Environment data processing method and device and self-driven equipment

Abstract

The invention provides an environment data processing method and device and self-driven equipment, and relates to the field of mobile positioning. According to the environment data processing method, a plurality of sensors are deployed to monitor environment data in real time, and the height and the driving path of the cutter head are adjusted, so that equipment can cover the whole operation area more effectively, and repeated operation and omission phenomena are reduced; meanwhile, time and energy waste caused by accidental shutdown is avoided through the obstacle avoidance and anti-falling functions, and the overall operation efficiency is further improved.

Description

Technical Field

[0001] This invention relates to the field of mobile positioning, and more specifically to an environmental data processing method, apparatus, and self-driving device. Background Technology

[0002] With the rapid development of artificial intelligence devices, the environmental monitoring technology of robots is also developing rapidly.

[0003] Most intelligent robots on the market rely on a single or a few sensors to perceive their environment, such as using only a single sensor at the top or bottom to detect the height of the lawn or obstacles. This approach has significant limitations, such as the inability to precisely control the cutting quality of the lawn on both sides, which can easily lead to over-trimming on one side and under-trimming on the other. At the same time, a single sensor cannot fully cover the front and sides of the driving path, increasing the risk of accidents caused by undetected dangerous environments such as cliffs. Summary of the Invention

[0004] The main objective of this invention is to propose an environmental data processing method and apparatus for gaining a deeper understanding of the surrounding environment.

[0005] A first aspect of the embodiments of this application provides an environmental data processing method, the method comprising:

[0006] The first and second sensors are located in different areas of the device body and collect environmental data from different directions.

[0007] Based on the environmental data collected by the first sensor and the second sensor, the vegetation information and road condition information in the current driving environment of the device are determined;

[0008] The operating strategy and / or driving strategy of the device are adjusted based on the vegetation and road conditions in the current driving environment.

[0009] In one example, the first sensor in the method provided in this application embodiment includes: a visual detection sensor, wherein determining the vegetation information and road condition information in the current driving environment of the device includes:

[0010] The first sensor detects path and vegetation information within a preset range and determines the target working area and target travel path of the device.

[0011] If the device enters the target work area according to the specified driving path, the second sensor detects and determines the vegetation and road conditions in the device's current driving environment.

[0012] In one example, the method provided in this application embodiment for determining the target working range and target travel path of the device includes:

[0013] The system analyzes the vegetation information detected by the first sensor within a preset range, identifies areas where the vegetation height is greater than the preset vegetation height, and determines these areas as the target working area.

[0014] Obtain several paths between the current location and the target work area;

[0015] The path length of each path and whether there is a target object during the path travel are calculated in turn to determine the target travel path.

[0016] In one example, adjusting the operating strategy and / or driving strategy of the device in the method provided in this application embodiment includes:

[0017] If the road condition information collected by the first sensor indicates that there is a target object in the first direction, then the road condition information of the second direction sensor is acquired.

[0018] If the road condition information collected by the second sensor indicates that there is a target object in the second direction, then stop driving and issue a warning;

[0019] If the road condition information collected by the second sensor indicates that there is no target object in the second direction, a driving instruction is generated, which is used to instruct driving along the second direction.

[0020] In one example, adjusting the operating strategy and / or driving strategy of the device in the method provided in this application embodiment includes:

[0021] The first vegetation information in the first area is detected by the first sensor, and the second vegetation information in the second area is detected by the second sensor.

[0022] Confirm whether the first vegetation information matches the preset vegetation information, and whether the second vegetation information matches the preset vegetation information, and generate confirmation information;

[0023] A working strategy is generated based on the confirmation information. The working strategy is used to indicate whether the cutting parameters of the device in the first direction need to be adjusted, and whether the cutting parameters of the device in the second direction need to be adjusted. The cutting parameters include at least one of the following: cutting height, cutting speed, and cutting cycle.

[0024] In one example, the method provided in this application embodiment generates a working strategy based on the confirmation information, including:

[0025] The first vegetation information in the first area is detected by the first sensor, and the second vegetation information in the second area is detected by the second sensor.

[0026] Confirm whether the first vegetation information matches the preset vegetation information, and whether the second vegetation information matches the preset vegetation information, and generate confirmation information;

[0027] A driving strategy is generated based on the confirmation information;

[0028] The driving strategy includes at least one of the following: driving direction, driving speed, and driving cycle.

[0029] In one example, the environmental data collected in the method provided in this application embodiment includes:

[0030] Obtain the height information of the first sensor and / or the second sensor on the device body;

[0031] A preset error value is determined based on the height information from the first sensor and / or the second sensor;

[0032] According to a preset error value, the first sensor and / or the second sensor collect environmental data;

[0033] In one example, the environmental data collected in the method provided in this application embodiment includes:

[0034] Obtain the movement information of the device;

[0035] Based on the device's movement information, the timing sequence for collecting environmental data is determined, and environmental data is collected in response to the timing sequence.

[0036] In one example, the second sensor in the method provided by this application embodiment includes at least two sub-sensors. The first sensor and the first sub-sensor of the second sensor form a first straight line, and the first sensor and the second sub-sensor of the second sensor form a second straight line. The angle between the first straight line and the second straight line is greater than 10 degrees.

[0037] The environmental data processing method provided in this application embodiment monitors environmental data in real time by deploying multiple sensors and adjusting the cutter head height and travel path. This allows the equipment to more effectively cover the entire work area, reducing repetitive work and omissions. At the same time, obstacle avoidance and anti-fall functions prevent the waste of time and energy caused by unexpected downtime, further improving overall work efficiency.

[0038] A second aspect of this application provides an environmental data processing apparatus, the apparatus comprising:

[0039] The acquisition module acquires environmental data collected by the first sensor and the second sensor, which are located in different areas of the device body and collect environmental data from different directions.

[0040] The processing module determines the vegetation information and road condition information in the current driving environment of the device based on the environmental data collected by the first sensor and the second sensor.

[0041] The operating strategy and / or driving strategy of the device are adjusted based on the vegetation and road conditions in the current driving environment.

[0042] The environmental data processing device provided in this application embodiment detects the vegetation height on the left, middle and right sides of the device by deploying multiple sensors. Combined with precise cutter head control, it achieves highly personalized intelligent cutting while effectively reducing energy consumption. The collaborative work of multiple sensors significantly improves the system's ability to perceive the condition of the lawn. Whether in flat areas or complex terrain, it can obtain more accurate information, thereby better adapting to various cutting needs and enhancing detection accuracy and coverage.

[0043] A third aspect of the embodiments of this application provides a self-driving device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.

[0044] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims. Attached Figure Description

[0045] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings:

[0046] Figure 1 This is a schematic diagram of the structure of an environmental data processing system provided in one embodiment of this application;

[0047] Figure 2 This is a flowchart of an environmental data processing method provided in one embodiment of this application;

[0048] Figure 3A This is an application example of the environmental data processing method provided in one embodiment of this application. Figure 1 ;

[0049] Figure 3B This is an application example of the environmental data processing method provided in one embodiment of this application. Figure 2 ;

[0050] Figure 4AFigure 3 shows an application example of the environmental data processing method provided in one embodiment of this application;

[0051] Figure 4B Figure 4 shows an application example of the environmental data processing method provided in one embodiment of this application;

[0052] Figure 5 This is an application example of the environmental data processing method provided in one embodiment of this application. Figure 5 ;

[0053] Figure 6A This is a schematic diagram of the position structure of the first and second sensors in an environmental data processing method provided in one embodiment of this application. Figure 1 ;

[0054] Figure 6B This is a schematic diagram of the position structure of the first and second sensors in an environmental data processing method provided in one embodiment of this application. Figure 2 ;

[0055] Figure 6C This is a schematic diagram of the position structure of the first and second sensors in an environmental data processing method provided in one embodiment of this application. Detailed Implementation

[0056] The main objective of this application is to propose an environmental data processing method and apparatus, which aims to achieve accurate detection of target objects in a work scenario, improve the efficiency and accuracy of equipment operation, and meet user needs.

[0057] When autonomous mobile devices perform lawn mowing tasks, the environment they encounter is often complex and varied, especially with scattered soil and gravel. These unstructured obstacles not only increase the difficulty of the robot's movement but also significantly increase the probability of it deviating from the preset route. This deviation may be due to the robot's wheels slipping when contacting uneven ground, or the path adjustment caused by the robot needing to take obstacle avoidance measures after encountering obstacles.

[0058] Most existing self-moving devices rely on a single or a few sensors to perceive environmental information, a design with numerous limitations. For example, many lawnmowers detect lawn height or obstacles using only a single sensor on the top or bottom. While this approach can achieve basic cutting functionality to some extent, it falls short of meeting higher-level requirements for cutting quality and safety.

[0059] For example, when a single sensor detects lawn height, it often only obtains localized information from directly below or above the lawnmower, failing to accurately reflect the actual height of the lawn on both sides. This can lead to the lawnmower being unable to precisely control the cutting quality on both sides during the mowing process, resulting in one side being over-cut while the other side is under-cut, severely impacting the overall aesthetics and comfort of the lawn.

[0060] Furthermore, single sensors have significant limitations in detecting obstacles and hazardous environments. Due to the limited sensing range of sensors, they often cannot fully cover the area in front of and to the sides of the lawnmower's path. This can lead to accidents during operation due to the failure to detect dangerous environments such as cliffs or steep downhill slopes, posing a serious threat to the life and property safety of users.

[0061] To address the issue that a single sensor in the aforementioned equipment cannot detect driving errors caused by complex environmental factors, this application provides an environmental data processing method. This method integrates multiple sensors and allows them to work collaboratively to achieve comprehensive perception and precise control of the lawn environment.

[0062] The solutions in this application embodiment can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0063] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0064] The following is a brief description of the application environment of the environmental data processing method provided in the embodiments of this application:

[0065] Please see Figure 1 This application provides an environmental data processing system 10, which includes at least a processor 101 and a sensor 102. The processor 101 is connected to the sensor 102, and the processor acquires environmental data collected by the sensor. The sensor 102 may include multiple sets of sensors, and each set of sensors may be of the same or different types. The sensors may include ultrasonic sensors, laser rangefinders, infrared sensors, or visual acquisition sensors. The sensor 102 can measure the changes in the height and density of the lawn in the current working environment in real time and accurately, and capture information on the micro-topography of the ground, including the undulation of the lawn and the differences in the growth status of different areas. The sensor 102 can also detect road condition information, such as the undulation of the road surface, the boundary cliffs of the road surface, and obstacles in the preset path.

[0066] The processor 101 acquires environmental data collected by the sensor 102, and the self-driving device can intelligently adjust the height of its blade to ensure the best mowing effect on lawns at different heights. At the same time, for areas with high lawn density, the lawnmower can optimize its travel speed or cutting mode to reduce clogging and improve work efficiency.

[0067] The processing system provided in this application collects environmental data for each operation through multiple sets of sensors, continuously optimizes algorithms and path planning strategies, improves the adaptability and operational efficiency of the equipment in various environments, and can also intelligently plan the optimal obstacle avoidance path by combining sensor data and preset path information, and adjust the travel direction of the equipment in real time so that it can quickly and accurately return to the preset path.

[0068] Please see Figure 2 This application provides an environmental data processing method applied to a processor 101. The method is used to collect environmental data during equipment operation and adjust operating and driving strategies. The environmental data processing method includes the following steps 201-203:

[0069] Step 201: Obtain environmental data collected by the first and second sensors.

[0070] Self-driven devices include those equipped with motors and processors, which can send signals through the processor to control the motors and drive the device to move, such as intelligent robots, intelligent lawnmowers, or other work equipment. The following description may refer to them as robots or devices for short.

[0071] Intelligent lawnmowers utilize sensors deployed in multiple directions on their chassis to detect movement (such as ultrasonic sensors, laser rangefinders, infrared sensors, or vision sensors).

[0072] The first and second sensors mentioned above are located in different areas of the device and collect environmental data from different directions. This is intended to comprehensively capture data about the surrounding environment from multiple dimensions to ensure comprehensive coverage and in-depth analysis of information.

[0073] For example, the first sensor in a self-driving device, such as an ultrasonic sensor, is located at the front of the device and measures the height of the lawn by emitting ultrasonic waves and receiving the reflected signals. This sensor can accurately capture the distance between the lawn surface and the device, thereby calculating the lawn height. The second sensor, such as an infrared sensor or an optical sensor, is located at the bottom or side of the device and assesses the lawn's density by measuring the degree to which the lawn absorbs or reflects light. Denseer lawns absorb more light, while sparser lawns reflect more light; the sensor generates lawn density data based on these differences.

[0074] For example, the first and second sensors can also detect changes in the tilt or acceleration of the equipment, or detect the contact status between the bottom of the equipment and the ground, to determine whether the lawnmower is on a safe ground, thereby triggering an early warning mechanism to prevent it from falling off cliffs or steep slopes. The fusion and comprehensive analysis of data from multiple sensors can effectively avoid safety hazards caused by misjudgment or omission by a single sensor.

[0075] For example, the first type of sensor, such as an ultrasonic sensor, uses the principle of sound wave ranging. It calculates the distance and infers the height of the lawn by emitting ultrasonic waves and measuring the time difference between their reflection and their impact with an obstacle. This type of sensor has the advantages of a wide measurement range and high accuracy. The second type of sensor, such as an infrared sensor or an optical sensor, uses optical principles for measurement. An infrared sensor assesses the density of the lawn by emitting infrared light and receiving the signal intensity after it is absorbed or reflected by the lawn; an optical sensor may capture images of the lawn and use image processing algorithms to analyze the density and distribution of the lawn.

[0076] For example, embodiments of this application may also be equipped with multiple sets of different types of sensors for joint detection. In addition to the aforementioned first and second sensors, gyroscopes, accelerometers, and ground contact sensors may also be included. Gyroscopes and accelerometers can sense changes in the device's attitude and acceleration.

[0077] Step 202: Based on the environmental data collected by the first and second sensors, determine the vegetation information and road condition information in the current driving environment of the equipment.

[0078] The sensor obtains vegetation information in the current driving environment by detecting changes in the height and density of the lawn in the current working environment. The vegetation information includes: the height and density of the vegetation, the undulation of the lawn, and the differences in growth status in different areas. The vegetation can include flowers, grasses and trees, but the following description uses lawn as a substitute, which does not mean that this application is limited to this.

[0079] The sensor acquires road information by detecting ground micro-topography within a preset range and obstacles (such as rocks, trees, toys, etc.) on the driving path. The road information includes: path length, path slope, and whether there are target objects in the path.

[0080] The first sensor (such as an ultrasonic sensor) calculates the round-trip time of the sound waves by emitting sound waves and receiving the reflected signals, thereby estimating the height of the lawn. The second sensor (such as an optical sensor) assesses the density and health of the lawn by analyzing the intensity or color differences of the light reflected from it. This information is converted into digital signals and sent to the central processing unit of the lawnmower.

[0081] The processor fuses and verifies data from multiple sensors to eliminate errors and interference from individual sensors. For example, when data from ultrasonic and infrared sensors are inconsistent, the processor performs weight allocation and correction according to a preset algorithm to ensure the accuracy of the final data. Simultaneously, the processor performs diversity analysis on the data, extracting different characteristics and trends of the lawn, providing a basis for the lawnmower's intelligent decision-making. Through these processing and analysis steps, the intelligent lawnmower can more accurately perceive its environment and make smarter mowing decisions.

[0082] Step 203: Adjust the working strategy and / or driving strategy of the device based on the vegetation information and road condition information in the current driving environment of the device.

[0083] Based on collected vegetation and road condition information, the device dynamically adjusts its mowing and driving strategies. For example, when it detects that a certain area of ​​the lawn is too tall or too dense, the device automatically lowers the mowing height, increases the cutting frequency, or adopts a slower driving speed to ensure the lawn is mowed evenly and thoroughly. In areas with dense obstacles, the device plans avoidance paths in advance to prevent collisions, while using gyroscopes to maintain driving stability and prevent rollovers due to terrain changes. Furthermore, by analyzing historical data, the device can learn lawn growth patterns and user preferences, gradually optimizing the mowing plan for more personalized and efficient lawn management. This intelligent mowing and driving strategy not only improves mowing efficiency but also reduces human intervention, enhancing the user experience.

[0084] The method provided in this application uses multiple sets of sensors to detect information such as changes in ground height and distance to obstacles, providing the device with real-time data for obstacle avoidance and path adjustment. Through precise measurement and adjustment, the sensors are raised to help the robot cross or bypass obstacles, reducing path deviations caused by environmental factors.

[0085] The processing method provided in this application, by deploying multiple sensors to monitor environmental data in real time and adjusting the cutter head height and travel path, enables the equipment to more effectively cover the entire work area, reducing repetitive work and omissions. Simultaneously, obstacle avoidance and fall prevention functions prevent time and energy waste caused by unexpected downtime, further improving overall work efficiency.

[0086] In an optional embodiment, the first sensor in the method provided by this application includes a visual detection sensor. Then, in step 202, vegetation information and road condition information in the current driving environment of the device are determined. Steps 301 to 302:

[0087] Step 301: Detect path information and vegetation information within a preset range using the first sensor, and determine the target working area and target travel path of the device;

[0088] The first sensor can use a camera combined with image processing technology to capture real-time image information from the front. By analyzing the color, texture, or specific patterns in the image (such as the density and height changes of the lawn), it can accurately identify which areas need to be cut. When the sensor detects that the lawn in front is higher than a preset threshold, it will send a signal to the control system.

[0089] This application embodiment can integrate a high-definition camera and an advanced image recognition algorithm to detect environmental data, perform a comprehensive scan of the lawn, identify the area to be cut and its location, and ensure that the area is located before the machine gets close through efficient image processing capabilities and real-time performance.

[0090] Step 302: If the device enters the target work area according to the target driving path, the second sensor detects and determines the vegetation information and road condition information in the current driving environment of the device.

[0091] The second sensor may include a vision sensor, a height sensor, a distance sensor, or a density sensor, etc.

[0092] During operation, the processor can be combined with the GPS positioning system to transmit the identified location information of the area to be cut to the central control unit, providing accurate navigation for subsequent directional cutting.

[0093] When the machine approaches the area to be cut as marked by the AI ​​vision system, the central control unit adjusts the mower's path based on the location information to ensure it accurately enters the area. It can also use algorithms to pre-set reasonable delay thresholds to ensure that the cutter head can respond accurately after the sensor enters the cutting area but before reaching the designated area.

[0094] At the same time, the bottom sensor starts working, adjusting the blade height and cutting speed in real time according to the height of the lawn to ensure precise cutting during missed mowing and re-mowing.

[0095] In special circumstances (such as complex terrain or high-density vegetation), the system can automatically adjust the cutting strategy, such as increasing the number of cuts or reducing the travel speed, to ensure the cutting effect.

[0096] Please refer to the attached document. Figure 3A and attached Figure 3B The work scenario shown is attached. Figure 3A A diagram used to indicate the inspection work area. Figure 1 , attached Figure 3B A diagram used to indicate the testing and inspection work area. Figure 2 This allows for a clear observation of the processing steps of the method provided in this application in detecting the work area and performing intelligent operations:

[0097] In the appendix Figure 3A In this method, the work area can be located in front of the equipment. Therefore, the method provided in this application can first determine the target work area using a first sensor and detect whether the equipment has moved to the target work area. After the processor receives the signal from the visual detection sensor, the control system will activate the cutter head motor, causing the cutter head to start rotating and performing the cutting operation. Conversely, if no area to be cut is detected, the cutter head remains stationary to achieve energy saving. This process may also integrate variable frequency control technology to automatically adjust the cutter head speed and cutting depth according to the hardness and height of the lawn, further optimizing the cutting effect and energy consumption.

[0098] The processing method provided in this application embodiment monitors the lawn status in real time through multiple sensors, quickly responds to areas that need to be cut, avoids the blindness and repetitive work in traditional methods, and significantly improves the overall work efficiency.

[0099] In the appendix Figure 3B In this method, the area to be cut can be on the side of the equipment. Therefore, the method provided in this application can first detect the difference between the left and right sides of the lawn, and then control the equipment to operate. Specifically, the equipment moves at a constant speed along the lawn path, and the height data of the lawn on the left and right sides are measured and compared in real time. If the height of the lawn on the right side is found to be significantly higher than the preset value on the left side, it is determined that there is a missed area on the right side. Subsequently, based on the GPS positioning system or the built-in navigation algorithm, the control system adjusts the vehicle body to shift to the right until the cutter head completely covers and cuts the missed lawn area on the right side.

[0100] The processing method provided in this application embodiment ensures that missed mowing areas can be effectively identified and processed even in complex environments through a multi-sensor detection mechanism. This avoids uneven lawn conditions caused by human error or blind spots, reduces the need for manual intervention, and improves work efficiency and cutting quality.

[0101] In an optional embodiment, the first sensor in the method provided by this application includes a visual detection sensor. Then, step 203, determining the target working range and target travel path of the device, includes steps 401 to 403:

[0102] Step 401: Analyze the vegetation information detected by the first sensor within a preset range, obtain the area where the vegetation height is greater than the preset vegetation height, and determine it as the target working area;

[0103] Of course, it can also be an area with a vegetation density greater than the preset vegetation density, and be designated as the target working area; or it can be a target vegetation area specified by the user.

[0104] Step 402: Obtain several paths between the current location and the target work area;

[0105] For example, the coordinates of several boundary lines of the target working area can be obtained, thereby determining several paths between the current position and the target working area.

[0106] By combining information such as ground elevation and obstacle distance, a digital map of the surrounding environment is quickly constructed. Based on this, the algorithm automatically calculates multiple safe and efficient driving paths, ensuring that the device can flexibly navigate complex terrain environments, such as uneven lawns, sculptures in gardens, or around trees.

[0107] Step 403: Calculate the path length of each path and whether there is a target object during the path travel to determine the target travel path.

[0108] When selecting the optimal path, the lawnmower considers not only the straight-line distance and slope, but also analyzes potential obstacles such as suddenly appearing pets, toys, or low-lying vegetation edges. Through real-time data processing and decision-making, the lawnmower can dynamically adjust its route to avoid collisions while ensuring efficient operation. Elevation sensors play a crucial role in this process, helping the lawnmower adjust flexibly when encountering gentle slopes or low obstacles, maintaining stable operation and significantly reducing path deviations and work interruptions caused by environmental factors.

[0109] In an optional embodiment, determining the target working range and target travel path of the device in step 203 of the method provided in this application includes: steps 501 to 503:

[0110] Step 501: If the road condition information collected by the first sensor indicates that there is a target object in the first direction, then obtain the road condition information from the second direction sensor.

[0111] Target objects may include: specific plants (such as weeds on a lawn or ornamental plants that need to be preserved), obstacles (such as stones, debris, fences, etc.), charging stations, users, animals, and other objects that can identify a specific target.

[0112] Step 502: If the road condition information collected by the second sensor indicates that there is a target object in the second direction, then stop driving and issue a warning;

[0113] Different sensors are mounted in different locations on the smart lawnmower to create complementary detection angles. For example, the first sensor might be responsible for detecting road conditions ahead, while the second sensor is specifically designed to monitor the sides or rear. This arrangement ensures that the appropriate sensors provide accurate road condition information whenever the device is traveling in any direction.

[0114] If the sensors detect a potential hazard (such as a cliff edge), a warning mechanism will be triggered, stopping the device and alerting the user through sound, lights, or other means. This way, even when the device is in autonomous driving mode, accidents can be avoided at critical moments.

[0115] Step 503: If the road condition information collected by the second sensor indicates that there is no target object in the second direction, a driving command is generated, which is used to instruct driving along the second direction.

[0116] The road condition information collected by the sensors is transmitted to the central processor for analysis in real time. By comparing data from different sensors, the system can more accurately determine the safety of the current environment and thus make the correct driving decisions.

[0117] Please refer to the attached document. Figure 4A and attached Figure 4B , attached Figure 4A Schematic diagram 3, used to indicate road condition detection scenarios, attached. Figure 4B Schematic diagram four, used to indicate the road condition detection scenario, clearly shows the process by which the method provided in this application detects road condition information to prevent fall risks:

[0118] In the appendix Figure 4A In the image, a depression in the road directly in front of the device can be clearly observed, potentially posing a risk of falling; a single sensor can detect this. However, for more complex road conditions, such as those shown in the attached image... Figure 4B If a single sensor can clearly observe a depression in the road to the right front of the device, it will be unable to detect the depression on the right side, meaning there is a large blind spot in the detection angle. If a device with only one sensor is driven to the right, it may not detect the cliff while the wheels on both sides have already fallen off, causing the device to fall and be damaged. However, the multiple sensors provided in this application can detect road condition information from multiple angles and aspects, preventing the side tires from getting stuck and improving the safety of the device.

[0119] Regarding the appendix Figure 4A In road condition scenarios, the method provided in this application can stop and reverse after detecting a risk of falling on the road ahead, and mark the risk area on the map.

[0120] Regarding the appendix Figure 4B In the road condition scenario, the method provided in this application can stop and reverse after detecting a risk of falling on the right front road condition, and mark the risk area on the map.

[0121] In an optional embodiment, determining the target working range and target travel path of the device in step 203 of the method provided in this application includes steps 601 to 603:

[0122] Step 601: Detect the first vegetation information in the first area using the first sensor module, and detect the second vegetation information in the second area using the second sensor;

[0123] Step 602: Confirm whether the first vegetation information matches the preset vegetation information and whether the second vegetation information matches the preset vegetation information, and generate confirmation information.

[0124] The confirmation information may include: whether the first vegetation information is greater than the maximum threshold of the preset vegetation information, or whether the second vegetation information is greater than the maximum threshold of the preset vegetation information;

[0125] The highest threshold can indicate either the threshold for vegetation height or the threshold for vegetation density.

[0126] Step 603: Generate a working strategy based on the confirmation information.

[0127] The working strategy is used to indicate whether the cutting parameters of the device in the first direction need to be adjusted, and whether the cutting parameters of the device in the second direction need to be adjusted. The cutting parameters include at least one of the following: cutting height, cutting speed, and cutting cycle.

[0128] For example, if the first vegetation information is greater than the highest threshold of the preset vegetation information, and the second vegetation information is less than or equal to the highest threshold of the preset vegetation information, then the cutting parameter in the first direction is increased and the cutting parameter in the second direction is decreased.

[0129] Please refer to the appendix here. Figure 5The scenario shown is as follows: In this scenario, the equipment operates along a bow-shaped cutting path. During the operation, environmental information (such as lawn height, obstacle position, etc.) is detected in real time. Based on the information fed back by the sensors, the position of the lawnmower (such as tilt angle, steering angle, etc.) is automatically adjusted, and the optimal driving route is dynamically planned to ensure that the cutter head can always be kept in the best cutting position. For example, if the height of the left side of the vehicle is lower than that of the right side, it will shift to the right and adjust the driving path at the same time.

[0130] In an optional embodiment, step 603 of the method provided in this application, which generates a working strategy based on the confirmation information, includes steps 701 to 703:

[0131] Step 701: Detect the first vegetation information in the first area using the first sensor module, and detect the second vegetation information in the second area using the second sensor;

[0132] Step 702: Confirm whether the first vegetation information matches the preset vegetation information and whether the second vegetation information matches the preset vegetation information, and generate confirmation information;

[0133] Step 703: Generate a driving strategy based on the confirmation information.

[0134] Employing an advanced path smoothing algorithm, the lawnmower maintains stability during turns and straight-line travel, reducing the frequency and magnitude of positional adjustments.

[0135] The driving strategy includes at least one of the following: driving direction, driving speed, and driving cycle.

[0136] For example, if the first vegetation information is greater than the highest threshold of the preset vegetation information, and the second vegetation information is less than or equal to the highest threshold of the preset vegetation information, then travel in the first direction.

[0137] If the first vegetation information is less than or equal to the highest threshold of the preset vegetation information, and the second vegetation information is greater than the highest threshold of the preset vegetation information, then travel in the second direction.

[0138] The method provided in this application embodiment can also intelligently adjust the height of its blade to ensure optimal mowing results on lawns of different heights. Simultaneously, for areas with high lawn density, the lawnmower can optimize its travel speed or cutting mode to reduce clogging and improve operational efficiency.

[0139] Each sensor in the method provided in this application has a different error value. The deviation value will also change due to different working environments, power-on and power-off factors. Therefore, the error needs to be corrected before use.

[0140] Through algorithm design, at the start of each operation, the system automatically adapts to and converges to a reasonable error value based on parameters such as installation height, and performs detection height analysis based on this error value, eliminating the need for manual calibration. For example, the device acquires the height information of the first sensor and / or the second sensor on the device body; determines a preset error value based on the height information of the first sensor and / or the second sensor; and collects environmental data based on the preset error value.

[0141] The method provided in this application employs efficient data processing algorithms, such as the sliding window algorithm or the fast Fourier transform (for stability processing in continuous signals), to ensure that sensor data can be processed and analyzed in real time.

[0142] The method provided in this application introduces a filtering algorithm (such as a Kalman filter or a median filter) to smooth the sensor data, reducing data fluctuations caused by environmental interference or sensor errors. The cutter head start command is triggered only when the detected data is continuously and stably within a certain range (i.e., meeting the cutting height requirements).

[0143] The method provided in this application embodiment can dynamically adjust the timing of issuing cutting commands based on the difference between the machine's travel speed and the speed feedback from the sensor, ensuring that the cutter head starts working at the optimal time and avoiding missed cuts; for example, acquiring the movement information of the device; determining the acquisition sequence of environmental data based on the movement information of the device and acquiring environmental data in response to the acquisition sequence.

[0144] In an optional embodiment, the first sensor and the second sensor in the method provided in this application are located at the front end and the bottom of the device, respectively, to achieve comprehensive monitoring of the height and density of the lawn.

[0145] The first sensor, typically employing ultrasonic or laser ranging technology, measures the distance between the lawn surface and the sensor, thus indirectly reflecting the lawn's height. This sensor can scan continuously, ensuring the real-time nature and accuracy of the data.

[0146] The second sensor focuses more on detecting lawn density. It may use pressure sensing or optical recognition technology to assess the lawn's density by analyzing the feedback force or light penetration when the bottom of the lawnmower contacts the lawn. The two sensors work together to provide the lawnmower with comprehensive information on the lawn's condition.

[0147] In an optional embodiment, both the first sensor and the second sensor may include a plurality of sensors. For example, the second sensor may include at least two sub-sensors. The first sub-sensors of the first sensor and the second sensor form a first straight line, and the second sub-sensors of the first sensor and the second sensor form a second straight line. The angle between the first straight line and the second straight line is greater than 10 degrees.

[0148] Please refer to the attached document. Figure 6A The structure shown in the attached figure has an angle of 120 degrees between the first and second straight lines.

[0149] Please refer to the attached document. Figure 6B The structure shown in the attached diagram has a 40-degree angle between the first and second straight lines. Figures 6A to 6B This is the simplest example; other combinations and angles can also be included.

[0150] Please refer to the attached document. Figure 6C The structure shown in the attached figure includes a first sub-sensor and a second sub-sensor, which can form two sets of angles. Each set of angles can be different or the same, and no limiting description is given here.

[0151] The method provided in this application embodiment constructs a comprehensive safety protection network by deploying multiple sets of sensors on the front, left and right sides. This network can promptly detect and avoid dangerous terrain such as pits and cliffs, effectively preventing the lawnmower from falling and greatly enhancing the safety of the operation process.

[0152] The method provided in this application embodiment can employ a self-calibration algorithm when calculating vegetation information and road condition information. It dynamically adjusts the error model based on initial installation parameters and environmental variables to ensure the long-term accuracy and consistency of the measured values.

[0153] The method provided in this application embodiment can employ a dynamic temporal acquisition framework when calculating vegetation information and road condition information: constructing an acquisition framework that can dynamically adjust the number of temporal frames according to the movement speed, thereby achieving an optimal balance between data acquisition speed and system responsiveness.

[0154] The method provided in this application embodiment can employ an intelligent data filtering and cleaning engine when calculating vegetation information and road condition information: by using anomaly detection algorithms and data quality control strategies, the raw data is preprocessed to automatically identify and remove invalid or abnormal data.

[0155] The method provided in this application embodiment can employ multidimensional filtering and analysis tools when calculating vegetation information and road condition information: by using multidimensional data analysis and filtering algorithms, it can deeply analyze the data distribution patterns under different scenarios, significantly reduce data fluctuations, and enhance the consistency of results. Furthermore, by comprehensively applying statistical methods such as mean and variance, it can further improve the stability and reliability of the detection results.

[0156] The method provided in this application embodiment can use a decision tree model to construct vegetation information and road condition information: using the decision tree model in machine learning, an accurate judgment model is established based on historical data and real-time sensor feedback, and the most reasonable height value is output.

[0157] By designing a complex and precise tree model processing mechanism, combining deviation values ​​and data analysis results, a reasonable judgment range is determined, and finally a stable and accurate height value is output, enabling the lawnmower to make optimal decisions based on complex and ever-changing operating environments.

[0158] The method embodiments provided in this application can be executed in a mobile robot or a similar computing device. The mobile robot may include one or more processors or programmable logic devices (PLDs) and a memory for storing data. In an exemplary embodiment, the mobile robot may also include a transmission device for communication functions and an input / output device.

[0159] In an optional embodiment, the device used in this application may further include multiple sensors, which can be mounted on the bottom of the lawnmower to monitor the height of the lawn below the cutter head in real time. These sensors should have a fast response capability to ensure that changes in lawn height can be quickly detected as the machine moves.

[0160] In an optional embodiment, the device used in this application may further include a cutter head control system: directly connected to the sensors, receiving signals from the sensors, and controlling the start and stop of the cutter head. This system needs to have a built-in fast response mechanism to ensure that it can immediately drive the cutter head to start working after receiving a cutting command.

[0161] Those skilled in the art will understand that mobile devices may also include more or fewer components than those shown in the above modules, or have different configurations that have the same or more functions as those shown in the above robots.

[0162] One embodiment of this application provides an environmental data processing apparatus, including: an acquisition module and a processing module, wherein the acquisition module and the processing module are connected.

[0163] The acquisition module is used to acquire environmental data collected by the first and second sensors.

[0164] The first sensor and the second sensor are located in different areas of the device body, and the first sensor and the second sensor collect environmental data from different directions;

[0165] The processing module determines the vegetation information and road condition information in the current driving environment of the device based on the environmental data collected by the first sensor and the second sensor.

[0166] The operating strategy and / or driving strategy of the device are adjusted based on the vegetation and road conditions in the current driving environment.

[0167] The acquisition and processing modules mentioned above can be hardware physical modules or software processing modules, and no limiting description is given here.

[0168] In an optional embodiment, the acquisition module in the device provided in this application embodiment may include a first sensor and a second sensor. Both the first sensor and the second sensor may include at least one set of sensors. The types of each set of sensors may be the same or different. The sensors may include ultrasonic sensors, laser rangefinders, infrared sensors or visual acquisition sensors. The sensors can measure the height and density of vegetation, the undulation of vegetation, and the differences in growth status in different areas in the current working environment in real time and accurately. They can also detect road condition information, such as road surface undulations, road boundary cliffs, and obstacles in the preset path.

[0169] In an optional embodiment, the first sensor in the device provided by this application includes: a visual detection sensor, and the processing module determines vegetation information and road condition information in the current driving environment of the device based on the visual detection sensor, including:

[0170] The first sensor detects path and vegetation information within a preset range and determines the target working area and target travel path of the device.

[0171] If the device enters the target work area according to the specified driving path, the second sensor detects and determines the vegetation and road conditions in the device's current driving environment.

[0172] In an optional embodiment, the processing module in the environmental data processing device provided in this application is used to determine the target working range and target travel path of the device, including:

[0173] The system analyzes the vegetation information detected by the first sensor within a preset range, identifies areas where the vegetation height is greater than the preset vegetation height, and determines these areas as the target working area.

[0174] Obtain several paths between the current location and the target work area;

[0175] The path length of each path and whether there is a target object during the path travel are calculated in turn to determine the target travel path.

[0176] In an optional embodiment, the processing module in the environmental data processing device provided in this application is used to adjust the working strategy and / or driving strategy of the device, including:

[0177] If the road condition information collected by the first sensor indicates that there is a target object in the first direction, then the road condition information of the second direction sensor is acquired.

[0178] If the road condition information collected by the second sensor indicates that there is a target object in the second direction, then stop driving and issue a warning;

[0179] If the road condition information collected by the second sensor indicates that there is no target object in the second direction, a driving instruction is generated, which is used to instruct driving along the second direction.

[0180] In an optional embodiment, the processing module in the environmental data processing device provided in this application is used to adjust the working strategy and / or driving strategy of the device, including:

[0181] The first vegetation information in the first area is detected by the first sensor, and the second vegetation information in the second area is detected by the second sensor.

[0182] Confirm whether the first vegetation information matches the preset vegetation information, and whether the second vegetation information matches the preset vegetation information, and generate confirmation information;

[0183] A work strategy is generated based on the confirmation information;

[0184] The working strategy is used to indicate whether the cutting parameters of the device in the first direction need to be adjusted, and whether the cutting parameters of the device in the second direction need to be adjusted. The cutting parameters include at least one of the following: cutting height, cutting speed, and cutting cycle.

[0185] In an optional embodiment, the processing module in the environmental data processing apparatus provided in this application is used to generate a working strategy based on the confirmation information, including:

[0186] The first vegetation information in the first area is detected by the first sensor, and the second vegetation information in the second area is detected by the second sensor.

[0187] Confirm whether the first vegetation information matches the preset vegetation information, and whether the second vegetation information matches the preset vegetation information, and generate confirmation information;

[0188] A driving strategy is generated based on the confirmation information;

[0189] The driving strategy includes at least one of the following: driving direction, driving speed, and driving cycle.

[0190] In an optional embodiment, the environmental data collected by the processing module in the environmental data processing apparatus provided in this application includes:

[0191] Obtain the height information of the first sensor and / or the second sensor on the device body;

[0192] A preset error value is determined based on the height information from the first sensor and / or the second sensor;

[0193] The first sensor and / or the second sensor collect environmental data according to a preset error value.

[0194] The processing module provided in this application embodiment can use a self-calibration algorithm when calculating vegetation information and road condition information. It dynamically adjusts the error model based on the initial installation parameters and environmental variables to ensure the long-term accuracy and consistency of the measured values.

[0195] The method provided in this application embodiment can employ an intelligent data filtering and cleaning engine when calculating vegetation information and road condition information: by using anomaly detection algorithms and data quality control strategies, the raw data is preprocessed to automatically identify and remove invalid or abnormal data.

[0196] The method provided in this application embodiment can employ multidimensional filtering and analysis tools when calculating vegetation information and road condition information: by using multidimensional data analysis and filtering algorithms, it can deeply analyze the data distribution patterns under different scenarios, significantly reduce data fluctuations, and enhance the consistency of results. Furthermore, by comprehensively applying statistical methods such as mean and variance, it can further improve the stability and reliability of the detection results.

[0197] In an optional embodiment, the environmental data collected by the processing module in the environmental data processing apparatus provided in this application includes:

[0198] Obtain the movement information of the device;

[0199] Based on the device's movement information, the timing sequence for collecting environmental data is determined, and environmental data is collected in response to the timing sequence.

[0200] The processing module provided in this application embodiment can adopt a dynamic time-series acquisition framework when calculating vegetation information and road condition information: construct an acquisition framework that can dynamically adjust the number of time-series frames according to the movement speed, so as to achieve the optimal balance between data acquisition speed and system responsiveness.

[0201] In an optional embodiment, the second sensor in the environmental data processing device provided in this application includes at least two sub-sensors. The first sensor and the first sub-sensor of the second sensor form a first straight line, and the first sensor and the second sub-sensor of the second sensor form a second straight line. The angle between the first straight line and the second straight line is greater than 10 degrees.

[0202] The environmental data processing device provided in this application embodiment monitors environmental data in real time by deploying multiple sensors and adjusting the cutter head height and travel path. The device can more effectively cover the entire working area, reduce repetitive work and omissions. At the same time, obstacle avoidance and anti-fall functions avoid the waste of time and energy caused by unexpected downtime, further improving the overall work efficiency.

[0203] In one embodiment, a self-driving device is provided, on which a computer program is stored, which, when executed by a processor, can perform any step of the environmental data processing method of the self-driving device described above.

[0204] In an optional embodiment, the self-driving device provided in this application may include multiple sets of sensors and processors, with the sensors and processors connected together.

[0205] The aforementioned sensors may include: a front sensor, a left sensor group, and a right sensor group; each of the front sensor, the left sensor group, and the right sensor group may include at least one group of sensors, and the types of each group of sensors may be the same or different. The sensors may include ultrasonic sensors, laser rangefinders, infrared sensors, or visual acquisition sensors.

[0206] Front sensor: It can be located at the front of the lawnmower and uses infrared ranging, ultrasonic sensing technology or visual detection technology to detect in real time whether there is a cliff or deep pit in front. Once detected, it will immediately trigger the braking mechanism to ensure the safety of the equipment.

[0207] Left-side sensor group: Includes a grass density sensor and a cliff detection sensor. The grass density sensor (such as an optically based sensor) analyzes the light reflectance of the grass on the left side to assess grass density, thereby adjusting the height of the cutter head and the cutting speed; the cliff detection sensor is the same as the front-side sensor, ensuring safety on the left side;

[0208] Right sensor group: It has the same function as the left sensor group, but it monitors the right environment to achieve symmetrical and balanced control on both sides;

[0209] The processor can also integrate GPS positioning, path planning algorithms and motion control modules, and achieve intelligent path planning, cutting strategy adjustment and obstacle avoidance control based on comprehensive analysis of data from various sensors.

[0210] In an optional embodiment, the aforementioned front sensor can also be used to identify the working area within a preset area and to confirm whether the device has traveled into the working area, for example:

[0211] The highly integrated first sensor suite (centered around a high-definition camera and supplemented by advanced image processing algorithms) performs a 360-degree scan of the environment within a preset range. This sensor suite not only captures high-definition image information in real time, but also uses advanced image recognition and processing technology to perform in-depth analysis of multi-dimensional features in the image, including but not limited to color distribution, texture features, and specific patterns identified through machine learning models (such as the lushness of the lawn, height gradient changes, etc.).

[0212] In the image analysis phase, a refined segmentation algorithm, combined with a pre-trained deep learning model, can be used to accurately classify each pixel or region in the image, thereby precisely distinguishing between grass areas that need to be cut and natural areas or obstacles that do not require intervention. This process not only improves the accuracy of recognition but also significantly enhances the system's adaptability and robustness.

[0213] When the height of the lawn detected by the sensor exceeds the preset optimal cutting threshold, the internal logic mechanism is immediately triggered, generating a corresponding control signal. This signal seamlessly connects to the equipment's control system via an efficient communication protocol, instructing it to automatically adjust its movement and direction based on the identified target working area and the planned optimal travel path, ensuring precise positioning to the area to be cut.

[0214] Furthermore, to further optimize system performance, a feedback adjustment mechanism can be introduced. During the cutting process, sensors continuously monitor the work results and transmit real-time feedback data to the algorithm module. Based on this data, the algorithm module dynamically adjusts cutting parameters (such as blade height and cutting speed).

[0215] Once the device accurately navigates to the target work area, it immediately activates a multimodal second sensor array (integrating visual, altitude, distance, and density perception capabilities) to comprehensively capture vegetation density, altitude changes, and road condition details in the current driving environment. This integrated perception strategy not only improves the accuracy of information acquisition but also enhances the system's robustness.

[0216] Subsequently, combining efficient image processing technology with real-time data analysis capabilities, the collected information is processed instantly to quickly identify the area to be cut and its precise location. Simultaneously, this process is tightly integrated with the GPS positioning system, transmitting the identified area location information to the central control unit in real time, thus constructing a precise navigation framework tailored for directional cutting.

[0217] As the machine gradually approaches the cutting area precisely marked by the AI ​​vision system, the central control unit intelligently adjusts the mower's trajectory based on pre-stored position coordinates and real-time path data, ensuring the machine accurately enters the designated work area. Furthermore, through built-in advanced algorithms and preset reasonable operation delay thresholds, the cutter head system can respond precisely and synchronously when the sensor first detects the cutting edge, completing preparations before reaching the predetermined position for seamless operation.

[0218] Throughout the operation, the intelligent sensors at the bottom continuously monitor changes in lawn height and dynamically adjust the working height and cutting speed of the cutter head based on the monitoring results, achieving efficient and precise cutting control. This dynamic adjustment mechanism effectively avoids missed cuts and over-cuts, ensuring the neatness, beauty, and healthy growth of the lawn.

[0219] The working process of the self-driven device provided in this application embodiment may include:

[0220] After the equipment is started, an environmental scan is performed first to confirm that the surrounding environment is safe before starting work.

[0221] During operation, the front sensor of the equipment continuously monitors for cliffs ahead to ensure driving safety.

[0222] Simultaneously, the left and right sensor groups of the equipment monitor the grass density and cliff conditions in their respective areas. The processor dynamically adjusts the blade height, cutting speed, and travel direction based on the collected data to achieve precise cutting and obstacle avoidance.

[0223] When the equipment performs complex paths such as bow-shaped cutting, the system intelligently adjusts its posture based on real-time feedback from sensors to ensure the continuity and efficiency of the cutting path.

[0224] The self-driving device provided in this application deploys multiple high-precision sensors at the bottom and key locations of the machine body to achieve all-round environmental perception and intelligent control, thereby significantly improving the environmental perception capability and autonomous decision-making level of the intelligent lawnmower and effectively solving the limitations of a single sensor system.

[0225] The self-driving device provided in this application, through multiple sets of sensors, can achieve comprehensive and uniform cutting of the working area, avoiding problems of untrimmed or over-trimmed areas caused by blind spots in detection; it enhances the safety of the lawnmower and reduces the potential accident risk caused by undetected obstacles such as cliffs.

[0226] Specific limitations regarding the data processing device of the aforementioned self-driven device can be found in the limitations of the environmental data processing method for the self-driven device described above, and will not be repeated here. Each module in the target object detection device of the aforementioned self-driven device can be implemented entirely or partially through software, hardware, or a combination thereof.

[0227] The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0228] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, can perform any step of the environmental data processing method for the self-driving device described above.

[0229] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

Claims

1. An environmental data processing method, characterized by, The method is applied to a self-driving device, and comprises the following steps: Acquiring environmental data collected by a first sensor and a second sensor, the first sensor and the second sensor being located at different regions of a device body, and the first sensor and the second sensor collecting environmental data in different directions; Determining vegetation information and road condition information in a current driving environment of the device according to the environmental data collected by the first sensor and the second sensor; Adjusting a working strategy and / or a driving strategy of the device according to the vegetation information and the road condition information in the current driving environment of the device.

2. The environmental data processing method of claim 1, wherein, The first sensor comprises a visual detection sensor, and the determining of the vegetation information and the road condition information in the current driving environment of the device comprises the following steps: Detecting path information and vegetation information in a preset range by the first sensor, and determining a target working region and a target driving path of the device; If the device drives into the target working region according to the target driving path, detecting the vegetation information and the road condition information in the current driving environment of the device by a second sensor.

3. The device environment data processing method of claim 2, wherein, The determining of the target working region and the target driving path of the device comprises the following steps: Analyzing the vegetation information in the preset range detected by the first sensor, acquiring a region with vegetation height greater than a preset vegetation height, and determining the region as the target working region; Acquiring a plurality of paths between a current position and the target working region; Calculating path length and whether there is a target object in a path driving process of each path in sequence, so as to determine the target driving path.

4. The device environment data processing method according to claim 1 or 2, characterized by, The adjusting of the working strategy and / or the driving strategy of the device comprises the following steps: If the road condition information collected by the first sensor indicates that there is a target object in a first direction, acquiring road condition information of a second direction sensor; If the road condition information collected by the second sensor indicates that there is a target object in a second direction, stopping driving and prompting; If the road condition information collected by the second sensor indicates that there is no target object in the second direction, generating a driving instruction, the driving instruction being used for indicating driving along the second direction.

5. The device environment data processing method according to claim 1 or 2, characterized by, The adjusting of the working strategy and / or the driving strategy of the device comprises the following steps: Detecting first vegetation information of a first region by a first sensor, and detecting second vegetation information of a second region by a second sensor; Confirming whether the first vegetation information matches preset vegetation information and whether the second vegetation information matches the preset vegetation information, and generating confirmation information; Generating a working strategy according to the confirmation information, the working strategy being used for indicating whether a cutting parameter of the device in a first direction needs to be adjusted and whether a cutting parameter of the device in a second direction needs to be adjusted, the cutting parameter at least comprising one of a cutting height, a cutting speed and a cutting working period.

6. The environmental data processing method of claim 1 or 2, wherein, The generating of the working strategy according to the confirmation information comprises the following steps: Detecting first vegetation information of a first region by a first sensor, and detecting second vegetation information of a second region by a second sensor; Confirming whether the first vegetation information matches preset vegetation information and whether the second vegetation information matches the preset vegetation information, and generating confirmation information; Generating a driving strategy according to the confirmation information; The driving strategy at least comprises one of a driving direction, a driving speed and a driving period.

7. The device environment data processing method of claim 1, wherein, The collected environmental data includes: Obtaining height information of the first sensor and / or the second sensor on the device body; Determining a preset error value according to the height information of the first sensor and / or the second sensor; According to the preset error value, the first sensor and / or the second sensor collects environmental data; And / or, Obtaining movement information of the device; According to the movement information of the device, determining the collection timing of the environmental data and collecting the environmental data in response to the collection timing.

8. The device environmental data processing method of claim 1 or 2, wherein: The second sensor includes at least two sub-sensors, the first sub-sensor of the first sensor and the second sensor constitutes a first straight line, the second sub-sensor of the first sensor and the second sensor constitutes a second straight line, and the first straight line and the second straight line constitute an angle greater than 10 degrees.

9. An environmental data processing apparatus, characterized by comprising: The device includes: An acquisition module that acquires environmental data collected by a first sensor and a second sensor, the first sensor and the second sensor being located at different areas of a device body, the first sensor and the second sensor collecting environmental data in different directions; A processing module that determines vegetation information and road condition information in a current driving environment of the device according to the environmental data collected by the first sensor and the second sensor; Adjusting the working strategy and / or driving strategy of the device according to the vegetation information and road condition information in the current driving environment of the device.

10. A self-driving device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to execute the method in any one of claims 1 to 8.

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