Mine road boundary dynamic updating method, system and device based on local point cloud map and storage medium

Abstract

The invention discloses a mine road boundary dynamic updating method, system and device based on a local point cloud map, and a storage medium, the method generates a local point cloud map for a scene in which road boundaries are frequently updated in soil loading and dumping, and automatically extracts boundary vectors in the local point cloud map, so that the accuracy of the boundaries can be ensured, and the reliability of the road boundaries is improved. The requirement for map boundary position freshness can be met, vector extraction is combined with a rule class post-processing mode through a semantic segmentation model, the accuracy of boundary vector extraction is improved, and a high-precision map is provided for intelligent driving of mine roads.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent driving technology and relates to high-precision map updating, and specifically to a method, system, device and storage medium for dynamically updating mine road boundaries based on local point cloud maps. Background Art

[0002] In recent years, amidst the shift towards intelligent and autonomous driving, high-precision maps have become a core cornerstone of autonomous driving systems, playing a crucial role in key areas such as intelligent mining. Mining environments are characterized by complex and varied terrain, often located in remote, harsh environments. These areas are not only subject to frequent changes due to geological activity, but also to constant changes in ground conditions caused by mechanical operations and material transportation within the mines. To ensure the safety and reliability of autonomous driving systems in these environments, it is crucial to dynamically generate and continuously update real-time high-precision maps that reflect the latest terrain features. High-precision maps not only provide accurate positioning information but also assist autonomous vehicles in path planning, collision avoidance, and safe and smooth driving.

[0003] Existing road boundary extraction methods primarily target structured lane lines or curbs in urban areas, where updates are less frequent and boundaries are relatively regular. Therefore, the traditional approach involves using a data collection vehicle to perform comprehensive data collection before creating a high-precision map. However, in mining scenarios, road boundaries can change at any time, placing even higher demands on real-time performance, requiring rapid reflection of boundary updates. Using traditional methods for comprehensive data collection followed by high-precision map creation would be prohibitively time-consuming and would fail to meet map freshness requirements. Summary of the Invention

[0004] In response to the above problems, the main purpose of the present invention is to design a method, system, equipment and storage medium for dynamically updating the boundaries of mine roads based on local point cloud maps, so as to solve the problems of low map accuracy and freshness in the context of changing geological activities on mine roads.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions: A method for dynamically updating the boundary of a mine road based on a local point cloud map, the method comprising the following steps: Use LiDAR to generate a point cloud map of the loading and dumping area and upload it to the cloud, where the trained semantic segmentation model is deployed. Based on the point cloud data of the soil loading and dumping area stored in the cloud, model semantic extraction is performed, and a semantic label is assigned to each point cloud to obtain the retaining wall and road surface point clouds; Obtain the interception range of the region of interest based on the area of the road point cloud, intercept the region of interest from the original point cloud data, extract the boundary candidate points, and extract the boundary vector by reordering; The generated boundary vectors are filtered and pushed to the high-precision map engine for updating.

[0006] As a further description of the present invention, using laser radar to generate a point cloud map of the loading and dumping area and uploading it to the cloud includes the following steps: The mapping mode is triggered every time a mining vehicle is loading and dumping soil; Starting from 30 meters away from the retaining wall, a key frame point cloud is obtained from the lidar every 1 meter; Eliminate the point cloud within 10 meters from the center of each frame of the point cloud car system; Continue to superimpose new keyframe point clouds until the mining vehicle stops at the storage location; Transfer data to the data platform cloud for storage.

[0007] As a further description of the present invention, model semantic extraction includes the following steps: Obtain local point cloud data of the soil loading and dumping area stored in the cloud; Using the semantic segmentation model in the cloud, the semantic labels of each point cloud are inferred; The inference parameters include block size equal to 16 meters, step length equal to 8 meters, and number of block sampling points equal to 12000; The extracted semantic information includes the retaining wall and road surface point clouds, which are used as input for boundary vector extraction.

[0008] As a further description of the present invention, the boundary vector extraction is based on the semantically extracted retaining wall and road surface point clouds, and the following steps are performed in sequence: obtaining the region of interest, extracting candidate boundary points, and reordering to obtain the boundary vector.

[0009] As a further description of the present invention, boundary vector extraction includes the following steps: Get the region of interest: Clustering the road point cloud obtained by semantic extraction of the model; Find the road cluster category with the largest number of point clouds in the clustering results, and obtain the maximum and minimum X, Y values of the road; Based on the maximum value of X and Y, increase the expansion degree, and based on the minimum value of X and Y, reduce the expansion degree to obtain the interception range of the region of interest; Extract candidate boundary points: Extract the region of interest from the original point cloud data; Obtain the point cloud of the retaining wall in the area of interest through semantic labeling; Obtain each retaining wall object in the retaining wall point cloud by clustering, and extract the boundary point cloud of each retaining wall cluster; Traverse all the extracted boundary points, set a range threshold, and select all points with the semantic label "road surface" within the threshold range. The selected points are the candidate boundary points; Reorder to get the boundary vector: Calculate the distance between two adjacent points of the boundary candidate points in sequence, and find the two endpoints with the largest adjacent distance, the starting end and the ending end; The 3D position information of all point clouds from the starting end to the ending end is saved in sequence to obtain the boundary vector.

[0010] As a further description of the present invention, by setting a threshold value of the number of point clouds, the boundary vectors are screened, including the following steps: If the number of points in the boundary vector exceeds the set point cloud number threshold, it will be directly filtered; If the number of points in the boundary vector is lower than the set point cloud number threshold, clustering is performed, the number of vector points in each cluster is counted, and point cloud segments with less than 80 points are removed; The filtered boundary vectors are saved as GeoJSON vector data in the form of Polyline and pushed to the high-precision map engine for updating.

[0011] A mine road boundary dynamic update system based on a local point cloud map, which is used to implement the above method, and includes a data acquisition module, a data processing module, a region extraction module, a boundary vector extraction module, a vector screening module, and a map engine update module; The data acquisition module is used to generate point cloud data of the soil loading and dumping area through the laser radar and upload it to the data processing module; The data processing module is used to deploy the trained semantic segmentation model to process the uploaded point cloud data of the loading and dumping area, assign a semantic label to each point cloud, and the semantic labels include wall and road surface, to obtain the retaining wall and road surface point clouds; The region extraction module determines the interception range of the region of interest based on the road surface point cloud area and intercepts the region from the original point cloud data; The boundary vector extraction module is used to extract candidate boundary points from the region of interest intercepted by the region extraction module, and extract boundary vectors by reordering; The vector screening module is used to screen the boundary vectors generated by the boundary vector extraction module to obtain screened boundary vectors; The map engine update module is used to receive the boundary vectors filtered by the vector screening module and push them to the high-precision map engine for updating, thereby realizing dynamic updating of the mine road boundaries.

[0012] As a further description of the present invention, the semantic segmentation model uses the convpoint model to perform point cloud semantic segmentation; Boundary vector extraction uses DBSCAN clustering and Alpha Shape algorithm to extract boundary point cloud; When the number of points in the boundary vector is lower than the set point cloud number threshold, DBSCAN clustering is used to retain the point cloud segments with more than 80 cluster points. The boundary vector data received by the HD map engine is GeoJSON vector data in the form of Polyline.

[0013] An electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, and wherein the memory is used to store a computer program; The processor is configured to execute the above-mentioned dynamic update method by running the computer program stored in the memory.

[0014] A computer-readable storage medium stores a computer program, wherein the computer program implements the above-mentioned dynamic update method when executed by a processor.

[0015] Compared with the prior art, the technical effects of the present invention are: The present invention provides a method, system, device and storage medium for dynamically updating mine road boundaries based on local point cloud maps. The method generates a local point cloud map for scenarios where road boundaries are frequently updated during soil loading and dumping, and automatically extracts boundary vectors from the local point cloud map, thereby ensuring the accuracy of the boundaries and meeting the requirements for the freshness of the map boundary positions. Vector extraction is combined with a semantic segmentation model and rule-based post-processing to improve the accuracy of boundary vector extraction, providing high-precision maps for intelligent driving on mine roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the updating method of the present invention. DETAILED DESCRIPTION

[0017] The present invention is described in detail below with reference to the accompanying drawings: In one embodiment of the present invention, a method for dynamically updating the boundary of a mine road based on a local point cloud map is disclosed. Figure 1 As shown in the figure, for scenarios where road boundaries are frequently updated, such as soil loading and dumping, a local point cloud map is generated, and then the road boundary vectors are extracted from the point cloud map for map update, while meeting the requirements of high-precision map accuracy and freshness. Specifically, the method includes the following steps: Use LiDAR to generate a point cloud map of the loading and dumping area and upload it to the cloud, where the trained semantic segmentation model is deployed. Based on the point cloud data of the soil loading and dumping area stored in the cloud, model semantic extraction is performed, and a semantic label is assigned to each point cloud to obtain the retaining wall and road surface point clouds; Obtain the interception range of the region of interest based on the area of the road point cloud, intercept the region of interest from the original point cloud data, extract the boundary candidate points, and extract the boundary vector by reordering; The generated boundary vectors are filtered and pushed to the high-precision map engine for updating.

[0018] In this embodiment, the above steps are specifically analyzed as follows: 1. Use LiDAR to generate a point cloud map of the loading and dumping area and upload it to the cloud Specifically, when vehicles are working in an open-pit mine, a point cloud map of the loading and dumping area is generated using LiDAR and uploaded to the cloud. This process involves the following steps: The mapping mode is triggered every time a mining vehicle is loading and dumping soil; Starting from 30 meters away from the retaining wall, a key frame point cloud is obtained from the lidar every 1 meter; In order to reduce transmission pressure and space occupation, the point cloud within 10 meters from the center of each frame of the point cloud car system is eliminated; Continue to superimpose new keyframe point clouds until the mining vehicle stops at the storage location; Transfer data to the data platform cloud for storage.

[0019] 2. Model Semantic Extraction Specifically, the trained semantic segmentation model deployed on the cloud is the convpoint point cloud semantic segmentation model, or other large-scale point cloud semantic segmentation models, such as randlanet and kpconv. Model semantic extraction includes the following steps: Obtain local point cloud data of the soil loading and dumping area stored in the cloud. The point cloud coverage of each upload is generally within 200×200 meters, and the number of point clouds is within the range of 100,000-200,000; Using the semantic segmentation model in the cloud, the semantic labels of each point cloud are inferred; To ensure the semantic segmentation model's segmentation accuracy meets the requirements for subsequent boundary vector extraction and to keep the inference time under 1 minute, the relevant inference parameters include a block size of 16 meters, a step size of 8 meters, and a block sampling point count of 12,000. The extracted semantic information includes the retaining wall and road surface point clouds, which are used as input for boundary vector extraction.

[0020] 3. Boundary Vector Extraction Specifically, the boundary vector extraction is based on the semantically extracted retaining wall and road surface point clouds, and the following steps are performed in sequence: obtaining the region of interest, extracting candidate boundary points, and reordering to obtain the boundary vector.

[0021] (1) Obtaining the region of interest To reduce redundant point cloud interference (typically residual point clouds identified as mountains) and improve processing efficiency, the region of interest (ROI) for subsequent processing must be calculated. Specifically, DBSCAN clustering is performed on the road point cloud obtained through semantic extraction of the model. The clustering results identify the road cluster with the largest number of point clouds and the maximum and minimum X and Y values of that road. Based on the maximum X and Y values, an expansion factor is added, and based on the minimum X and Y values, an expansion factor is reduced to determine the interception range of the ROI.

[0022] It should be noted that the above expansion degree is usually set to 5 meters, which can be set according to actual needs and is not limited to a single data value.

[0023] (2) Extracting alternative boundary points Based on the interception range of the region of interest, candidate boundary points are obtained from the original point cloud data. Specifically, the region of interest is intercepted from the original point cloud data, and the retaining wall point cloud in the region of interest is obtained using semantic labels. Each retaining wall object is obtained from the retaining wall point cloud through DBSCAN clustering, and the alpha shape algorithm is used to extract the boundary point cloud of the retaining wall for each retaining wall cluster. Since the output boundary only considers the intersection of the output road surface and the retaining wall, it is necessary to filter out the boundary point cloud that does not belong to this area. In this embodiment, all extracted boundary points are traversed in sequence, and by setting a range threshold, all points with the semantic label of road surface within the threshold range are selected as candidate boundary points.

[0024] If the range threshold is set to 1 meter, all the extracted boundary points are traversed in turn. If there is a point cloud with the semantic label of road surface within 1 meter of the point, the point is retained; otherwise, the point is removed to obtain the candidate points of the boundary point cloud.

[0025] (3) Reorder to obtain boundary vector Specifically, the distance between two adjacent points of the obtained boundary candidate points is calculated in sequence to find the two endpoints with the largest adjacent distance. These two points are the starting and ending position point clouds of the boundary of this section of the retaining wall, that is, the starting end and the ending end; the 3D (XYZ) position information of all point clouds from the starting end to the ending end is saved in sequence to obtain the boundary vector.

[0026] 4. Boundary vector screening and push to the HD map engine for update Since the generated local point cloud data may be too small or too sparse, the extracted boundary vector cannot truly cover the entire boundary area. Directly uploading it to the map engine for replacement may result in incomplete boundary vectors on the cloud. Therefore, the number of vector points in the generated boundary vector will be counted after each processing is completed.

[0027] Specifically, by setting a point cloud number threshold, the boundary vectors are filtered. If the number of points in the boundary vector exceeds the set point cloud number threshold, it is directly filtered; if the number of points in the boundary vector is lower than the set point cloud number threshold, DBSCAN clustering is performed to count the number of vector points in each cluster, and point cloud segments with less than 80 points are removed; the filtered boundary vectors are saved as GeoJSON vector data in the form of Polyline and pushed to the high-precision map engine for update.

[0028] In this embodiment, the point cloud number threshold is set to 300. If the number of vector points is less than 300, DBSCAN clustering is required. The number of vector points in each cluster is counted in turn. If the number exceeds 80, it is retained. Otherwise, the point cloud segment is removed and not updated.

[0029] It should be noted that, in this embodiment, the above-mentioned clustering algorithms include but are not limited to DBSCAN clustering, and also include meanshift (mean shift), GMM (Gaussian mixture clustering) algorithm, etc.

[0030] In another embodiment of the present invention, a system for dynamically updating the boundary of a mine road based on a local point cloud map is disclosed, the system comprising a data acquisition module, a data processing module, a region extraction module, a boundary vector extraction module, a vector screening module, and a map engine update module; The data acquisition module is used to generate point cloud data of the soil loading and dumping area through the laser radar and upload it to the data processing module; The data processing module is used to deploy the trained semantic segmentation model to process the uploaded point cloud data of the loading and dumping area, assign a semantic label to each point cloud, and the semantic labels include wall and road surface, to obtain the retaining wall and road surface point clouds; The region extraction module determines the interception range of the region of interest based on the road surface point cloud area and intercepts the region from the original point cloud data; The boundary vector extraction module is used to extract candidate boundary points from the region of interest intercepted by the region extraction module, and extract boundary vectors by reordering; The vector screening module is used to screen the boundary vectors generated by the boundary vector extraction module to obtain screened boundary vectors; The map engine update module is used to receive the boundary vectors filtered by the vector screening module and push them to the high-precision map engine for updating, thereby realizing dynamic updating of the mine road boundaries.

[0031] Specifically, in this embodiment, the semantic segmentation model uses the convpoint model to perform point cloud semantic segmentation; Boundary vector extraction uses DBSCAN clustering and Alpha Shape algorithm to extract boundary point cloud; When the number of points in the boundary vector is lower than the set point cloud number threshold, DBSCAN clustering is used to retain the point cloud segments with more than 80 cluster points. The boundary vector data received by the HD map engine is GeoJSON vector data in the form of Polyline.

[0032] The above embodiments disclose the dynamic update method and system of the present invention. Compared with the prior art, the present invention has the following advantages: 1. This invention generates a local point cloud map for scenarios where road boundaries are frequently updated during soil loading and dumping, and automatically extracts boundary vectors from the local point cloud map, ensuring both boundary accuracy and the freshness of the map boundary position. 2. The present invention adopts a semantic segmentation model for vector extraction, combined with a rule-based post-processing approach to improve the accuracy of boundary vector extraction.

[0033] Another embodiment of the present invention further includes an electronic device, which may include a processor and a memory storing computer program instructions.

[0034] Specifically, in this embodiment, the processor may include a central processing unit (CPU), a specific integrated circuit, or may be configured as one or more integrated circuits of this embodiment. The memory may include a large-capacity storage for data or instructions, including but not limited to a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include removable or non-removable (or fixed) media. In certain embodiments, the memory is non-volatile solid-state memory. In certain embodiments, the memory includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmable ROM, a programmable ROM (PROM), an erasable programm ...

[0035] The processor implements the dynamic update method disclosed above by reading and executing computer program instructions stored in the memory.

[0036] It should also be noted that the electronic device of this embodiment may also include a communication interface and a communication bus. The processor, memory, and communication interface are connected via the communication bus and communicate with each other. The communication interface is primarily used to enable communication between the various units, modules, devices, or equipment in the embodiments of the present invention.

[0037] The communication bus mentioned above includes hardware, software or a combination of both, coupling the components of the online data flow device to each other. Where appropriate, the communication bus may include one or more buses.

[0038] In addition, in combination with the dynamic update method in the above embodiment, the embodiment of the present invention can be implemented by providing a computer storage medium, which stores computer program instructions; the computer program instructions are executed by a processor to execute the above dynamic update method.

[0039] It should be clear that the present invention is not limited to the methods, systems, and devices disclosed above, but also includes various changes, modifications, and additions made by those skilled in the art based on the ideas of the present invention, or changes in the order of steps.

[0040] When the present invention is implemented in hardware, it may be an electronic circuit, an application-specific integrated circuit, appropriate firmware, a plug-in, a function card, etc.; when implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The programs or code segments may be stored in a machine-readable medium or uploaded via a data signal carried in a carrier wave over a transmission medium or communication link. "Machine-readable medium" may include any medium capable of storing or transmitting information, such as an electronic circuit, a semiconductor memory device, ROM, flash memory, erasable ROM (EROM), a floppy disk, an optical disk, a hard disk, an optical fiber, a radio frequency link, etc. The code segments may be downloaded via a computer network such as the Internet or an intranet.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of the present invention. Other modifications or equivalent substitutions made to the technical solutions of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for dynamically updating the boundary of a mine road based on a local point cloud map, characterized in that: The method comprises the following steps: Use LiDAR to generate a point cloud map of the loading and dumping area and upload it to the cloud, where the trained semantic segmentation model is deployed. Based on the point cloud data of the soil loading and dumping area stored in the cloud, model semantic extraction is performed, and a semantic label is assigned to each point cloud to obtain the retaining wall and road surface point clouds; Obtain the interception range of the region of interest based on the area of the road point cloud, intercept the region of interest from the original point cloud data, extract the boundary candidate points, and extract the boundary vector by reordering; The generated boundary vectors are filtered and pushed to the high-precision map engine for updating.

2. The method for dynamically updating the boundary of a mine road based on a local point cloud map according to claim 1, characterized in that: Using LiDAR to generate a point cloud map of the loading and dumping area and upload it to the cloud includes the following steps: The mapping mode is triggered every time a mining vehicle is loading and dumping soil; Starting from 30 meters away from the retaining wall, a key frame point cloud is obtained from the lidar every 1 meter; Eliminate the point cloud within 10 meters from the center of each frame of the point cloud car system; Continue to superimpose new keyframe point clouds until the mining vehicle stops at the storage location; Transfer data to the data platform cloud for storage.

3. The method for dynamically updating the boundary of a mine road based on a local point cloud map according to claim 1, characterized in that: Model semantic extraction includes the following steps: Obtain local point cloud data of the soil loading and dumping area stored in the cloud; Using the semantic segmentation model in the cloud, the semantic labels of each point cloud are inferred; The inference parameters include block size equal to 16 meters, step length equal to 8 meters, and number of block sampling points equal to 12000; The extracted semantic information includes the retaining wall and road surface point clouds, which are used as input for boundary vector extraction.

4. The method for dynamically updating the boundary of a mine road based on a local point cloud map according to claim 1, characterized in that: Boundary vector extraction is based on the semantically extracted retaining wall and road surface point clouds, and the following steps are performed in sequence: obtaining the region of interest, extracting candidate boundary points, and reordering to obtain boundary vectors.

5. The method for dynamically updating the boundary of a mine road based on a local point cloud map according to claim 4, characterized in that: Boundary vector extraction includes the following steps: Get the region of interest: Clustering the road point cloud obtained by semantic extraction of the model; Find the road cluster category with the largest number of point clouds in the clustering results, and obtain the maximum and minimum X, Y values of the road; Based on the maximum value of X and Y, increase the expansion degree, and based on the minimum value of X and Y, reduce the expansion degree to obtain the interception range of the region of interest; Extract candidate boundary points: Extract the region of interest from the original point cloud data; Obtain the point cloud of the retaining wall in the area of interest through semantic labeling; Obtain each retaining wall object in the retaining wall point cloud by clustering, and extract the boundary point cloud of each retaining wall cluster; Traverse all the extracted boundary points, set a range threshold, and select all points with the semantic label "road surface" within the threshold range. The selected points are the candidate boundary points; Reorder to get the boundary vector: Calculate the distance between two adjacent points of the boundary candidate points in sequence, and find the two endpoints with the largest adjacent distance, the starting end and the ending end; The 3D position information of all point clouds from the starting end to the ending end is saved in sequence to obtain the boundary vector.

6. The method for dynamically updating the boundary of a mine road based on a local point cloud map according to claim 1, characterized in that: By setting the point cloud quantity threshold, the boundary vectors are screened, including the following steps: If the number of points in the boundary vector exceeds the set point cloud number threshold, it will be directly filtered; If the number of points in the boundary vector is lower than the set point cloud number threshold, clustering is performed, the number of vector points in each cluster is counted, and point cloud segments with less than 80 points are removed; The filtered boundary vectors are saved as GeoJSON vector data in the form of Polyline and pushed to the high-precision map engine for updating.

7. A system for dynamically updating mine road boundaries based on a local point cloud map according to any one of claims 1 to 6, characterized in that: The system includes a data acquisition module, a data processing module, a region extraction module, a boundary vector extraction module, a vector screening module, and a map engine update module; The data acquisition module is used to generate point cloud data of the soil loading and dumping area through the laser radar and upload it to the data processing module; The data processing module is used to deploy the trained semantic segmentation model to process the uploaded point cloud data of the loading and dumping area, assign a semantic label to each point cloud, and the semantic labels include wall and road surface, to obtain the retaining wall and road surface point clouds; The region extraction module determines the interception range of the region of interest based on the road surface point cloud area and intercepts the region from the original point cloud data; The boundary vector extraction module is used to extract candidate boundary points from the region of interest intercepted by the region extraction module, and extract boundary vectors by reordering; The vector screening module is used to screen the boundary vectors generated by the boundary vector extraction module to obtain screened boundary vectors; The map engine update module is used to receive the boundary vectors filtered by the vector screening module and push them to the high-precision map engine for updating, thereby realizing dynamic updating of the mine road boundaries.

8. The system for dynamically updating the boundary of a mine road based on a local point cloud map according to claim 7, characterized in that: The semantic segmentation model uses the convpoint model to perform point cloud semantic segmentation; Boundary vector extraction uses DBSCAN clustering and Alpha Shape algorithm to extract boundary point cloud; When the number of points in the boundary vector is lower than the set point cloud number threshold, DBSCAN clustering is used to retain the point cloud segments with more than 80 cluster points. The boundary vector data received by the HD map engine is GeoJSON vector data in the form of Polyline.

9. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein: The processor, the communication interface and the memory communicate with each other via the communication bus, wherein the memory is used to store computer programs; The processor is configured to execute the method according to any one of claims 1 to 6 by running the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program implements the method according to any one of claims 1 to 6 when executed by a processor.

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