Obstacle avoidance detection system and method based on non-repeated scanning laser radar

Abstract

The invention provides an obstacle avoidance detection system and method based on a non-repeated scanning laser radar, and the system comprises a sensing module which obtains environment data in a non-repeated scanning manner; the obstacle detection and recognition module is used for receiving the environment data, detecting obstacles and recognizing the types of the obstacles; the path planning module is used for determining a planning path for avoiding the obstacle based on the environment data, the identified obstacle and the category thereof, and the vehicle state; the decision module is used for formulating a driving strategy of the vehicle based on the planned path and other related information; the control and execution module is used for converting the driving strategy into a vehicle control instruction and executing the vehicle control instruction; and the real-time monitoring and feedback module is used for monitoring the surrounding environment of the vehicle in real time, transmitting environment data to the obstacle detection module, and updating a planned path and a driving strategy. The panoramic data can be quickly collected by applying the non-repeated scanning sensing module, and data redundancy is avoided, so that the problem of low processing efficiency caused by data repetition is avoided.

Description

Technical Field

[0001] The present invention relates to the field of unmanned driving technology, and in particular to an obstacle avoidance detection system and method based on a non-repetitive scanning laser radar. Background Art

[0002] Currently, the autonomous obstacle avoidance capability of mobile devices in complex environments is becoming a research focus. Traditional LiDAR systems collect environmental information by repeated scanning, but this method has the disadvantages of data duplication and low collection efficiency. Data duplication and low efficiency will lead to delays in the entire processing and calculation process. During driving, delays in operational control may cause traffic accidents, thus posing major safety hazards. Conventional autonomous driving systems, although they follow the predetermined route, are often unable to respond flexibly to complex and changeable road conditions and unpredictable emergencies. Summary of the invention

[0003] In view of the defects in the prior art, the object of the present invention is to provide an obstacle avoidance detection system and method based on non-repetitive scanning laser radar.

[0004] According to one aspect of the present invention, there is provided an obstacle avoidance detection system based on a non-repetitive scanning laser radar, comprising:

[0005] The perception module uses a non-repetitive scanning method to obtain environmental data;

[0006] An obstacle detection and identification module receives the environmental data from the perception module, detects obstacles and identifies their types;

[0007] A path planning module, which determines a planned path to avoid obstacles based on the environmental data, the identified obstacles and their categories, and the vehicle status, combined with the target path;

[0008] A decision module, which formulates a driving strategy for the vehicle based on the planned path;

[0009] A control and execution module converts the driving strategy into a vehicle control command and executes it;

[0010] The real-time monitoring and feedback module monitors the vehicle's surrounding environment in real time, transmits the monitored environmental data to the obstacle detection and recognition module, and updates the planned path and the driving strategy in turn.

[0011] Preferably, the perception module includes:

[0012] A collection unit, wherein the collection unit is a laser radar with a 360-degree horizontal scanning capability; the laser radar scans at a fixed angle to form a laser beam on one or more horizontal planes; the laser radar performs an all-round scan to obtain environmental data of a panoramic view;

[0013] The processing unit pre-processes the environmental data obtained by the acquisition unit, including: eliminating noise, correcting scanning errors, and filtering out abnormal values.

[0014] Preferably, the obstacle detection and recognition module includes:

[0015] The ground segmentation unit uses height information and terrain features to distinguish the laser point cloud into ground point cloud and non-ground point cloud;

[0016] An obstacle sensing unit, which identifies obstacles in the non-ground point cloud;

[0017] The object segmentation unit segments and marks the identified obstacles to extract their shape, size, location information and category.

[0018] Preferably, the path planning module includes:

[0019] A data unit, for acquiring environmental data of the perception module and vehicle body status information;

[0020] Planning unit: based on the environmental data, the vehicle body status information, and the obstacle and category information of the obstacle detection and recognition module, combined with the target path, generates a planned path.

[0021] Preferably, the driving strategy of the decision module includes acceleration time point, acceleration duration, acceleration data; deceleration time point, deceleration duration, deceleration data; turning time point, direction, angle, and turning duration.

[0022] Preferably, the vehicle control instructions of the control and execution module include controlling the throttle, brakes, and steering.

[0023] Preferably, the real-time monitoring and feedback module includes:

[0024] The monitoring unit collects the surrounding environment of the vehicle in real time during driving and identifies dynamic obstacles and local obstacles that appear;

[0025] The path planning module comprises:

[0026] A data unit, which obtains environmental data from the perception module, vehicle body status information, and obstacle information from the monitoring unit;

[0027] A local planning unit receives the environmental data from the data unit and obtains an environmental data grid map; creates multiple possible paths using a path generation algorithm based on vehicle body status information, obstacle information from the monitoring unit, and a target path; smoothes and locally optimizes the multiple possible paths, calculates a cost value for each path, and selects a path with a minimum cost value as a local optimal path;

[0028] The global planning unit merges the local optimal route with the original planned path, and adjusts the weights of the two paths to obtain a global path as an updated planned path.

[0029] According to a second aspect of the present invention, there is provided an obstacle avoidance detection method based on a non-repetitive scanning laser radar, comprising:

[0030] Use non-repetitive scanning to obtain environmental data;

[0031] Receiving the environmental data, detecting obstacles and identifying their categories;

[0032] Determining a planned path to avoid obstacles based on the environmental data, the identified obstacles and their categories, and the vehicle status, in combination with the target path;

[0033] Formulate a driving strategy for the vehicle based on the planned path;

[0034] Convert the driving strategy into vehicle control instructions and execute them;

[0035] The vehicle's surrounding environment is monitored in real time, obstacle detection and identification are performed again based on the monitored environmental data, and the planned path and the driving strategy are updated in sequence.

[0036] According to a third aspect of the present invention, there is provided a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can be used to execute any one of the methods described, or to run the system described, when executing the program.

[0037] According to a fourth aspect of the present invention, a computer-readable storage medium stores a computer program thereon, wherein the program, when executed by a processor, can be used to execute any of the methods described above, or to run the system described above.

[0038] Compared with the prior art, the embodiments of the present invention have at least one of the following beneficial effects:

[0039] In an embodiment of the present invention, the obstacle avoidance detection system based on non-repetitive scanning lidar can quickly collect panoramic data without data redundancy by using a non-repetitive scanning perception module, thereby avoiding the problem of low processing efficiency caused by data duplication.

[0040] In the embodiments of the present invention, the obstacle avoidance detection system based on non-repetitive scanning laser radar can timely sense obstacles in the surrounding environment and formulate obstacle avoidance strategies to avoid collisions with obstacles, thereby improving the driving safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0042] Figure 1 Schematic diagram of the structure of an obstacle avoidance detection system based on a non-repetitive scanning laser radar in one embodiment of the present invention;

[0043] Figure 2 The present invention is a flowchart of an obstacle avoidance detection method based on non-repetitive scanning laser radar in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0045] In one embodiment of the present invention, an obstacle avoidance detection system based on non-repetitive scanning laser radar is provided, including a perception module, an obstacle detection and recognition module, a path planning module, a decision module, a control and execution module, and a real-time monitoring and feedback module. The perception module acquires environmental data by non-repetitive scanning; the obstacle detection and recognition module receives the environmental data of the perception module, detects obstacles and identifies their categories; the path planning module determines the planned path to avoid obstacles based on the environmental data, the identified obstacles and their categories, and the vehicle status; the decision module formulates the vehicle's driving strategy based on the planned path; the control and execution module converts the driving strategy into a vehicle control instruction and executes it; the real-time monitoring and feedback module monitors the vehicle's surrounding environment in real time, transmits the environmental data to the obstacle detection and recognition module, and updates the planned path and driving strategy.

[0046] The above-mentioned embodiment, by using a non-repetitive scanning perception module, can quickly collect panoramic data without data redundancy, thereby avoiding the problem of low processing efficiency caused by data duplication.

[0047] In order to further avoid the problem of repeated data collection and redundancy, in one embodiment of the present invention, a preferred perception module is provided. Specifically, the perception module is divided into a collection unit and a processing unit. The collection unit adopts a laser radar. The laser radar is a non-repetitive scanning laser radar with a 360-degree horizontal scanning capability. The laser radar upgrades the perception level to 3D. The laser radar will scan within a certain angle range to form one or more laser beams on the horizontal plane. The laser radar can perform an all-round scan to obtain a panoramic view of the environment.

[0048] The non-repetitive collection of panoramic views makes it easier for the mobile robot with obstacle avoidance detection system to collect enough environmental feature points to autonomously complete mapping and vehicle positioning, eliminating the need to modify the spatial environment and greatly improving deployment efficiency. At the same time, the angular resolution of its LiDAR can be significantly improved with time integration, and the perception of small obstacles is greatly enhanced. "Time integration" in LiDAR refers to the accumulation of echo signals of multiple laser pulses over a period of time to improve the signal quality and detection capability of the sensor.

[0049] After the lidar collects the raw data (i.e. the panoramic view), it is pre-processed by the processing unit to eliminate noise, correct scanning errors, filter out outliers, etc. The processing process includes steps such as data filtering, correction, and registration to ensure the accuracy and reliability of subsequent analysis and to ensure the safe operation of the robot.

[0050] Of course, there is no restriction on the processing of data, as long as it can improve the accuracy and reliability of subsequent analysis. In other embodiments, other data preprocessing methods, such as normalization, etc., are also possible.

[0051] After the perception module has collected the point cloud data, obstacle detection and recognition can be performed. In a preferred embodiment, the obstacle detection and recognition module includes a ground segmentation unit, an obstacle perception unit and an object segmentation unit.

[0052] The ground segmentation unit uses height information and terrain features to distinguish the laser point cloud into ground point cloud and non-ground point cloud, separating the ground from other objects in the environment to more accurately identify and perceive obstacles.

[0053] The obstacle perception unit identifies obstacles in non-ground point clouds, such as vehicles, pedestrians, and buildings.

[0054] The object segmentation unit segments and labels the identified obstacles to extract their shape, size, location information and category. This helps with subsequent path planning, obstacle avoidance and navigation tasks.

[0055] Of course, the obstacle detection method is not limited. In other embodiments, other monitoring algorithms can be used to detect and identify different types of obstacles. For example, the template matching method is suitable for objects of specific shapes (such as traffic signs and lane lines), and the RANSAC method is suitable for identifying objects with spatial geometric features, such as pedestrians, buildings, and other vehicles.

[0056] In some other embodiments, a publishing module is provided to publish the data passed through the perception module and the obstacle detection and recognition module through ROS (Robot Operating System), so that other nodes, modules or systems can subscribe to and use the data for further processing and control.

[0057] The obstacle detection and recognition module in the above embodiment plays an important role in the unmanned driving system, which can realize real-time perception and understanding of the environment and provide key information support for the safe navigation and operation of the vehicle.

[0058] Furthermore, after obtaining the obstacle information, path planning is required. In a preferred embodiment, the path scale includes a data unit and a planning unit. The data unit obtains the environmental data of the perception module and the vehicle body state information (current driving speed and acceleration as well as the vehicle's orientation and heading angle). The planning unit generates a planned path based on the environmental data, vehicle body state information, and the obstacle and its category information of the obstacle detection and recognition module, combined with the target path.

[0059] In another preferred embodiment, the decision module formulates the vehicle's driving strategy and actions, including acceleration, deceleration, and steering, based on the generated planned path and other relevant information. Specifically, it refers to: acceleration time point, acceleration duration, acceleration data; deceleration time point, deceleration duration, deceleration data; steering time point, direction, angle, steering duration. The calculation formula is as follows:

[0060]

[0061] θ f =θ i +ω·t

[0062] Among them, v f is the final velocity, vi is the initial velocity, a is the acceleration, t is the acceleration time, θ f is the angle after turning, θ i is the initial turning angle.

[0063] Furthermore, in a preferred embodiment, the control and execution module converts the results of the decision module into vehicle control instructions, including controlling the throttle, brakes, steering, etc., to achieve safe obstacle avoidance driving, executes vehicle control instructions, allows the vehicle to travel along the planned path, and adjusts the driving posture in real time to cope with dynamic environmental changes.

[0064] The above embodiment can complete the automatic driving control under good road conditions. However, during the driving process of the vehicle, dynamic obstacles or local obstacles (such as pedestrians, vehicles, etc.) may appear, and local path planning is required to avoid collision. Therefore, in a preferred embodiment of the present invention, a real-time monitoring and feedback module and a better path planning module are designed. Specifically,

[0065] Real-time monitoring and feedback module, including:

[0066] The monitoring unit collects the surrounding environment of the vehicle in real time during driving and identifies dynamic obstacles and local obstacles. The monitoring unit can use sensors such as lidar and cameras to perceive the surrounding environment data. The environmental perception data is usually provided in the form of point clouds, images, etc., describing the roads, obstacles, traffic signs and other information around the vehicle, and obtaining obstacle information from this information.

[0067] The data from the monitoring unit is input into the path planning module to update the route. Specifically, the path planning module includes:

[0068] A data unit, which obtains environmental data from the perception module, vehicle body status information, and obstacle information from the monitoring unit;

[0069] The local planning unit, whose input is the data obtained by non-repetitive scanning of the perception module; the environmental data transmitted by the data unit, the vehicle body status information, the obstacle information of the monitoring unit; and the target path information; its output is the optimal local path. In the local planning unit, a grid map is constructed for the perceived environmental data; a path generation algorithm (such as A* algorithm, RRT or PRM) is used to create multiple possible paths; after generation, multiple possible paths are smoothed and locally optimized, and finally the cost value of each path is calculated, and the path with the smallest cost value is selected as the local optimal path.

[0070] The global planning unit evaluates the feasibility of the local optimal path and merges it with the original planned path; adjusts the path weight of the merged path to obtain a global path for updating the original planned path.

[0071] In the above embodiment, based on the selected driving path and the current environmental status, local planning and global planning can be used to timely correct and adjust the latest and optimal route, thereby further ensuring the safety and efficiency of driving.

[0072] Further, in a specific embodiment, if Figure 2 As shown,

[0073] The left column is the real-time monitoring and feedback module. In this unit:

[0074] While the vehicle is driving, the surrounding environment is sensed and monitored through sensors.

[0075] Call the GetMrptpose function to get the current posture information; determine whether there is relevant positioning information. If so, proceed to the next step of local and global path planning. If not, receive new point cloud data from the Lidar sensor and call the GetMrptpose function again to get the current posture information. This process is to obtain vehicle body information. In some other implementations, it can also be implemented in the path planning module.

[0076] Generate a local ROI map and calculate the maximum and minimum heights; based on the maximum and minimum heights, obtain a local obstacle list for small and large targets; based on the local obstacle list, integrate it into the global target list and update it in real time to form global target information. Finally, publish the global target and related image data for use by other nodes to support the vehicle's driving decision-making and environmental perception.

[0077] Figure 2 The second column is the perception module, which is synchronized with the real-time monitoring and feedback unit. The perception module obtains various data, such as point cloud data obtained by non-repetitive scanning of the laser radar, inertial navigation data, and GPS data. It also processes the data, including noise reduction sampling, to establish an environmental grid map.

[0078] Figure 2 The third column shows that in the path planning module, the data unit receives data from the real-time monitoring and feedback unit and the perception module to determine whether the vehicle's own information and the environmental grid map data output by the perception module are normal. If the data is normal, the system will further determine the vehicle's driving strategy or behavior. Specifically:

[0079] Local planning unit: Based on the perceived environmental grid data, multiple possible paths are created using a path generation algorithm (such as A* algorithm, RRT or PRM); after generation, multiple possible paths are smoothed and locally optimized, and finally the cost value of each path is calculated, and the path with the smallest cost value is selected as the local optimal path.

[0080] The cost value itself is usually a comprehensive indicator used to evaluate the quality of the trajectory: the cost value can include multiple factors, such as: path length (total length of the trajectory), turning angle (larger turns usually increase driving difficulty), speed change (the amplitude of acceleration and deceleration, frequent speed changes may lead to uncomfortable driving experience), obstacle distance (distance from obstacles to ensure safety). The cost value C can be calculated by weighted sum, the formula is as follows:

[0081] C=w1·L+w2·A+w3·V+w4·D

[0082] Where L is the path length, A is the amount of change in turning, V is the speed change, and D is the minimum distance to the obstacle.

[0083] The path with the lowest cost is selected from the generated candidate paths as the local optimal path.

[0084] Global planning unit: Integrates the local optimal candidate path into the current global plan. First, the feasibility of the local path is evaluated and merged with the global path. Next, the merged path is optimized to reduce abrupt turns and acceleration and deceleration behaviors, usually using Bezier curves or spline curves for smoothing to ensure its smoothness and comfort. At the same time, the path weight is adjusted to maintain overall efficiency. Finally, the updated global path is released to the decision module so that the vehicle can perform motion control according to the new path and continuously monitor the environment to adapt to changes.

[0085] Figure 2 The right column is the decision module, which uses the updated path as the vehicle's motion command, and then obtains the lateral control (steering) and longitudinal control (acceleration / deceleration) of the vehicle through the control algorithm to make the vehicle drive along the planned path. Finally, the control and execution module drives according to the lateral control (steering) and longitudinal control (acceleration / deceleration) of the decision module.

[0086] Based on the same inventive concept, in another embodiment of the present invention, there is provided an obstacle avoidance detection method based on a non-repetitive scanning laser radar, comprising:

[0087] Use non-repetitive scanning to obtain environmental data;

[0088] Receive environmental data, detect obstacles and identify their categories;

[0089] Determine a planned path to avoid obstacles based on environmental data, identified obstacles and their categories, and vehicle status;

[0090] Formulate vehicle driving strategies based on the planned path and other relevant information;

[0091] Convert driving strategies into vehicle control instructions and execute them;

[0092] Monitor the vehicle's surrounding environment in real time, transmit the environmental data to the obstacle detection module, and update the planned path and driving strategy.

[0093] The specific steps in the above example of the present invention can refer to the implementation technology of each module / unit corresponding to the obstacle avoidance detection system based on non-repetitive scanning laser radar in the above embodiment, which will not be repeated here.

[0094] The obstacle avoidance detection system and method based on non-repetitive scanning laser radar in the above embodiment can improve driving safety: unmanned vehicles face various complex road environments and traffic conditions during actual driving, such as road obstacles, pedestrians, vehicles, etc. The obstacle avoidance detection system can timely perceive obstacles in the surrounding environment, and formulate obstacle avoidance strategies through intelligent algorithms to avoid collisions with obstacles, thereby improving the driving safety of the vehicle. It can improve the level of automation: the obstacle avoidance detection system is an important part of the unmanned driving system, which realizes the autonomous perception and decision-making ability of unmanned vehicles in complex environments. Through the obstacle avoidance detection system, unmanned vehicles can autonomously avoid obstacles and adjust the driving path without human intervention, realize fully automated driving, and improve the automation level of the vehicle. It can improve efficiency and comfort: the obstacle avoidance detection system can quickly and accurately perceive the surrounding environment, formulate the optimal driving path and action strategy, thereby effectively improving the efficiency of vehicle driving. At the same time, the obstacle avoidance detection system can avoid frequent lane changes or sudden braking of the vehicle during driving, reduce the tension and discomfort during driving, and improve the driving comfort. Can promote the development of intelligent transportation: Obstacle avoidance detection technology is an important part of the intelligent transportation system, which provides key technical support for the construction and development of the intelligent transportation system. Through the application of the obstacle avoidance detection system, it is possible to achieve the goals of optimizing traffic flow, reducing traffic accidents, and rationally utilizing road resources, thus promoting the development and popularization of intelligent transportation.

[0095] Based on the same inventive concept, in other embodiments of the present invention, a terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it can be used to execute the obstacle avoidance detection method based on non-repetitive scanning laser radar, or to run the obstacle avoidance detection system based on non-repetitive scanning laser radar.

[0096] Based on the same inventive concept, in other embodiments of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, it can be used to execute the obstacle avoidance detection method based on non-repetitive scanning laser radar, or to run the obstacle avoidance detection system based on non-repetitive scanning laser radar.

[0097] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the essence of the present invention. The above preferred features can be used in any combination without conflicting with each other.

Claims

1. An obstacle avoidance detection system based on non-repetitive scanning laser radar, characterized in that: include: The perception module uses a non-repetitive scanning method to obtain environmental data; An obstacle detection and identification module receives the environmental data from the perception module, detects obstacles and identifies their types; A path planning module, which determines a planned path to avoid obstacles based on the environmental data, the identified obstacles and their categories, and the vehicle status, combined with the target path; A decision module, which formulates a driving strategy for the vehicle based on the planned path; A control and execution module converts the driving strategy into a vehicle control command and executes it; The real-time monitoring and feedback module monitors the vehicle's surrounding environment in real time, transmits the monitored environmental data to the obstacle detection and recognition module, and updates the planned path and the driving strategy in turn.

2. The obstacle avoidance detection system based on non-repetitive scanning laser radar according to claim 1, characterized in that: The perception module comprises: A collection unit, wherein the collection unit is a laser radar with a 360-degree horizontal scanning capability; the laser radar scans at a fixed angle to form a laser beam on one or more horizontal planes; the laser radar performs an all-round scan to obtain environmental data of a panoramic view; The processing unit pre-processes the environmental data obtained by the acquisition unit, including: eliminating noise, correcting scanning errors, and filtering out abnormal values.

3. The obstacle avoidance detection system based on non-repetitive scanning laser radar according to claim 2, characterized in that: The obstacle detection and recognition module includes: The ground segmentation unit uses height information and terrain features to distinguish the laser point cloud into ground point cloud and non-ground point cloud; An obstacle sensing unit, which identifies obstacles in the non-ground point cloud; The object segmentation unit segments and marks the identified obstacles to extract their shape, size, location information and category.

4. The obstacle avoidance detection system based on non-repetitive scanning laser radar according to claim 1, characterized in that: The path planning module comprises: A data unit, for acquiring environmental data of the perception module and vehicle body status information; Planning unit: based on the environmental data, the vehicle body status information, and the obstacle and category information of the obstacle detection and recognition module, combined with the target path, generates a planned path.

5. The obstacle avoidance detection system based on non-repetitive scanning laser radar according to claim 1, characterized in that: The driving strategy of the decision module includes acceleration time point, acceleration duration, acceleration data; deceleration time point, deceleration duration, deceleration data; turning time point, direction, angle, and turning duration.

6. The obstacle avoidance detection system based on non-repetitive scanning laser radar according to claim 1, characterized in that: The vehicle control instructions of the control and execution module include controlling the throttle, brakes, and steering.

7. The obstacle avoidance detection system based on non-repetitive scanning laser radar according to claim 1, characterized in that: The real-time monitoring and feedback module includes: The monitoring unit collects the surrounding environment of the vehicle in real time during driving and identifies dynamic obstacles and local obstacles that appear; The path planning module comprises: A data unit, which obtains environmental data from the perception module, vehicle body status information, and obstacle information from the monitoring unit; A local planning unit receives the environmental data from the data unit and obtains an environmental data grid map; creates multiple possible paths using a path generation algorithm based on the vehicle body state information, the obstacle information from the monitoring unit, and the target path; smoothes and locally optimizes the multiple possible paths, calculates the cost value of each path, and selects the path with the smallest cost value as the local optimal path; The global planning unit merges the local optimal route with the original planned path, and adjusts the weights of the two paths to obtain a global path as an updated planned path.

8. An obstacle avoidance detection method based on non-repetitive scanning laser radar, characterized in that: include: Use non-repetitive scanning to obtain environmental data; Receiving the environmental data, detecting obstacles and identifying their categories; Determining a planned path to avoid obstacles based on the environmental data, the identified obstacles and their categories, and the vehicle status, in combination with the target path; Formulate a driving strategy for the vehicle based on the planned path; Convert the driving strategy into vehicle control instructions and execute them; The vehicle's surrounding environment is monitored in real time, obstacle detection and identification are performed again based on the monitored environmental data, and the planned path and the driving strategy are updated in sequence.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it can be used to execute the method described in any one of claims 1 to 7, or to run the system described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it can be used to execute the method described in any one of claims 1 to 7, or to run the system described in claim 8.

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