A disinfection method, system, and apparatus for a disinfection robot

Abstract

The present application relates to the technical field of robot control, in particular to a disinfection method, system and equipment of a disinfection robot, in which, based on an obstacle image, the height type, pickup type and volume of the obstacle are judged, a reasonable disinfection strategy is set according to the height characteristics of the obstacle, and the disinfection quality and efficiency are improved; meanwhile, the type and volume characteristics of the obstacle are also considered, a suitable disinfection method is formulated, and the disinfection quality and efficiency are further improved, and the flexibility and efficiency of the disinfection robot are improved.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, specifically to a disinfection method, system, and equipment for a disinfection robot. Background Technology

[0002] In today's rapidly developing technological landscape, the inefficient palletizing and handling functions of robots can no longer meet the needs of modern industry and the intelligent era. New types of robots applicable to various complex and extreme environments have become a hot research topic. With the rapid development of the robotics industry, autonomous navigation disinfection robots have become a focus of development and investment in this emerging field. These robots can use sensors to perceive their surroundings and estimate their position and status, enabling them to complete purification tasks in assigned areas even in unfamiliar environments with obstacles, irregular shapes, and high personnel flow.

[0003] Disinfection robots can replace manual disinfection of indoor public places, saving labor costs, reducing the risks associated with manual disinfection, and decreasing the probability of cross-infection. However, current disinfection robots are not yet fully intelligent, their flexible disinfection capabilities are limited, and their disinfection efficiency and quality are low. Summary of the Invention

[0004] This invention provides a disinfection method, system, and device for a disinfection robot.

[0005] The technical solution of this invention is as follows:

[0006] A disinfection method using a disinfection robot includes the following operations:

[0007] The S1 disinfection robot disinfects according to a preset path. If it detects an obstacle during the disinfection process, it acquires an image of the obstacle.

[0008] S2 obtains the obstacle state type based on the obstacle image;

[0009] If the obstacle is a dynamic obstacle, the disinfection robot stops driving and disinfecting. Once the disinfection robot can no longer detect dynamic obstacles in its field of vision, the disinfection robot continues driving and disinfecting.

[0010] If the obstacle's state type is static obstacle, execute S3;

[0011] S3 determines the obstacle height type based on the obstacle image;

[0012] If the obstacle height type is the first height type, the disinfection robot disinfects the obstacle in place;

[0013] If the obstacle height type is the second height type, execute S4;

[0014] S4 classifies the obstacles based on the obstacle image to obtain the obstacle picking type;

[0015] If the obstacle pickup type is the first pickup type, the disinfection robot disinfects the obstacle in place;

[0016] If the obstacle pickup type is the second pickup type, execute S5;

[0017] S5 obtains the size information of the obstacle, and obtains the volume of the obstacle based on the size information;

[0018] If the volume of the obstacle does not exceed the storage volume of the trash can, the disinfection robot will pick up the obstacle and put it into the trash can, updating the storage volume of the trash can;

[0019] If the volume of the obstacle exceeds the storage volume of the trash can, the disinfection robot will disinfect the obstacle in place.

[0020] In the disinfection method described above, the operation of obtaining the obstacle height type in S3 specifically involves: acquiring an image of the disinfection robot, converting it to a scale, and comparing it with an obstacle image of the same scale. If the obstacle height in the obstacle image is not less than the height of the disinfection robot, then the obstacle height type is a first height type; if the obstacle height in the obstacle image is less than the height of the disinfection robot, then the obstacle height type is a second height type.

[0021] In the disinfection method described above, during operation S3, if the obstacle height type is a first height type, the disinfection robot stops moving when the distance between it and the obstacle is a first distance, and disinfects the obstacle in place.

[0022] In the disinfection method described above, the operation of obtaining obstacle size information in S5 is as follows:

[0023] Obtain an image of the obstacle from the first perspective of the disinfection robot, and obtain the first length and second length of the obstacle;

[0024] Obtain an image of the obstacle from the second perspective of the disinfection robot, and obtain the third length of the obstacle;

[0025] Based on the first length, second length, and third length of the obstacle, the size information of the obstacle is obtained;

[0026] The angle between the first viewpoint and the second viewpoint is 90°.

[0027] The operation to obtain the volume of the obstacle in S5 is specifically as follows:

[0028] Multiply the first length by the second and third lengths to obtain the first volume, and record the first volume as the obstacle volume.

[0029] The disinfection method described above, prior to the operation in S5 where the disinfection robot picks up the obstacle and places it in the trash can, further includes:

[0030] The front, left, right, and back images of the obstacle are acquired to obtain a full-view image of the obstacle. The full-view image of the obstacle is then processed by affine transformation to obtain the obstacle pose information. Based on the obstacle pose information, the operation in S5 where the disinfection robot picks up the obstacle and puts it into the trash can is executed.

[0031] The disinfection method described above, step S5 further includes determining whether the longest length of the obstacle exceeds the shortest length of the trash can;

[0032] If the volume of the obstacle does not exceed the storage volume of the trash can, and the longest length of the obstacle does not exceed the shortest length of the trash can, the disinfection robot will pick up the obstacle and put it into the trash can, updating the storage volume of the trash can;

[0033] If the volume of the obstacle does not exceed the storage volume of the trash can, and the longest length of the obstacle exceeds the shortest length of the trash can, the disinfection robot will disinfect the obstacle in place.

[0034] If the volume of the obstacle exceeds the storage volume of the trash can, the disinfection robot will disinfect the obstacle in place.

[0035] A disinfection system for a disinfection robot includes:

[0036] The obstacle image generation module is used by the disinfection robot to disinfect according to a preset path. If an obstacle is detected during the disinfection process, the obstacle image is acquired.

[0037] The obstacle state type generation and judgment module is used to obtain the obstacle state type based on the obstacle image; if the obstacle state type is a dynamic obstacle, the disinfection robot stops driving and disinfecting until no dynamic obstacle is detected in the detection field of the disinfection robot, then the disinfection robot continues driving and disinfecting; if the obstacle state type is a static obstacle, the operation in the obstacle height type generation and judgment module is executed.

[0038] An obstacle height type generation and judgment module is used to obtain the obstacle height type based on the obstacle image; if the obstacle height type is a first height type, the disinfection robot disinfects the obstacle in place; if the obstacle height type is a second height type, the operation in the obstacle pickup type generation and judgment module is executed.

[0039] The obstacle pickup type generation and judgment module is used to classify the obstacles based on the obstacle image to obtain the obstacle pickup type; if the obstacle pickup type is the first pickup type, the disinfection robot disinfects the obstacle in place; if the obstacle pickup type is the second pickup type, the operation in the obstacle volume generation and judgment module is executed.

[0040] The obstacle volume generation and judgment module is used to obtain the size information of the obstacle and obtain the obstacle volume based on the obstacle size information; if the obstacle volume does not exceed the storage volume of the trash can, the disinfection robot picks up the obstacle and puts it into the trash can, updating the storage volume of the trash can; if the obstacle volume exceeds the storage volume of the trash can, the disinfection robot disinfects the obstacle in place.

[0041] A disinfection device for a disinfection robot includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the disinfection method described above.

[0042] A computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the disinfection method as described above.

[0043] The beneficial effects of this invention are as follows:

[0044] This invention provides a disinfection method for a disinfection robot. Based on obstacle images, it determines the obstacle height type, obstacle pickup type, and obstacle volume. According to the height characteristics of the obstacle, it sets a reasonable disinfection strategy to improve disinfection quality and efficiency. At the same time, it also considers the type and volume characteristics of the obstacle to formulate a suitable disinfection method, further improving disinfection quality and efficiency. The entire disinfection method improves the flexibility and efficiency of the disinfection robot. Attached Figure Description

[0045] The solutions and advantages of this application will become clear to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0046] In the attached diagram:

[0047] Figure 1 This is a schematic diagram of the disinfection method in the embodiment;

[0048] Figure 2 This is a schematic diagram of the disinfection system in the embodiment;

[0049] Figure 3 This is a schematic diagram of the disinfection equipment in the embodiment. Detailed Implementation

[0050] Exemplary embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings.

[0051] This embodiment provides a disinfection method using a disinfection robot, see [link to relevant documentation]. Figure 1 This includes the following operations:

[0052] The S1 disinfection robot disinfects according to a preset path. If it detects an obstacle during the disinfection process, it acquires an image of the obstacle.

[0053] S2 obtains the obstacle state type based on the obstacle image;

[0054] If the obstacle is a dynamic obstacle, the disinfection robot stops driving and disinfecting. Once the disinfection robot can no longer detect dynamic obstacles in its field of vision, the disinfection robot continues driving and disinfecting.

[0055] If the obstacle's state type is static obstacle, execute S3;

[0056] S3 determines the obstacle height type based on the obstacle image;

[0057] If the obstacle height type is the first height type, the disinfection robot disinfects the obstacle in place;

[0058] If the obstacle height type is the second height type, execute S4;

[0059] S4 classifies the obstacles based on the obstacle image to obtain the obstacle picking type;

[0060] If the obstacle pickup type is the first pickup type, the disinfection robot disinfects the obstacle in place;

[0061] If the obstacle pickup type is the second pickup type, execute S5;

[0062] S5 obtains the size information of the obstacle, and obtains the volume of the obstacle based on the size information;

[0063] If the volume of the obstacle does not exceed the storage volume of the trash can, the disinfection robot will pick up the obstacle and put it into the trash can, updating the storage volume of the trash can;

[0064] If the volume of the obstacle exceeds the storage volume of the trash can, the disinfection robot will disinfect the obstacle in place.

[0065] The S1 disinfection robot disinfects according to a preset path. If it detects an obstacle during the disinfection process, it acquires an image of the obstacle.

[0066] Pre-defined path planning. The spatial information of the hospital wards is input into the disinfection robot in advance. The disinfection robot builds a map based on the spatial information and obtains a pre-defined path according to the A* algorithm. The disinfection robot travels and disinfects according to the pre-defined path. If an obstacle is detected during the disinfection process, the obstacle image is immediately acquired.

[0067] Obstacle detection. The disinfection robot probes along a preset path. If the robot receives a reflected wave from an object, it detects an obstacle; if it does not receive a reflected wave, it does not detect an obstacle.

[0068] The disinfection robot's image acquisition and detection functions can be achieved through its radar and camera, respectively. Before acquiring images of obstacles, the camera needs to be Halcon calibrated to set its intrinsic and extrinsic parameters, reducing distortion and thus obtaining accurate image information.

[0069] S2 determines the obstacle state type based on the obstacle image; if the obstacle state type is a dynamic obstacle, the disinfection robot stops driving and disinfecting until no dynamic obstacle is detected in the disinfection robot's field of vision, then the disinfection robot continues driving and disinfecting; if the obstacle state type is a static obstacle, proceed to S3.

[0070] The specific steps to determine the obstacle state type are as follows: acquire several images within a preset acquisition period, compare the obstacle positions in the images, and if the obstacle position changes, it is a dynamic obstacle; if the obstacle position does not change, it is a static obstacle. The preset acquisition period is 1-10 seconds, preferably 5 seconds. By comparing the obstacle positions in the obstacle images within 5 seconds, the obstacle state type can be quickly identified.

[0071] S3 determines the obstacle height type based on the obstacle image. If the obstacle height type is the first height type, the disinfection robot disinfects the obstacle in place; if the obstacle height type is the second height type, S4 is executed.

[0072] The specific steps to obtain the obstacle height type are as follows: acquire an image of the disinfection robot, convert it to the correct scale, and compare it with an obstacle image of the same scale. If the obstacle height in the obstacle image is not less than the height of the disinfection robot, the obstacle height type is the first height type; if the obstacle height in the obstacle image is less than the height of the disinfection robot, the obstacle height type is the second height type.

[0073] If the obstacle height type is the first height type, the disinfection robot will stop when it reaches the first distance from the obstacle and disinfect the obstacle in place. The first distance is 1-2m, preferably 1.5m. Obstacles corresponding to the first height type include hospital beds, tables, cabinets, medical equipment, etc. These obstacles are difficult to move. When disinfecting these obstacles, the disinfection robot maintains a certain distance from the obstacle to increase the intensity of the disinfection spray and increase the disinfection area.

[0074] S4 classifies obstacles based on their images to determine the obstacle pickup type. If the obstacle pickup type is the first pickup type, the disinfection robot disinfects the obstacle in place. If the obstacle pickup type is the second pickup type, S5 is executed.

[0075] Before the classification operation, feature extraction processing of the obstacle image is performed. Specifically, the feature extraction process involves: obtaining candidate boxes based on the obstacle's position in the obstacle image; sequentially extracting the center point and obstacle region from the candidate boxes to obtain a first feature map; fusing the candidate boxes with the first feature map to obtain a part feature extraction image; and then performing the classification operation in step S3 on the part feature extraction image. More specifically: based on the obstacle's position in the obstacle image, a candidate box generation network processes the obstacle image to obtain multiple levels of anchor boxes; these anchor boxes are fused according to their hierarchical positions to obtain candidate boxes; next, a keypoint extraction network extracts obstacle information from the candidate boxes, followed by feature extraction using a part region extraction network to obtain a first feature map containing detailed obstacle information; finally, the candidate boxes are fused with the first feature map to obtain the part feature extraction image.

[0076] The classification process is as follows: acquire several obstacle images, perform label processing, feed the labeled obstacle images into a neural network for training to obtain neural network parameters, input the neural network parameters into the neural network to obtain a trained neural network, and use the trained neural network to classify and process the obstacle images to obtain the obstacle picking type.

[0077] If the obstacle pickup type is the first type, the disinfection robot stops when it reaches the second distance from the obstacle and disinfects the obstacle in place. The second distance is 0.2-1m, preferably 0.5m.

[0078] If the obstacle pickup type is the first type, when the disinfection robot disinfects the obstacle in place, it will upload the obstacle's location information to the cloud, and the cloud will notify the staff to clean up the obstacle.

[0079] The first type of item to be picked up is non-medical waste, such as mineral water bottles, tissues, and plastic bags. The second type of item to be picked up is medical waste, such as cotton swabs, adhesive bandages, and gauze.

[0080] S5 acquires the size information of the obstacle and obtains the volume of the obstacle based on the size information. If the volume of the obstacle does not exceed the storage volume of the trash can, the disinfection robot picks up the obstacle and puts it into the trash can, updating the storage volume of the trash can. If the volume of the obstacle exceeds the storage volume of the trash can, the disinfection robot disinfects the obstacle in place.

[0081] The operation to obtain obstacle size information is as follows: Obtain an image of the obstacle from the first viewpoint of the disinfection robot to obtain its first and second lengths; obtain an image of the obstacle from the second viewpoint of the disinfection robot to obtain its third length; based on the first, second, and third lengths of the obstacle, obtain the obstacle size information, with the angle between the first and second viewpoints being 90°. When obtaining the images of the obstacle from the first and second viewpoints of the disinfection robot, the distance between the disinfection robot and the obstacle is the same.

[0082] The specific operation to obtain the obstacle volume is as follows: multiply the first length by the second and third lengths to obtain the first volume, and record the first volume as the obstacle volume. The first length, second length, and third length are the maximum length, maximum height, and maximum width of the obstacle, respectively. Using the smallest circumscribed hexahedral volume of the obstacle (the first volume) as the obstacle volume facilitates calculation. Furthermore, the first volume, which is always greater than or equal to the actual volume of the obstacle, is used as a threshold and compared with the storage volume of the trash can to ensure that the trash can can accommodate the obstacle.

[0083] Before the disinfection robot picks up the obstacle and places it in the trash can, the process includes: acquiring front, left, right, and back images of the obstacle to obtain a full-view image; performing an affine transformation on the full-view image to obtain the obstacle's pose information; and executing the obstacle-picking-the-obstacle-in-the-trash-can operation in step S5 based on the obstacle pose information. Images are taken from four directions (forming a 360° angle) of the obstacle to obtain front, left, right, and back images. An affine transformation is used to map the obstacle information onto the camera coordinate system to determine the obstacle's pose. Based on hand-eye calibration, the positional relationship between the obstacle and the disinfection robot's robotic arm is obtained, and the robotic arm is controlled to grasp and place the obstacle into the trash can. When grasping the obstacle, the disinfection robot uses the obstacle pose information to determine the surface with the smallest cross-sectional area within the obstacle, and then uses this surface to pick up the obstacle and place it in the trash can.

[0084] In addition, to ensure that the trash can can hold obstacles, S5 also includes determining whether the longest length of the obstacle exceeds the shortest length of the trash can; if the volume of the obstacle does not exceed the storage volume of the trash can, and the longest length of the obstacle does not exceed the shortest length of the trash can, the disinfection robot picks up the obstacle and puts it into the trash can, updating the storage volume of the trash can; if the volume of the obstacle does not exceed the storage volume of the trash can, and the longest length of the obstacle exceeds the shortest length of the trash can, the disinfection robot disinfects the obstacle in place; if the volume of the obstacle exceeds the storage volume of the trash can, the disinfection robot disinfects the obstacle in place.

[0085] The longest length of an obstacle is the maximum straight-line distance between any two points on the obstacle. The longest length can be obtained by acquiring several obstacle images and comparing the maximum distance between two points in the images to find the maximum distance between the two points. The shortest length of a trash can is the shortest side at the opening (entrance) of the trash can. By comparing the longest length of the obstacle with the shortest length of the trash can, it can be ensured that the trash can be put into the trash can after it is picked up.

[0086] If the size of an obstacle exceeds the storage volume of the trash can, the disinfection robot will mark the obstacle's location information while disinfecting it on-site. The location information will then be uploaded to the cloud, which will notify staff to remove the obstacle. Alternatively, after the trash can has been completely cleaned, if the trash can's storage volume exceeds the size of the obstacle, the disinfection robot will pick up the obstacle and place it in the trash can based on the marked obstacle location information.

[0087] Before updating the trash can's storage volume, the process also includes compressing the trash can to increase its remaining storage volume.

[0088] This embodiment provides a disinfection system for a disinfection robot, see [link / reference]. Figure 2 ,include:

[0089] The obstacle image generation module is used by the disinfection robot to disinfect according to a preset path. If an obstacle is detected during the disinfection process, the obstacle image is acquired.

[0090] The obstacle state type generation and judgment module is used to obtain the obstacle state type based on the obstacle image. If the obstacle state type is a dynamic obstacle, the disinfection robot stops driving and disinfecting. If the robot to be disinfected cannot detect the dynamic obstacle in its field of vision, the disinfection robot continues to drive and disinfect. If the obstacle state type is a static obstacle, the operation in the obstacle height type generation and judgment module is executed.

[0091] The obstacle height type generation and judgment module is used to obtain the obstacle height type based on the obstacle image; if the obstacle height type is the first height type, the disinfection robot disinfects the obstacle in place; if the obstacle height type is the second height type, the operation in the obstacle picking type generation and judgment module is executed.

[0092] The obstacle pickup type generation and judgment module is used to classify obstacles based on obstacle images to obtain obstacle pickup types. If the obstacle pickup type is the first pickup type, the disinfection robot disinfects the obstacle in place. If the obstacle pickup type is the second pickup type, the operation in the obstacle volume generation and judgment module is executed.

[0093] The obstacle volume generation and judgment module is used to obtain the size information of the obstacle and obtain the obstacle volume based on the obstacle size information. If the obstacle volume does not exceed the storage volume of the trash can, the disinfection robot picks up the obstacle and puts it into the trash can, updating the storage volume of the trash can. If the obstacle volume exceeds the storage volume of the trash can, the disinfection robot disinfects the obstacle in place.

[0094] This embodiment provides a disinfection device for a disinfection robot, see [link / reference]. Figure 3 It includes a processor and a memory, wherein the processor implements the above-described disinfection method when executing a computer program stored in the memory.

[0095] A computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described disinfection method.

[0096] This embodiment provides a disinfection method for a disinfection robot. Based on obstacle images, it determines the obstacle height type, obstacle pickup type, and obstacle volume. According to the height characteristics of the obstacle, it sets a reasonable disinfection strategy to improve disinfection quality and efficiency. At the same time, it also considers the type and volume characteristics of the obstacle to formulate a suitable disinfection method, further improving disinfection quality and efficiency. The entire disinfection method improves the flexibility and efficiency of the disinfection robot.

Claims

1. A disinfection method using a disinfection robot, characterized in that, This includes the following operations: The S1 disinfection robot disinfects according to a preset path. If it detects an obstacle during the disinfection process, it acquires an image of the obstacle. S2 Based on the obstacle image, the obstacle state type is obtained; If the obstacle is a dynamic obstacle, the disinfection robot stops driving and disinfecting. Once the disinfection robot can no longer detect dynamic obstacles in its field of vision, the disinfection robot continues driving and disinfecting. If the obstacle's state type is static obstacle, execute S3; S3. Based on the obstacle image, obtain the obstacle height type; If the obstacle height type is the first height type, the disinfection robot disinfects the obstacle in place; If the obstacle height type is the second height type, execute S4; S4 Based on the obstacle image, the obstacles are classified to obtain the obstacle picking type; If the obstacle pickup type is the first pickup type, the disinfection robot disinfects the obstacle in place; If the obstacle pickup type is the second pickup type, execute S5; S5 Obtain the size information of the obstacle, and obtain the volume of the obstacle based on the size information; If the volume of the obstacle does not exceed the storage volume of the trash can, the disinfection robot will pick up the obstacle and put it into the trash can, updating the storage volume of the trash can; If the volume of the obstacle exceeds the storage volume of the trash can, the disinfection robot will disinfect the obstacle in place.

2. The disinfection method according to claim 1, characterized in that, The operation to obtain the obstacle height type in S3 is specifically as follows: An image of the disinfection robot is acquired, and after scaling, it is compared with an obstacle image of the same scale. If the height of the obstacle in the obstacle image is not less than the height of the disinfection robot, then the obstacle height type is the first height type; if the height of the obstacle in the obstacle image is less than the height of the disinfection robot, then the obstacle height type is the second height type.

3. The disinfection method according to claim 1, characterized in that, In the operation of S3, if the obstacle height type is the first height type, when the disinfection robot travels to a distance of the first distance from the obstacle, it stops traveling and disinfects the obstacle in place.

4. The disinfection method according to claim 1, characterized in that, The operation to obtain obstacle size information in S5 is as follows: Obtain an image of the obstacle from the first perspective of the disinfection robot, and obtain the first length and second length of the obstacle; Obtain an image of the obstacle from the second perspective of the disinfection robot, and obtain the third length of the obstacle; Based on the first length, second length, and third length of the obstacle, the size information of the obstacle is obtained; The angle between the first viewpoint and the second viewpoint is 90°.

5. The disinfection method according to claim 4, characterized in that, The operation to obtain the volume of the obstacle in S5 is specifically as follows: Multiply the first length by the second and third lengths to obtain the first volume, and record the first volume as the obstacle volume.

6. The disinfection method according to claim 1, characterized in that, Before the disinfection robot in S5 picks up the obstacle and places it in the trash can, the following steps are also included: The front, left, right, and back images of the obstacle are acquired to obtain a full-view image of the obstacle. The full-view image of the obstacle is then processed by affine transformation to obtain the obstacle pose information. Based on the obstacle pose information, the operation in S5 where the disinfection robot picks up the obstacle and puts it into the trash can is executed.

7. The disinfection method according to claim 1, characterized in that, S5 also includes determining whether the longest length of the obstacle exceeds the shortest length of the trash can; If the volume of the obstacle does not exceed the storage volume of the trash can, and the longest length of the obstacle does not exceed the shortest length of the trash can, the disinfection robot will pick up the obstacle and put it into the trash can, updating the storage volume of the trash can; If the volume of the obstacle does not exceed the storage volume of the trash can, and the longest length of the obstacle exceeds the shortest length of the trash can, the disinfection robot will disinfect the obstacle in place. If the volume of the obstacle exceeds the storage volume of the trash can, the disinfection robot will disinfect the obstacle in place.

8. A disinfection system for a disinfection robot, characterized in that, include: The obstacle image generation module is used by the disinfection robot to disinfect according to a preset path. If an obstacle is detected during the disinfection process, the obstacle image is acquired. An obstacle state type generation and judgment module is used to obtain the obstacle state type based on the obstacle image; If the obstacle is a dynamic obstacle, the disinfection robot stops driving and disinfects until no dynamic obstacle is detected in the robot's field of vision, then the robot continues driving and disinfecting. If the obstacle is a static obstacle, the operation in the obstacle height type generation and judgment module is executed. An obstacle height type generation and judgment module is used to obtain the obstacle height type based on the obstacle image; if the obstacle height type is a first height type, the disinfection robot disinfects the obstacle in place; if the obstacle height type is a second height type, the operation in the obstacle pickup type generation and judgment module is executed. An obstacle pickup type generation and judgment module is used to classify the obstacles based on the obstacle image to obtain the obstacle pickup type; If the obstacle pickup type is the first pickup type, the disinfection robot disinfects the obstacle in place; if the obstacle pickup type is the second pickup type, the operation in the obstacle volume generation and judgment module is executed. An obstacle volume generation and determination module is used to obtain the size information of the obstacle and, based on the obstacle size information, obtain the obstacle volume. If the size of the obstacle does not exceed the storage volume of the trash can, the disinfection robot picks up the obstacle and puts it into the trash can, updating the storage volume of the trash can; if the size of the obstacle exceeds the storage volume of the trash can, the disinfection robot disinfects the obstacle in place.

9. A disinfection device for a disinfection robot, characterized in that, It includes a processor and a memory, wherein the processor implements the disinfection method as described in any one of claims 1-7 when executing a computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the disinfection method as described in any one of claims 1-7.

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