Digital twin point cloud editing asynchronous updating method

Abstract

The invention discloses a digital twin point cloud editing asynchronous updating method, which comprises the following steps that: a rear end selects a proper point cloud processing algorithm for each mapping stage in sequence, and generates a corresponding mapping task so as to generate a primitive of a three-dimensional model, and uploads the primitive to MinIO as a primitive file; selecting the point cloud data needing to be edited through the front-end interface, and performing editing operation on the point cloud data so as to generate new point cloud data; the front end sends editing operation to the rear end regularly, if it is judged that the editing operation can affect the primitives, corresponding change records are generated, and otherwise, the scene case database is directly updated; the back end reads the change record regularly, and once the editing operation of the point cloud data is not applied yet, a proper point cloud processing algorithm is called to carry out editing operation and primitive segmentation on the related point cloud data, so that new primitives are generated and uploaded to MinIO, and a primitive file is updated, so that the operation threshold is reduced, the accuracy of an editing result is improved, and the editing efficiency of the point cloud data is improved. And the data maintenance work is simplified.

Description

Technical Field

[0001] The present invention relates to a method for editing and processing point cloud data, and in particular to a method for asynchronously updating digital twin point cloud editing, and belongs to the technical field of Internet of Things. Background Art

[0002] In the digital twin technology application system, the general process of point cloud data editing service is as follows: First, point cloud data is imported. The point cloud data can be actual scene data acquired by a 3D scanner or virtual scene data generated by a computer.

[0003] Secondly, the imported point cloud data is preprocessed and registered to generate the original 3D model. Preprocessing includes noise filtering, outlier removal, data downsampling, etc. to improve data quality. The registration principle is to align point cloud data collected from multiple perspectives or at different times to the same coordinate system to ensure the consistency of subsequent processing.

[0004] Next, the original 3D model is edited. The basic editing methods include adding, deleting, moving, rotating, and scaling points to modify and adjust the 3D model. Specific editing is used to further process the preliminary 3D model, such as adding driving areas, recommended routes, speed limit areas, fences, annotations, etc., to optimize and enhance the point cloud information.

[0005] Finally, save the edited 3D model in a specific file format for subsequent use or export to other software or devices.

[0006] However, the calculation time of the above-mentioned point cloud data editing service process is too long to meet the rapid calculation and real-time update of the digital twin model. The specific technical defects are as follows: a. High operation threshold: Since it requires personnel with professional knowledge and skills to perform editing operations, the operation threshold is high and it is difficult to promote and use; b. Unsatisfactory editing results: Due to misoperation or tool limitations during the editing process, the accuracy of the editing results is insufficient; c. Difficult data maintenance: Since the editing results need to be saved in the corresponding database or file, when the data changes, it is necessary to re-publish the editing tool or update the database, which makes model updates difficult and the workload is large.

[0007] Therefore, the present invention urgently needs to develop and design a new method for editing and updating point cloud data. Summary of the invention

[0008] In response to the above-mentioned existing technical problems, the present invention proposes an asynchronous update method for digital twin point cloud editing, which aims to lower the operation threshold, improve the accuracy of editing results, and simplify the workload of data maintenance.

[0009] To achieve the above technical objectives, the present invention provides a digital twin point cloud editing asynchronous update method, using a digital twin system mainly composed of a mapping device, a DDT front end and back end, MinIO, and a robot, including the following steps: Collect point cloud data in the scene through mapping devices and upload it to MinIO; The backend selects appropriate point cloud processing algorithms for each mapping stage according to the characteristics of point cloud data and mapping requirements, and generates corresponding mapping tasks; all mapping tasks enter the task queue in order, and are dequeued from the task queue in order for mapping processing, thereby generating the primitives of the 3D model and uploading them to MinIO as primitive files; Select the point cloud data to be edited through the front-end interface and perform editing operations on it to generate new point cloud data; the front-end regularly sends the editing operations to the back-end. After receiving the editing operations, if the back-end determines that it will affect the primitives, it generates the corresponding change records, otherwise it directly updates the scene case database; The backend periodically reads the change records. Once it finds an editing operation that has not been applied to the point cloud data, it calls the appropriate point cloud processing algorithm based on the change record to perform editing operations and primitive segmentation on the relevant point cloud data, thereby generating new primitives, which are then uploaded to MinIO and updated the primitive files.

[0010] The method of the present invention further comprises: a point cloud data preprocessing algorithm, a point cloud data registration algorithm, a point cloud data three-dimensional reconstruction algorithm, a point cloud data primitive segmentation algorithm, and a point cloud data post-processing algorithm.

[0011] The method of the present invention further includes: dequeuing from the task queue in order to perform mapping processing, including: For the currently queued mapping task, start the corresponding algorithm container, download the corresponding point cloud data from MinIO, and import it into the algorithm container; start the operation of the point cloud processing algorithm, and monitor the running status and results of the algorithm container; once the point cloud processing algorithm completes the operation and generates the results, upload the results to MinIO; Then process the return value and final status, update the backend status displayed on the front end, close the corresponding algorithm container, and update the task queue; The next mapping task to be dequeued repeats the above steps until there are no pending mapping tasks in the task queue.

[0012] The method of the present invention further includes starting a corresponding algorithm container according to the point cloud processing algorithm, including: According to the selected point cloud processing algorithm, start the corresponding algorithm container in K8S; Then judge the startup status of the algorithm container: If the startup is successful, the backend status will be in the map building state, and the backend status displayed on the front end will be updated; If the startup fails, process the return value and final status, and update the backend status displayed on the front end.

[0013] The method of the present invention further includes, after the operation of the point cloud processing algorithm, the following steps: The backend starts the WebSocket service; Check the backend status through the frontend. If the backend status is in the map building state, the frontend initiates a WebSocket connection and transmits data with the WebSocket service through the port range exposed by K8S.

[0014] The method of the present invention further includes selecting point cloud data to be edited through the front-end interface and performing editing operations thereon, including: Select the point cloud data to be edited through the front-end interface; The front end downloads the corresponding point cloud data from MinIO through the back end, performs editing operations on it, and reflects the editing operations on the point cloud data in real time.

[0015] The method of the present invention is further characterized in that the editing operation includes: basic editing and specific editing; The basic editing includes adding, deleting, moving, rotating and scaling points; The specific editing includes adding driving areas, recommended routes, speed limit areas, fences, and labels.

[0016] In the method of the present invention, the change record includes the ID of the edited point cloud data, the type of editing operation, and corresponding parameters.

[0017] The method of the present invention further includes calling a suitable point cloud processing algorithm according to the change record, performing editing operations and primitive segmentation on the corresponding point cloud data, thereby generating new primitives, including: The backend downloads the corresponding point cloud data from MinIO, calls the appropriate point cloud processing algorithm according to the change record, starts the corresponding algorithm container, and imports the point cloud data into the algorithm container; starts the operation of the point cloud processing algorithm, and monitors the running status and results of the algorithm container; after the operation is completed, generates the edited point cloud data, and closes the corresponding algorithm container; Call the point cloud data segmentation algorithm, start the corresponding algorithm container, and import the edited point cloud data into the algorithm container; start the operation of the point cloud data segmentation algorithm, and monitor the running status and results of the algorithm container; after the operation is completed, generate new primitives and close the corresponding algorithm container.

[0018] The method of the present invention further includes, after the updating of the metafile, the following steps: The backend notifies the frontend user to refresh the editing operations applied to the point cloud data; Once the front-end user refreshes, he can query the updated primitives and the change records corresponding to the editing operations that have not been applied to the point cloud data, and load and render the latest primitives.

[0019] In summary, the innovation of the present invention lies in decoupling the editing operation of point cloud data from the point cloud processing algorithm, and realizing free combination through dynamic binding, so that the editing operation and the operation of the point cloud processing algorithm can be carried out separately, thereby improving the flexibility and efficiency of editing, solving the problem of difficulty in updating the digital twin model, lowering the operation threshold, improving the accuracy of the editing results, and simplifying the work of data maintenance.

[0020] Compared with conventional point cloud data editing services, the present invention has the following technical advantages: 1. The present invention separates the point cloud data from the point cloud processing algorithm, so that the editing results are independent of the original point cloud data. When the data changes, only the point cloud processing algorithm needs to be updated to realize the asynchronous update of the digital twin 3D model, making the update of the digital twin 3D model simple and easy, greatly simplifying the work of data maintenance.

[0021] 2. The present invention realizes multiple editing and reuse of point cloud data, avoids the work of repeatedly making point cloud processing algorithms, makes maintenance work simple and easy, and greatly reduces the workload of developers.

[0022] 3. The present invention obtains more ideal editing results and improves the accuracy of editing results by providing efficient and accurate point cloud processing algorithms and tools. At the same time, by providing methods and tools to simplify editing operations, the operating threshold is lowered, so that non-professionals can easily edit and update point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a schematic diagram of the steps of an embodiment of the method of the present invention; Figure 2 Flow chart of step S1 in the method embodiment of the present invention; Figure 3Flow chart of steps S2 and S3 in the method embodiment of the present invention. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0026] Furthermore, it should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, modules and / or units, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, modules, units and / or their collections. It should also be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0027] like Figure 1 As shown, this embodiment provides a digital twin point cloud editing asynchronous update method, using a digital twin system mainly composed of a mapping device, a DDT front end and back end, MinIO, and a robot.

[0028] It should be noted that DDT (Dynamic digital twins) is an editing and display platform for digital twin technology, which can collect, store and process a large amount of data in real time, generate digital twin copies through data analysis and modeling, and then achieve real-time simulation and monitoring of the physical world by establishing virtual digital copies. It is mainly used in application scenarios such as intelligent manufacturing, intelligent inspection, status monitoring and early warning analysis of equipment, production lines, factories, etc. MinIO is a cloud storage service used to store large-scale, distributed unstructured data, such as point cloud data.

[0029] S1. Collect SLAM data and generate 3D models: collect point cloud data in the scene through mapping equipment and upload it to MinIO; the backend selects appropriate point cloud processing algorithms for each mapping stage according to the characteristics of point cloud data and mapping requirements, and generates corresponding mapping tasks; all mapping tasks enter the task queue in order, and then exit the task queue in order for mapping processing, thereby generating the primitives of the 3D model and uploading it to MinIO as primitive files. The specific steps are described below.

[0030] S1-1. Collect point cloud data in the scene through mapping devices such as 3D scanners, lidar or cameras, or robots carrying mapping devices, and upload the point cloud data to MinIO.

[0031] S1-2. The backend selects appropriate point cloud processing algorithms for the preprocessing stage, registration stage, 3D reconstruction stage, and primitive segmentation stage according to the characteristics of the point cloud data and mapping requirements, and generates corresponding mapping tasks.

[0032] In specific implementation, mapping usually refers to the process of building a complete 3D model from raw point cloud data in the initial stage. This process may involve multiple mapping stages, such as preprocessing, registration, 3D reconstruction, primitive segmentation, etc., and may also involve multiple point cloud processing algorithms. The point cloud processing algorithm can be an algorithm provided by a third-party library or software, or a custom-developed algorithm, including but not limited to: point cloud data preprocessing algorithm, point cloud data registration algorithm, point cloud data 3D reconstruction algorithm, point cloud data primitive segmentation algorithm, point cloud data post-processing algorithm, which are introduced as follows.

[0033] 1) Point cloud data preprocessing algorithm: By filtering, denoising, resampling, etc., the point cloud data is processed to remove noise and abnormal points, and optimize the quality and density of the point cloud data. In specific implementation, the algorithm can adopt statistical filtering methods, distance-based filtering methods, shape-based filtering methods, etc.

[0034] 2) Point cloud data registration algorithm: Register point cloud data at different positions and angles to align them in the same coordinate system to form a 3D model. In specific implementation, the algorithm can adopt feature-based registration methods, geometry-based registration methods, optimization-based registration methods, etc.

[0035] 3) Point cloud data reconstruction algorithm: Convert point cloud data into a 3D model or surface to achieve visualization and analysis of point cloud data. In specific implementation, the algorithm can adopt a reconstruction method based on triangular mesh, a reconstruction method based on voxels, a reconstruction method based on implicit surfaces, etc.

[0036] 4) Point cloud data segmentation algorithm: The point cloud map is divided into 50m×50m primitives. Each primitive is composed of several point clouds, and each point cloud has corresponding position information, height information, and color information.

[0037] 5) Point cloud data post-processing algorithm: read the 3D model and change records, including the ID of the edited point cloud data, the type of edit, and the corresponding parameters. The algorithm is based on the edited point cloud data, considers the distribution characteristics and density of the surrounding point clouds, and efficiently updates the coordinates of the associated points through the coordinate transformation matrix.

[0038] In addition, when selecting a suitable point cloud processing algorithm based on the characteristics of point cloud data and mapping requirements, the following conditions should be considered: the applicable scenarios of the algorithm, accuracy requirements, computing resource requirements, and algorithm availability, etc. For example, different point cloud processing algorithms are suitable for different scenarios. Some point cloud processing algorithms are suitable for 3D reconstruction of indoor environments, while others are suitable for 3D reconstruction of outdoor environments.

[0039] In addition, each point cloud processing algorithm has a corresponding algorithm container. The algorithm container refers to the operating environment created to run the algorithm, which contains the dependencies and configurations required by the algorithm. Each algorithm container can be customized according to the characteristics and requirements of the algorithm to provide the best operating environment.

[0040] From the above, we can see that the key point of this step is to separate the point cloud data from the point cloud processing algorithm, so that the editing results and the original data are independent of each other, and multiple editing and reuse of the point cloud data are realized. And the most appropriate point cloud processing algorithm can be selected to edit and optimize the point cloud data, which improves the accuracy and efficiency of point cloud processing.

[0041] S1-3, such as Figure 2 As shown, in the specific implementation, the corresponding assembly parameters are obtained, including the address of the algorithm, that is, the location or path where the algorithm is located; the description error, that is, a detailed description of the error or exception that occurred during the operation of the algorithm; and the bag file address, that is, the file address containing the point cloud data.

[0042] S1-4. All mapping tasks enter the task queue in order, and are dequeued from the task queue in order for mapping processing, thereby generating the primitives of the 3D model and uploading them to MinIO as primitive files. The specific contents are described below.

[0043] It should be noted that this step can be implemented by creating a task queue or using a queue management system. All mapping tasks are queued to generate a task queue, which can not only ensure the order and fairness of the tasks, but also avoid resource competition and performance degradation caused by multiple mapping tasks being carried out simultaneously. The task queue manages mapping tasks based on the first-in-first-out principle. New mapping tasks are directly added to the end of the queue and wait for processing. The system monitors the resource occupancy and operating load of each algorithm container, thereby allocating mapping tasks to the adapted algorithm container. In addition, the task queue can be configured with the number of mapping tasks that can be started simultaneously.

[0044] like Figure 2As shown in the figure, in the specific implementation, first, for the current mapping task, the corresponding algorithm container is started in K8S (Kubernetes, referred to as K8S, is an open source system for automatic deployment, scaling and management of containerized applications) according to the point cloud processing algorithm. In addition, the startup status of the algorithm container is judged: if the startup is successful, the backend status is in the mapping state, and the backend status displayed on the front end is updated; if the startup fails, the return value and the final status are processed, and the backend status displayed on the front end is updated.

[0045] Secondly, download the corresponding point cloud data from MinIO and import it into the algorithm container. Start the operation of the point cloud processing algorithm, and monitor the running status and results of the algorithm container. During the operation, the backend starts the WebSocket service (it is a network communication protocol, and it is a protocol for full-duplex communication on a single TCP connection provided by HTML5.) and transmits data with the port interval exposed by K8S, including: radar, collection, full map data, map incremental data, compressed data, etc. At this time, check the backend status through the front end. If the backend status is in the map construction, the front end initiates a WebSocket connection and transmits data with the WebSocket service through the port interval exposed by K8S.

[0046] Furthermore, once the point cloud processing algorithm has completed the operation and generated the results, the results are uploaded to MinIO. The return value and final status are then processed, and the backend status displayed on the front end is updated. The backend closes the algorithm container and updates the task queue.

[0047] Next, determine whether there is any data to be processed in the task queue; if so, the next mapping task is dequeued for mapping processing, and the above steps are repeated until there are no mapping tasks to be processed in the task queue, thereby releasing resources and allocating resources for new reconstruction tasks; if not, end.

[0048] Finally, the point cloud data of the 3D model is stored on MinIO according to the "metadata" file format requirements, thereby constructing the original metadata file on MinIO.

[0049] S2. Edit the operation model and record the operation history: select the point cloud data to be edited through the front-end interface and perform editing operations on it to generate new point cloud data; the front-end regularly sends the editing operations to the back-end. After receiving the editing operations, if the back-end determines that it affects the primitives, it generates the corresponding change records, otherwise it directly updates the scene case database. The specific steps are described as follows.

[0050] It should be noted that since the original point cloud data may have some problems or need further optimization and editing, the point cloud data needs to be edited through the front end after the 3D model is generated. The front end interface provides point cloud visualization and editing functions. Users can select the point cloud data to be edited on the front end interface, and then perform respective editing operations on the selected point cloud data.

[0051] S2-1, such as Figure 3 As shown, in the specific implementation, the point cloud data to be edited is selected through the front-end interface, and the front-end downloads the corresponding point cloud data from MinIO through the back-end, performs editing operations on it, and reflects the editing operations on the point cloud data in real time. The editing operations include: basic editing and specific editing; the basic editing includes adding, deleting, moving, rotating, and scaling points; the specific editing includes adding driving areas, recommended routes, speed limit areas, fences, and annotations.

[0052] S2-2, such as Figure 3 As shown, in the specific implementation, in order to ensure the real-time and effectiveness of the editing, the front end will periodically (such as at regular intervals or when the user completes a set of editing actions) send these editing operations to the back end in the form of data packets.

[0053] S2-3, such as Figure 3 As shown in the figure, different editing operations have different degrees and ways of affecting point cloud data during implementation. For example, if the editing operation changes the coordinate position, normal direction or color of the point cloud data, then these changes are likely to be reflected in the geometry and attributes of the primitives. Once the primitives are affected, the primitive files need to be updated. Therefore, after receiving the editing operation sent by the front end, the back end first determines whether the editing operation is of the type of adding, deleting, moving or rotating. If so, it will affect the primitives. Specifically, there are two situations.

[0054] ‌a. If the backend determines that the editing operation does affect the relevant primitives, it will generate one or more change records based on the specific content of the editing operation. These change records describe in detail the ID of the edited point cloud data, the type of editing operation (add, delete, move, rotate, etc.), and the corresponding parameters.

[0055] b. If the backend determines that the editing operation does not affect any primitives, it will directly update the point cloud data in the scene case database and end the process without generating a change record. This direct update ensures the real-time and consistency of the database while reducing unnecessary storage and computing overhead.

[0056] From the above, it can be seen that the front-end and back-end of the method of the present invention can work together to achieve multiple editing and reuse of point cloud data, avoid the work of repeated algorithm creation, and greatly simplify the workload of developers. At the same time, by generating change records and processing direct updates that do not affect editing operations, the system can maintain the integrity and consistency of the data and provide reliable data support for applications such as digital twins.

[0057] S3, triggering the algorithm queue and automatically updating the model: the backend periodically reads the change records. Once an edit operation that has not been applied to the point cloud data is found, the appropriate point cloud processing algorithm is called according to the change record to perform edit operations and primitive segmentation on the relevant point cloud data, thereby generating new primitives, which are then uploaded to MinIO and the primitive files are updated. The specific steps are described below.

[0058] S3-1, such as Figure 3 As shown in the figure, in the specific implementation, the backend configures a scheduled task, which runs at a certain time interval (such as every minute, every hour, etc.) to check the change records. When the scheduled task runs, the backend scans the change records stored in the system and traverses the change records to check whether there are any edit operations that have not been applied to the point cloud data; if no unapplied edit operations are found, the process ends and waits for the next scheduled task to run; if unapplied edit operations are found, proceed to the next step.

[0059] S3-2, such as Figure 3 As shown, during specific implementation, the backend will parse the JSON data in the change record to obtain the ID of the edited point cloud data, the type of editing operation, and the corresponding parameters.

[0060] S3-3, such as Figure 3 As shown, in the specific implementation, the backend downloads the point cloud data to be edited from MinIO according to the information of the change record and saves it locally for subsequent editing operations and primitive segmentation.

[0061] First, the backend uses the primitive toolkit method program to call the appropriate point cloud processing algorithm based on the information in the change record and starts the corresponding algorithm container. The point cloud processing algorithm can be an algorithm provided by a third-party library or software, or a custom-developed algorithm. Then, the corresponding point cloud data is imported into the algorithm container, the operation of the point cloud processing algorithm is started, and the operation status and results of the algorithm container are monitored. In addition, the point cloud processing algorithm modifies the point cloud data accordingly according to the type of editing operation and the corresponding parameters in the change record. After the operation is completed, the edited point cloud data is generated and the corresponding algorithm container is closed.

[0062] Secondly, call the point cloud data segmentation algorithm and start the corresponding algorithm container, import the edited point cloud data into the algorithm container, start the operation of the point cloud data segmentation algorithm, and monitor the operation status and results of the algorithm container. After the operation is completed, close the corresponding algorithm container, generate new primitives, upload them to MinIO, and update the primitive file.

[0063] In this way, the original 3D model and metafile can be edited again, and the latest result can be saved after editing. In addition, the backend records the storage path and version information of the new metafile in the database as a new version of the metafile. Afterwards, the user can obtain the new version of the metafile through the front-end interface or other means for further analysis, processing or visualization operations.

[0064] S3-4, such as Figure 3 As shown, in the specific implementation, the backend notifies the frontend user to refresh the editing operations that have been applied to the point cloud data; once the frontend user refreshes, the updated primitives and the change records corresponding to the editing operations that have not been applied to the point cloud data can be queried, and the latest primitives can be loaded and rendered.

[0065] In summary, in the past, the practice of building the point cloud data editing function into the point cloud processing algorithm, although it achieved a high degree of coupling, encountered great difficulties in improving convenience and accuracy. The method of the present invention innovatively separates the point cloud data editing function from the point cloud processing algorithm. In this way, whether it is to enhance the convenience of point cloud editing or to improve the accuracy of point cloud editing, there is no need for developers to republish the point cloud processing algorithm. At the same time, the same point cloud data can also be flexibly shared by multiple point cloud processing algorithms. Obviously, by decoupling the point cloud data editing function from the point cloud processing algorithm, the present invention not only significantly improves the flexibility and maintainability of the point cloud processing algorithm, but also effectively saves memory usage, thereby improving the execution efficiency of the point cloud processing algorithm. In addition, the method of the present invention is widely applicable to a variety of point cloud processing application scenarios such as three-dimensional modeling, map construction, and object recognition, demonstrating its strong versatility and practicality.

[0066] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A digital twin point cloud editing asynchronous update method, characterized in that: Using the digital twin system mainly composed of mapping equipment, DDT front-end and back-end, MinIO, and robots, the following steps are included: Collect point cloud data in the scene through mapping devices and upload it to MinIO; The backend selects appropriate point cloud processing algorithms for each mapping stage according to the characteristics of point cloud data and mapping requirements, and generates corresponding mapping tasks; all mapping tasks enter the task queue in order, and are dequeued from the task queue in order for mapping processing, thereby generating the primitives of the 3D model and uploading them to MinIO as primitive files; Select the point cloud data to be edited through the front-end interface and perform editing operations on it to generate new point cloud data; the front-end regularly sends the editing operations to the back-end. After receiving the editing operations, if the back-end determines that it will affect the primitives, it generates the corresponding change records, otherwise it directly updates the scene case database; The backend periodically reads the change records. Once it finds an editing operation that has not been applied to the point cloud data, it calls the appropriate point cloud processing algorithm based on the change record to perform editing operations and primitive segmentation on the relevant point cloud data, thereby generating new primitives, which are then uploaded to MinIO and updated the primitive files.

2. A digital twin point cloud editing asynchronous update method according to claim 1, characterized in that: The point cloud processing algorithm includes: a point cloud data preprocessing algorithm, a point cloud data registration algorithm, a point cloud data three-dimensional reconstruction algorithm, a point cloud data primitive segmentation algorithm, and a point cloud data post-processing algorithm.

3. A digital twin point cloud editing asynchronous update method according to claim 1, characterized in that: The step of sequentially dequeuing from the task queue for map building includes: For the currently queued mapping task, start the corresponding algorithm container, download the corresponding point cloud data from MinIO, and import it into the algorithm container; start the operation of the point cloud processing algorithm, and monitor the running status and results of the algorithm container; once the point cloud processing algorithm completes the operation and generates the results, upload the results to MinIO; Then process the return value and final status, update the backend status displayed on the front end, close the corresponding algorithm container, and update the task queue; The next mapping task to be dequeued repeats the above steps until there are no pending mapping tasks in the task queue.

4. A digital twin point cloud editing asynchronous update method according to claim 3, characterized in that: The starting of a corresponding algorithm container according to the point cloud processing algorithm includes: According to the selected point cloud processing algorithm, start the corresponding algorithm container in K8S; Then judge the startup status of the algorithm container: If the startup is successful, the backend status will be in the map building state, and the backend status displayed on the front end will be updated; If the startup fails, process the return value and final status, and update the backend status displayed on the front end.

5. A digital twin point cloud editing asynchronous update method according to claim 3 or 4, characterized in that: After starting the operation of the point cloud processing algorithm, the method further includes: The backend starts the WebSocket service; Check the backend status through the frontend. If the backend status is in the map building state, the frontend initiates a WebSocket connection and transmits data with the WebSocket service through the port range exposed by K8S.

6. A digital twin point cloud editing asynchronous update method according to claim 1, characterized in that: The point cloud data to be edited is selected through the front-end interface, and the editing operation is performed on it, including: Select the point cloud data to be edited through the front-end interface; The front end downloads the corresponding point cloud data from MinIO through the back end, performs editing operations on it, and reflects the editing operations on the point cloud data in real time.

7. A digital twin point cloud editing asynchronous update method according to claim 1 or 6, characterized in that: The editing operation includes: basic editing and special editing; The basic editing includes adding, deleting, moving, rotating and scaling points; The specific editing includes adding driving areas, recommended routes, speed limit areas, fences, and labels.

8. The method for asynchronously updating digital twin point cloud editing according to claim 1, characterized in that: The change record includes: the ID of the edited point cloud data, the type of editing operation, and corresponding parameters.

9. A digital twin point cloud editing asynchronous update method according to claim 1 or 8, characterized in that: The method of calling a suitable point cloud processing algorithm according to the change record, performing editing operations and primitive segmentation on the corresponding point cloud data, thereby generating new primitives, includes: The backend downloads the corresponding point cloud data from MinIO, calls the appropriate point cloud processing algorithm according to the change record, starts the corresponding algorithm container, and imports the point cloud data into the algorithm container; starts the operation of the point cloud processing algorithm, and monitors the running status and results of the algorithm container; after the operation is completed, generates new point cloud data and closes the corresponding algorithm container; Call the point cloud data segmentation algorithm, start the corresponding algorithm container, and import the edited point cloud data into the algorithm container; start the operation of the point cloud data segmentation algorithm, and monitor the running status and results of the algorithm container; after the operation is completed, generate new primitives and close the corresponding algorithm container.

10. A digital twin point cloud editing asynchronous update method according to claim 1, characterized in that: After the metafile is updated, the following steps are also included: The backend notifies the frontend user to refresh the editing operations applied to the point cloud data; Once the front-end user refreshes, he can query the updated primitives and the change records corresponding to the editing operations that have not been applied to the point cloud data, and load and render the latest primitives.

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