Method and system for managing global multi-source-oriented mass point cloud data

Abstract

The invention discloses a global multi-source-oriented mass point cloud data management method and a global multi-source-oriented mass point cloud data management system. The method comprises the following steps: in an acquisition stage, selecting a multi-source laser scanner to acquire data according to different scenes, retaining original characteristics, unifying coordinates, constructing an octree index according to a minimum space grid size, and dividing files. During naming, a'region + time 'unique naming system is formed by adopting a 28-character rule of'administrative division code prefix-frame point cloud data time-file suffix'. For storage, a seven-level storage structure is established, and a point cloud information management database constructs three-layer mapping of'filename-storage position-real scene '. For user demands, corresponding hierarchical data are extracted according to demands by means of an octree hierarchical index mechanism, diversified use scenes are met, and data processing efficiency and user experience are improved. The system response speed is improved, the user experience is effectively improved, and the data can better serve various actual application scenes.

Description

Technical Field

[0001] The present invention belongs to the field of three-dimensional spatial information acquisition and processing, and more specifically, relates to a method and system for managing massive point cloud data for global multi-source. Background Art

[0002] In the field of three-dimensional spatial information collection and processing, with the continuous innovation of technologies such as laser scanning, drone aerial surveying, and mobile measurement, the scale of point cloud data, the core data that accurately describes geographic space and object form, is growing at an astonishing rate. In particular, nationwide point cloud data, covering multi-dimensional information such as land, water, and underground space, has exceeded the exabyte level and even reached the zettabyte and terabyte levels, posing unprecedented challenges to traditional point cloud data management technologies.

[0003] Existing point cloud data management methods fall into two main categories. One focuses on overall data management based on the characteristics of the acquisition scene, such as stratifying and partitioning data based on factors such as spatial location and acquisition time. The other focuses on processing individual point cloud files, improving data access efficiency by constructing spatial index structures such as KD trees and octrees. These technologies can achieve relatively efficient data storage and retrieval when processing small-scale point cloud data below the terabyte level or data from specific areas. However, their limitations become increasingly significant when applied to the management of ultra-large-scale point cloud data at the national level.

[0004] First, existing technologies cannot meet the demand for efficient storage and rapid retrieval of massive point cloud data nationwide. Due to the large scale and wide distribution of data, traditional indexing and retrieval mechanisms struggle to quickly locate and extract target data files when faced with diverse user query conditions (such as administrative divisions, time ranges, spatial resolutions, etc.), resulting in low data retrieval efficiency. Second, at the user interaction level, existing systems struggle to provide smooth point cloud data preview, application, and download services. For ultra-large-scale point cloud data, low-resolution previews cannot truly present data details, while high-resolution data transmission faces bottlenecks in network bandwidth and transmission efficiency, failing to meet users' needs for real-time viewing and interaction with data. Furthermore, in scenarios with concurrent multi-user access, existing systems lack effective resource scheduling and load balancing mechanisms, making it difficult to ensure service stability and responsiveness, severely impacting user experience. Finally, multi-source, heterogeneous point cloud data (such as data collected by different equipment, such as airborne, ground, and underwater) lacks unified and standardized management standards, making data integration difficult and data sharing and collaborative applications difficult to achieve.

[0005] Therefore, an innovative technical solution is urgently needed to achieve systematic management of massive point cloud data covering 9.6 million square kilometers, break through the bottlenecks of existing technologies in storage, retrieval, interaction and services, and build an efficient, stable and easy-to-use national point cloud data management system to meet the growing application needs in multiple fields such as geographic information, urban planning, and resource exploration. Summary of the Invention

[0006] This invention aims to address the challenges of managing massive point cloud data nationwide. Through innovative methods and systems, it enables efficient storage and rapid retrieval of global, ultra-large-scale point cloud data, establishing a comprehensive user interaction mechanism to meet needs such as data preview, application, and download. It also optimizes the system architecture, enhancing responsiveness and service stability in high-concurrency scenarios, and integrating heterogeneous data from multiple sources, providing a data management foundation for "Point Cloud China" and meeting the needs of diverse applications.

[0007] In view of the above defects or improvement needs of the existing technology, the present invention provides a method for managing massive point cloud data across multiple sources, including

[0008] S1. Targeted selection of multi-source laser scanners, including airborne, mobile, ground-based, airborne blue-green, and underwater, for point cloud data acquisition based on different acquisition scenarios. The original point cloud characteristics are retained while converting relative coordinates to a unified coordinate system based on the data characteristics of different devices. An octree index is constructed based on the minimum spatial grid size and the original point cloud file is partitioned.

[0009] S2. Use a 28-character naming convention: "Administrative division code prefix - frame point cloud data time - file suffix." The 9-digit administrative division code strictly adheres to national standards, accurately identifying the geographic region of the point cloud data. The 14-digit frame point cloud time records the instantaneous information collected, ensuring unique identification of data within the same region. This creates a unique naming system based on the dual dimensions of "region + time."

[0010] S3. Establish a seven-level storage structure consisting of "China - Provincial - Prefectural - County - Township / Township / Street - Data Collection Time - Requirement Hierarchy Octree Index"; the point cloud information management database stores standardized named file indexes and constructs a three-level mapping relationship between "file name - storage location - real-world scenario";

[0011] S4. When users raise data requirements, the system locates and extracts point cloud data at the corresponding level through the octree hierarchical indexing mechanism. If users need to perform high-precision modeling of a certain area, the system extracts high-resolution hierarchical data based on the octree index to meet the modeling requirements for details. If only macroscopic scene browsing is required, low-resolution hierarchical data is extracted to reduce data transmission and processing volume, improve response speed, and thus meet users' diverse data usage scenarios.

[0012] Furthermore, the S1 original point cloud characteristics include point cloud range, point cloud density, whether the point cloud is uniform, and whether it is a relative position point cloud or an absolute position point cloud.

[0013] Furthermore, in S1, the multi-source laser scanners including airborne, mobile, ground-based, airborne blue-green and underwater are selected to collect point cloud data according to different collection scenarios. Specifically:

[0014] The airborne 3D laser scanner, whose acquisition equipment is a multi-rotor UAV or a small or medium-sized manned aircraft, is the main means of collecting massive data in the entire area and is used in scenarios such as large-scale terrain mapping, forestry remote sensing, and point cloud collection in mountainous areas or uninhabited areas.

[0015] The mobile 3D laser scanner, whose acquisition equipment is a mobile measuring vehicle, rail vehicle, or electric platform equipped with a cross-sectional 3D laser scanner, is an important supplementary means for collecting massive data across the entire area and is used in municipal and transportation scenarios such as tunnels, pipeline corridors, and bridges.

[0016] The terrestrial 3D laser scanner is a collection device that is a supplementary measurement method for key targets in the global massive data collection. It is used to construct the ultra-high-requirement and fine data required for key target measurement and monitoring, such as large venues and important infrastructure.

[0017] The airborne blue-green laser scanner, whose acquisition equipment is a multi-rotor drone or a small or medium-sized manned aircraft carrier, is a supplementary measurement method for non-land targets for full-area massive data collection and is used in lake, river and coastline measurement scenarios.

[0018] Furthermore, the original point cloud characteristics are:

[0019] The point cloud data collected by the airborne three-dimensional laser scanner has coordinates of relative positions;

[0020] The point cloud data collected by the mobile 3D laser scanner is spiral data with uneven density. The data density in the forward direction is affected by the forward speed of the carrier. The point cloud data includes both ground point cloud data and underground point cloud data. The density of the point cloud data is affected by the acquisition principle and is uneven. The point density is high in areas close to the sensor and low in areas far away.

[0021] The point cloud data collected by the ground-based 3D laser scanner is non-uniform circular area data, and its point cloud density is related to the distance between the target and the scanner; the point cloud coordinates are relative position coordinates;

[0022] The point cloud data collected by the airborne blue-green laser scanner is strip water line and coastline data. Since it is a multi-rotor drone or a small or medium-sized manned aircraft carrier, its point cloud density is uniform;

[0023] The point cloud data collected by the underwater laser scanner is underwater laser point cloud data within a small range. Its point cloud density is affected by the acquisition sensor and is uneven, and is a relative position coordinate.

[0024] Furthermore, in S1, relative coordinates are converted into a unified coordinate system according to different device data characteristics. The specific method is as follows:

[0025] The point cloud data collected by the airborne 3D laser scanner is converted to the BeiDou coordinate system by the airborne BeiDou + inertial navigation system in conjunction with the control points along the flight path;

[0026] The point cloud data collected by the mobile 3D laser scanner is converted to the BeiDou coordinate system through the vehicle-mounted BeiDou + inertial navigation system or the control points along the line;

[0027] The point cloud data collected by the terrestrial 3D laser scanner is converted into the Beidou coordinate system through at least three control points of line of sight;

[0028] The point cloud data collected by the airborne blue-green laser scanner and the point cloud data collected by the underwater laser scanner are relative position coordinates; the relative position point cloud is the absolute coordinate of the point cloud in this area without a universal coordinate system, and all points are in the current local engineering measurement coordinate system.

[0029] Furthermore, the specific method of constructing an octree index according to the minimum spatial grid size and dividing the original point cloud file in S1 is:

[0030] For the original single point cloud file F, firstly, we build a point cloud octree index based on the given minimum spatial grid size δ in meters:

[0031] For the original point cloud file F, let the range of the point cloud data it contains in three-dimensional space be X max -X min ,Y max -Y min ,Z max -Z min , where (X min ,Y min ,Z min ) and (X max ,Y max ,Z max ) are the minimum and maximum coordinate values ​​of the point cloud data in the X, Y, and Z axis directions respectively;

[0032] The spatial extent of the entire point cloud data can be represented as a three-dimensional cube:

[0033] C=[X max ,X min ]×[Ymax ,Y min ]×[Z max ,Z min ]

[0034] This cube will serve as the initial space for octree construction;

[0035] The three-dimensional space range C determined above is used as the root node Root of the octree; the root node represents the spatial range of the entire original point cloud file. At this time, the size of the root node S Root for:

[0036]

[0037] For any node Node, divide it into 8 child nodes Child1, Child2, ..., Child8 by dividing it into two equal parts along the three dimensions X, Y, and Z.

[0038] Assume that the range of the node Node in the X, Y, and Z directions are [X node_max ,X node_min ],[Y node_max ,Y node_min ],[Z node_max ,Z node_min ], then the i-th child node Child i The range can be determined as follows:

[0039]

[0040] Node size S Node The calculation formula is

[0041]

[0042] The minimum spatial grid size δ is introduced as one of the termination conditions for partitioning; when the size of the node Node S Node Greater than δ, and the number of point clouds contained in the node is N Node When it is greater than the set threshold T, the node continues to be divided; otherwise, the division is stopped and the node is marked as a leaf node;

[0043] After completing the point cloud octree indexing of the original point cloud file F, the original point cloud file F is further divided as follows:

[0044] Assume that the data size of the original point cloud file F is T, in GB, and divide it into sub-files of at most 1 GB in size;

[0045] Assume the number of sub-files is N, then:

[0046]

[0047] The original point cloud file F is divided according to the number of files N. The divided files include N-1 1GB sub-files and a file with a size of T-N+1GB.

[0048] Furthermore, the 14-bit frame point cloud time in S2 is specifically:

[0049] The 1st to 4th digits correspond to the natural year when the frame point cloud data was collected, the 5th to 6th digits correspond to the natural month when the frame point cloud data was collected, the 7th to 8th digits correspond to the natural day when the frame point cloud data was collected, the 9th to 10th digits correspond to the 24-hour time when the frame point cloud data was collected, the 11th to 12th digits correspond to the minutes when the frame point cloud data was collected, and the 13th to 14th digits correspond to the seconds when the frame point cloud data was collected. For point cloud data within the same administrative area, the frame point cloud time provides a unique identifier to distinguish it from other data.

[0050] Furthermore, in S4, when the user raises a data requirement, the specific method for locating and extracting the point cloud data of the corresponding level through the octree hierarchical index mechanism is as follows:

[0051] User request analysis: Assume that the spatial range specified by the user is a three-dimensional rectangular area, whose coordinate range in the Cartesian coordinate system is [X1, X2] × [Y1, Y2] × [Z1, Z2], and the resolution requirement is R; after receiving the request, the task management system first analyzes these parameters;

[0052] Octree index positioning: Octree index is based on point cloud data block processing; each octree node corresponds to a specific spatial area. Assume that the volume of the spatial area corresponding to an octree node is V, and the number of points contained in the node is N; according to the original characteristics of the point cloud data, the index is established with the minimum spatial grid size δ. The grid size δ has a certain relationship with the resolution R. When the point cloud is relatively evenly distributed, it can be expressed as

[0053] The system starts traversing from the root node of the octree based on the spatial range [X1,X2]×[Y1,Y2]×[Z1,Z2] requested by the user; for each node, it determines whether the corresponding spatial area intersects with the spatial range specified by the user; if there is an intersection, it further calculates the resolution of the points within the node

[0054] If R node ≥R and the spatial area of ​​the node has sufficient overlap with the user-specified range, then the node is marked as a candidate node; the node is traversed to the lower level nodes until the lowest level candidate node set that meets the conditions is found;

[0055] Data extraction and screening: Extract point cloud data from the located candidate node set; assuming the candidate node set is {C1, C2, ..., C n}, for each candidate node C i , which contains the point cloud data point set P i ;

[0056] According to the user-specified spatial range [X1,X2]×[Y1,Y2]×[Z1,Z2], from P i Filter out the points whose coordinates are within this range and get the filtered point set P i ′ ;

[0057] Then according to the resolution requirement R, if P i ′ If there are too many or too few points in the image, further processing may be required. When there are too many points, the sampling algorithm can be used to process the P points according to the resolution requirements. i ′ Sampling is performed to obtain a point set P that meets the resolution requirements i ″;

[0058] Data transmission and delivery: The point set P that has been filtered and processed is sent to the i ″Merge to form a data set P that ultimately meets user needs.

[0059] Furthermore, it also includes:

[0060] S5: User Data Processing, Storage, and Management: Manage registered users and be responsible for their information management, including their usernames, passwords, and various personal information, which is fully protected by law; manage registered users' historical selection data, historical browsing data, planned purchase data, and purchased data; manage updates to users' historical purchase data; and manage changes to users' personal information;

[0061] S6: Data Request and Request Response: Responsible for processing requests submitted by users, including registration-related, data selection, data preview, data interactive browsing, data resolution selection, data download, historical data browsing, data update, etc., and responding to corresponding requests;

[0062] S7: High-concurrency response and processing: Assess user scale and user needs, handle high-concurrency tasks, and provide services to multiple users at the same time.

[0063] As a second aspect of the present invention, a global multi-source massive point cloud data management system is provided, comprising:

[0064] The multi-source acquisition and indexing unit is used to select multi-source laser scanners, including airborne, mobile, ground-based, airborne blue-green, and underwater, to collect point cloud data according to different acquisition scenarios. It retains the characteristics of the original point cloud while converting relative coordinates into a unified coordinate system based on the data characteristics of different devices. It also constructs an octree index based on the minimum spatial grid size and divides the original point cloud file.

[0065] Standardized naming units use a 28-character naming convention of "administrative division code prefix - frame point cloud data time - file suffix". The 9-digit administrative division code strictly follows national standards to accurately lock the geographical area of ​​the point cloud data; the 14-digit frame point cloud time records the instantaneous information of acquisition to ensure the unique identification of data in the same area, forming a unique naming system with two dimensions of "region + time".

[0066] The seven-level storage structure building unit is used to establish a seven-level storage structure of "China - Provincial - Prefectural - County - Township / Town / Street - Data Collection Time - Demand Hierarchy Octree Index"; the point cloud information management database stores standardized named file indexes and builds a three-level mapping relationship of "file name - storage location - real scene";

[0067] The customized data extraction unit is used to locate and extract point cloud data of the corresponding level through the octree hierarchical indexing mechanism when the user makes a data request; if the user needs to perform high-precision modeling of a certain area, the system extracts high-resolution hierarchical data based on the octree index to meet the modeling requirements for details; if only macro scene browsing is required, low-resolution hierarchical data is extracted to reduce data transmission and processing volume, improve response speed, and thus meet the user's diverse data usage scenarios.

[0068] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0069] 1. The present invention's method for managing massive point cloud data across the entire domain and from multiple sources achieves efficient data management and precise indexing by constructing a unique octree indexing system and file naming rules. In single file management, an octree is constructed based on the density, uniformity of distribution, and other characteristics of the original point cloud data. A multi-level octree is constructed for areas with a large number of points to accurately reflect the details of the point cloud, while the number of levels is reduced for areas with sparse points to balance storage and retrieval efficiency. In file naming, a 28-character naming rule combining administrative division codes and frame point cloud time is adopted to form a unique naming system with two dimensions of "region + time". This not only facilitates the rapid location of data by region and time, but also provides a clear identifier for data storage, retrieval, and management, greatly improving the accuracy and efficiency of data management.

[0070] 2. The present invention's method for managing massive point cloud data across the entire region and from multiple sources optimizes the data storage and search process by establishing a seven-level data storage structure and a comprehensive database mapping relationship. A seven-level storage structure of "China - Provincial - Prefectural - County - Township / Town / Street - Data Collection Time - Demand Hierarchy Octree Index" is established. The point cloud information management database stores standardized named file indexes, and constructs a three-layer mapping relationship of "file name - storage location - real scene." This structure enables users to quickly search for the corresponding original point cloud file based on the file name during operation, accurately locating the real three-dimensional landscape at the time of data collection, greatly improving the speed of data search, ensuring the orderliness of data storage, and providing a solid foundation for the long-term management and application of massive point cloud data.

[0071] 3. The present invention's method for managing massive point cloud data across the entire domain and from multiple sources meets users' diverse data usage needs through a customized data indexing and extraction mechanism. When a user raises a data requirement, the octree hierarchical indexing mechanism is used to quickly locate and extract point cloud data of the corresponding level based on the user's required spatial range, resolution, and other parameters. For example, in a high-precision modeling scenario, the system extracts high-resolution level data; when browsing a macroscopic scene, it extracts low-resolution level data. This not only meets users' needs for data of different precisions, but also reduces unnecessary data transmission and processing, improves system response speed, effectively enhances user experience, and enables data to better serve various practical application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of a method for managing massive point cloud data across multiple sources in an entire region according to an embodiment of the present invention;

[0073] Figure 2 This is a schematic diagram of naming point cloud data according to an embodiment of the present invention;

[0074] Figure 3 This is the overall process of the embodiment of the present invention;

[0075] Figure 4 Detailed process of an embodiment of the present invention;

[0076] Figure 5 2 is a diagram of system units according to an embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0078] Example 1

[0079] Please refer to Figure 1 This embodiment 1 provides a method for managing massive point cloud data across multiple sources, including:

[0080] S1. Targeted selection of multi-source laser scanners, including airborne, mobile, ground-based, airborne blue-green, and underwater, for point cloud data acquisition based on different acquisition scenarios. The original point cloud characteristics are retained while converting relative coordinates to a unified coordinate system based on the data characteristics of different devices. An octree index is constructed based on the minimum spatial grid size and the original point cloud file is partitioned.

[0081] S2. Use a 28-character naming convention: "Administrative division code prefix - frame point cloud data time - file suffix." The 9-digit administrative division code strictly adheres to national standards, accurately identifying the geographic region of the point cloud data. The 14-digit frame point cloud time records the instantaneous information collected, ensuring unique identification of data within the same region. This creates a unique naming system based on the dual dimensions of "region + time."

[0082] S3. Establish a seven-level storage structure consisting of "China - Provincial - Prefectural - County - Township / Township / Street - Data Collection Time - Requirement Hierarchy Octree Index"; the point cloud information management database stores standardized named file indexes and constructs a three-level mapping relationship between "file name - storage location - real-world scenario";

[0083] S4. When users raise data requirements, the system locates and extracts point cloud data at the corresponding level through the octree hierarchical indexing mechanism. If users need to perform high-precision modeling of a certain area, the system extracts high-resolution hierarchical data based on the octree index to meet the modeling requirements for details. If only macroscopic scene browsing is required, low-resolution hierarchical data is extracted to reduce data transmission and processing volume, improve response speed, and thus meet users' diverse data usage scenarios.

[0084] This embodiment 1 further explains the above steps.

[0085] (1) Point cloud data processing, storage and management

[0086] Please refer to Figure 1 as well as Figure 2 , process the collected original single point cloud file and construct an octree in blocks; name the data according to the administrative division code and collection time; store the file storage location in the management system, and the data organization method of the management system complies with the national administrative division code.

[0087] 1.1 Single point cloud file management:

[0088] Process multi-source point cloud data collected by airborne 3D laser scanners, mobile 3D laser scanners, terrestrial 3D laser scanners, airborne blue-green laser scanners, and underwater laser scanners, retaining the characteristics of the original point cloud data, establishing a point cloud octree index with a given minimum spatial grid size, and dividing the original file into sub-files of up to 1GB in size.

[0089] The point cloud data collected by airborne 3D laser scanners is typically highly uniform, flight path data. These data are collected using multi-rotor drones or small to medium-sized manned aircraft. They are the primary means of collecting massive amounts of data across a wide area, and are used in scenarios such as large-scale topographic mapping, forestry remote sensing, and point cloud collection in mountainous or uninhabited areas. The point cloud data density is relatively uniform, and the coordinates are generally relative. These coordinates can be converted to the BeiDou coordinate system using the airborne BeiDou+inertial navigation system and control points along the flight path.

[0090] The mobile 3D laser scanner and the acquisition equipment are mobile measuring vehicles, rail vehicles, and electric platforms equipped with cross-sectional 3D laser scanners; the collected point cloud data are usually spiral data with uneven density, and the data density in the forward direction is affected by the forward speed of the carrier. Since the mobile carrier for data acquisition has speed and stability requirements, it is an important supplementary means for the collection of massive data in the entire domain, and is used in municipal and transportation scenarios such as tunnels, pipeline corridors, and bridges. It includes ground point cloud data and underground point cloud data. The density of its point cloud data is affected by the acquisition principle and is uneven. The point density is high in areas close to the sensor, and low in areas far away. The point cloud coordinates are generally relative position coordinates, which can be converted to the Beidou coordinate system through the vehicle-mounted Beidou + inertial navigation system or control points along the line.

[0091] The ground-based 3D laser scanner, the acquisition equipment is a ground-based 3D laser scanner, which is a supplementary measurement method for key targets in the global massive data collection, and is used to construct ultra-high-requirement fine data required for key target measurement and monitoring; the collected point cloud data is usually uneven circular area data, and its point cloud density is related to the distance from the target to the scanner. The application distance of ground-based 3D laser scanning technology is limited, and it usually requires the target and the scanner to be within 100m. It is used to construct ultra-high-requirement fine data in the process of global massive data collection. The point cloud coordinates are generally relative position coordinates, which can be converted to the Beidou coordinate system through at least 3 control points with line of sight.

[0092] The airborne blue-green laser scanner, which uses a multi-rotor drone or small- to medium-sized manned aircraft, is a supplementary non-terrestrial target measurement method for global, massive data collection. It is used for surveying lakes, rivers, and coastlines. The collected point cloud data typically represents strips of water and coastline data. Due to the multi-rotor drone or small- to medium-sized manned aircraft, the point cloud density is relatively uniform. Coordinates are generally relative.

[0093] The point cloud data collected by the underwater laser scanner is usually underwater laser point cloud data within a small range, such as underwater caves, etc. Its point cloud density is affected by the acquisition sensor and is generally uneven, and is generally relative position coordinates.

[0094] The ground point cloud data is ground target data that can be directly collected by airborne, vehicle-mounted, or ground-based three-dimensional laser scanners.

[0095] The underground point cloud data is underground target data that cannot be directly collected, such as railway tunnels, subway tunnels, and pipeline corridors.

[0096] The original point cloud characteristics include point cloud range, point cloud density, whether the point cloud is uniform, and whether it is a relative position point cloud or an absolute position point cloud.

[0097] The relative position point cloud is a point cloud in which the area has no absolute coordinates in a universal coordinate system, and all points are in the current local engineering measurement coordinate system.

[0098] The specific method of constructing an octree index based on the minimum spatial grid size and dividing the original point cloud file is as follows:

[0099] For the original single point cloud file F, firstly, we build a point cloud octree index based on the given minimum spatial grid size δ in meters:

[0100] For the original point cloud file F, let the range of the point cloud data it contains in three-dimensional space be X max -X min ,Y max -Y min ,Z max -Z min , where (X min ,Y min ,Z min ) and (X max ,Y max ,Z max ) are the minimum and maximum coordinate values ​​of the point cloud data in the X, Y, and Z axis directions respectively;

[0101] The spatial extent of the entire point cloud data can be represented as a three-dimensional cube:

[0102] C=[X max ,X min ]×[Y max ,Y min ]×[Z max ,Z min ]

[0103] This cube will serve as the initial space for octree construction;

[0104] The three-dimensional space range C determined above is used as the root node Root of the octree; the root node represents the spatial range of the entire original point cloud file. At this time, the size of the root node S Root for:

[0105]

[0106] For any node Node, divide it into 8 child nodes Child1, Child2, ..., Child8 by dividing it into two equal parts along the three dimensions X, Y, and Z.

[0107] Assume that the range of the node Node in the X, Y, and Z directions are [X node_max ,X node_min ],[Y node_max ,Y node_min ],[Z node_max ,Z node_min ], then the i-th child node Child i The range can be determined as follows:

[0108]

[0109] Node size S Node The calculation formula is:

[0110]

[0111] The minimum spatial grid size δ is introduced as one of the termination conditions for partitioning; when the size of the node Node S Node Greater than δ, and the number of point clouds contained in the node is N Node When it is greater than the set threshold T, the node continues to be divided; otherwise, the division is stopped and the node is marked as a leaf node;

[0112] After completing the point cloud octree indexing of the original point cloud file F, the original point cloud file F is further divided as follows:

[0113] Assume that the data size of the original point cloud file F is T, in GB, and divide it into sub-files of at most 1 GB in size;

[0114] Assume the number of sub-files is N, then:

[0115]

[0116] The original point cloud file F is divided according to the number of files N. The divided files include N-1 1GB sub-files and a file with a size of T-N+1GB.

[0117] 1.2 Overall point cloud file naming

[0118] Please refer to Figure 2 , name the original point cloud data after division. Its name consists of four parts, a total of 28 characters: administrative division code prefix, underscore, frame point cloud data time, and file suffix.

[0119] The administrative division code is determined by the ten written standards of the Administrative Division Code of the People's Republic of China (GB / T2260-80, 82, 84, 86, 88, 91, 1995, 1999, 2002, 2007), the Rules for the Compilation of Administrative Division Codes Below the County Level (GB / T10114-2003), and the Regulations on the Administration of Administrative Divisions (Order No. 704 of the State Council of the People's Republic of China (2018), issued by the Ministry of Civil Affairs. The administrative division code, with the first and second digits representing the provincial administrative division (including provinces, autonomous regions, municipalities directly under the central government, and special administrative regions); the third and fourth digits representing the prefecture-level administrative division (including provincial administrative regions, prefecture-level cities, autonomous prefectures, regions, and leagues); the fifth and sixth digits representing the county-level administrative division (including municipal districts, municipal special zones, forest areas, mining areas, industrial and mining areas, industrial and agricultural areas, counties, autonomous counties, county-level cities, banners, autonomous banners, county-level towns, islands and reefs, and their maritime areas); and the seventh, eighth, and ninth digits representing townships, towns, and sub-districts. The administrative division code uniquely identifies the specific location of the original point cloud file within the entire land area.

[0120] The underscore is the character "_".

[0121] The frame point cloud data time is the time when the first point in the current point cloud file is collected. This data is given by the original data in a non-universal format. The data is in binary format and provided by the relevant hardware supplier. It is a non-public parameter. The frame point cloud data time is specifically: the 1st to 4th digits correspond to the natural year of the frame point cloud data collection, the 5th to 6th digits correspond to the natural month of the frame point cloud data collection, the 7th to 8th digits correspond to the natural day of the frame point cloud data collection, the 9th to 10th digits correspond to the 24-hour system of the frame point cloud data collection, the 11th to 12th digits correspond to the minutes of the frame point cloud data collection, and the 13th to 14th digits correspond to the seconds of the frame point cloud data collection. For point cloud data within the same administrative area, the frame point cloud time provides a unique identifier to distinguish it from other data.

[0122] The file suffix is ​​a common point cloud data format suffix, usually .las.

[0123] 1.3 Overall point cloud file management

[0124] Establish an overall data storage structure: China - provincial-level administrative divisions - prefecture-level administrative divisions - county-level administrative divisions - townships, towns, and streets - data collection time - demand hierarchical octree index, with a total of seven levels. The point cloud file names corresponding to this structure are stored in the point cloud information management database, and the corresponding original point cloud files are stored in the original point cloud data storage cluster, providing a basis for subsequent applications.

[0125] The point cloud information management database is a database that stores the names of original point cloud files. Since the point cloud file names fully identify the location and acquisition time of the point cloud, they form a one-to-one and unique three-layer mapping relationship with the specific file and the corresponding location: the user operates at the point cloud file name level, and after confirming the purchase operation, the unique corresponding original point cloud file is searched on demand. This file uniquely corresponds to the real three-dimensional landscape of the location at the time of data acquisition.

[0126] The original point cloud data storage cluster is a server cluster that stores original point cloud files. The server is responsible for responding to download requests from users after they purchase corresponding files and providing data download services to users through a high-speed network transmission system.

[0127] 1.4 Customized Data Extraction

[0128] When a user raises a data requirement, the specific method for locating and extracting the point cloud data of the corresponding level through the octree hierarchical indexing mechanism is as follows:

[0129] User request analysis: Assume that the spatial range specified by the user is a three-dimensional rectangular area, whose coordinate range in the Cartesian coordinate system is [X1, X2] × [Y1, Y2] × [Z1, Z2], and the resolution requirement is R; after receiving the request, the task management system first analyzes these parameters;

[0130] Octree index positioning: Octree index is based on point cloud data block processing; each octree node corresponds to a specific spatial area. Assume that the volume of the spatial area corresponding to an octree node is V, and the number of points contained in the node is N; according to the original characteristics of the point cloud data, the index is established with the minimum spatial grid size δ. The grid size δ has a certain relationship with the resolution R. When the point cloud is relatively evenly distributed, it can be expressed as

[0131] The system starts traversing from the root node of the octree based on the spatial range [X1,X2]×[Y1,Y2]×[Z1,Z2] requested by the user; for each node, it determines whether the corresponding spatial area intersects with the spatial range specified by the user; if there is an intersection, it further calculates the resolution of the points within the node

[0132] If R node≥R and the spatial area of ​​the node has sufficient overlap with the user-specified range, then the node is marked as a candidate node; the node is traversed to the lower level nodes until the lowest level candidate node set that meets the conditions is found;

[0133] Data extraction and screening: Extract point cloud data from the located candidate node set; assuming the candidate node set is {C1, C2, ..., C n}, for each candidate node C i , which contains the point cloud data point set P i ;

[0134] According to the user-specified spatial range [X1,X2]×[Y1,Y2]×[Z1,Z2], from P i Filter out the points whose coordinates are within this range and get the filtered point set P i ′ ;

[0135] Then according to the resolution requirement R, if P i ′ If there are too many or too few points in the image, further processing may be required. When there are too many points, the sampling algorithm can be used to process the P points according to the resolution requirements. i ′ Sampling is performed to obtain a point set P that meets the resolution requirements i ″;

[0136] Data transmission and delivery: The point set P that has been filtered and processed is sent to the i ″Merge to form a data set P that ultimately meets user needs.

[0137] Please refer to Figure 3 as well as Figure 4 Furthermore, in a preferred embodiment, the following steps are also included:

[0138] S5: User Data Processing, Storage, and Management: Manage registered users and be responsible for their information management, including their usernames, passwords, and various personal information, which is fully protected by law; manage registered users' historical selection data, historical browsing data, planned purchase data, and purchased data; manage updates to users' historical purchase data; and manage changes to users' personal information;

[0139] S6: Data Request and Request Response: Responsible for processing requests submitted by users, including registration-related, data selection, data preview, data interactive browsing, data resolution selection, data download, historical data browsing, data update, etc., and responding to corresponding requests;

[0140] S7: High-concurrency response and processing: Assess user scale and user needs, handle high-concurrency tasks, and provide services to multiple users at the same time.

[0141] This embodiment further explains the above steps.

[0142] (2) User data processing, storage and management

[0143] 2.1 User Registration and Information Management

[0144] When users interactively obtain the global point cloud data provided by the technology related to this patent, they need to first register an account on the website. This account is associated with the user and is managed by the registered user database. When registering, the user provides a mobile phone number and identity information, and after passing the review, they can enter the data browsing interface. User information is protected by relevant national laws, and any steps involved in this patent will not disclose user personal information. In addition, user registration and information management content also includes: password retrieval, personal information modification, personal data management, account cancellation and other functions.

[0145] The registered user database is a server dedicated to storing user information, which has a high security level in the system. When a user registers, the user information is added to the database, and the password is stored in encrypted form and compared with the input password each time the user logs in.

[0146] The user's personal information includes the user's ID number, mobile phone number, name, gender, work unit, nature of work, payment method, purpose of data use, commitment letter that the data will not be used for other commercial purposes, and all other information protected by law.

[0147] The password retrieval function provides two ways to retrieve the password: using the email address previously bound by the user and the mobile phone verification code.

[0148] The personal information modification is a process in which a user modifies his or her personal information independently when there is a relevant need, and updates the registered user database after submitting a request to the system.

[0149] The personal data management is a module that manages the user's historical selection records and historical purchase data after the user selects and purchases data, and is used to provide better retrospective services for the user.

[0150] The account cancellation is to cancel the account as needed by the user and delete the corresponding information in the registered user database.

[0151] 2.2 User Data Management

[0152] User data management primarily consists of two parts: user selection history management and user purchase history interface management. User selection history management involves determining the corresponding parameters for preview or interactive preview in the data display system after the user has selected the planned purchase parameters. User purchase history interface management manages the data that the user has purchased and provides data update services to users who are still in the service period when data updates or changes occur.

[0153] The planned purchase parameters refer to the administrative division code and spatial grid size selected by the user. When the user browses repeatedly, the file corresponding to the administrative division code is loaded by default, and the spatial grid size is the user's historical selection.

[0154] The preview and interactive preview are a type of non-transmission data interaction request and response. After a registered user issues a preview or interactive preview request, the server responds to the preview request and provides the user terminal with a preview effect of the original point cloud data of an appropriate scale in the form of a two-dimensional panoramic display or a three-dimensional interactive browsing.

[0155] The data update or change generally refers to the re-collection of point cloud data for a particular administrative area due to repeated collection needs. In this case, the updated local data is provided to the user while retaining the original data. In addition, when the administrative division code changes, all systems covered by this patent also undergo corresponding changes.

[0156] The service period means that after purchasing the data, the registered user has the right to download the relevant data and the data before and after the update within a certain period of time. At the same time, the registered user can apply to download the original point cloud data of all time nodes before purchasing the data.

[0157] (3) Data Request and Request Response

[0158] 3.1 Non-data interaction requests and responses

[0159] Non-data interaction requests primarily involve operations related to user information, specifically categorized as non-registered user requests and registered user requests. Non-registered users are restricted to browsing all website information and limited access to sample data. Registered users, in addition to browsing all website information, can access low-resolution sample data and interactively preview it. Registered users can submit non-data interaction requests to modify their personal registration information.

[0160] The sample data is low-resolution, non-important historical data provided by the data display terminal and is used for preview and interactive preview.

[0161] The preview is a low-resolution two-dimensional image of the target area that appears on the display terminal after the user submits a request, providing the user with an intuitive image of the selected range.

[0162] The interactive preview is a low-resolution three-dimensional point cloud of the target area that appears on the display terminal after the user submits a request, and supports the user to use external devices such as a mouse and keyboard to interact with the point cloud data used for preview, including moving, rotating and scaling.

[0163] 3.2 Non-transmission data interaction request and response

[0164] Before purchasing data, registered users need to understand the basic details of the data. These operations do not involve data downloading and therefore do not consume a large amount of network transmission resources. The main non-transmission data interaction requests include data preview, interactive data preview, and historical data browsing. After the registered user makes a request, the task management system responds and sends the task information to the point cloud information management server. The point cloud information management server loads the point cloud data from the point cloud data storage server cluster and sends it to the point cloud data processing server via a hardware link. The point cloud data processing server selects a single file that is at least two levels lower than the octree index level selected by the user and sends it to the display terminal, providing users with preview, interactive preview, and historical data browsing services.

[0165] The task management system is a management system that responds to requests after a registered user sends a request, and is responsible for the interaction between the user and the point cloud information management server.

[0166] The point cloud information management server is the carrier platform of the point cloud information management database in step S103, and is used to store the renamed point cloud index information instead of directly storing the point cloud files.

[0167] The point cloud data storage server cluster is consistent with the point cloud data storage server cluster in S103.

[0168] The point cloud data processing server is a high-performance computer that calculates the point cloud of the corresponding octree grid size required by registered users. It is a node that generates the required files from the original files when users preview and purchase data.

[0169] The hardware link refers to the point cloud data storage server cluster and the point cloud data processing server being connected by lines instead of wireless networks to ensure the efficiency and reliability of data transmission.

[0170] 3.3 Transmission Data Interaction Request and Response

[0171] After the user selects the point cloud parameters to be purchased, including the area where the point cloud data is located, the point cloud data collection time, and the minimum spatial grid size, and completes the purchase operation, the registered user data management system sends a request to the task management system. The task management system sends the task information to the point cloud information management server, and the point cloud information management server sends a request to the point cloud data storage server. The latter sends the corresponding data stored in the point cloud data storage server cluster to the point cloud data processing server via the hardware link. The point cloud data processing server is responsible for extracting the corresponding level original point cloud that meets the grid size requirements according to user needs, and providing data download services to users through the high-speed network transmission system.

[0172] The high-speed network transmission system is a switching network that provides required data to users.

[0173] (4) High-concurrency response and processing

[0174] When the number of registered users accessing the website is excessive, a high-concurrency response and processing module is deployed to provide data services to multiple users simultaneously and improve concurrent task processing capabilities. This module is deployed in at least two core control clusters. When there are too many web page data requests, the core control clusters monitor the utilization of each user information management server and allocate computing resources with low utilization to computing servers with high utilization, thus achieving reasonable allocation and optimization of computing resources and improving computing resource utilization.

[0175] Example 2

[0176] Please refer to Figure 5 This embodiment 2 provides a massive point cloud data management system for the entire domain and multiple sources, including:

[0177] The multi-source acquisition and indexing unit is used to select multi-source laser scanners, including airborne, mobile, ground-based, airborne blue-green, and underwater, to collect point cloud data according to different acquisition scenarios. It retains the characteristics of the original point cloud while converting relative coordinates into a unified coordinate system based on the data characteristics of different devices. It also constructs an octree index based on the minimum spatial grid size and divides the original point cloud file.

[0178] Standardized naming units use a 28-character naming convention of "administrative division code prefix - frame point cloud data time - file suffix". The 9-digit administrative division code strictly follows national standards to accurately lock the geographical area of ​​the point cloud data; the 14-digit frame point cloud time records the instantaneous information of acquisition to ensure the unique identification of data in the same area, forming a unique naming system with two dimensions of "region + time".

[0179] The seven-level storage structure building unit is used to establish a seven-level storage structure of "China - Provincial - Prefectural - County - Township / Town / Street - Data Collection Time - Demand Hierarchy Octree Index"; the point cloud information management database stores standardized named file indexes and builds a three-level mapping relationship of "file name - storage location - real scene";

[0180] The customized data extraction unit is used to locate and extract point cloud data of the corresponding level through the octree hierarchical indexing mechanism when the user makes a data request; if the user needs to perform high-precision modeling of a certain area, the system extracts high-resolution hierarchical data based on the octree index to meet the modeling requirements for details; if only macro scene browsing is required, low-resolution hierarchical data is extracted to reduce data transmission and processing volume, improve response speed, and thus meet the user's diverse data usage scenarios.

[0181] Example 3

[0182] This embodiment 3 also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement any step of a method for managing massive point cloud data for global multi-source.

[0183] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0184] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0185] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for managing massive point cloud data across multiple sources, characterized by: include: S1. Targeted selection of multi-source laser scanners, including airborne, mobile, ground-based, airborne blue-green, and underwater, for point cloud data acquisition based on different acquisition scenarios. The original point cloud characteristics are retained while converting relative coordinates to a unified coordinate system based on the data characteristics of different devices. An octree index is constructed based on the minimum spatial grid size and the original point cloud file is partitioned. S2. Use a 28-character naming convention: "Administrative division code prefix - frame point cloud data time - file suffix." The 9-digit administrative division code strictly adheres to national standards, accurately identifying the geographic region of the point cloud data. The 14-digit frame point cloud time records the instantaneous information collected, ensuring unique identification of data within the same region. This creates a unique naming system based on the dual dimensions of "region + time." S3. Establish a seven-level storage structure consisting of "China - Provincial - Prefectural - County - Township / Township / Street - Data Collection Time - Requirement Hierarchy Octree Index"; the point cloud information management database stores standardized named file indexes and constructs a three-level mapping relationship between "file name - storage location - actual scene"; S4. When users raise data requirements, the system locates and extracts point cloud data at the corresponding level through the octree hierarchical indexing mechanism. If users need to perform high-precision modeling of a certain area, the system extracts high-resolution hierarchical data based on the octree index to meet the modeling requirements for details. If only macroscopic scene browsing is required, low-resolution hierarchical data is extracted to reduce data transmission and processing volume, improve response speed, and thus meet users' diverse data usage scenarios.

2. The method for managing massive point cloud data across the entire region and multiple sources according to claim 1, characterized in that: The S1 original point cloud characteristics include point cloud range, point cloud density, whether the point cloud is uniform, and whether it is a relative position point cloud or an absolute position point cloud.

3. The method for managing global multi-source massive point cloud data according to claim 1, characterized in that: In S1, the multi-source laser scanners including airborne, mobile, ground-based, airborne blue-green and underwater are selected to collect point cloud data according to different collection scenarios. Specifically: The airborne 3D laser scanner, whose acquisition equipment is a multi-rotor UAV or a small or medium-sized manned aircraft, is the main means of collecting massive data in the entire area and is used in scenarios such as large-scale terrain mapping, forestry remote sensing, and point cloud collection in mountainous areas or uninhabited areas. The mobile 3D laser scanner, whose acquisition equipment is a mobile measuring vehicle, rail vehicle, or electric platform equipped with a cross-sectional 3D laser scanner, is an important supplementary means for collecting massive data across the entire area and is used in municipal and transportation scenarios such as tunnels, pipeline corridors, and bridges. The terrestrial 3D laser scanner is a collection device that is a supplementary measurement method for key targets in the global massive data collection, and is used to construct the ultra-high-requirement and fine data required for key target measurement and monitoring; The airborne blue-green laser scanner, whose acquisition equipment is a multi-rotor drone or a small or medium-sized manned aircraft carrier, is a supplementary measurement method for non-land targets for full-area massive data collection and is used in lake, river and coastline measurement scenarios.

4. The method for managing global multi-source massive point cloud data according to claim 2, characterized in that: The original point cloud characteristics are: The point cloud data collected by the airborne 3D laser scanner has relative position coordinates. Since it is a multi-rotor UAV or a small or medium-sized manned aircraft carrier, its point cloud density is uniform. The point cloud data collected by the mobile 3D laser scanner is spiral data with uneven density. The data density in the forward direction is affected by the forward speed of the carrier. The point cloud data includes both ground point cloud data and underground point cloud data. The density of the point cloud data is affected by the acquisition principle and is uneven. The point density is high in areas close to the sensor and low in areas far away. The point cloud data collected by the ground-based 3D laser scanner is non-uniform circular area data, and its point cloud density is related to the distance between the target and the scanner; the point cloud coordinates are relative position coordinates; The point cloud data collected by the airborne blue-green laser scanner is strip water line and coastline data. Since it is a multi-rotor drone or a small or medium-sized manned aircraft carrier, its point cloud density is uniform; The point cloud data collected by the underwater laser scanner is underwater laser point cloud data within a small range. Its point cloud density is affected by the acquisition sensor and is uneven, and is a relative position coordinate.

5. The method for managing global multi-source massive point cloud data according to claim 3, characterized in that: In S1, relative coordinates are converted into a unified coordinate system according to different device data characteristics. The specific method is as follows: The point cloud data collected by the airborne 3D laser scanner is converted to the BeiDou coordinate system by the airborne BeiDou + inertial navigation system in conjunction with the control points along the flight path; The point cloud data collected by the mobile 3D laser scanner is converted to the BeiDou coordinate system through the vehicle-mounted BeiDou + inertial navigation system or the control points along the line; The point cloud data collected by the terrestrial 3D laser scanner is converted into the Beidou coordinate system through at least three control points of line of sight; The point cloud data collected by the airborne blue-green laser scanner and the point cloud data collected by the underwater laser scanner are relative position coordinates; the relative position point cloud is the absolute coordinate of the point cloud in this area without a universal coordinate system, and all points are in the current local engineering measurement coordinate system.

6. The method for managing global multi-source massive point cloud data according to claim 1, characterized in that: The specific method of constructing an octree index according to the minimum spatial grid size and dividing the original point cloud file in S1 is: For the original single point cloud file F, firstly, we build a point cloud octree index based on the given minimum spatial grid size δ in meters: For the original point cloud file F, let the range of the point cloud data it contains in three-dimensional space be X max -X min ,Y max -Y min ,Z max -Z min , where (X min ,Y min ,Z min ) and (X max ,Y max ,Z max ) are the minimum and maximum coordinate values ​​of the point cloud data in the X, Y, and Z axis directions respectively; The spatial extent of the entire point cloud data can be represented as a three-dimensional cube: C=[X max ,X min ]×[Y max ,Y min ]×[Z max ,Z min ] This cube will serve as the initial space for octree construction; The three-dimensional space range C determined above is used as the root node Root of the octree; the root node represents the spatial range of the entire original point cloud file. At this time, the size of the root node S Root for: For any node Node, divide it into 8 child nodes Child1, Child2, ..., Child8 by dividing it into two equal parts along the three dimensions X, Y, and Z. Assume that the range of the node Node in the X, Y, and Z directions are [X node_max ,X node_min ],[Y node_max ,Y node_min ],[Z node_max ,Z node_min ], then the i-th child node Child i The range can be determined as follows: Node size S Node The calculation formula is The minimum spatial grid size δ is introduced as one of the termination conditions for partitioning; when the size of the node Node S Node Greater than δ, and the number of point clouds contained in the node is N Node When it is greater than the set threshold T, the node continues to be divided; otherwise, the division is stopped and the node is marked as a leaf node; After completing the point cloud octree indexing of the original point cloud file F, the original point cloud file F is further divided as follows: Assume that the data size of the original point cloud file F is T, in GB, and divide it into sub-files of at most 1 GB in size; Assume the number of sub-files is N, then: The original point cloud file F is divided according to the number of files N. The divided files include N-1 1GB sub-files and a file with a size of T-N+1GB.

7. The method for managing global multi-source massive point cloud data according to claim 1, characterized in that: The 14-bit frame point cloud time in S2 is specifically: The 1st to 4th digits correspond to the natural year when the frame point cloud data was collected, the 5th to 6th digits correspond to the natural month when the frame point cloud data was collected, the 7th to 8th digits correspond to the natural day when the frame point cloud data was collected, the 9th to 10th digits correspond to the 24-hour time when the frame point cloud data was collected, the 11th to 12th digits correspond to the minutes when the frame point cloud data was collected, and the 13th to 14th digits correspond to the seconds when the frame point cloud data was collected. For point cloud data within the same administrative area, the frame point cloud time provides a unique identifier to distinguish it from other data.

8. The method for managing massive point cloud data across multiple sources according to claim 1, characterized in that ,In S4, when the user raises data requirements, the specific method of locating and extracting the ,point cloud data of the corresponding level through the octree hierarchical indexing ,mechanism is as follows: User request analysis: Assume that the spatial range specified by the user is a three-dimensional rectangular area, whose coordinate range in the Cartesian coordinate system is [X1, X2] × [Y1, Y2] × [Z1, Z2], and the resolution requirement is R; after receiving the request, the task management system first analyzes these parameters; Octree index positioning: Octree index is based on point cloud data block processing; each octree node corresponds to a specific spatial area. Assume that the volume of the spatial area corresponding to an octree node is V, and the number of points contained in the node is N; according to the original characteristics of the point cloud data, the index is established with the minimum spatial grid size δ. The grid size δ has a certain relationship with the resolution R. When the point cloud is relatively evenly distributed, it can be expressed as The system starts traversing from the root node of the octree based on the spatial range [X1,X2]×[Y1,Y2]×[Z1,Z2] requested by the user; for each node, it determines whether the corresponding spatial area intersects with the spatial range specified by the user; if there is an intersection, it further calculates the resolution of the points within the node If R node ≥R and the spatial area of ​​the node has sufficient overlap with the user-specified range, then the node is marked as a candidate node; the node is traversed to the lower level nodes until the lowest level candidate node set that meets the conditions is found; Data extraction and screening: Extract point cloud data from the located candidate node set; assuming the candidate node set is {C1, C2, ..., C n }, for each candidate node C i , which contains the point cloud data point set P i ; According to the user-specified spatial range [X1,X2]×[Y1,Y2]×[Z1,Z2], from P o Filter out the points whose coordinates are within this range and get the filtered point set P i ′ ; Then according to the resolution requirement R, if P i ′ If there are too many or too few points in the image, further processing may be required. When there are too many points, the sampling algorithm can be used to process the P points according to the resolution requirements. o ′ Sampling is performed to obtain a point set P that meets the resolution requirements o ″; Data transmission and delivery: The point set P that has been filtered and processed is sent to the i ″Merge to form a data set P that ultimately meets user needs.

9. The method for managing global multi-source massive point cloud data according to claim 1, characterized in that: Also includes: S5: User Data Processing, Storage, and Management: Manage registered users and be responsible for their information management, including their usernames, passwords, and various personal information, which is fully protected by law; manage registered users' historical selection data, historical browsing data, planned purchase data, and purchased data; manage updates to users' historical purchase data; and manage changes to users' personal information; S6: Data Request and Request Response: Responsible for processing requests submitted by users, including registration-related, data selection, data preview, data interactive browsing, data resolution selection, data download, historical data browsing, data update, etc., and responding to corresponding requests; S7: High-concurrency response and processing: Assess user scale and user needs, handle high-concurrency tasks, and provide services to multiple users at the same time.

10. A massive point cloud data management system for the entire region and multiple sources, characterized by: include: The multi-source acquisition and indexing unit is used to select multi-source laser scanners, including airborne, mobile, ground-based, airborne blue-green, and underwater, to collect point cloud data according to different acquisition scenarios. It retains the characteristics of the original point cloud while converting relative coordinates into a unified coordinate system based on the data characteristics of different devices. It also constructs an octree index based on the minimum spatial grid size and divides the original point cloud file. Standardized naming units use a 28-character naming convention of "administrative division code prefix-frame point cloud data time-file suffix". The 9-digit administrative division code strictly follows national standards to accurately lock the geographical area of ​​the point cloud data. 14-bit frame point cloud time records the instantaneous information collected, ensuring the unique identification of data in the same area; forming a unique naming system with two dimensions of "area + time"; The seven-level storage structure building unit is used to establish a seven-level storage structure of "China - Provincial - Prefectural - County - Township / Town / Street - Data Collection Time - Demand Hierarchy Octree Index"; the point cloud information management database stores standardized named file indexes and builds a three-level mapping relationship of "file name - storage location - real scene"; The customized data extraction unit is used to locate and extract point cloud data of the corresponding level through the octree hierarchical indexing mechanism when the user makes a data request; if the user needs to perform high-precision modeling of a certain area, the system extracts high-resolution hierarchical data based on the octree index to meet the modeling requirements for details; if only macro scene browsing is required, low-resolution hierarchical data is extracted to reduce data transmission and processing volume, improve response speed, and thus meet the user's diverse data usage scenarios.

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