Laser point cloud data storage method and device based on time sequence and medium

Abstract

The invention discloses a laser point cloud data storage method and device based on a time sequence and a medium, and relates to the technical field of point cloud storage. The method comprises the following steps: acquiring a to-be-queried data file of a client; reading packet header information, and calculating the occupied space of the data file under the condition that the format of the packet header information is consistent with the original format; under the condition that the occupied space of the data file is consistent with the original occupied space, data of the byte bits are read, and the radar data type is composed of the byte bits; according to the data of the byte bit, the radar data type is obtained through judgment, and the data of the data file is loaded. According to the method, the point cloud data with the same data type are fused and then integrally stored, the point cloud data and the corresponding pose data are separately stored in two files, comparison results before and after optimization can be checked at the same time, the optimized point cloud data are prevented from being stored again, and the storage efficiency of the point cloud data is effectively improved.

Description

Technical Field

[0001] This application relates to the technical field of point cloud storage, and particularly to a method, device, and medium for storing laser point cloud data based on time series. Background Art

[0002] Three-dimensional point cloud storage technology is a data technology for storing objects or scenes in three-dimensional space. It obtains point cloud data by discretely sampling the surface of an actual object or scene. These points can be regarded as discrete representations of the scene surface in a given coordinate system. Three-dimensional point cloud data is usually obtained by devices such as laser scanners, cameras, and three-dimensional scanners, and has high-precision, high-resolution, and high-dimensional geometric information, which can intuitively represent information such as the shape, surface, and texture of objects in space.

[0003] There are various storage formats for three-dimensional point cloud data, including PCD (Point Cloud Data), PLY (Polygon File Format), and LAS (LiDAR Aerial Survey), etc. According to their characteristics and applicable scenarios, suitable formats can be selected for storage. Among them, LAS is a storage format for lidar data (LiDAR, Light Laser Detection and Ranging), which is more complex than other formats but allows different hardware and software providers to output an interoperable unified format. LAS files store data in the arrangement of each scan line, including the three-dimensional coordinates of laser points, multiple echo information, intensity information, scan angle, classification information, flight strip information, flight attitude information, project information, GPS (Global Positioning System) information, data point color information, etc.

[0004] Generally speaking, three-dimensional point cloud storage technology is a data technology for storing objects or scenes in three-dimensional space, which can provide high-precision, high-resolution, and high-dimensional geometric information, and has broad application prospects in fields such as three-dimensional modeling, scene reconstruction, robot navigation, virtual reality, and augmented reality.

[0005] PCD format data storage cannot store the time information of point cloud acquisition and other data information of the point cloud into a single file simultaneously. It can only be split into small files for separate storage. At the same time, the format of point cloud data information supported by this data is limited, and it is impossible to add custom data information. Las data format supports comprehensive storage of point cloud data information and can support various common point cloud data information. However, it cannot meet the requirement of quickly querying data based on time information. For each different query call, it is necessary to traverse the entire data file to determine whether the query conditions are met, which greatly increases the data query time.

[0006] Through the above analysis, the problems and defects existing in the prior art are as follows:

[0007] For the storage of point cloud data in the prior art, the storage space pressure is large, so that for each different query call, it is necessary to traverse the entire data file to determine whether the query conditions are met, which greatly increases the data query time, and it is impossible to split the pose trajectory data from the overall point cloud data. Summary of the Invention

[0008] The embodiments of the present application provide a method, device, and medium for storing laser point cloud data based on time series, which solve the problems in the prior art that the storage space pressure of point cloud data is large, and thus for each different query call, it is necessary to traverse the entire data file to determine whether the query conditions are met, the query time is long, and it is impossible to split the pose trajectory data from the overall point cloud data.

[0009] In a first aspect, the embodiments of the present application provide a method for storing laser point cloud data based on time series. The method includes: obtaining a data file to be queried by a client. The data file includes a header information module and an index information module. The header information module includes header information, radar data type, and the total number of nodes. The index information module includes the number of point clouds of a node, the file index offset address of the node, and the node timestamp; reading the header information, and when the format of the header information is consistent with the original format, calculating the occupied space of the data file, where the original format is the file format set when creating the data file; when the occupied space of the data file is consistent with the original occupied space, reading the data of the byte bits, and the radar data type is composed of byte bits; according to the data of the byte bits, determining the radar data type and loading the data of the data file.

[0010] In an implementation manner of the present application, after loading the data of the data file, the method further includes: obtaining a corresponding pose file according to the file index offset address and the node timestamp of the node in the data file; performing an optimization algorithm on the data of the pose file to minimize the reprojection error and obtain a trajectory data file.

[0011] In an implementation manner of the present application, before obtaining the total data information of the data file, the method further includes: collecting point cloud data and creating a temporary file and a pose file, storing the point cloud data in the temporary file; during the process of SLAM real-time mapping, the point cloud data respectively generates corresponding pose data; when the SLAM real-time mapping is completed, storing the pose data in the pose file; configuring a header information module, an index information module, and a data information module for the temporary file to obtain a data file.

[0012] In an implementation manner of the present application, after obtaining the data file, the method further includes: calculating the stored data of the data file to obtain the original occupied space of the data file; in the data file, appending the packet header information to the head of the data file.

[0013] In an implementation manner of the present application, configuring the header information module, the index information module, and the data information module in the data file specifically includes: setting the file format of the packet header information and respectively corresponding the radar data type and the binary byte bits to obtain the header information module; describing the number of point clouds of the node, the acquisition time information, and the file index offset address to obtain the index information module; arranging the point cloud data according to the acquisition time information to obtain the data information module.

[0014] In an implementation manner of the present application, calculating the occupied space of the data file specifically includes: loading the information data of all nodes according to the total number of nodes; when the loading is completed, obtaining the occupied space of the last node through the number of point clouds of the node and the radar data type in the data file; obtaining the total occupied space of the data file according to the occupied space of the last node and the file index offset address of the last node.

[0015] In an implementation manner of the present application, the method further includes: when querying multiple groups of node data, dividing the multiple groups of node data into multiple tasks, and each task processes a group of node data; creating multiple threads, allocating the tasks to each thread, and each thread is responsible for executing a task.

[0016] In an implementation manner of the present application, the radar data type includes the three-dimensional coordinate information of the laser point, the scanning angle information, the classification information, the flight strip information, the flight attitude information, the project information, the GPS information, the data point color information, the multiple echo information, and the intensity information.

[0017] Second aspect, an embodiment of the present application further provides a time-series based laser point cloud data storage device, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: obtain a data file to be queried by a client, the data file including a header information module and an index information module, the header information module including packet header information, radar data type and total number of nodes, and the index information module including the number of point clouds of a node, file index offset address and node timestamp; read the packet header information, and calculate the occupied space of the data file when the format of the packet header information is consistent with the original format, the original format being the file format set when creating the data file; when the occupied space of the data file is consistent with the original occupied space, read the data of the byte bits, the radar data type being composed of the byte bits; and determine the radar data type according to the data of the byte bits and load the data of the data file.

[0018] Third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for time-series based laser point cloud data storage, storing computer-executable instructions, and the computer-executable instructions are set to: obtain a data file to be queried by a client, the data file including a header information module and an index information module, the header information module including packet header information, radar data type and total number of nodes, and the index information module including the number of point clouds of a node, file index offset address and node timestamp; read the packet header information, and calculate the occupied space of the data file when the format of the packet header information is consistent with the original format, the original format being the file format set when creating the data file; when the occupied space of the data file is consistent with the original occupied space, read the data of the byte bits, the radar data type being composed of the byte bits; and determine the radar data type according to the data of the byte bits and load the data of the data file.

[0019] A method, device, and medium for storing laser point cloud data based on time series provided by an embodiment of the present application separate the point cloud data and the corresponding pose data into two files. Only by storing the optimized pose data in a new file, the comparison results before and after optimization can be viewed simultaneously, avoiding re-storing the optimized point cloud data. Thus, the amount of stored data is greatly reduced, the storage space pressure is reduced, and the SLAM real-time mapping data can be stored and managed more effectively, facilitating subsequent loading and use. The point cloud data of different times with the same data type is integrated and stored after fusion, effectively improving the storage efficiency of the point cloud data, saving storage space, and accelerating the query and reading speed at the same time, enabling fast reading of the point cloud data at different times. Appending the original point cloud data to the file header can ensure the integrity of the data and avoid data loss or confusion during the copying process. In addition, before reading the file, it is also possible to determine whether the data is lost or tampered with by others, improving the data security, further ensuring the integrity of the data, and avoiding meaningless reading. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0021] Figure 1 is a flowchart of a method for storing laser point cloud data based on time series provided by an embodiment of the present application;

[0022] Figure 2 is an internal structural schematic diagram of a device for storing laser point cloud data based on time series provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0024] An embodiment of the present application provides a method, device, and medium for storing laser point cloud data based on time series, which solves the problems in the prior art that the storage space pressure of point cloud data is large, and thus each different query call requires traversing the entire data file to determine whether the query conditions are met, the query time is long, and the pose trajectory data cannot be separated from the overall point cloud data.

[0025] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1 It is a flowchart of a method for storing laser point cloud data based on time series provided by an embodiment of the present application. As Figure 1 shown, a method for storing laser point cloud data based on time series provided by an embodiment of the present application specifically includes the following steps:

[0027] As an optional embodiment, before obtaining the total data information of the data file, the method may further include:

[0028] Step 101: Collect point cloud data, create a temporary file and a pose file, and store the point cloud data in the temporary file.

[0029] For ease of understanding, the specific process of the embodiment of the present application is first described. The first step can be the acquisition stage of point cloud data. The point cloud data is collected through a lidar and various other sensors, and each frame (here, one frame represents one node) of the collected point cloud data is stored in a temporary file in real time. The temporary file is a file stream format that continuously appends data according to the increasing time during the data acquisition process, and new data will always be appended to the end of the file.

[0030] Step 102: During the process of SLAM real-time mapping, the point cloud data respectively generates corresponding pose data.

[0031] During the acquisition of point cloud data, SLAM (Simultaneous Localization And Mapping) mapping is performed. During the mapping process, each frame of point cloud data generates a corresponding pose data.

[0032] Step 103: When the SLAM real-time mapping is completed, store the pose data in the pose file.

[0033] In this step, after the SLAM real-time mapping is completed, the above-mentioned pose data will be stored in the pose file. To facilitate the user to simultaneously compare and view the differences before and after the optimization action is executed, the point cloud data and the corresponding pose data are stored in two separate files. In this way, we only need to store the optimized pose data in a new file to simultaneously view the comparison results before and after the optimization, avoiding re-storing the optimized point cloud data, thereby greatly reducing the amount of stored data.

[0034] Step 104: Configure a header information module, an index information module, and a data information module for the temporary file to obtain a data file.

[0035] In this step, the entire point cloud data structure can be divided into four main parts: the header information module, the index information module, the data information module, and the pose information module. It can be stored in different file formats for easy reading, transmission, and processing.

[0036] The header information module can include: 1. Packet header information: a uint32_t data that contains a custom flag character used to determine whether the loaded file is a data file of this format; 2. File version number: a uint32_t data that records the change history of the data file and can be updated as the file is modified; 3. Radar data type: a uint32_t data from which the data format type of the acquired point cloud data can be obtained; 4. Total number of nodes: a uint32_t data that records the number of nodes in the point cloud data, facilitating subsequent data loading; 5. Global pose transformation matrix: a double×16 array data that contains a pose transformation matrix acting on the entire point cloud data.

[0037] The index information module can include: 1. File index offset address: a uint64_t data from which the file storage index position of any node data can be quickly obtained, enabling the desired data content to be read without traversing and loading the entire file; 2. Node timestamp: a uint64_t data that describes the point cloud data acquisition time information for each node; 3. Node point cloud count information: a uint32_t data that describes the total number of point clouds in the node. Combining with the radar data type in the header data, the total space occupied by the point cloud data of this node can be calculated.

[0038] The data information module can record the point cloud data arranged in the order of node time and can store point cloud data of different format types. The data information for a single point usually includes the three-dimensional coordinates of the laser point, multiple echo information, intensity information, scanning angle, classification information, flight strip information, flight attitude information, project information, GPS information, data point color information, etc.

[0039] The pose information module is a set of pose data that can be recorded in the order of node time. Each pose data contains a double×3 node displacement data and a double×4 node rotation quaternion data. In this way, each node data has corresponding pose data, and the corresponding pose data needs to be obtained during display and data format conversion processing.

[0040] During specific implementation, the header information module, index information module, and data information module in the data file are configured, which can specifically include:

[0041] Step 1041: Set the file format of the packet header information, and map the radar data types to the binary byte bits respectively to obtain the header information module.

[0042] To quickly query and determine the specific format content contained in the data packet and continuously be compatible with newly added data information in the future, each type of information is mapped to a data of one binary byte bit. By integrating and storing the point cloud data of the same data type at different times, the storage efficiency of the point cloud data can be effectively improved, the storage space can be saved, and at the same time, the query and reading speed can be accelerated, enabling fast reading of the point cloud data at different times.

[0043] It can be understood that the radar data types can include the three-dimensional coordinate information of laser points, scanning angle information, classification information, flight strip information, flight attitude information, project information, GPS information, data point color information, multiple echo information, and intensity information.

[0044] For example, it is stipulated that the intensity information corresponds to the first bit of the binary byte bit, and the GPS information corresponds to the second bit. Assuming that the point cloud data information contains both intensity information and GPS information at the same time, the corresponding binary bits will all become 1, and the expressed binary form is 00000000000000000000000000000011, which is 3 in decimal. In this way, by mapping different information to different byte bits, it is only necessary to judge the value of a certain byte bit to know whether this type of point cloud information is contained, and at the same time, the space size occupied by each point cloud information record can also be calculated based on this data, which is convenient for quickly loading and reading data later.

[0045] Step 1042: Describe the number of point clouds of the node, acquisition time information, and file index offset address to obtain the index information module.

[0046] Step 1043: Arrange the point cloud data according to the acquisition time information to obtain the data information module.

[0047] Further, after obtaining the data file, the method may further include:

[0048] Step 105: Calculate the stored data of the data file to obtain the original occupied space of the data file.

[0049] Step 106: In the data file, append the packet header information to the head of the data file.

[0050] After the SLAM real-time mapping is completed, append the packet header information and the index offset address to the head of the data file. Appending the original point cloud data to the file head can ensure the integrity of the data and avoid data loss or confusion during the copying process. In this way, the point cloud data storage stage ends.

[0051] Step 10: Obtain the data file to be queried by the client. The data file includes a header information module and an index information module. The header information module includes packet header information, radar data type, and total number of nodes. The index information module includes the number of point clouds of the node, file index offset address, and node timestamp.

[0052] In this step, it is a process of optimizing some parts where the trajectory paths are misaligned or deviated during the SLAM real-time mapping process.

[0053] Step 20: Read the packet header information. When the format of the packet header information is the same as the original format, calculate the occupied space of the data file. The original format is the file format set when creating the data file.

[0054] Furthermore, after the storage stage ends, the reading stage starts. When loading and reading this data file, it is necessary to first read the header information data of the data file, and determine whether the file data has the same type according to the packet header information. If so, continue the operation; otherwise, it proves that the data format is incorrect and the file cannot be loaded.

[0055] When specifically implemented, calculating the occupied space of the data file may specifically include:

[0056] Step 201: Load the information data of all nodes according to the total number of nodes.

[0057] Step 202: When the loading is completed, obtain the occupied space of the last node through the number of point clouds of the node in the data file and the radar data type.

[0058] Step 203: Obtain the total occupied space of the data file according to the occupied space of the last node and the file index offset address of the last node.

[0059] Step 30: When the occupied space of the data file is the same as the original occupied space, read the data of the byte bits. The radar data type is composed of byte bits.

[0060] Steps 10 - 30 can be regarded as an identity authentication to determine whether data loss occurs during the copy-paste or transmission of the data file, or whether others tamper with it without permission.

[0061] Step 40: According to the data of the byte bits, determine the radar data type and load the data of the data file.

[0062] As an optional embodiment, after loading the data of the data file, the method may further include:

[0063] Step 50: Obtain the corresponding pose file according to the file index offset address and node timestamp of the node in the data file.

[0064] In this step, during the optimization process, the corresponding pose data can be obtained according to the index position of the nodes. The node information and the pose data are arranged in one-to-one correspondence in chronological order.

[0065] Step 60: Apply an optimization algorithm to the data of the pose file to minimize the reprojection error and obtain a trajectory data file.

[0066] Preferably, the method may further include:

[0067] In the case of querying multiple sets of node data, divide the multiple sets of node data into multiple tasks, and each task processes a set of node data;

[0068] Create multiple threads, assign the tasks to each thread, and each thread is responsible for executing one task.

[0069] The above is the method embodiment proposed in this application. Based on the same inventive concept, the embodiments of this application also provide a time-series-based laser point cloud data storage device, the structure of which is as Figure 2 shown.

[0070] Figure 2 FIG. is a schematic internal structure diagram of a time-series-based laser point cloud data storage device provided by an embodiment of this application. As Figure 2 shown, the device includes:

[0071] At least one processor 201;

[0072] And a memory 202 communicatively connected to the at least one processor;

[0073] Wherein, the memory 202 stores instructions executable by the at least one processor. The instructions are executed by the at least one processor 201 so that the at least one processor 201 can: obtain the data file to be queried by the client, the data file includes a header information module and an index information module, the header information module includes a packet header information, a radar data type, and the total number of nodes, the index information module includes the number of point clouds of the nodes, the file index offset address, and the node timestamp; read the packet header information, and calculate the occupied space of the data file when the format of the packet header information is consistent with the original format, the original format is the file format set when creating the data file; when the occupied space of the data file is consistent with the original occupied space, read the data of the byte bits, and the radar data type is composed of the byte bits; determine the radar data type according to the data of the byte bits and load the data of the data file.

[0074] Some embodiments of this application provide corresponding to Figure 1A non-volatile computer storage medium for storing laser point cloud data based on time series, storing computer-executable instructions, and the computer-executable instructions are set to: obtain a data file to be queried by a client, where the data file includes a header information module and an index information module, the header information module includes header information, radar data type, and the total number of nodes, and the index information module includes the number of point clouds of a node, the file index offset address, and the node timestamp; read the header information, and calculate the occupied space of the data file when the format of the header information is consistent with the original format, where the original format is the file format set when creating the data file; when the occupied space of the data file is consistent with the original occupied space, read the data of the byte bits, and the radar data type is composed of the byte bits; determine the radar data type according to the data of the byte bits and load the data of the data file.

[0075] Each embodiment in this application is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0076] The systems and media provided by the embodiments of this application correspond one-to-one with the methods. Therefore, the systems and media also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be elaborated here.

[0077] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate for implementing in the process Figure 1 a process or multiple processes and / or blocksFigure 1 means for the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one Figure 1 one or more processes and / or boxes Figure 1 or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one or more processes and / or boxes Figure 1 or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0082] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0083] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0084] It should also be noted that the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that comprises a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus that comprises the element.

[0085] The above are only examples of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for storing laser point cloud data based on time series, characterized in that, The method includes: Obtain a data file to be queried by the client. The data file includes a header information module and an index information module. The header information module includes header packet information, radar data type, and total number of nodes. The index information module includes the number of point clouds of a node, file index offset address, and node timestamp; Read the header packet information. When the format of the header packet information is consistent with the original format, calculate the occupied space of the data file. The original format is the file format set when creating the data file; When the occupied space of the data file is consistent with the original occupied space, read the data of the byte bits. The radar data type is composed of the byte bits; Judge the radar data type according to the data of the byte bits and load the data of the data file.

2. The method for storing laser point cloud data based on time series according to claim 1, characterized in that, After loading the data of the data file, the method further includes: Obtain the corresponding pose file according to the file index offset address and node timestamp of the node in the data file; Perform an optimization algorithm on the data of the pose file to minimize the reprojection error and obtain a trajectory data file.

3. The method for storing laser point cloud data based on time series according to claim 1, characterized in that, Before obtaining the total data information of the data file, the method further includes: Collect point cloud data and create a temporary file and a pose file, and store the point cloud data in the temporary file; During the process of SLAM real-time mapping, the point cloud data respectively generate corresponding pose data; When the SLAM real-time mapping is completed, store the pose data in the pose file; Configure the header information module, index information module, and data information module for the temporary file to obtain the data file.

4. The method for storing laser point cloud data based on time series according to claim 3, characterized in that, After obtaining the data file, the method further includes: Calculate the stored data of the data file to obtain the original occupied space of the data file; In the data file, append the header packet information to the head of the data file.

5. The method for storing laser point cloud data based on time series according to claim 4, characterized in that, Configure the header information module, index information module, and data information module in the data file, specifically including: Set the file format of the header packet information, and respectively correspond the radar data type to the binary byte bits to obtain the header information module; Describe the number of point clouds of the node, acquisition time information, and file index offset address to obtain the index information module; Arrange the point cloud data according to the acquisition time information to obtain the data information module.

6. The method for storing laser point cloud data based on time series according to claim 1, characterized in that, Calculate the occupied space of the data file, specifically including: Load the information data of all nodes according to the total number of nodes; When the loading is completed, obtain the occupied space of the last node through the number of point clouds of the node in the data file and the radar data type; According to the occupied space of the last node and the file index offset address of the last node, obtain the total occupied space of the data file.

7. The method for storing laser point cloud data based on time series according to claim 1, characterized in that, The method further includes: When querying multiple groups of node data, divide the multiple groups of node data into multiple tasks, and each task processes a group of node data; Create multiple threads, assign the tasks to each thread, and each thread is responsible for executing a task.

8. The method for storing laser point cloud data based on time series according to claim 1, characterized in that,The radar data types include three-dimensional coordinate information of laser points, scanning angle information, classification information, flight strip information, flight attitude information, project information, GPS information, data point color information, multiple echo information, and intensity information.

9. A laser point cloud data storage device based on time series, characterized in that, The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to: obtain a data file to be queried by a client, the data file including a header information module and an index information module, the header information module including header packet information, radar data types, and the total number of nodes, and the index information module including the number of point clouds of a node, a file index offset address, and a node timestamp; read the header packet information, and calculate the occupied space of the data file when the format of the header packet information is consistent with the original format, the original format being the file format set when creating the data file; when the occupied space of the data file is consistent with the original occupied space, read the data of the byte bits, the radar data types being composed of the byte bits; judge the radar data types based on the data of the byte bits and load the data of the data file.

10. A non - volatile computer storage medium for storing laser point cloud data based on time series, storing computer - executable instructions, characterized in that, The computer-executable instructions are set to: obtain a data file to be queried by a client, the data file including a header information module and an index information module, the header information module including header packet information, radar data types, and the total number of nodes, and the index information module including the number of point clouds of a node, a file index offset address, and a node timestamp; read the header packet information, and calculate the occupied space of the data file when the format of the header packet information is consistent with the original format, the original format being the file format set when creating the data file; when the occupied space of the data file is consistent with the original occupied space, read the data of the byte bits, the radar data types being composed of the byte bits; judge the radar data types based on the data of the byte bits and load the data of the data file.

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