[0014] Start all laser line laying instruments and synchronize the time between each laser line laying instrument through the wireless communication module. After the time synchronization is completed, the laser line laying instruments in adjacent spatial positions project high-precision point lasers or narrow line lasers at each other and are captured by the laser generator of the other party.

[0015] Configure a communication terminal and connect all laser line-laying instruments to it. Based on the communication connection results, assign numbers a1, a2, a3, ..., a... to each laser line-laying instrument. m And set m laser emission nodes.

[0016] Furthermore, the process of adjusting the acquisition direction and spatial position of each laser line-finding instrument includes:

[0017] By using collinear or planar constraints, and combining the laser emission and reception angles between each laser line laying instrument, the relative azimuth, elevation, and distance between the laser line laying instruments are obtained. Then, based on the relative azimuth, elevation, and distance between each laser line laying instrument, laser connection lines are set between the corresponding laser emission nodes.

[0018] Based on the data acquisition range of the high-precision point laser or narrow-line laser of the laser line feeder, a conical association space is set for each laser connection line to represent the data acquisition range of the corresponding high-precision point laser or narrow-line laser.

[0019] Establish a building laser acquisition network based on the size of the target building, and input the laser emission nodes with laser connection lines into the building laser acquisition network. Traverse the mapping space composed of the conical associated space of all laser connection lines, and then determine whether the mapping space completely covers the building laser acquisition network.

[0020] If it is determined that the survey space does not completely cover the building laser acquisition network, then mark the uncovered space location in the building laser acquisition network and record it as an omitted survey space; otherwise, do not perform any operation.

[0021] Based on the location of the missed mapping space, the laser emission node is matched with its adjacent position. A greedy algorithm is used to move the matched laser emission node in the spatial position of the building laser acquisition network with the minimum moving distance of the laser emission node and without creating new missed mapping spaces.

[0022] If it is determined that the survey space completely covers the building laser acquisition network, no operation is performed. Then, based on the spatial location of each laser emission node in the building laser acquisition network, the position of the laser line laying instrument in the target building is adjusted.

[0023] Furthermore, the multi-core data processing mechanism includes a point scanning core, a line scanning core, a surface scanning core, and a contour scanning core;

[0024] The point scanning kernel is used to project laser points onto the target surface to form a point cloud;

[0025] The line scanning core is used to project a laser line that sweeps across the target surface along a specific path;

[0026] The surface scanning kernel is used to project structured light patterns onto the target surface;

[0027] The contour scanning kernel is used to project moving laser points or laser lines along the edge contour of the target.

[0028] Furthermore, the process of acquiring various laser data using a laser beam patterner includes:

[0029] Based on the target building location of the surveying task, all corresponding laser line laying instruments are retrieved. The positions of the retrieved laser line laying instruments are adjusted according to step one. After the retrieved laser line laying instruments have completed the position adjustment, each laser line laying instrument emits laser signals to its data acquisition range according to the multi-core acquisition mechanism. The generated laser data is labeled with the type name according to the type of scanning core. The type name includes point cloud data, line and surface data, plane data and contour image data.

[0030] Furthermore, the process of establishing the data block set includes:

[0031] A multi-dimensional coordinate system is established to divide various types of laser data into several data blocks, and spatiotemporal coordinates are set for each data block. The spatiotemporal coordinates are represented as (spatial coordinates, time coordinates), where the time coordinate is the acquisition time of the data block.

[0032] Data blocks with the same spatiotemporal coordinates are superimposed sequentially in the order of point cloud-line-plane-contour, and then the superimposed data blocks are spliced ​​together according to the spatiotemporal coordinates to obtain the data block set of the corresponding laser line laying instrument.

[0033] Point cloud data: Fine registration is performed using iterative nearest point or its variant to eliminate minor deviations caused by different viewpoints, and then the point cloud data of the overlapping areas are weighted and averaged to complete the fusion operation;

[0034] Line and surface data: Set multiple common feature points on the line and surface data, match the common feature points of each line and surface data with each other, and connect or fit different line and surface data according to the matching results;

[0035] Planar data and contour image data: Several normal vectors are marked on the planar data and contour image data. Then, the normal vectors and distances of different planar data or contour image data are compared. If the normal vectors and distances are consistent, it is determined that the corresponding two planar data or contour image data are consistent, and the corresponding two planar data or contour image data are fused.

[0036] Otherwise, if they are determined to be different planes, then based on the positions of the different normal vectors, the difference data parts are divided in the two plane data or contour image data, and the difference data parts are weighted and fused.

[0037] Furthermore, the process of creating the voxel mesh includes:

[0038] Each data block within the fully merged data block set is sequentially transformed into multiple feature association vectors representing different features;

[0039] The multidimensional coordinate system is transformed into a regular voxel grid, which is composed of several grid cells, and each grid cell is composed of multiple sub-grid cells. Then, according to the spatiotemporal coordinate distribution of each data block, the feature association vector is input into each sub-grid cell respectively.

[0040] Multiple matrix dimensions can be set for the voxel mesh, including geometric feature dimension, attribute feature dimension and semantic feature dimension, so that by selecting different matrix dimensions, the voxel mesh can simultaneously display feature correlation vectors of multiple matrix dimensions.

[0041] Furthermore, the process of creating a local visualization map includes:

[0042] Several tree nodes are set according to the structural attributes of the target building. The structural attributes include building component boundaries, structural partitions and spatial scale. The structural attributes corresponding to each tree node are mapped to a voxel grid. Tree nodes that are associated with only one grid cell are recorded as child nodes, and tree nodes that are associated with more than one grid cell are still recorded as tree nodes. In this way, a spatial association tree is constructed in the voxel grid.

[0043] Generate a local visualization topology map of the corresponding data type based on the data contained in each sub-grid cell, and set a time slider;

[0044] When a user selects or clicks to view an area and uploads a viewing method, the system retrieves the corresponding child nodes based on the tree node corresponding to the viewing area, and the local visualization topology associated with the child nodes of the corresponding data format based on the viewing method. These are then stitched together to obtain a local visualization map, which is then sent to the user.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention establishes a multi-dimensional coordinate system to map various types of laser data into it for data fusion, resulting in a data block set. This achieves comprehensive consideration of multiple dimensions of laser data, enabling the organic combination of different types of data, eliminating redundancy and conflicts, and improving data quality and usability. Simultaneously, the data block set is further fused, and a voxel grid is established. The fusion result is input into the voxel grid, providing a more scientific and efficient structure for subsequent data management and analysis, facilitating precise processing and manipulation of architectural spatial data.

[0047] 2. Based on the structural attributes of the target building, tree nodes and child nodes are set within the voxel grid to construct a spatial relationship tree. The spatial relationship tree clearly displays the hierarchical relationships and spatial connections between building data, making the data organization more orderly. Based on the user-uploaded viewing area and viewing method, relevant data can be accurately retrieved from the spatial relationship tree to construct a local visualization map. This provides users with high flexibility and targeting, allowing them to customize viewing content and methods according to their needs, quickly obtain building data information of interest, improve the visualization effect and utilization value of the data, and help users more intuitively understand the structure and characteristics of the target building, providing strong support for building design, construction, and management. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention.

[0049] Figure 1This is a flowchart of the method of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0051] like Figure 1 As shown, a building surveying method based on a laser line laying instrument includes the following steps:

[0052] Step 1: Install several laser line-finding instruments on the target building. Adjust the acquisition direction and spatial position of each laser line-finding instrument according to the spatial structure of the target building to establish a building laser acquisition network.

[0053] Step 2: Set up a multi-core acquisition mechanism for each laser line-laying instrument. Then, the laser line-laying instrument emits laser signals to its data acquisition range according to the multi-core acquisition mechanism and generates various types of laser data.

[0054] Step 3: Establish a multi-dimensional coordinate system, map the various types of laser data from the laser line-finding instrument onto the multi-dimensional coordinate system, perform data fusion to obtain a set of data blocks, then fuse the set of data blocks from the laser line-finding instrument, establish a voxel grid, and then input the fusion result of the set of data blocks into the voxel grid.

[0055] Step 4: Obtain the structural attributes of the target building. Based on the structural attributes of the target building, set tree nodes and child nodes in the voxel grid to obtain a spatial association tree. Based on the viewing area and viewing method uploaded by the user, retrieve the corresponding data in the spatial association tree to construct a local visualization map.

[0056] Furthermore, step one is achieved through the following process:

[0057] The acquisition direction and spatial position of each laser line-finding instrument are adjusted according to the spatial structure of the target building:

[0058] Install m laser line-laying devices inside the target building. Each laser line-laying device consists of a laser generator, an optical lens, and a wireless communication module, where m is an integer greater than 10.

[0059] Start all laser line laying instruments and synchronize the time between each laser line laying instrument through the wireless communication module. After the time synchronization is completed, the laser line laying instruments in adjacent spatial positions project high-precision point lasers or narrow line lasers at each other and are captured by the laser generator of the other party.

[0060] Configure a communication terminal and connect all laser line-laying instruments to it. Based on the communication connection results, assign numbers a1, a2, a3, ..., a... to each laser line-laying instrument. m And set m laser emission nodes;

[0061] By using collinear or planar constraints, and combining the laser emission and reception angles between each laser line laying instrument, the relative azimuth, elevation, and distance between the laser line laying instruments are obtained. Then, based on the relative azimuth, elevation, and distance between each laser line laying instrument, laser connection lines are set between the corresponding laser emission nodes.

[0062] Based on the data acquisition range of the high-precision point laser or narrow-line laser of the laser line feeder, a conical association space is set for each laser connection line to represent the data acquisition range of the corresponding high-precision point laser or narrow-line laser.

[0063] Establish a building laser acquisition network based on the size of the target building, and input the laser emission nodes with laser connection lines into the building laser acquisition network. Traverse the mapping space composed of the conical associated space of all laser connection lines, and then determine whether the mapping space completely covers the building laser acquisition network.

[0064] If it is determined that the survey space does not completely cover the building laser acquisition network, then mark the uncovered space location in the building laser acquisition network and record it as an omitted survey space; otherwise, do not perform any operation.

[0065] Based on the location of the missed mapping space, the laser emission node is matched with its adjacent position. A greedy algorithm is used to move the matched laser emission node in the spatial position of the building laser acquisition network with the minimum moving distance of the laser emission node and without creating new missed mapping spaces.

[0066] If it is determined that the survey space completely covers the building laser acquisition network, no operation is performed. Then, based on the spatial location of each laser emission node in the building laser acquisition network, the position of the laser line laying instrument in the target building is adjusted.

[0067] Through the above steps, several laser line-laying instruments are installed on the target building, and their acquisition direction and spatial position are adjusted according to the building's spatial structure to construct a building laser acquisition network. This approach enables data acquisition from various key parts and angles of the building, ensuring comprehensive data coverage of the entire target building space and avoiding data blind spots. Simultaneously, precise position and orientation adjustments ensure that the data acquired by the laser line-laying instruments accurately reflects the actual structure and spatial characteristics of the building, providing a high-quality, high-precision data foundation for subsequent analysis and processing.

[0068] Furthermore, step two is achieved through the following process:

[0069] A multi-core acquisition mechanism is set up for the laser line laying instrument corresponding to each laser emission node in the building laser acquisition network. The multi-core data processing mechanism includes point scanning core, line scanning core, surface scanning core and contour scanning core.

[0070] The point scanning kernel is used to project laser points onto the target surface to form a point cloud;

[0071] The line scanning core is used to project laser lines along a specific path (such as S-shape or grid shape) across the target surface;

[0072] The surface scanning kernel is used to project structured light patterns (such as gratings or pseudo-random dot matrices) onto the target surface;

[0073] The contour scanning kernel is used to project moving laser points or laser lines along the edge contour of the target;

[0074] Users upload surveying tasks, such as "scanning the outline of the west facade curtain wall unit" and "detecting the flatness of the third floor slab". Based on the target building location of the surveying task, all corresponding laser line laying instruments are retrieved.

[0075] According to step one, the position of the retrieved laser line-laying instrument is adjusted. After the retrieved laser line-laying instrument completes the position adjustment, each laser line-laying instrument emits laser signals to its data acquisition range according to the multi-core acquisition mechanism, and labels the generated laser data with type names according to the type of scanning core. The type names include point cloud data, line and surface data, plane data and contour image data.

[0076] Through the above process, the acquisition direction and spatial position of the laser line-layout instrument are adjusted according to the building's spatial structure to ensure that the acquired data accurately reflects the actual situation of the building. Furthermore, the multi-core acquisition mechanism enables the laser line-layout instrument to emit laser signals and generate various types of laser data, greatly improving the efficiency and richness of data acquisition.

[0077] Furthermore, step three is achieved through the following process:

[0078] A multi-dimensional coordinate system is established. For any laser line-emitting instrument, the various types of laser data it collects have the property of spatiotemporal consistency. Then, the various types of laser data are divided into several data blocks, and spatiotemporal coordinates are set for each data block. The spatiotemporal coordinates are represented as (spatial coordinates, time coordinates), where the time coordinate is the acquisition time of the data block.

[0079] Data blocks with the same spatiotemporal coordinates are superimposed sequentially in the order of point cloud-line-plane-contour, and then the superimposed data blocks are spliced ​​together according to the spatiotemporal coordinates to obtain the data block set of the corresponding laser line laying instrument.

[0080] The data block set corresponding to the laser line laying instrument performing the same surveying task is mapped onto a multi-dimensional coordinate system according to the spatial distribution of laser emission nodes in the building laser acquisition network;

[0081] Since each laser line laying instrument performs the same surveying task at the same time, and according to the position adjustment results in step one, the data acquisition range of laser line laying instruments in adjacent spatial positions overlaps or has the same part. This is represented in the multidimensional coordinate system as the same spatiotemporal location within the target building corresponding to data blocks with the same spatiotemporal coordinates in different data block sets.

[0082] Then, data fusion is performed on data blocks that overlap in the multidimensional coordinate system:

[0083] For point cloud data: use iterative nearest point or its variant for fine registration to eliminate minor deviations caused by different viewpoints, and then perform weighted averaging on the point cloud data of overlapping areas to complete the fusion operation;

[0084] Line and surface data: Set multiple common feature points on the line and surface data, match the common feature points of each line and surface data with each other, and connect or fit different line and surface data according to the matching results;

[0085] Planar data and contour image data: Several normal vectors are marked on the planar data and contour image data. Then, the normal vectors and distances of different planar data or contour image data are compared. If the normal vectors and distances are consistent, it is determined that the corresponding two planar data or contour image data are consistent, and the corresponding two planar data or contour image data are fused.

[0086] Otherwise, if they are determined to be different planes, then based on the positions of the different normal vectors, the difference data parts are divided in the two plane data or contour image data, and the difference data parts are weighted and fused.

[0087] Each data block within the fully merged data block set is sequentially transformed into multiple feature association vectors representing different features. The feature types include: geometric features (spatiotemporal coordinates, normal vectors, point cloud distribution statistics), attribute features (point clouds, lines, surfaces, contours, and planes), and semantic features (such as "wall", "column", and "ground").

[0088] The multidimensional coordinate system is transformed into a regular voxel grid, which is composed of several grid cells, and each grid cell is composed of multiple sub-grid cells. Then, according to the spatiotemporal coordinate distribution of each data block, the feature association vector is input into each sub-grid cell respectively.

[0089] Multiple matrix dimensions can be set for the voxel mesh, including geometric feature dimension, attribute feature dimension and semantic feature dimension, so that by selecting different matrix dimensions, the voxel mesh can simultaneously display feature correlation vectors of multiple matrix dimensions.

[0090] Through the above process, a multi-dimensional coordinate system is established, and various types of laser data are mapped and fused to obtain a set of data blocks. These data blocks are then further fused, and a voxel grid is created. This multi-level data fusion method can fully integrate different types of data, uncover the relationships between data, improve data quality and usability, and provide a solid data foundation for subsequent analysis and applications.

[0091] Furthermore, step four is achieved through the following process:

[0092] Several tree nodes are set according to the structural attributes of the target building. The structural attributes include building component boundaries (such as division according to major components such as walls, columns, floors, and roofs), structural zoning (functional areas, structural units, construction structural segments, etc. of the building), and spatial scale (divided hierarchically from the whole to the part, such as the whole building -> floors -> rooms -> components).

[0093] The structural properties of each tree node are mapped to the voxel grid, and tree nodes that are associated with only one grid cell are recorded as child nodes, while tree nodes that are associated with more than one grid cell are still recorded as tree nodes, thereby constructing a spatial association tree in the voxel grid.

[0094] Generate a local visualization topology map of the corresponding data type based on the data contained in each sub-grid cell, and set a time slider;

[0095] When a user selects or clicks to view an area and uploads a viewing method, the system retrieves the corresponding child nodes based on the tree node corresponding to the viewing area, and the local visualization topology associated with the child nodes of the corresponding data format based on the viewing method. These are then stitched together to obtain a local visualization map, which is then sent to the user.

[0096] Through the above process, tree nodes and child nodes are set according to the structural attributes of the target building to obtain a spatial relationship tree. Users can upload viewing areas and viewing methods according to their needs, and retrieve corresponding data from the spatial relationship tree to construct a local visualization map. This method can provide users with an intuitive and personalized display of building information, enabling them to more clearly understand the internal structure of the building and detailed information of each area, which is helpful for building design, construction management, maintenance and other work.

[0097] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A building surveying method based on a laser line laying instrument, characterized in that, Includes the following steps: Step 1: Install several laser line-finding instruments on the target building. Adjust the acquisition direction and spatial position of each laser line-finding instrument according to the spatial structure of the target building to establish a building laser acquisition network. Step 2: Set up a multi-core acquisition mechanism for each laser line-laying instrument. Then, the laser line-laying instrument emits laser signals to its data acquisition range according to the multi-core acquisition mechanism and generates various types of laser data. Step 3: Establish a multi-dimensional coordinate system, map the various types of laser data from the laser line-finding instrument onto the multi-dimensional coordinate system, perform data fusion to obtain a set of data blocks, then fuse the set of data blocks from the laser line-finding instrument, establish a voxel grid, and then input the fusion result of the set of data blocks into the voxel grid. Step 4: Obtain the structural attributes of the target building. Based on the structural attributes of the target building, set tree nodes and child nodes in the voxel grid to obtain a spatial association tree. Based on the viewing area and viewing method uploaded by the user, retrieve the corresponding data in the spatial association tree to construct a local visualization map.

2. The building surveying method based on a laser line laying instrument according to claim 1, characterized in that, The process of installing a laser line-finding device on the target building includes: The acquisition direction and spatial position of each laser line-finding instrument are adjusted according to the spatial structure of the target building: Install m laser line-laying devices inside the target building. Each laser line-laying device consists of a laser generator, an optical lens, and a wireless communication module, where m is an integer greater than 10. Start all laser line laying instruments and synchronize the time between each laser line laying instrument through the wireless communication module. After the time synchronization is completed, the laser line laying instruments in adjacent spatial positions project high-precision point lasers or narrow line lasers at each other and are captured by the laser generator of the other party. Set up a communication terminal and connect all laser line emitters to the communication terminal. Based on the communication connection results, set up m laser emission nodes.

3. The building surveying method based on a laser line laying instrument according to claim 2, characterized in that, The process of adjusting the acquisition direction and spatial position of each laser line-finding instrument includes: By using collinear or planar constraints, and combining the laser emission and reception angles between each laser line laying instrument, the relative azimuth, elevation, and distance between the laser line laying instruments are obtained. Then, based on the relative azimuth, elevation, and distance between each laser line laying instrument, laser connection lines are set between the corresponding laser emission nodes. Based on the data acquisition range of the high-precision point laser or narrow line laser of the laser line feeder, a conical association space is set for each laser connection line; Establish a building laser acquisition network and input laser emission nodes with laser connection lines into the building laser acquisition network. Traverse the mapping space composed of the conical associated spaces of all laser connection lines, and then determine whether the mapping space completely covers the building laser acquisition network. If it is determined that the survey space does not completely cover the building laser acquisition network, then mark the uncovered space location in the building laser acquisition network and record it as an omitted survey space; otherwise, do not perform any operation. Based on the location of the missed mapping space, the laser emission node is matched with its adjacent position. A greedy algorithm is used to move the matched laser emission node in the spatial position of the building laser acquisition network with the minimum moving distance of the laser emission node and without creating new missed mapping spaces. If it is determined that the survey space completely covers the building laser acquisition network, no operation is performed. Then, based on the spatial location of each laser emission node in the building laser acquisition network, the position of the laser line laying instrument in the target building is adjusted.

4. The building surveying method based on a laser line laying instrument according to claim 3, characterized in that, The multi-core data processing mechanism includes point scanning core, line scanning core, surface scanning core, and contour scanning core; The point scanning kernel is used to project laser points onto the target surface to form a point cloud; The line scanning core is used to project a laser line that sweeps across the target surface along a specific path; The surface scanning kernel is used to project structured light patterns onto the target surface; The contour scanning kernel is used to project moving laser points or laser lines along the edge contour of the target.

5. A building surveying method based on a laser line laying instrument according to claim 4, characterized in that, The process of a laser beam patterner acquiring various laser data includes: Based on the target building location of the surveying task, all corresponding laser line laying instruments are retrieved. The positions of the retrieved laser line laying instruments are adjusted according to step one. After the retrieved laser line laying instruments have completed the position adjustment, each laser line laying instrument emits laser signals to its data acquisition range according to the multi-core acquisition mechanism. The generated laser data is labeled with the type name according to the type of scanning core. The type name includes point cloud data, line and surface data, plane data and contour image data.

6. The building surveying method based on a laser line laying instrument according to claim 5, characterized in that, The process of establishing the data block set includes: A multi-dimensional coordinate system is established to divide various types of laser data into several data blocks, and spatiotemporal coordinates are set for each data block. The spatiotemporal coordinates are represented as (spatial coordinates, time coordinates), where the time coordinate is the acquisition time of the data block. Data blocks with the same spatiotemporal coordinates are superimposed sequentially in the order of point cloud-line-plane-contour, and then the superimposed data blocks are stitched together according to their spatiotemporal coordinates to obtain the data block set of the corresponding laser line-finding instrument.

7. A building surveying method based on a laser line laying instrument according to claim 6, characterized in that, The process of creating a voxel mesh includes: Each data block within the fully merged data block set is sequentially transformed into multiple feature association vectors representing different features; The multidimensional coordinate system is transformed into a regular voxel grid, which is composed of several grid cells, and each grid cell is composed of multiple sub-grid cells. Then, according to the spatiotemporal coordinate distribution of each data block, the feature association vector is input into each sub-grid cell respectively. Multiple matrix dimensions can be set for the voxel mesh, including geometric feature dimension, attribute feature dimension and semantic feature dimension, so that by selecting different matrix dimensions, the voxel mesh can simultaneously display feature correlation vectors of multiple matrix dimensions.

8. A building surveying method based on a laser line laying instrument according to claim 7, characterized in that, The process of creating a local visualization map includes: Several tree nodes are set according to the structural attributes of the target building. The structural attributes include building component boundaries, structural partitions and spatial scale. The structural attributes corresponding to each tree node are mapped to a voxel grid. Tree nodes that are associated with only one grid cell are recorded as child nodes, and tree nodes that are associated with more than one grid cell are still recorded as tree nodes. In this way, a spatial association tree is constructed in the voxel grid. Generate a local visualization topology map of the corresponding data type based on the data contained in each sub-grid cell, and set a time slider; When a user selects or clicks to view an area and uploads a viewing method, the system retrieves the corresponding child nodes based on the tree node corresponding to the viewing area, and the local visualization topology associated with the child nodes of the corresponding data format based on the viewing method. These are then stitched together to obtain a local visualization map, which is then sent to the user.

Images