Multi-terminal-oriented point cloud data format standardization conversion method

Abstract

The invention discloses a multi-terminal-oriented point cloud data format standardization conversion method, and relates to the technical field of three-dimensional laser scanning, and the method comprises the steps: carrying out the down-sampling processing of partitioned point cloud data, generating multi-level resolution point cloud representation, and carrying out the dynamic dilution operation of multi-resolution point cloud data, and obtaining a diluted point cloud data file; performing format conversion on the diluted point cloud data by using a plug-in type modularization method, and exporting a point cloud data format file adaptive to multiple platforms; dynamically adjusting the resolution ratio and the blocking range of the point cloud data according to the blocked point cloud data loaded in real time, adapting to a newly added point cloud format by loading a new plug-in, and outputting the optimized point cloud data; in combination with substation scene characteristics and point cloud geometric characteristics, point cloud data can be efficiently segmented and diluted according to importance scores of points, so that the data volume is greatly reduced on the basis of ensuring information integrity of a key area.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional laser scanning technology, and in particular to a point cloud data format standardized conversion method for multiple terminals. Background Art

[0002] In recent years, with the rapid development of 3D laser scanning technology, point cloud data has been widely used in digital modeling, equipment status monitoring and intelligent operation and maintenance of substations. Existing point cloud acquisition technology usually relies on fixed scanning equipment to generate point cloud data through multi-angle scanning and post-stitching. However, due to the complex structure of substation scenes and irregular distribution of equipment, traditional methods have certain limitations in acquisition efficiency, data integrity and subsequent processing convenience. In addition, the multi-terminal adaptation requirements of point cloud data are becoming increasingly prominent, especially in scenarios such as real-time rendering and intelligent decision-making. Different terminals have different requirements for the resolution, file size and loading speed of point cloud data.

[0003] At present, point cloud data processing technology has made certain progress in the fields of denoising, blocking and compression, but there are still the following shortcomings: First, the existing denoising algorithm lacks specificity when eliminating invalid points and is prone to losing effective information in key areas; second, the existing blocking methods mostly rely on fixed parameter settings and fail to fully combine the spatial characteristics of point cloud data, resulting in inefficient blocking results; third, the multi-terminal adaptation solution lacks dynamic adjustment capabilities and cannot flexibly optimize point cloud resolution and loading speed according to terminal performance and user needs. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a point cloud data format standardized conversion method for multiple terminals to solve the problems of low efficiency and insufficient adaptability in the point cloud data collection, processing and multi-terminal adaptation process in complex substation scenarios.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for standardizing and converting point cloud data formats for multiple terminals, which includes collecting three-dimensional space point cloud data, and performing preprocessing to generate a standardized point cloud data file;

[0008] The standardized point cloud data is processed into blocks using a spatial segmentation algorithm, and structured block point cloud data is output;

[0009] Downsample the block point cloud data to generate point cloud representations with multiple levels of resolution;

[0010] Performing dynamic dilution operation on multi-resolution point cloud data to obtain a diluted point cloud data file;

[0011] Use the plug-in modular method to convert the diluted point cloud data format and export point cloud data format files that are compatible with multiple platforms;

[0012] Deploy point cloud data format files to multiple terminal devices, dynamically load segmented data based on terminal performance and user needs, and output real-time loaded segmented point cloud data;

[0013] According to the block point cloud data loaded in real time, the resolution and block range of the point cloud data are dynamically adjusted, and the new point cloud format is adapted by loading new plug-ins to output optimized point cloud data.

[0014] As a preferred solution of the point cloud data format standardization conversion method for multiple terminals described in the present invention, the three-dimensional point cloud data is collected and preprocessed to generate a standardized point cloud data file, including the following steps:

[0015] Plan the scanning path according to the structural complexity of the substation;

[0016] Use a handheld laser radar scanning device to scan along the planned path and collect spatial point cloud data in real time;

[0017] Use the SLAM algorithm to perform real-time positioning of its own position and preliminary stitching of point cloud data, and output the original point cloud data file in the local coordinate system;

[0018] Perform denoising on the point cloud data in the original point cloud data file;

[0019] Combined with the scene characteristics of the substation, invalid points are removed from the point cloud data after denoising;

[0020] Convert the point cloud data after invalid points are removed from the local coordinate system to the global coordinate system;

[0021] For the point cloud data in the global coordinate system, the voxel grid method is used to homogenize the point cloud density;

[0022] The color of the homogenized point cloud data is supplemented and corrected to generate a standardized point cloud data file.

[0023] As a preferred solution of the method for standardizing and converting point cloud data formats for multiple terminals described in the present invention, the standardized point cloud data is processed into blocks using a spatial segmentation algorithm, and the output of structured block point cloud data includes the following steps:

[0024] Perform spatial range analysis on the standardized point cloud data file, determine the overall bounding box of the point cloud data, and initialize the segmentation parameters;

[0025] According to the density of point cloud data, determine the maximum segmentation depth of the octree;

[0026] Based on the overall bounding box and maximum segmentation depth of the point cloud data, the point cloud data is recursively divided into blocks using the octree algorithm, and a unique spatial index number is generated for each block;

[0027] An index table is created for the generated block point cloud data, and the point cloud data with a unique spatial index number is stored as a structured block point cloud data file according to the storage path recorded in the index table.

[0028] As a preferred solution of the method for standardizing and converting point cloud data formats for multiple terminals of the present invention, downsampling the block point cloud data to generate a point cloud representation with multiple levels of resolution includes the following steps:

[0029] Determine the number of layers of multi-level resolution and the downsampling ratio of each resolution layer based on the target application scenario and terminal performance requirements;

[0030] Save the raw data of the block point cloud as a high-resolution layer file in a standard format;

[0031] According to the voxel size of the medium-resolution layer, the block point cloud data in the high-resolution layer file is divided into a three-dimensional voxel grid;

[0032] Aggregate each voxel in the three-dimensional voxel grid to generate block point cloud data at a medium resolution level;

[0033] The medium-resolution point cloud data of each block is downsampled again to generate a low-resolution point cloud representation.

[0034] As a preferred solution of the method for standardizing and converting point cloud data formats for multiple terminals of the present invention, the following steps are included: performing a dynamic dilution operation on multi-resolution point cloud data to obtain a diluted point cloud data file:

[0035] Load multi-level resolution point cloud data for each block;

[0036] Extract the geometric features of point cloud data at each level of resolution through neighborhood analysis of point cloud;

[0037] According to the semantic rules of the substation, the importance of the points is scored by combining the spatial position and attribute information of the point cloud. The expression is:

[0038]

[0039] Among them, S(P i ) represents point P i The importance score of i) represents point P i The curvature, C(P i ) represents point P i The normal vector, D(P i ) represents point P i The local density, G(P i ) represents point P i The attribute value of represents the average value of the attribute, H(P i ) represents point P i The neighborhood entropy, σ(P i ) represents point P i The standard deviation of the neighborhood attributes, P i Represents a point in the point cloud, and i represents the index of the point;

[0040] Based on the scoring results, distinguish between critical areas and low-importance areas;

[0041] According to the geometric characteristics and differentiation results, dynamic dilution rules are designed to dynamically adjust the dilution ratio. The expression is:

[0042]

[0043] Among them, R(P i ) represents point P i The dilution retention probability of α represents the dilution strength of the control transition region, β represents the decay rate of the dilution probability in the control transition region, γ represents the dilution probability of the low importance region, T1 represents the high threshold of the importance score, and T2 represents the low threshold of the importance score;

[0044] Perform dynamic dilution operation on point cloud data according to dynamic dilution rules, directly retain the point sets in important areas, and randomly sample the point sets in secondary areas according to the target dilution ratio;

[0045] The retained important area point set and the diluted secondary area point set are merged to obtain a diluted point cloud data file.

[0046] As a preferred solution of the method for standardizing and converting point cloud data formats for multiple terminals of the present invention, the following steps are included: converting the format of the diluted point cloud data using a plug-in modular method, and exporting a point cloud data format file adapted to multiple platforms:

[0047] Read the local coordinates of each point from the diluted point cloud data file;

[0048] According to the requirements of the target 3D design platform, obtain the definition of the target global coordinate system;

[0049] Use the linear transformation formula to transform local coordinates into global coordinates;

[0050] Replace the local coordinates in the point cloud file with the converted global coordinates;

[0051] Use the mainstream point cloud compression library to compress the point cloud data file according to the corresponding compression standard, quantize the floating point coordinates into integers, and convert the file format;

[0052] Based on the converted file, check whether it meets the compatibility requirements of the target platform and verify whether the compressed point cloud can be correctly decoded and loaded to obtain the compressed point cloud file;

[0053] According to the point cloud format specification of the target platform, the attribute fields of the compressed point cloud file are mapped to the compatible fields of the target platform, and the point cloud data format file adapted to multiple platforms is exported.

[0054] As a preferred solution of the point cloud data format standardization conversion method for multiple terminals of the present invention, the point cloud data format file is deployed to multiple terminal devices, the block data is dynamically loaded according to the terminal performance and user needs, and the real-time loaded block point cloud data is output, which includes the following steps:

[0055] Classify and deploy point cloud data format files to corresponding terminal devices according to data resolution levels and terminal requirements;

[0056] Dynamically determine the point cloud blocks that need to be loaded based on the user's view range;

[0057] Select the corresponding resolution level according to the terminal device type;

[0058] The required point cloud data files are loaded block by block and stored in the memory. The loaded blocks that are beyond the user's view range are dynamically unloaded to release memory resources and output the loaded block point cloud data in real time.

[0059] As a preferred solution of the point cloud data format standardization conversion method for multiple terminals of the present invention, the point cloud data resolution and block range are dynamically adjusted according to the block point cloud data loaded in real time, and the new point cloud format is adapted by loading a new plug-in, and the optimized point cloud data is outputted, including the following steps:

[0060] Capture users' real-time operations on multiple terminal devices and update the interaction status;

[0061] Dynamically adjust the point cloud resolution level according to user operations and viewpoint distance;

[0062] According to the resolution level and view range, the newly added block data is retrieved from the index table, and through the modular plug-in mechanism, the plug-in of the newly added point cloud format is dynamically loaded to output the optimized point cloud data.

[0063] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the multi-terminal point cloud data format standardization conversion method as described in the first aspect of the present invention is implemented.

[0064] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for standardized conversion of point cloud data formats for multiple terminals as described in the first aspect of the present invention.

[0065] The beneficial effects of the present invention are as follows: by planning the scanning path and using the SLAM algorithm for real-time positioning and preliminary splicing, the scanning problem caused by the complexity of the substation structure is effectively solved; through point cloud denoising, invalid point elimination and global coordinate conversion, the quality of data processing is further improved, ensuring that the generated point cloud data is more standardized and structured; the voxel grid homogenization and multi-level resolution downsampling technology are adopted to make the point cloud data more efficient in storage and transmission; the octree blocking and dynamic dilution algorithm, combined with the substation scene characteristics and point cloud geometric features, can efficiently segment and dilute the point cloud data according to the importance score of the point, thereby greatly reducing the amount of data while ensuring the integrity of the key area information; the standardized conversion of the point cloud format and the multi-terminal compatible deployment are realized through the modular plug-in mechanism, so that the point cloud data can adapt to the needs of different platforms and support real-time loading and dynamic adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0067] Figure 1 This is a flowchart of the method for standardized conversion of point cloud data formats for multiple terminals in Example 1.

[0068] Figure 2 Schematic diagram of point cloud representation with multiple levels of resolution in Example 1. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0070] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0071] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0072] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a point cloud data format standardization conversion method for multiple terminals, comprising the following steps:

[0073] S1. Collect three-dimensional point cloud data, perform preprocessing, and generate standardized point cloud data files.

[0074] S1.1. Plan the scanning path according to the structural complexity of the substation; use a handheld laser radar scanning device to scan along the planned path and collect spatial point cloud data in real time; use the SLAM algorithm to complete the real-time positioning of its own position and the preliminary stitching of point cloud data, and output the original point cloud data file in the local coordinate system; denoise the point cloud data in the original point cloud data file.

[0075] Specifically, the denoising process uses a denoising algorithm based on statistical analysis, and the specific operations are as follows:

[0076] Define a point cloud neighborhood radius r, select neighborhood points within a fixed radius (recommended value: r = 0.05 ~ 0.1 meters, depending on the point cloud density of the acquisition environment); for any point in the point cloud, count the number of points in its neighborhood, and calculate the deviation from the point to the mean of the neighborhood points. The expression is:

[0077]

[0078] Among them, d i represents the deviation of the i-th point, N i Indicates the number of neighboring points of the i-th point, within the set neighborhood radius r, from point p i The number of points less than r, j represents the index p of the point in the neighborhood j represents the jth neighborhood point.

[0079] like Then point p i is eliminated, where σd is the standard deviation of the global deviation d, is the global mean.

[0080] S1.2. In combination with the scene characteristics of the substation, invalid points are eliminated from the point cloud data after denoising; the point cloud data after invalid points are eliminated is converted from the local coordinate system to the global coordinate system; for the point cloud data in the global coordinate system, the voxel grid method is used to homogenize the point cloud density; the color of the homogenized point cloud data is supplemented and corrected to generate a standardized point cloud data file.

[0081] Specifically, isolated point removal is an isolated point detection algorithm based on point cloud density. For the neighboring points in the point cloud that are less than the set threshold T min Points (such as T min =5), mark it as an isolated point and remove it.

[0082] If N i <T min , then point p i was eliminated.

[0083] The conversion from the local coordinate system to the global coordinate system is to calculate the conversion matrix between the acquisition device coordinate system and the global coordinate system through the global positioning reference points (such as GPS reference points or fixed calibration points) arranged in the substation. The expression is:

[0084]

[0085] Among them, T represents the coordinate transformation matrix, R represents the rotation matrix, and t represents the translation vector.

[0086] The local coordinates of each point are transformed, and the expression is:

[0087]

[0088] in, Represents the transformed point p i The three-dimensional point cloud coordinates in the global coordinate system, Represents point p i The original coordinates in the local coordinate system.

[0089] The specific operations of point cloud density homogenization are as follows:

[0090] Voxel division: Define the resolution s of the voxel grid (recommended value: s = 0.01 to 0.1 meters, depending on the accuracy requirements of the application scenario) and divide the three-dimensional space into voxels of size s×s×s.

[0091] Point cloud dilution: For each voxel, only one representative point is retained (such as the center point of the voxel or the average value of the points within the voxel), and other points are removed to achieve uniform point cloud density.

[0092] Point cloud data supplement and color correction means that if the lidar does not collect color information, it can add color attributes to each point through projection mapping combined with high-resolution panoramic images.

[0093] S2. Use the spatial segmentation algorithm to divide the standardized point cloud data into blocks and output structured block point cloud data.

[0094] S2.1. Perform spatial range analysis on the standardized point cloud data file, determine the overall bounding box of the point cloud data, and initialize the segmentation parameters.

[0095] Furthermore, the spatial range analysis is to use the maximum and minimum coordinate values ​​to represent the three-dimensional spatial range of the point cloud data.

[0096] S2.2. Determine the maximum segmentation depth of the octree based on the density of the point cloud data.

[0097] The density of point cloud data can be defined as the number of points per unit volume, and the density is usually analyzed by statistically analyzing the distribution of points in the point cloud.

[0098] The volume of the segmentation unit at the maximum segmentation depth should match the density of local points as much as possible. Too deep a segmentation will result in too small blocks, increasing the storage and indexing burden; too shallow a segmentation may not capture local details.

[0099] S2.3. Based on the overall bounding box and maximum segmentation depth of the point cloud data, the point cloud data is recursively divided into blocks using the octree algorithm, and a unique spatial index number is generated for each block; an index table is established for the generated block point cloud data, and the point cloud data with a unique spatial index number is stored as a structured block point cloud data file according to the storage path recorded in the index table.

[0100] Specifically, the octree is a recursive space partitioning algorithm that divides the three-dimensional space into 8 subspaces (ie, 8 sub-blocks). After each partition, each sub-block will continue to be recursively partitioned until the maximum partitioning depth is reached or other termination conditions are met.

[0101] Recursive processing operations:

[0102] Divide the spatial range of each node into two equal parts along the coordinate axis to generate 8 child nodes; traverse the point cloud data and assign each point to the corresponding child node; if the segmentation depth of the node has reached the termination condition, stop the segmentation, otherwise, continue to recursively segment the child nodes.

[0103] S3. Downsample the block point cloud data to generate point cloud representations with multiple levels of resolution.

[0104] S3.1. Determine the number of layers of multi-level resolution and the downsampling ratio of each resolution layer based on the target application scenario and terminal performance requirements.

[0105] Specifically, common resolution layers:

[0106] High-resolution layer: Complete original point cloud data, preserving all details.

[0107] Medium-resolution layer: generated by downsampling, retaining the main geometric features.

[0108] Low-resolution layer: further downsampled to retain only the coarse global structure.

[0109] S3.2. Save the original data of the block point cloud as a high-resolution layer file in a standard format; divide the block point cloud data in the high-resolution layer file into a three-dimensional voxel grid according to the voxel size of the medium-resolution layer; aggregate each voxel in the three-dimensional voxel grid to generate block point cloud data of the medium-resolution layer; downsample the medium-resolution point cloud data of each block again to generate a point cloud representation of the low-resolution layer.

[0110] Specifically, the definition of a voxel grid: A voxel grid organizes point cloud data by dividing the three-dimensional space into cubic units of fixed size, called voxels.

[0111] The voxel size is determined by the downsampling ratio of the mid-resolution layer.

[0112] Voxel aggregation: For each voxel, the points within the voxel are aggregated into a representative point to generate a medium-resolution point cloud.

[0113] S4. Perform a dynamic dilution operation on the multi-resolution point cloud data to obtain a diluted point cloud data file.

[0114] S4.1. Load the multi-level resolution point cloud data of each block; extract the geometric features of each level of resolution point cloud data through neighborhood analysis of the point cloud; and score the importance of the point according to the semantic rules of the substation, combined with the spatial position and attribute information of the point cloud. The expression is:

[0115]

[0116] Among them, S(P i ) represents point P i The importance score of i ) represents point P i The curvature, C(P i ) represents point P i The consistency of the normal vector, D(P i ) represents point P i The local density, G(Pi ) represents point P i The attribute value of represents the average value of the attribute, H(P i ) represents point P i The neighborhood entropy, σ(P i ) represents point P i The standard deviation of the neighborhood attributes, P i Represents a point in the point cloud, and i represents the index of the point.

[0117] The expression for curvature is:

[0118] Among them, λ max represents the maximum eigenvalue of the neighborhood point covariance matrix, λ j Represents the eigenvalue of the jth neighborhood point.

[0119] The expression for the consistency of the normal vector is:

[0120] Among them, k represents the number of neighborhood points, n i represents the normal vector of the i-th point, n j Represents the normal vector of the jth neighborhood point.

[0121] The expression of local density is:

[0122] The expression of neighborhood entropy is:

[0123] The expression of attribute standard deviation is:

[0124] S4.2. Based on the scoring results, distinguish between critical areas and low-importance areas.

[0125] The regions are divided into three categories:

[0126] Critical areas: direct retention; transition areas: dynamic dilution; low importance areas: strong dilution.

[0127] S4.3. According to the geometric characteristics and the differentiation results, a dynamic dilution rule is designed to dynamically adjust the dilution ratio. The expression is:

[0128]

[0129] Among them, R(P i ) represents point P iThe dilution retention probability of α represents the dilution strength of the control transition area, β represents the attenuation rate of the dilution probability in the control transition area, γ represents the dilution probability of the low importance area, T1 represents the high threshold of the importance score, which is used to distinguish the key area from the transition area, and T2 represents the low threshold of the importance score, which is used to distinguish the low importance area from the transition area;

[0130] According to the dynamic dilution rule, the point cloud data is dynamically diluted. The important area point set is directly retained, and the secondary area point set is randomly sampled according to the target dilution ratio. The retained important area point set and the diluted secondary area point set are merged to obtain the diluted point cloud data file.

[0131] It should be noted that the multi-resolution point cloud data is diluted using dynamic dilution rules. The diluted point cloud data file retains detailed information in key areas while reducing the number of points in low-importance areas, thereby optimizing storage and computing efficiency and meeting application requirements in specific fields such as substations.

[0132] S5. Use a plug-in modular method to convert the format of the diluted point cloud data and export a point cloud data format file that is compatible with multiple platforms.

[0133] S5.1. Read the local coordinates of each point from the diluted point cloud file;

[0134] According to the requirements of the target 3D design platform, obtain the definition of the target global coordinate system; use the linear transformation formula to convert the local coordinates into global coordinates; replace the local coordinates in the point cloud file with the converted global coordinates; use the mainstream point cloud compression library to compress the point cloud data file according to the corresponding compression standard, quantize the floating-point coordinates into integers, and convert the file format.

[0135] Quantization formula for floating-point coordinates:

[0136]

[0137] Among them, Q(x) represents the quantized integer coordinate value, x represents the original floating-point coordinate value of the point, round represents the rounding operation, converting consecutive floating-point numbers to the nearest integer value, and x min Indicates the minimum value of the coordinate value in the point cloud data, step indicates the quantization step size, and the coordinate increment corresponding to each integer unit.

[0138] S5.2. Based on the converted file, check whether it meets the compatibility requirements of the target platform, and verify whether the compressed point cloud can be correctly decoded and loaded to obtain the compressed point cloud file; according to the point cloud format specification of the target platform, map the attribute fields of the compressed point cloud file to the compatible fields of the target platform, and export the point cloud data format file that is compatible with multiple platforms.

[0139] It should be noted that after the point cloud data is converted and compressed, the file is checked to see if it complies with the technical specifications of the target platform. Files that do not comply with the specifications of the target platform may not be loaded or used correctly. This check ensures that the point cloud data can be run directly on the target platform, reducing the workload of later developers to repair format issues.

[0140] S6. Deploy the point cloud data format file to multiple terminal devices, dynamically load the block data according to the terminal performance and user needs, and output the real-time loaded block point cloud data.

[0141] S6.1. Classify and deploy point cloud data format files to corresponding terminal devices according to data resolution levels and terminal requirements; dynamically determine the point cloud blocks that need to be loaded based on the user's view range.

[0142] Specifically, the resolution level refers to the hierarchical processing of point cloud data, which is divided into different levels according to the density or accuracy of the points:

[0143] High-resolution layer: complete point cloud data with the highest point density, suitable for high-performance devices (such as desktop workstations).

[0144] Medium resolution level: The point cloud data is sparsely processed to reduce the point density, which is suitable for medium-performance devices (such as laptops or tablets).

[0145] Low-resolution layer: further sparse, suitable for low-performance devices (such as mobile phones or embedded devices).

[0146] S6.2. Select the corresponding resolution level according to the terminal device type; load the required point cloud data files block by block and store them in the memory, and dynamically unload the loaded blocks that are beyond the user's view range to release memory resources and output the real-time loaded block point cloud data.

[0147] Specifically, the dynamic unloading mechanism refers to regularly checking the loaded block data in the memory, determining which blocks no longer overlap with the user's view range, and preferentially unloading the blocks outside the view range. For larger block files, they can be unloaded in batches.

[0148] S7. According to the block point cloud data loaded in real time, the resolution and block range of the point cloud data are dynamically adjusted, and the new point cloud format is adapted by loading a new plug-in to output the optimized point cloud data.

[0149] S7.1. Capture the real-time operation of users on multiple terminal devices and update the interactive status; dynamically adjust the point cloud resolution level according to user operations and viewpoint distance; retrieve the newly added block data from the index table according to the resolution level and view range, and dynamically load the newly added point cloud format plug-in through the modular plug-in mechanism to output the optimized point cloud data.

[0150] Specifically, the viewpoint distance refers to the distance between the user's camera position and the point cloud data:

[0151] Close distance: Users need to observe the details of the point cloud and should load high-resolution data.

[0152] Medium Distance: Medium resolution data can be loaded to balance detail and performance.

[0153] Long distance: Only low-resolution data needs to be loaded to reduce the number of points drawn.

[0154] This embodiment also provides a computer device, which is suitable for the case of a standardized conversion method of point cloud data format for multiple terminals, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the standardized conversion method of point cloud data format for multiple terminals as proposed in the above embodiment.

[0155] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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 a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0156] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing standardized conversion of point cloud data formats for multiple terminals proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0157] In summary, the present invention effectively solves the scanning problem caused by the complexity of the substation structure by: planning the scanning path and using the SLAM algorithm for real-time positioning and preliminary splicing; further improves the quality of data processing through point cloud denoising, invalid point elimination and global coordinate transformation, ensuring that the generated point cloud data is more standardized and structured; adopts voxel grid homogenization and multi-level resolution downsampling technology to make point cloud data more efficient in storage and transmission; octree blocking and dynamic dilution algorithm, combined with substation scene characteristics and point cloud geometric features, can efficiently segment and dilute point cloud data according to the importance score of the point, thereby greatly reducing the amount of data while ensuring the integrity of key area information; modular plug-in mechanism is used to achieve standardized conversion of point cloud format and multi-terminal compatible deployment, so that point cloud data can adapt to the needs of different platforms and support real-time loading and dynamic adjustment.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A point cloud data format standardization conversion method for multiple terminals, characterized by: include, Collect 3D point cloud data, perform preprocessing, and generate standardized point cloud data files; The standardized point cloud data is processed into blocks using a spatial segmentation algorithm, and structured block point cloud data is output; Downsample the block point cloud data to generate point cloud representations with multiple levels of resolution; Performing dynamic dilution operation on multi-resolution point cloud data to obtain a diluted point cloud data file; Use the plug-in modular method to convert the diluted point cloud data format and export point cloud data format files that are compatible with multiple platforms; Deploy point cloud data format files to multiple terminal devices, dynamically load segmented data based on terminal performance and user needs, and output real-time loaded segmented point cloud data; According to the block point cloud data loaded in real time, the resolution and block range of the point cloud data are dynamically adjusted, and the new point cloud format is adapted by loading new plug-ins to output optimized point cloud data.

2. The method for standardizing point cloud data formats for multiple terminals as claimed in claim 1, characterized in that: Collecting 3D point cloud data and preprocessing it to generate standardized point cloud data files includes the following steps: Plan the scanning path according to the structural complexity of the substation; Use a handheld laser radar scanning device to scan along the planned path and collect spatial point cloud data in real time; Use the SLAM algorithm to perform real-time positioning of its own position and preliminary stitching of point cloud data, and output the original point cloud data file in the local coordinate system; Perform denoising on the point cloud data in the original point cloud data file; Combined with the scene characteristics of the substation, invalid points are removed from the point cloud data after denoising; Convert the point cloud data after invalid points are removed from the local coordinate system to the global coordinate system; For the point cloud data in the global coordinate system, the voxel grid method is used to homogenize the point cloud density; The color of the homogenized point cloud data is supplemented and corrected to generate a standardized point cloud data file.

3. The method for standardizing point cloud data formats for multiple terminals as claimed in claim 2, characterized in that: The standardized point cloud data is processed into blocks using a spatial segmentation algorithm, and the output of structured block point cloud data includes the following steps: Perform spatial range analysis on the standardized point cloud data file, determine the overall bounding box of the point cloud data, and initialize the segmentation parameters; According to the density of point cloud data, determine the maximum segmentation depth of the octree; Based on the overall bounding box and maximum segmentation depth of the point cloud data, the point cloud data is recursively divided into blocks using the octree algorithm, and a unique spatial index number is generated for each block; An index table is created for the generated block point cloud data, and the point cloud data with a unique spatial index number is stored as a structured block point cloud data file according to the storage path recorded in the index table.

4. The method for standardizing point cloud data formats for multiple terminals as claimed in claim 3, characterized in that: Downsampling the block point cloud data to generate a multi-level resolution point cloud representation includes the following steps: Determine the number of layers of multi-level resolution and the downsampling ratio of each resolution layer based on the target application scenario and terminal performance requirements; Save the raw data of the block point cloud as a high-resolution layer file in a standard format; According to the voxel size of the medium-resolution layer, the block point cloud data in the high-resolution layer file is divided into a three-dimensional voxel grid; Aggregate each voxel in the three-dimensional voxel grid to generate block point cloud data at a medium resolution level; The medium-resolution point cloud data of each block is downsampled again to generate a low-resolution point cloud representation.

5. The method for standardizing point cloud data formats for multiple terminals as claimed in claim 4, characterized in that: Performing dynamic dilution operation on multi-resolution point cloud data to obtain the diluted point cloud data file includes the following steps: Load multi-level resolution point cloud data for each block; Extract the geometric features of point cloud data at each level of resolution through neighborhood analysis of point cloud; According to the semantic rules of the substation, the importance of the points is scored by combining the spatial position and attribute information of the point cloud. The expression is: Among them, S(P i ) represents point P i The importance score of i ) represents point P i The curvature, C(P i ) represents point P i The normal vector, D(P i ) represents point P i The local density, G(P i ) represents point P i The attribute value of represents the average value of the attribute, H(P i ) represents point P i The neighborhood entropy, σ(P i ) represents point P i The standard deviation of the neighborhood attributes, P i Represents a point in the point cloud, and i represents the index of the point; Based on the scoring results, distinguish between critical areas and low-importance areas; According to the geometric characteristics and differentiation results, dynamic dilution rules are designed to dynamically adjust the dilution ratio. The expression is: Among them, R(P i ) represents point P i The dilution retention probability of α represents the dilution strength of the control transition region, β represents the decay rate of the dilution probability in the control transition region, γ represents the dilution probability of the low importance region, T1 represents the high threshold of the importance score, and T2 represents the low threshold of the importance score; Perform dynamic dilution operation on point cloud data according to dynamic dilution rules, directly retain the point sets in important areas, and randomly sample the point sets in secondary areas according to the target dilution ratio; The retained important area point set and the diluted secondary area point set are merged to obtain a diluted point cloud data file.

6. The method for standardizing and converting point cloud data formats for multiple terminals according to claim 5, characterized in that: The plug-in modular method is used to convert the diluted point cloud data format and export the point cloud data format file suitable for multiple platforms, including the following steps: Read the local coordinates of each point from the diluted point cloud data file; According to the requirements of the target 3D design platform, obtain the definition of the target global coordinate system; Use the linear transformation formula to transform local coordinates into global coordinates; Replace the local coordinates in the point cloud file with the converted global coordinates; Use the mainstream point cloud compression library to compress the point cloud data file according to the corresponding compression standard, quantize the floating point coordinates into integers, and convert the file format; Based on the converted file, check whether it meets the compatibility requirements of the target platform and verify whether the compressed point cloud can be correctly decoded and loaded to obtain the compressed point cloud file; According to the point cloud format specification of the target platform, the attribute fields of the compressed point cloud file are mapped to the compatible fields of the target platform, and the point cloud data format file adapted to multiple platforms is exported.

7. The method for standardizing point cloud data formats for multiple terminals according to claim 6, characterized in that: Deploy point cloud data format files to multiple terminal devices, dynamically load block data according to terminal performance and user needs, and output real-time loaded block point cloud data. The following steps are included: Classify and deploy point cloud data format files to corresponding terminal devices according to data resolution levels and terminal requirements; Dynamically determine the point cloud blocks that need to be loaded based on the user's view range; Select the corresponding resolution level according to the terminal device type; The required point cloud data files are loaded block by block and stored in the memory. The loaded blocks that are beyond the user's view range are dynamically unloaded to release memory resources and output the loaded block point cloud data in real time.

8. The method for standardizing point cloud data formats for multiple terminals as claimed in claim 7, characterized in that: According to the real-time loaded block point cloud data, the resolution and block range of the point cloud data are dynamically adjusted, and the new point cloud format is adapted by loading a new plug-in. The output of the optimized point cloud data includes the following steps: Capture users' real-time operations on multiple terminal devices and update the interaction status; Dynamically adjust the point cloud resolution level according to user operations and viewpoint distance; According to the resolution level and view range, the newly added block data is retrieved from the index table, and through the modular plug-in mechanism, the plug-in of the newly added point cloud format is dynamically loaded to output the optimized point cloud data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the point cloud data format standardization conversion method for multiple terminals described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the point cloud data format standardization conversion method for multiple terminals described in any one of claims 1 to 8 are implemented.

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