Control method of a mowing device, control device of a mowing device and mower

Abstract

The application relates to a control method of a mowing device, a control device of the mowing device and a mower. The method comprises the following steps: in response to a starting instruction of the mowing device, acquiring mowing parameters, determining a mowing strategy according to the mowing parameters, and controlling the mowing device to mow in a target mowing area according to the mowing strategy. The method can improve the mowing effect of the mowing device.

Description

Technical Field

[0001] This application relates to the field of automatic control technology, and in particular to a control method for lawn mowing equipment, a control device for lawn mowing equipment, and a lawnmower. Background Technology

[0002] With the continuous development of software and hardware technology and the continuous improvement of users' living standards, some household chores can gradually be completed through smart devices. For example, the maintenance of garden lawns can be done by smart lawnmowers. Smart lawnmowers can automatically and intelligently trim garden lawns according to the instructions of the operator. Smart lawnmowers can mow lawns according to boundary lines / fixed paths, random collision mowing, visual navigation mowing, radar navigation mowing, etc.

[0003] Currently, the effectiveness of intelligent lawn mowers in maintaining garden lawns still needs improvement. Summary of the Invention

[0004] Therefore, it is necessary to provide a control method for lawn mowing equipment, a control device for lawn mowing equipment, and a lawn mower that can improve the mowing effect of lawn mowers, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a control method for a lawn mowing device, comprising: in response to a start command for the lawn mowing device, acquiring mowing parameters, the mowing parameters including at least one of lawn status data of a target mowing area, environmental perception parameters, and preset lawn parameters; determining a mowing strategy based on the mowing parameters, the mowing strategy including at least one of mowing time, blade control parameters, and mowing path; and controlling the lawn mowing device to mow the lawn in the target mowing area according to the mowing strategy.

[0006] In one embodiment, determining a mowing strategy based on mowing parameters includes at least one of the following: determining mowing time based on environmental perception parameters and / or lawn status data; determining blade control parameters based on preset lawn parameters and / or lawn status data, wherein the blade control parameters include at least one of blade rotation speed, blade height, single mowing frequency, and mowing path spacing, and the preset lawn parameters include lawn pattern and lawn type; and determining the mowing path for the target mowing area based on map data of the target mowing area and environmental perception parameters.

[0007] In one embodiment, determining the mowing time based on environmental sensing parameters and / or lawn status data includes: determining whether the lawn in the target mowing area is wet based on weather data included in the environmental sensing parameters and / or lawn humidity data included in the lawn status data; if the lawn is wet, predicting the mowing time based on the weather data; if the lawn is not wet, determining the current time as the mowing time.

[0008] In one embodiment, determining the mowing time based on environmental sensing parameters and / or lawn status data includes: detecting whether the grass height data exceeds a grass height threshold based on the grass height data included in the environmental sensing parameters and / or lawn status data; if the grass height data exceeds the grass height threshold, determining the current time as the mowing time; if the grass height data does not exceed the grass height threshold, predicting the mowing time based on the lawn growth rate, wherein the lawn growth rate is predicted and determined based on the historical lawn height and / or seasonal information of the target mowing area.

[0009] In one embodiment, determining the cutter head control parameters based on preset lawn parameters and / or lawn state data includes: querying candidate cutter head control parameters corresponding to a lawn type from a preset lawn type parameter mapping table, wherein the candidate cutter head control parameters include a target mowing height and a cutter head rotation speed, and the lawn type parameter mapping table includes the mapping relationship between each lawn type and the candidate cutter head control parameters; correcting the cutter head rotation speed based on the lawn density data included in the lawn state data to obtain a target cutter head rotation speed; and determining the target cutter head rotation speed and the target mowing height as the cutter head control parameters.

[0010] In one embodiment, before acquiring mowing parameters in response to a start command for the mowing device, the method further includes: receiving a start command sent by a terminal; or, generating a start command in response to a mowing cycle completion command.

[0011] In one embodiment, the process of determining the mowing cycle includes: determining candidate lawn growth rates for the target mowing area based on multiple historical lawn heights over a historical time period; updating the candidate lawn growth rates with weighted averages based on seasonal weights included in the environmental perception parameters to obtain the target lawn growth rate for the target mowing area; and predicting the mowing cycle based on the target lawn growth rate.

[0012] In one embodiment, controlling a mowing device to mow grass in a target mowing area according to a mowing strategy includes: acquiring an environmental image through an image acquisition component included in the mowing device; determining obstacle data of the target mowing area based on the environmental image, the obstacle data including obstacle type and / or obstacle range; updating the mowing path included in the mowing strategy based on the obstacle data; and controlling the mowing device to mow grass according to the updated mowing path.

[0013] In one embodiment, the method further includes: determining a mowing effect quantification value of the target mowing area based on the lawn height and / or lawn density included in the lawn status data, and sending the mowing effect quantification value to the terminal, the mowing effect quantification value being used to characterize the mowing effect of the lawnmower; and / or determining a health status quantification value of the target mowing area based on the lawn color and / or lawn density included in the lawn status data, and sending the health status quantification value to the terminal, the health status quantification value being used to characterize the lawn health status of the target mowing area.

[0014] In one embodiment, the method further includes at least one of the following: if the tilt angle of the mowing device is detected to be greater than an angle threshold, controlling the mowing device to stop mowing and return to a historical position; if a collision is detected, controlling the mowing device to stop mowing; if the blade current of the mowing device is detected to be greater than a current threshold, controlling the mowing device to stop mowing and clear the blockage; if the battery power of the mowing device is detected to be less than or equal to a battery power threshold, controlling the mowing device to return to a charging station.

[0015] In one embodiment, after controlling the mowing equipment to mow the grass in the target mowing area according to the mowing strategy, the method further includes: obtaining user feedback information and performing semantic recognition on the user feedback information to obtain semantic recognition results; and updating the mowing strategy according to the semantic recognition results.

[0016] Secondly, this application also provides a control device for a lawn mowing equipment, comprising: a start module, configured to acquire mowing parameters in response to a start command for the lawn mowing equipment, the mowing parameters including at least one of lawn status data of the target mowing area, environmental perception parameters, and preset lawn parameters; a mowing strategy determination module, configured to determine a mowing strategy based on the mowing parameters, the mowing strategy including at least one of mowing time, blade control parameters, and mowing path; and a mowing control module, configured to control the lawn mowing equipment to mow the lawn in the target mowing area according to the mowing strategy.

[0017] Thirdly, this application also provides a lawnmower, 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 the method described in the first aspect.

[0018] Fourthly, this application also 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 the first aspect.

[0019] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0020] The aforementioned control method, control device, and lawnmower for lawn mowing equipment involve the lawnmower responding to a start command and acquiring mowing parameters. These parameters include at least one of the following: lawn status data of the target mowing area, environmental perception parameters, and preset lawn parameters. Based on these parameters, a mowing strategy is determined, including at least one of mowing time, blade control parameters, and mowing path. This mowing strategy enables intelligent control of the lawnmower. By dynamically generating mowing strategies (including at least one of mowing time, blade control parameters, and mowing path) based on perceived mowing parameters, the mowing effect and user experience are improved. This method determines a mowing strategy that matches the current situation based on the current mowing parameters, and then controls the lawnmower to mow in the target mowing area based on the mowing strategy. By intelligently generating and configuring mowing strategies, the mowing effect of the lawnmower is improved. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an application environment diagram of a control method for a lawn mowing device in one embodiment.

[0023] Figure 2 This is a flowchart illustrating the control method of a lawn mowing device in one embodiment;

[0024] Figure 3 This is a flowchart illustrating step 202 in one embodiment;

[0025] Figure 4 This is a flowchart illustrating step 301 in one embodiment;

[0026] Figure 5 This is a flowchart illustrating step 301 in another embodiment;

[0027] Figure 6 This is a flowchart illustrating step 302 in one embodiment;

[0028] Figure 7 This is a flowchart illustrating the steps for determining the mowing cycle in one embodiment;

[0029] Figure 8 This is a flowchart illustrating step 203 in one embodiment;

[0030] Figure 9 This is a schematic diagram of the interaction process with the terminal in one embodiment;

[0031] Figure 10 This is a flowchart illustrating the control steps of a lawnmower in one embodiment;

[0032] Figure 11 This is a schematic diagram of the user feedback process in one embodiment;

[0033] Figure 12 This is a flowchart illustrating the control method for a lawn mowing device in another embodiment;

[0034] Figure 13 This is a structural block diagram of the control device for a lawnmower in one embodiment;

[0035] Figure 14 This is a diagram of the internal structure of a lawnmower in one embodiment. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0037] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0038] The control method for lawn mowing equipment provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown includes at least a lawnmower 101 and may also include a terminal 102.

[0039] The lawnmower 101 is used to receive and respond to the start command to obtain mowing parameters, determine the mowing strategy according to the mowing parameters, and control the lawnmower 101 to mow the grass according to the mowing strategy; further, the lawnmower 101 may include a control module 101-1, which can realize the above-mentioned functions of the lawnmower 101.

[0040] Terminal 102 can be used to send various commands to lawnmower 101. These commands may include start commands and setting commands to configure the mowing parameters of lawnmower 101. Terminal 102 can also receive various notification messages sent by lawnmower 101. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses.

[0041] In real-world scenarios, smart lawnmowers have become an important tool for maintaining garden lawns. However, currently, smart lawnmowers are mainly operated manually or remotely, with the work area defined by preset boundary lines or buried wires in the ground. This control method has caused some problems.

[0042] This control method has fixed mowing height, speed, and frequency, treating ornamental and sports lawns the same, failing to meet diverse needs. Furthermore, it results in insufficient mowing when lawns grow rapidly and over-mowing when growth is slow, wasting energy and blade life. This method also suffers from random or monotonous paths and patterns. Additionally, it requires users to manually set area boundaries, start / stop mowing, manage charging / maintenance, and adjust mowing parameters based on lawn condition, placing a heavy burden on users. Moreover, this control method lacks adaptive strategies for different lawn types (dense grass, post-mowing lawns, grass of varying lengths, weeds, and multiple grass species), failing to adjust mowing parameters according to lawn condition, thus affecting lawn health and aesthetics.

[0043] To address this, this application achieves intelligent control of lawn mowing equipment through mowing strategies. It realizes the determination of mowing strategies that match the current situation based on the current mowing parameters, enabling the lawn mower to automatically perform customized lawn mowing or customized lawn pattern mowing without manual intervention, based on lawn usage, lawn condition / grass length, user settings, etc. It also automatically manages the entire process of mowing tasks, charging, obstacle avoidance, and lawn health maintenance. By intelligently generating and configuring mowing strategies, the mowing effect of the lawn mowing equipment is improved.

[0044] In one exemplary embodiment, such as Figure 2 As shown, a control method for a lawn mowing device is provided, which is applied to... Figure 1 The following steps are used as an example to illustrate the control module of a lawnmower, including steps 201 to 203.

[0045] Step 201: In response to the start command for the lawn mowing equipment, obtain the mowing parameters.

[0046] In this application, mowing parameters refer to parameters affecting the mowing area corresponding to the lawn in the target mowing area. Mowing parameters can be the lawn's own state data, environmental data of the spatial area corresponding to the target mowing area, or a lawn type or pattern preset by the user. Specifically, mowing parameters include at least one of the lawn state data of the target mowing area, environmental perception parameters, and preset lawn parameters; preset lawn parameters can be lawn type and / or lawn pattern.

[0047] During implementation, the control module responds to the start command for the lawn mowing equipment by acquiring mowing parameters. During execution, the control module can retrieve pre-stored mowing parameters from storage, such as predetermined preset lawn parameters. The control module can also receive mowing parameters carried by the start command, such as extracting preset lawn parameters carried by the start command. Furthermore, the control module can acquire mowing parameters through the sensing components of the lawn mowing equipment, such as acquiring lawn status data and environmental perception parameters of the target mowing area through the sensing components. The lawn status data may include at least one of the following: lawn height data, lawn density data, lawn color data, and lawn humidity data of the target mowing area. The environmental perception parameters may include at least one of the following: lawn image, weather data, and lawn point cloud data of the target mowing area.

[0048] Furthermore, the control module can acquire lawn images through the image acquisition component of the lawn mower, acquire lawn moisture data through the soil moisture sensor of the lawn mower, and acquire lawn point cloud data through LiDAR. Furthermore, the control module can determine at least one of grass height data and lawn density data through lawn images and / or lawn point cloud data.

[0049] It should be noted that the mowing parameters can be collected after the mowing equipment is started to determine the mowing strategy. During the subsequent mowing process, the mowing equipment can also collect one or more parameters including the mowing parameters in real time and update the mowing strategy in real time.

[0050] In this application, the start command is an instruction to control the lawn mowing equipment to start. This start command can be sent from an external device to the lawn mowing equipment, or it can be generated by the lawn mowing equipment itself. During execution, the start command can be sent from a terminal to the lawn mowing equipment, corresponding to a remote start method; the start command can also be generated by the lawn mowing equipment after triggering its control (start button), corresponding to a local start method; or the start command can be automatically generated by the lawn mowing equipment according to a preset start cycle, corresponding to an automatic start method. In one optional embodiment provided by this application, before step 201, the method further includes: receiving a start command sent from a terminal; or, generating a start command in response to a lawn mowing cycle completion instruction; or, generating a start command in response to a trigger signal from the start button.

[0051] During execution, the terminal can run the control program for the lawnmower and send a start command to the lawnmower by triggering the start button on the control program's interactive interface; the control module receives the start command sent by the terminal to the lawnmower through the interactive interface of the control program. The terminal can also preset the lawnmower's mowing cycle (auto-start cycle) in the control program; the lawnmower generates a start command after detecting that the mowing cycle has expired.

[0052] Step 202: Determine the mowing strategy based on the mowing parameters.

[0053] In this application, the mowing strategy is a control strategy used to control a mowing device to perform mowing. The mowing strategy can control at least one of the mowing time, the cutter head, and the mowing path of the mowing device; wherein, the mowing strategy includes at least one of the mowing time, cutter head control parameters, and the mowing path. Optionally, the cutter head control parameters include at least one of the cutter head rotation speed, cutter head height, single mowing frequency, and mowing path spacing. It should be noted that the mowing path can be updated based on map data from the GPS component and / or sensing data from the sensing component of the mowing device; the sensing data includes environmental images and environmental point cloud data.

[0054] During implementation, the control module determines the mowing strategy corresponding to each of the multiple mowing parameters. During execution, the control module can determine the mowing time of the mowing equipment based on the mowing parameters, determine the blade control parameters of the mowing equipment based on the mowing parameters, and determine the mowing path of the mowing equipment based on the mowing parameters.

[0055] Step 203: Control the mowing equipment to mow the grass in the target mowing area according to the mowing strategy.

[0056] During implementation, the control module controls the various components of the mowing equipment to mow the grass in the target mowing area according to the mowing strategy. Furthermore, during the mowing process, the control module can also acquire lawn status data and environmental perception parameters of the target mowing area in real time, and update the mowing strategy in real time according to the lawn status data and environmental perception parameters, so as to mow the grass according to the updated mowing strategy.

[0057] During the mowing process, the control module can also monitor the equipment status of the mowing equipment in real time. If it detects that the mowing equipment is tilted, colliding, or has abnormal power consumption, it will stop mowing or return to a previous safe position. The equipment status can be determined by the mowing equipment's sensing components. The tilt status of the mowing equipment can be determined by a gyroscope, the collision status can be determined by radar or image acquisition components, and the power consumption status of the mowing equipment can be determined by the mowing equipment's BMS (battery management system).

[0058] In the above-mentioned control method for lawn mowing equipment, the lawn mowing equipment responds to the start command and acquires mowing parameters, which include at least one of the lawn status data of the target mowing area, environmental perception parameters, and preset lawn parameters. Based on the mowing parameters, a mowing strategy is determined, which includes at least one of the mowing time, blade control parameters, and mowing path. The mowing strategy enables intelligent control of the lawn mowing equipment, realizing the determination of a mowing strategy that matches the current situation based on the current mowing parameters. Then, based on the mowing strategy, the lawn mowing equipment is controlled to mow the target mowing area. By intelligently generating and configuring mowing strategies, the mowing effect of the lawn mowing equipment is improved.

[0059] Based on the above exemplary embodiment, the following provides a control method for a lawnmower in one or more exemplary embodiments, which is applied to... Figure 1 Taking the control module as an example, the explanation includes the following:

[0060] In determining the mowing strategy, the control module can determine the corresponding mowing strategy based on one or more parameters included in the mowing parameters; in one optional implementation provided in this application, such as Figure 3 As shown, step 202 includes steps 301 to 303:

[0061] Step 301: Determine the mowing time based on environmental perception parameters and / or lawn condition data.

[0062] During implementation, the control module can determine the mowing time based on environmental sensing parameters, or based on lawn status data, or based on environmental sensing parameters and / or lawn status data.

[0063] During execution, the control module determines whether to set the current time as the mowing time based on environmental perception parameters and / or lawn status data. In other words, the control module determines whether to start mowing immediately after activation based on environmental perception parameters and / or lawn status data. If the current time is not set as the mowing time, the control module can predict the mowing time based on environmental perception parameters and / or lawn status data.

[0064] Step 302: Determine the cutter head control parameters based on preset lawn parameters and / or lawn condition data.

[0065] During implementation, the control module reads the preset lawn parameters and queries the database / data table for the cutter head control parameters corresponding to the preset lawn parameters.

[0066] Furthermore, the cutter head control parameters can also be determined based on turf condition data; the control module can determine the cutter head height based on the grass height and / or turf density data included in the turf condition data. Additionally, the cutter head control parameters can also be determined jointly based on preset turf parameters and turf condition data. The control module can first determine candidate cutter head control parameters based on the preset turf parameters, and then correct the candidate cutter head control parameters based on the turf condition data to obtain the final cutter head control parameters.

[0067] For example, if the grass height is greater than n times the preset mowing height, the grass is mowed in n times. The first cut reaches n1% of the height. If the density is greater than 0.8 (dense), the speed is reduced by n2% and the blade rotation speed is increased by n3%. If the density is less than or equal to 0.5 (sparse), the speed is increased by n4%. If the height variance is large, the path overlap is increased to n5%.

[0068] Step 303: Determine the mowing path for the target mowing area based on the map data and environmental perception parameters of the target mowing area.

[0069] During implementation, the control module determines the mowing route for the target mowing area based on map data and corrects the route according to environmental perception parameters, thus obtaining the mowing path for the target area. Furthermore, the location of the mowing equipment can be determined based on location perception parameters, and the mowing path can be determined based on the location and map data. The mowing path includes the starting position, route taken, and ending position of the mowing equipment. The mowing path can be determined using a greedy algorithm.

[0070] During execution, map data can be uploaded by users, downloaded from the network, or determined by image acquisition components, radar components, and / or positioning components during the first operation in the target lawn mowing area.

[0071] For example, when a user uses the lawnmower for the first time, they can manually push or remotely control it to walk around the boundary, record the GPS trajectory point sequence, close the trajectory to form a boundary polygon, and obtain map data of the target lawn area; or, they can use image semantic segmentation to identify lawn and non-lawn areas (such as lawn / road / soil), extract the boundary contour of the lawn area, convert the pixel coordinates to world coordinates, and obtain map data of the target lawn area; or, they can perform ground segmentation based on point cloud, detect height differences (height difference between lawn and curb, wall), extract boundary points, and obtain map data of the target lawn area.

[0072] One optional implementation provided in this application determines one or more mowing strategies by using one or more parameters in the mowing parameters. By using mowing parameters from multiple dimensions, the reliability and accuracy of the mowing strategy are improved. Furthermore, it can also support users to customize lawn patterns and mowing cycles, enriching the mower strategies and improving the mower's adaptability to the real-time state of the lawn.

[0073] In determining the mowing time, the suitability of the current time for mowing can be determined based on the lawn's condition, specifically by using lawn moisture data included in the lawn condition data. One optional implementation provided in this application is as follows: Figure 4 As shown, step 301 includes steps 401 to 403:

[0074] Step 401: Determine whether the lawn in the target mowing area is wet based on the weather data included in the environmental perception parameters and / or the lawn humidity data included in the lawn status data.

[0075] During implementation, the control module detects whether the air humidity is greater than a preset air humidity threshold based on weather data included in the environmental perception parameters, and / or whether the lawn humidity is greater than a preset lawn humidity threshold based on lawn status data included in the lawn status data. Based on the detection result of at least one of the two, it determines whether the lawn in the target mowing area is in a wet state. If the lawn is in a wet state, that is, the air humidity is equal to or greater than the preset air humidity threshold, and / or the lawn humidity is equal to or greater than the preset lawn humidity threshold, the lawn in the target mowing area is determined to be in a wet state, and step 402 is executed. If the lawn is not in a wet state, that is, the air humidity is less than the preset air humidity threshold, and / or the lawn humidity is less than the preset lawn humidity threshold, the lawn in the target mowing area is determined to be not in a wet state, and step 403 is executed.

[0076] Furthermore, if the probability of abnormal weather in the weather data is greater than or equal to the probability threshold, step 402 can also be executed; abnormal weather includes: rainy weather, snowy weather, sandstorm weather, etc.

[0077] During execution, the control module extracts air humidity data from the weather data and detects whether the air humidity is greater than the preset air humidity threshold, and / or, the control module obtains lawn humidity data collected by the soil moisture sensor and detects whether the lawn humidity is greater than the preset lawn humidity threshold.

[0078] Step 402: Predict the mowing time based on weather data.

[0079] During implementation, the control module predicts the target time when the lawn in the target mowing area will become dry grass based on air humidity or lawn humidity and weather data, and determines the target time as the mowing time.

[0080] For example, in the case of artificial watering of the lawn, the control module predicts the time required for the lawn to dry based on the current lawn humidity and the temperature, wind and humidity data included in the weather data, and predicts the mowing time based on the current time and the required time.

[0081] Step 403: Determine the current time as the lawn mowing time.

[0082] During implementation, the control module determines the current time as the mowing time, meaning that the mowing equipment will start mowing as soon as it is started.

[0083] It should be noted that the mowing time can also be determined based on a time window. The time window can be determined based on the user's preset data. If the mowing time is within the time window, the mowing time is determined to be available. If the mowing time is not within the time window, the mowing time will be delayed until it is within the time window. The time window can be used to represent the working period of the mowing equipment, which can be a period of time excluding rest periods.

[0084] One optional implementation provided in this application determines the mowing time by combining weather data and lawn humidity data, avoiding the operation of the mowing equipment in abnormal weather or the operation of the mowing equipment on wet grass, thereby increasing the blade life of the mowing equipment and improving the mowing effect.

[0085] In addition, the mowing time can be determined based on the lawn height to avoid mowing before the lawn height reaches the threshold, thus avoiding resource waste; another optional implementation provided in this application, such as Figure 5 As shown, step 301 includes steps 501 to 503:

[0086] Step 501: Based on the environmental perception parameters and / or the grass height data included in the lawn status data, detect whether the grass height data exceeds the grass height threshold.

[0087] During implementation, the control module determines grass height data through image recognition based on the lawn image included in the environmental perception parameters, and / or the control module reads grass height data included in the lawn status data and detects whether the grass height data exceeds the grass height threshold; if the grass height data exceeds the grass height threshold, step 502 is executed; if the grass height data does not exceed the grass height threshold, step 503 is executed.

[0088] During execution, the control module can acquire images of the lawn through the image acquisition component, and then determine the grass height data through image recognition; the control module can also acquire grass height data through the ultrasonic component.

[0089] Step 502: Determine the current time as the lawn mowing time.

[0090] During implementation, the control module determines the current time as the mowing time, meaning that the mowing equipment will start mowing as soon as it is started.

[0091] Step 503: Predict mowing time based on lawn growth rate.

[0092] During implementation, the control module acquires the lawn growth rate, calculates the time required for the lawn to reach a threshold height based on the current grass height data and the lawn growth rate, and predicts the mowing time based on the current time and the required time. The lawn growth rate refers to the speed at which the lawn grows, and can be used to characterize the number of centimeters the lawn grows in one day. The lawn growth rate can be predicted and determined based on historical lawn height and / or seasonal information. The calculation process of the lawn growth rate will be described in detail in subsequent embodiments; please refer to the descriptions in those embodiments.

[0093] It should be noted that the mowing time can also be determined based on a time window. The time window can be determined based on the user's preset data. If the mowing time is within the time window, the mowing time is determined to be available. If the mowing time is not within the time window, the mowing time will be delayed until it is within the time window. The time window can be used to represent the working period of the mowing equipment, which can be a period of time excluding rest periods.

[0094] Another optional implementation provided in this application determines the mowing time by taking into account the lawn height, and mowing is carried out only when the lawn height reaches a threshold, which saves the power consumption of the mowing equipment and improves the mowing effect.

[0095] In determining the cutterhead control parameters, the parameters can be determined jointly based on the lawn type and pattern; in one optional embodiment provided in this application, such as... Figure 6 As shown, step 302 includes steps 601 to 603:

[0096] Step 601: Query the candidate cutter head control parameters corresponding to the lawn type from the preset lawn type parameter mapping table.

[0097] During implementation, users can preset the lawn type of the target mowing area in advance, and the control module can determine the mowing path spacing according to the lawn type. Furthermore, users can also extract the lawn pattern of the preset target mowing area, and the control module can also determine the blade rotation speed, blade height and single mowing frequency according to the lawn pattern. The lawn type parameter mapping table includes the mapping relationship between each lawn type and the candidate blade control parameters.

[0098] In this application, the mowing path spacing refers to the horizontal distance between the center points of the cutter head in two adjacent strokes of the mowing equipment. When the mowing path spacing is less than the cutting width of the cutter head, there is an overlapping coverage area between adjacent strokes. The larger the overlap ratio, the more uniform the mowing coverage and the higher the mowing quality. The control module can dynamically adjust the mowing path spacing according to the lawn type and lawn pattern requirements.

[0099] Alternatively, during implementation, the control module can query the candidate cutter head control parameters corresponding to the lawn type from a preset lawn type parameter mapping table; among which, the candidate cutter head control parameters include the target mowing height and the cutter head rotation speed.

[0100] For example, for ornamental lawns, setting the edge precision to 2cm ensures high accuracy and clear edges; for golf greens, setting the edge precision to 1cm ensures extremely high accuracy; for recreational lawns, setting the edge precision to 5cm ensures medium accuracy; for wildflower lawns, setting the edge precision to 10cm ensures natural boundaries; for parallel stripes, calculate the bounding box of the lawn boundary, rotate the coordinate system to a specified direction (e.g., 0°, 45°), and generate parallel horizontal lines in the rotated coordinate system, ensuring the row spacing equals the stripe width, with each line alternating directions (reducing turns): the first row from left to right, the second row from right to left, clipping to within the lawn boundary, avoiding obstacles, and rotating back to the original coordinate system to form alternating light and dark parallel stripes; for checkerboard patterns, divide the lawn into square grids (e.g., 1m × 1m), and for each square (i, If (i+j) is even: mow horizontally (0°); if (i+j) is odd: mow vertically (90°). Generate parallel line paths within each square, connecting all squares in the shortest path order to form a checkerboard pattern. The grass blades in adjacent squares have different tilt directions. For the spiral pattern, start from the lawn boundary and generate paths along the boundary (following the boundary outline), shrinking the boundary inward (distance = row spacing), repeating mowing until the center area, forming a spiral pattern from the outside in. For the diagonal pattern, similar to parallel lines, but with the direction set to 45° or 135°, forming diagonal stripes. For concentric circle patterns, the center point of the lawn is found, and concentric rings are generated outward from the center, with the ring spacing equal to the line spacing. These are then cropped to the lawn boundary to form concentric circles or concentric ellipses. For custom logo patterns, the user uploads a logo image (a black and white image, where white represents the mowing area). The logo is scaled to the lawn size, and a contour tracking algorithm is used to extract the logo boundary. The logo's interior is filled (using parallel lines with a line spacing of 0.3m). The pixel coordinates are converted to world coordinates to display the user-defined logo pattern on the lawn. This process yields the blade control parameters corresponding to each lawn pattern.

[0101] Step 602: Based on the lawn density data included in the lawn condition data, the cutter head speed is corrected to obtain the target cutter head speed.

[0102] During implementation, the control module determines the cutting path spacing, blade speed, blade height, and single-mowing frequency corresponding to the selected lawn pattern and lawn type as the blade control parameters.

[0103] Alternatively, the control module can adjust the cutter head speed based on the lawn density data included in the lawn status data to obtain the target cutter head speed; for example, the cutter head speed can be increased when the lawn density is high, and decreased when the cutter head density is low.

[0104] Step 603: Determine the target cutter head rotation speed and target mowing height as cutter head control parameters.

[0105] During implementation, the control module determines the target cutter head speed and target mowing height as cutter head control parameters, so as to perform mowing based on the target cutter head parameters.

[0106] One optional implementation provided in this application determines the cutter head control parameters by selecting one or more of the following: lawn pattern, lawn type, and lawn density data. This allows the lawn mowing equipment to maintain the lawn according to the type, pattern, and actual condition of the lawn selected by the user, thereby improving the adaptability of the lawn mowing equipment and thus improving the mowing effect.

[0107] In practical scenarios, setting lawn mowing equipment to automatically start on a periodic basis can save users' management effort and improve their user experience. The mowing cycle can be determined based on the lawn's growth rate. In one optional implementation provided in this application, such as... Figure 7 As shown, the process of determining the mowing cycle includes steps 701 to 703:

[0108] Step 701: Determine the candidate lawn growth rate of the target mowing area based on multiple historical lawn heights over a historical time period.

[0109] During implementation, the control module acquires multiple historical lawn heights of the target mowing area over historical time periods, and determines the candidate lawn growth rate of the target mowing area based on the historical lawn heights and intervals.

[0110] For example, obtain historical lawn heights collected at multiple adjacent historical time points, calculate the difference between each pair of adjacent historical lawn heights, divide the difference by the interval time to obtain multiple lawn growth rates, calculate the average of each lawn growth rate, and obtain the candidate lawn growth rate; predicted grass height = current grass height + growth rate × number of days.

[0111] Step 702: Based on the seasonal weights included in the environmental perception parameters, the candidate lawn growth rate is updated with weights to obtain the target lawn growth rate of the target mowing area.

[0112] During implementation, the control module updates the candidate lawn growth rate with weighted average based on the seasonal weights included in the environmental perception parameters to obtain the target lawn growth rate of the target mowing area. During execution, the control module extracts the seasonal data included in the environmental perception parameters, queries the seasonal weights corresponding to the seasonal data, and multiplies the seasonal weights with the candidate lawn growth rates to obtain the target lawn growth rate of the target mowing area.

[0113] For example, extract historical mowing records: (timestamp, grass height before mowing, grass height after mowing), calculate grass height growth between two adjacent mowing times: time interval = (i+1)th time - (i)th time; height growth = (i+1)th height before mowing - (i)th height after mowing); average daily growth rate = height growth / time interval; calculate the average growth rate of the last 10 mowing times; seasonal adjustment: spring coefficient 1.5 (fast growth), summer coefficient, autumn coefficient 0.8, winter coefficient 0.3 (slow growth); weather adjustment: sufficient rainfall (>20mm): coefficient × 1.3, drought (<5mm): coefficient × 0.7, high temperature (>25°C): coefficient × 1.2, low temperature (<15°C): coefficient × 0.8. After obtaining the weights according to the above rules, multiply them by the candidate lawn growth rate to obtain the target lawn growth rate.

[0114] Step 703: Predict the mowing cycle based on the target lawn growth rate.

[0115] During implementation, the control module predicts the mowing cycle based on the target lawn growth rate and current grass height data. During execution, it calculates the time required for the current grass height to grow to a grass height threshold and uses this time as the mowing cycle.

[0116] For example, if the number of days required to reach the threshold = (threshold - current grass height) / growth rate, then the next mowing date = current date + number of days.

[0117] One optional implementation provided in this application predicts the lawn growth rate and thus the mowing cycle, enabling the mowing equipment to predetermine its start-up time and mowing cycle. This avoids manual start-up by the user or poor start-up timing of the mowing equipment, thereby improving the mowing effect of the mowing equipment.

[0118] During the mowing process, the mowing path can be updated based on data from the surrounding environment / obstacles to avoid collisions with obstacles; one optional implementation provided in this application is as follows: Figure 8 As shown, step 203 includes steps 801 to 803:

[0119] Step 801: Acquire an environmental image using the image acquisition component included in the lawn mowing device.

[0120] During implementation, the control module acquires environmental images in real time through the image acquisition components included in the lawn mowing equipment while controlling the lawn mowing equipment to mow the grass; the environmental images can be environmental images along the lawn mowing path determined above.

[0121] Step 802: Determine obstacle data for the target mowing area based on the environmental image.

[0122] During implementation, the control module performs image recognition on the environmental image to obtain obstacle data corresponding to the environmental image in the target mowing area; the obstacle data includes obstacle type and / or obstacle range.

[0123] Step 803: Update the mowing strategy including the mowing path based on obstacle data, and control the mowing equipment to mow the grass according to the updated mowing path.

[0124] During implementation, the control module updates the mowing path included in the mowing strategy based on obstacle data, and controls the mowing equipment to mow the grass according to the updated mowing path; during execution, the control module can update the mowing path according to the obstacle type and obstacle range.

[0125] For small obstacles, a detour path can be determined based on the obstacle's range, and the mowing path can be updated based on the detour path. For large obstacles, the mowing path can be re-determined based on the obstacle's range and map data. For dynamic obstacles, the system can stop and wait for a first preset time. If the obstacle is removed after the first preset time, the system can continue mowing according to the mowing path. If the obstacle is still there after the first preset time, the system can return to the charging station and wait for a second preset time. The second preset time is longer than the first preset time.

[0126] One optional implementation method provided in this application achieves obstacle avoidance control of the lawn mowing equipment through dynamic obstacle avoidance. By using different obstacle types and strategies, the continuous and smooth lawn mowing process is ensured while guaranteeing obstacle avoidance, thereby improving the lawn mowing feasibility of the lawn mowing equipment.

[0127] In real-world scenarios, lawn mowing equipment can also return quantitative values ​​of mowing effectiveness and / or lawn health to the user based on the condition of the lawn; in one optional implementation provided in this application, such as Figure 9 As shown, the method further includes steps 901 and / or 902:

[0128] Step 901: Based on the lawn status data including lawn height and / or lawn density, determine the mowing effect quantification value of the target mowing area, and send the mowing effect quantification value to the terminal.

[0129] During implementation, the control module can determine the overall mowing effect quantification value of the target mowing area based on the lawn status data, including lawn height and / or lawn density, before mowing; the mowing effect quantification value is used to characterize the mowing effect of the lawnmower.

[0130] Alternatively, the control module can acquire target lawn status data after mowing is completed, determine the mowing effect quantification value of the target mowing area based on the lawn height and / or lawn density of the target lawn status data, and send the mowing effect quantification value to the terminal.

[0131] The mowing effect quantification value can be a mowing score, which can be used to characterize the effect of the mower on the target mowing area after mowing. The mowing effect quantification value can be determined based on the following example; correspondingly, the health level quantification value can be a health score, which can be used to characterize the health level of the grass in the target mowing area. The health level quantification value can be determined with reference to the following example.

[0132] For example, height reduction is calculated as height_reduction = average grass height before mowing - average grass height after mowing; uniformity is calculated as uniformity = 1.0 / (1.0 + height standard deviation / 10.0), with higher scores indicating greater uniformity; coverage rate is calculated as coverage_rate = actual covered area / total lawn area; omission detection: detects areas in the post-mowing grass height map that exceed a threshold - counts the omission area; pattern quality (pattern pattern only): evaluates the sharpness of the stripes and the symmetry of the pattern to obtain a comprehensive score.

[0133] The overall score is calculated as follows: overall_score = 0.3 × uniformity + 0.3 × coverage_rate + 0.2 × height_reduction + 0.2 × pattern_quality. This overall score is the quantitative value of the lawn mowing effect.

[0134] Step 902: Based on the lawn status data including lawn color and / or lawn density, determine the health status quantification value of the target mowing area and send the health status quantification value to the terminal.

[0135] During execution, the control module determines the quantified health level of the target mowing area based on the lawn status data, including lawn color and / or lawn density, and sends the quantified health level value to the terminal. The quantified health level value characterizes the lawn health level of the target mowing area.

[0136] For example, statistical information includes: number of mowings, total mowing area, total time spent, average grass height, density, and quantified health status within a time period; trend analysis includes: grass height change curve over time, quantified health status change curve over time, and density change trend; problem identification includes: identifying problem areas in the lawn (yellowing, wilting, low density), marking their location and area; optimization suggestions include: parameter adjustment suggestions (frequency, height), and special care suggestions (fertilization, watering); and visualization charts include: grass height trend chart, quantified health status trend chart, and coverage heatmap, with the visualization chart serving as the quantified health status value.

[0137] Furthermore, the control module can also send status notifications for the lawn mowing equipment, generating notification messages based on the equipment's status and sending them to the terminal. For example, notification type 1: Task Completed: Title: "Mowing Completed," Content: Time Spent, Coverage, Attachment: Post-Mowing Photo; Notification Type 2: Next Mowing Reminder: Title: "Automatic Mowing to Tomorrow," Content: Estimated Start Time, Time Spent; Notification Type 3: Health Warning: Title: "Lawn Health Decreases," Content: Detected Problems (e.g., Reduced Density), Action: View Detailed Report; Notification Type 4: Maintenance Reminder: Title: "Maintenance Required," Content: Blade Usage Time, Recommended Inspection or Replacement, Action: View Maintenance Guide.

[0138] One optional implementation provided in this application determines the mowing effect quantification value and / or health level quantification value of the lawn in the target mowing area through lawn status data, and sends the mowing effect quantification value and / or health level quantification value to the terminal, so that the user can intuitively perceive the mowing effect of the mowing equipment through the terminal, thereby improving the user's perception.

[0139] In real-world scenarios, lawn mowing equipment still presents some hazards, such as tilting, collisions, blade jamming, and power failure. To address these hazards, the equipment can be controlled to return to a previously safe position. One optional implementation provided in this application is as follows: Figure 10 As shown, the method further includes at least one of steps 1001 to 1004:

[0140] Step 1001: If the tilt angle of the mowing equipment is detected to be greater than the angle threshold, control the mowing equipment to stop mowing and return to the historical position.

[0141] During implementation, the control module detects the tilt angle of the lawnmower using its sensing components. If the tilt angle exceeds a threshold, the module stops the lawnmower and returns to a previous position. This previous position can be a safe position from a previous period.

[0142] During execution, the control module can obtain the tilt angle of the lawnmower through the gyroscope of the lawnmower.

[0143] Step 1002: If a collision is detected with the mowing equipment, control the mowing equipment to stop mowing.

[0144] During implementation, the control module can detect the movement status of the lawn mower through its sensing components. If a collision is detected, the control module can stop the lawn mower and return it to its previous position.

[0145] During execution, the control module can obtain the motion status of the lawn mower through the gyroscope of the lawn mower.

[0146] Step 1003: If the current of the blade of the mowing device is detected to be greater than the current threshold, control the mowing device to stop mowing and clear the blockage.

[0147] During implementation, the control module can detect the blade current of the mower through the current output module of the mower. If the current of the blade is detected to be greater than the current threshold, the control module can stop the mower and clear the blockage.

[0148] During execution, the control module can obtain the blade current of the mower through the current output module of the mower. If the current of the blade of the mower is detected to be greater than the current threshold, the control module will stop the mower and reverse the blade to clear the blockage.

[0149] Step 1004: If the battery level of the lawn mower is detected to be less than or equal to the battery threshold, control the lawn mower to return to the charging station.

[0150] During implementation, the control module detects the battery level of the lawnmower through the BMS system. If the detected battery level is less than or equal to the battery threshold, the control module controls the lawnmower to return to the charging station.

[0151] For example, tilt angle: if the tilt angle > 30°, an emergency stop is initiated and the device returns to a safe position; collision detection: if a collision is detected, an emergency stop is initiated and the device reverses 0.5m; cutter head stall: if the cutter head current > a threshold, the cutter head is stopped and the blockage is cleared; GPS signal loss: if the GPS signal is weak, local navigation (visual / LiDAR) is switched; battery power: if the battery power < 10%, the task is aborted and the device returns to the charging station.

[0152] One or more optional embodiments provided in this application enhance the safety of the lawn mowing equipment by using its components, thereby improving the mowing effect.

[0153] In real-world scenarios, users can also control the mowing strategy of the lawnmower using natural language; in one optional implementation provided in this application, such as Figure 11 As shown, the method further includes steps 1101 to 1102:

[0154] Step 1101: Obtain user feedback information and perform semantic recognition on the user feedback information to obtain semantic recognition results.

[0155] During implementation, the control module acquires user feedback information input by the user through the control program and performs semantic recognition on the user feedback information to obtain semantic recognition results.

[0156] For example, if a user inputs "stripes are not obvious", "uneven", "cut too high", "cut too low", "too frequent" or "not frequent enough", the system will obtain the semantic recognition results corresponding to the user's feedback.

[0157] Step 1102: Update the mowing strategy based on the semantic recognition results.

[0158] During implementation, the control module queries the strategy set based on the semantic recognition results to obtain the lawn mowing strategy corresponding to the semantic recognition results, and updates the lawn mowing strategy based on the query results.

[0159] For example, user satisfaction ratings (1-5 points): If the rating is low: if the comment includes "stripes are not obvious", the stripe width will be increased by 20%; if the comment includes "uneven", the path overlap will be increased by 5%. Height preference feedback: if the user comments "cut too high" or "cut too low", the system will automatically adjust: if the cut is too high, it will be reduced by 5mm; if the cut is too low, it will be increased by 5mm. Frequency preference feedback: if the user comments "too frequent" or "not frequent enough", the system will automatically adjust the frequency.

[0160] One optional implementation method provided in this application enables the lawn mowing equipment to self-learn and self-update through user feedback, thereby realizing direct interactive control of the mowing strategy by the user, improving the user's operating experience and perception, and enhancing the mowing effect of the lawn mowing equipment.

[0161] In one embodiment, see Figure 12 The diagram illustrates a flowchart of a control method for a lawn mowing device provided in an embodiment of this application. This control method for the lawn mowing device can be applied to... Figure 1 In the control module shown. For example... Figure 12 As shown, the control method for this lawn mowing equipment may include the following steps:

[0162] Step 1201: Obtain and respond to the start command to obtain the mowing parameters.

[0163] Step 1202: Determine the mowing time based on environmental perception parameters and / or lawn condition data.

[0164] Step 1203: Determine the cutter head control parameters based on preset lawn parameters and / or lawn condition data.

[0165] Optionally, the cutter head control parameters include at least one of the following: cutter head speed, cutter head height, single mowing frequency, and mowing path spacing; the preset lawn parameters include lawn pattern and lawn type.

[0166] Step 1204: Determine the mowing path for the target mowing area based on the map data and environmental perception parameters of the target mowing area.

[0167] Step 1205: Acquire environmental images using the image acquisition components included in the lawn mowing equipment.

[0168] Step 1206: Based on the environmental image, determine the obstacle data of the target mowing area. The obstacle data includes obstacle type and / or obstacle range.

[0169] Step 1207: Update the mowing strategy including the mowing path based on obstacle data, and control the mowing equipment to mow the grass according to the updated mowing path.

[0170] Step 1208: Obtain target lawn status data, and determine the mowing effect quantification value of the target mowing area based on the lawn height and / or lawn density included in the lawn status data.

[0171] Step 1209: Send the lawn mowing effect quantification value to the terminal.

[0172] It should be noted that any one or more of steps 1201 to 1209 can be combined to form a new implementation method according to the needs of implementation and deployment. Furthermore, any one or more technical features in the technical solution composed of steps 1201 to 1209 can also be combined to form a new implementation method according to the actual deployment needs, or technical features in one or more optional implementation methods provided by one or more of the above embodiments can be combined to form a new implementation method. These will not be elaborated on here.

[0173] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0174] Based on the same inventive concept, this application also provides a control device for a lawn mower to implement the control method for the lawn mower described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the control device for the lawn mower provided below can be found in the limitations of the control method for the lawn mower described above, and will not be repeated here.

[0175] In one exemplary embodiment, such as Figure 13 As shown, a control device for a lawn mowing equipment is provided, including: a start module 1301, a mowing strategy determination module 1302, and a mowing control module 1303, wherein: the start module 1301 is used to acquire mowing parameters in response to a start command for the lawn mowing equipment, the mowing parameters including at least one of lawn status data of the target mowing area, environmental perception parameters, and preset lawn parameters; the mowing strategy determination module 1302 is used to determine a mowing strategy based on the mowing parameters, the mowing strategy including at least one of mowing time, blade control parameters, and mowing path; the mowing control module 1303 is used to control the lawn mowing equipment to mow the lawn in the target mowing area according to the mowing strategy.

[0176] In one embodiment, the mowing strategy determination module 1302 includes a mowing time determination unit, a blade control parameter determination unit, and a mowing path determination unit, wherein: the mowing time determination unit is used to determine the mowing time based on environmental perception parameters and / or lawn status data; the blade control parameter determination unit is used to determine blade control parameters based on preset lawn parameters and / or lawn status data, the blade control parameters including at least one of blade rotation speed, blade height, single mowing frequency, and mowing path spacing, the preset lawn parameters including lawn pattern and lawn type; the mowing path determination unit is used to determine the mowing path of the target mowing area based on map data of the target mowing area and environmental perception parameters.

[0177] In one embodiment, the lawn mowing time determination unit includes a lawn state determination unit and either a first prediction unit or a second prediction unit, wherein: the lawn state determination unit is used to determine whether the lawn in the target mowing area is wet based on weather data included in the environmental perception parameters and / or lawn humidity data included in the lawn state data; the first prediction unit is used to predict the mowing time based on the weather data if the lawn is wet; and the second prediction unit is used to determine the current time as the mowing time if the lawn is not wet.

[0178] In one embodiment, the mowing time determination unit includes a grass height detection unit, and either a first determination unit or a second determination unit, wherein: the grass height detection unit is used to: detect whether the grass height data exceeds a grass height threshold based on the grass height data included in the environmental perception parameters and / or lawn status data; the first determination unit is used to determine the current time as the mowing time if the grass height data exceeds the grass height threshold; the second determination unit is used to predict the mowing time based on the lawn growth rate if the grass height data does not exceed the grass height threshold, wherein the lawn growth rate is predicted and determined based on the historical lawn height and / or seasonal information of the target mowing area.

[0179] In one embodiment, the cutter head control parameter determination unit includes a third determination unit and a fourth determination unit, wherein: the third determination unit is used to query candidate cutter head control parameters corresponding to the lawn type from a preset lawn type parameter mapping table, the candidate cutter head control parameters including the target mowing height and the cutter head rotation speed; the fourth determination unit is used to correct the cutter head rotation speed according to the lawn density data included in the lawn state data to obtain the target cutter head rotation speed, and determine the target cutter head rotation speed and the target mowing height as the cutter head control parameters.

[0180] In one embodiment, the device further includes a start command acquisition module, which is used to receive a start command sent by the terminal; or, in response to a lawn mowing cycle completion command, generate a start command.

[0181] In one embodiment, the device further includes a first lawn growth rate acquisition module, a lawn growth rate update module, and a mowing cycle prediction module, wherein: the first lawn growth rate acquisition module is used to determine candidate lawn growth rates for the target mowing area based on multiple historical lawn heights of the target mowing area over a historical time period; the lawn growth rate update module is used to perform weighted updates on the candidate lawn growth rates based on seasonal weights included in the environmental perception parameters to obtain the target lawn growth rate for the target mowing area; and the mowing cycle prediction module is used to predict the mowing cycle based on the target lawn growth rate.

[0182] In one embodiment, the lawn mowing control module 1303 includes an environmental image acquisition unit, an obstacle determination unit, and a lawn mowing path update unit, wherein: the environmental image acquisition unit is used to acquire environmental images through image acquisition components included in the lawn mowing device; the obstacle determination unit is used to determine obstacle data of the target lawn mowing area based on the environmental images, the obstacle data including obstacle type and / or obstacle range; the lawn mowing path update unit is used to update the lawn mowing path included in the lawn mowing strategy based on the obstacle data, and control the lawn mowing device to mow the lawn based on the updated lawn mowing path.

[0183] In one embodiment, the device further includes a lawn mowing effect quantification value sending module and / or a health level quantification value sending module, wherein: the lawn mowing effect quantification value sending module is used to determine the lawn mowing effect quantification value of the target mowing area based on the lawn height and / or lawn density included in the lawn status data, and send the lawn mowing effect quantification value to the terminal, the lawn mowing effect quantification value being used to characterize the lawn mowing effect of the lawnmower; the health level quantification value sending module is used to determine the health level quantification value of the target mowing area based on the lawn color and / or lawn density included in the lawn status data, and send the health level quantification value to the terminal, the health level quantification value being used to characterize the lawn health level of the target mowing area.

[0184] In one embodiment, the device further includes at least one of a first return module, a second return module, a clearing module, and a third return module, wherein: the first return module is used to control the mowing device to stop mowing and return to a historical position when the tilt angle of the mowing device is detected to be greater than an angle threshold; the second return module is used to control the mowing device to stop mowing when a collision is detected; the clearing module is used to control the mowing device to stop mowing and clear the blockage when the blade current of the mowing device is detected to be greater than a current threshold; and the third return module is used to control the mowing device to return to a charging station when the battery power of the mowing device is detected to be less than or equal to a battery power threshold.

[0185] In one embodiment, the device further includes a user feedback module and a strategy update module, wherein: the user feedback module is used to obtain user feedback information and perform semantic recognition on the user feedback information to obtain semantic recognition results; the strategy update module is used to update the lawn mowing strategy according to the semantic recognition results.

[0186] Each module in the control device of the aforementioned lawnmower can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the lawnmower in hardware form or independent of it, or stored in the lawnmower's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0187] In one exemplary embodiment, a lawnmower is provided, which may include a control module. The internal structure diagram of the control module may be as follows: Figure 14As shown, the control module includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores control data for the lawnmower. The I / O interfaces allow the processor to exchange information with external devices. The communication interface allows communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a control method for the lawnmower.

[0188] Those skilled in the art will understand that Figure 14 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the lawnmower to which the present application is applied. A specific lawnmower may include more or fewer parts than shown in the figure, or combine certain parts, or have different part arrangements.

[0189] In one exemplary embodiment, a lawnmower is provided, including a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, performs the following steps: in response to a start command for the lawnmower, acquiring mowing parameters, the mowing parameters including at least one of lawn state data of a target mowing area, environmental perception parameters, and preset lawn parameters; determining a mowing strategy based on the mowing parameters, the mowing strategy including at least one of mowing time, blade control parameters, and mowing path; and controlling the lawnmower to mow the target mowing area according to the mowing strategy.

[0190] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the mowing time based on environmental perception parameters and / or lawn status data; determining the blade control parameters based on preset lawn parameters and / or lawn status data, wherein the blade control parameters include at least one of blade rotation speed, blade height, single mowing frequency, and mowing path spacing, and the preset lawn parameters include lawn pattern and lawn type; and determining the mowing path of the target mowing area based on map data of the target mowing area and environmental perception parameters.

[0191] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining whether the lawn of the target mowing area is wet based on weather data included in the environmental perception parameters and / or lawn humidity data included in the lawn status data; if the lawn is wet, predicting the mowing time based on the weather data; if the lawn is not wet, determining the current time as the mowing time.

[0192] In one embodiment, when the processor executes the computer program, it further performs the following steps: based on the grass height data included in the environmental perception parameters and / or lawn status data, it detects whether the grass height data exceeds a grass height threshold; if the grass height data exceeds the grass height threshold, it determines the current time as the mowing time; if the grass height data does not exceed the grass height threshold, it predicts the mowing time based on the lawn growth rate, which is predicted and determined based on the historical lawn height and / or seasonal information of the target mowing area.

[0193] In one embodiment, when the processor executes the computer program, it further performs the following steps: querying candidate cutter head control parameters corresponding to the lawn type from a preset lawn type parameter mapping table, the candidate cutter head control parameters including the target mowing height and the cutter head speed; correcting the cutter head speed according to the lawn density data included in the lawn status data to obtain the target cutter head speed; and determining the target cutter head speed and the target mowing height as the cutter head control parameters.

[0194] In one embodiment, the processor, when executing a computer program, further performs the following steps: receiving a start command sent by a terminal; or, in response to a lawn mowing cycle completion command, generating a start command.

[0195] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining candidate lawn growth rates for the target lawn area based on multiple historical lawn heights over a historical time period; updating the candidate lawn growth rates with weights based on seasonal weights included in the environmental perception parameters to obtain the target lawn growth rate for the target lawn area; and predicting the mowing cycle based on the target lawn growth rate.

[0196] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring an environmental image through an image acquisition component included in the lawn mowing device; determining obstacle data for a target mowing area based on the environmental image, the obstacle data including obstacle type and / or obstacle range; updating the mowing path included in the mowing strategy based on the obstacle data; and controlling the lawn mowing device to mow the lawn based on the updated mowing path.

[0197] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a mowing effect quantification value for the target mowing area based on the lawn height and / or lawn density included in the lawn status data, and sending the mowing effect quantification value to the terminal, the mowing effect quantification value being used to characterize the mowing effect of the lawnmower; and / or determining a health level quantification value for the target mowing area based on the lawn color and / or lawn density included in the lawn status data, and sending the health level quantification value to the terminal, the health level quantification value being used to characterize the lawn health level of the target mowing area.

[0198] In one embodiment, when the processor executes the computer program, it further implements the following steps: if the tilt angle of the mowing device is detected to be greater than an angle threshold, it controls the mowing device to stop mowing and return to a historical position; if a collision is detected, it controls the mowing device to stop mowing; if the blade current of the mowing device is detected to be greater than a current threshold, it controls the mowing device to stop mowing and clear the blockage; if the battery power of the mowing device is detected to be less than or equal to a battery power threshold, it controls the mowing device to return to the charging station.

[0199] In one embodiment, when the processor executes the computer program, it also performs the following steps: obtaining user feedback information and performing semantic recognition on the user feedback information to obtain semantic recognition results; updating the lawn mowing strategy based on the semantic recognition results.

[0200] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps: in response to a start command for a lawn mowing device, acquiring mowing parameters, the mowing parameters including at least one of lawn status data of a target mowing area, environmental perception parameters, and preset lawn parameters; determining a mowing strategy based on the mowing parameters, the mowing strategy including at least one of mowing time, blade control parameters, and mowing path; and controlling the lawn mowing device to mow the target mowing area according to the mowing strategy.

[0201] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the mowing time based on environmental perception parameters and / or lawn status data; determining the blade control parameters based on preset lawn parameters and / or lawn status data, wherein the blade control parameters include at least one of blade rotation speed, blade height, single mowing frequency, and mowing path spacing, and the preset lawn parameters include lawn pattern and lawn type; and determining the mowing path of the target mowing area based on map data of the target mowing area and environmental perception parameters.

[0202] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining whether the lawn of the target mowing area is wet based on weather data included in the environmental perception parameters and / or lawn humidity data included in the lawn status data; if the lawn is wet, predicting the mowing time based on the weather data; if the lawn is not wet, determining the current time as the mowing time.

[0203] In one embodiment, when the processor executes the computer program, it further performs the following steps: based on the grass height data included in the environmental perception parameters and / or lawn status data, it detects whether the grass height data exceeds a grass height threshold; if the grass height data exceeds the grass height threshold, it determines the current time as the mowing time; if the grass height data does not exceed the grass height threshold, it predicts the mowing time based on the lawn growth rate, which is predicted and determined based on the historical lawn height and / or seasonal information of the target mowing area.

[0204] In one embodiment, when the processor executes the computer program, it further performs the following steps: querying candidate cutter head control parameters corresponding to the lawn type from a preset lawn type parameter mapping table, the candidate cutter head control parameters including the target mowing height and the cutter head speed; correcting the cutter head speed according to the lawn density data included in the lawn status data to obtain the target cutter head speed; and determining the target cutter head speed and the target mowing height as the cutter head control parameters.

[0205] In one embodiment, the processor, when executing a computer program, further performs the following steps: receiving a start command sent by a terminal; or, in response to a lawn mowing cycle completion command, generating a start command.

[0206] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining candidate lawn growth rates for the target lawn area based on multiple historical lawn heights over a historical time period; updating the candidate lawn growth rates with weights based on seasonal weights included in the environmental perception parameters to obtain the target lawn growth rate for the target lawn area; and predicting the mowing cycle based on the target lawn growth rate.

[0207] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring an environmental image through an image acquisition component included in the lawn mowing device; determining obstacle data for a target mowing area based on the environmental image, the obstacle data including obstacle type and / or obstacle range; updating the mowing path included in the mowing strategy based on the obstacle data; and controlling the lawn mowing device to mow the lawn based on the updated mowing path.

[0208] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a mowing effect quantification value for the target mowing area based on the lawn height and / or lawn density included in the lawn status data, and sending the mowing effect quantification value to the terminal, the mowing effect quantification value being used to characterize the mowing effect of the lawnmower; and / or determining a health level quantification value for the target mowing area based on the lawn color and / or lawn density included in the lawn status data, and sending the health level quantification value to the terminal, the health level quantification value being used to characterize the lawn health level of the target mowing area.

[0209] In one embodiment, when the processor executes the computer program, it further implements the following steps: if the tilt angle of the mowing device is detected to be greater than an angle threshold, it controls the mowing device to stop mowing and return to a historical position; if a collision is detected, it controls the mowing device to stop mowing; if the blade current of the mowing device is detected to be greater than a current threshold, it controls the mowing device to stop mowing and clear the blockage; if the battery power of the mowing device is detected to be less than or equal to a battery power threshold, it controls the mowing device to return to the charging station.

[0210] In one embodiment, when the processor executes the computer program, it also performs the following steps: obtaining user feedback information and performing semantic recognition on the user feedback information to obtain semantic recognition results; updating the lawn mowing strategy based on the semantic recognition results.

[0211] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps: in response to a start command for a lawn mowing device, acquiring mowing parameters, the mowing parameters including at least one of lawn status data of a target mowing area, environmental perception parameters, and preset lawn parameters; determining a mowing strategy based on the mowing parameters, the mowing strategy including at least one of mowing time, blade control parameters, and mowing path; and controlling the lawn mowing device to mow the target mowing area according to the mowing strategy.

[0212] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the mowing time based on environmental perception parameters and / or lawn status data; determining the blade control parameters based on preset lawn parameters and / or lawn status data, wherein the blade control parameters include at least one of blade rotation speed, blade height, single mowing frequency, and mowing path spacing, and the preset lawn parameters include lawn pattern and lawn type; and determining the mowing path of the target mowing area based on map data of the target mowing area and environmental perception parameters.

[0213] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining whether the lawn of the target mowing area is wet based on weather data included in the environmental perception parameters and / or lawn humidity data included in the lawn status data; if the lawn is wet, predicting the mowing time based on the weather data; if the lawn is not wet, determining the current time as the mowing time.

[0214] In one embodiment, when the processor executes the computer program, it further performs the following steps: based on the grass height data included in the environmental perception parameters and / or lawn status data, it detects whether the grass height data exceeds a grass height threshold; if the grass height data exceeds the grass height threshold, it determines the current time as the mowing time; if the grass height data does not exceed the grass height threshold, it predicts the mowing time based on the lawn growth rate, which is predicted and determined based on the historical lawn height and / or seasonal information of the target mowing area.

[0215] In one embodiment, when the processor executes the computer program, it further performs the following steps: querying candidate cutter head control parameters corresponding to the lawn type from a preset lawn type parameter mapping table, the candidate cutter head control parameters including the target mowing height and the cutter head speed; correcting the cutter head speed according to the lawn density data included in the lawn status data to obtain the target cutter head speed; and determining the target cutter head speed and the target mowing height as the cutter head control parameters.

[0216] In one embodiment, the processor, when executing a computer program, further performs the following steps: receiving a start command sent by a terminal; or, in response to a lawn mowing cycle completion command, generating a start command.

[0217] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining candidate lawn growth rates for the target lawn area based on multiple historical lawn heights over a historical time period; updating the candidate lawn growth rates with weights based on seasonal weights included in the environmental perception parameters to obtain the target lawn growth rate for the target lawn area; and predicting the mowing cycle based on the target lawn growth rate.

[0218] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring an environmental image through an image acquisition component included in the lawn mowing device; determining obstacle data for a target mowing area based on the environmental image, the obstacle data including obstacle type and / or obstacle range; updating the mowing path included in the mowing strategy based on the obstacle data; and controlling the lawn mowing device to mow the lawn based on the updated mowing path.

[0219] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a mowing effect quantification value for the target mowing area based on the lawn height and / or lawn density included in the lawn status data, and sending the mowing effect quantification value to the terminal, the mowing effect quantification value being used to characterize the mowing effect of the lawnmower; and / or determining a health level quantification value for the target mowing area based on the lawn color and / or lawn density included in the lawn status data, and sending the health level quantification value to the terminal, the health level quantification value being used to characterize the lawn health level of the target mowing area.

[0220] In one embodiment, when the processor executes the computer program, it further implements the following steps: if the tilt angle of the mowing device is detected to be greater than an angle threshold, it controls the mowing device to stop mowing and return to a historical position; if a collision is detected, it controls the mowing device to stop mowing; if the blade current of the mowing device is detected to be greater than a current threshold, it controls the mowing device to stop mowing and clear the blockage; if the battery power of the mowing device is detected to be less than or equal to a battery power threshold, it controls the mowing device to return to the charging station.

[0221] In one embodiment, when the processor executes the computer program, it also performs the following steps: obtaining user feedback information and performing semantic recognition on the user feedback information to obtain semantic recognition results; updating the lawn mowing strategy based on the semantic recognition results.

[0222] It should be noted that the user information (including but not limited to user device information, user personal information, user feedback information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0223] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0224] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0225] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A control method for a lawn mowing device, characterized in that, The method includes: In response to a start command for the lawn mowing equipment, lawn mowing parameters are acquired, including at least one of lawn status data of the target lawn mowing area, environmental sensing parameters, and preset lawn parameters. A mowing strategy is determined based on the mowing parameters, wherein the mowing strategy includes at least one of mowing time, blade control parameters, and mowing path. According to the mowing strategy, the mowing equipment is controlled to mow the grass in the target mowing area.

2. The method according to claim 1, characterized in that, The step of determining the mowing strategy based on the mowing parameters includes at least one of the following: The mowing time is determined based on the environmental perception parameters and / or the lawn condition data; The cutter head control parameters are determined based on the preset lawn parameters and the lawn status data. The cutter head control parameters include at least one of the following: cutter head rotation speed, cutter head height, single mowing frequency, and mowing path spacing. The preset lawn parameters include lawn pattern and lawn type. Based on the map data of the target mowing area and the environmental perception parameters, the mowing path of the target mowing area is determined.

3. The method according to claim 2, characterized in that, Determining the mowing time based on the environmental perception parameters and / or the lawn condition data includes: Based on the weather data included in the environmental perception parameters and / or the lawn humidity data included in the lawn status data, determine whether the lawn in the target mowing area is in a wet state. If the lawn is wet, the mowing time is predicted based on the weather data; If the lawn is not wet, the current time is determined as the mowing time.

4. The method according to claim 2, characterized in that, Determining the mowing time based on the environmental perception parameters and / or the lawn condition data includes: Based on the environmental sensing parameters and / or the grass height data included in the lawn status data, detect whether the grass height data exceeds the grass height threshold; If the grass height data exceeds the grass height threshold, the current time is determined as the mowing time; If the grass height data does not exceed the grass height threshold, the mowing time is predicted based on the lawn growth rate, which is determined based on the historical lawn height and seasonal information of the target mowing area.

5. The method according to claim 2, characterized in that, The step of determining the cutter head control parameters based on the preset lawn parameters and the lawn state data includes: The candidate cutter head control parameters corresponding to the lawn type are queried from the preset lawn type parameter mapping table. The candidate cutter head control parameters include the target mowing height and the cutter head rotation speed. The lawn type parameter mapping table includes the mapping relationship between each lawn type and the candidate cutter head control parameters. The target cutter head speed is obtained by correcting the cutter head speed based on the lawn density data, which is included in the lawn condition data. The target blade rotation speed and the target mowing height are determined as the blade control parameters.

6. The method according to claim 1, characterized in that, Before acquiring mowing parameters in response to a start command for the mowing equipment, the method further includes: The receiving terminal sends the start command; or, The start command is generated in response to the completion command of the mowing cycle.

7. The method according to claim 6, characterized in that, The process of determining the mowing cycle includes: Based on multiple historical lawn heights of the target mowing area over a historical time period, determine the candidate lawn growth rate of the target mowing area; The candidate lawn growth rate is updated by weighting the seasonal weights included in the environmental perception parameters to obtain the target lawn growth rate of the target mowing area. The mowing cycle is predicted based on the target lawn growth rate.

8. The method according to any one of claims 1-7, characterized in that, The step of controlling the mowing equipment to mow the grass in the target mowing area according to the mowing strategy includes: The lawn mowing equipment acquires environmental images using its image acquisition components. Based on the environmental image, obstacle data for the target mowing area is determined, including obstacle type and / or obstacle range; The mowing strategy includes a mowing path that is updated based on the obstacle data, and the mowing equipment is controlled to mow the grass based on the updated mowing path.

9. The method according to any one of claims 1-7, characterized in that, The method further includes: Based on the lawn condition data, including lawn height and / or lawn density, a quantitative value for the mowing effect of the target mowing area is determined, and this quantitative value is sent to the terminal. The quantitative value characterizes the mowing effect of the mowing equipment; and / or, Based on the lawn condition data, including lawn color and / or lawn density, a quantitative value of the health status of the target mowing area is determined, and the quantitative value of the health status is sent to the terminal. The quantitative value of the health status is used to characterize the lawn health status of the target mowing area.

10. The method according to any one of claims 1-7, characterized in that, The method further includes at least one of the following: If the tilt angle of the mowing device is detected to be greater than the angle threshold, the mowing device is controlled to stop mowing and return to the historical position; If a collision is detected with the lawn mowing equipment, the lawn mowing equipment shall be controlled to stop mowing. If the current of the blade of the mowing device is detected to be greater than the current threshold, the mowing device is controlled to stop mowing and clear the blockage. If the battery level of the lawnmower is detected to be less than or equal to a battery threshold, the lawnmower is controlled to return to the charging station.

11. The method according to any one of claims 1-7, characterized in that, After controlling the mowing equipment to mow the grass in the target mowing area according to the mowing strategy, the method further includes: Obtain user feedback information and perform semantic recognition on the user feedback information to obtain semantic recognition results; The mowing strategy is updated based on the semantic recognition results.

12. A control device for a lawn mowing machine, characterized in that, The device includes: The startup module is used to respond to a startup command for the lawn mowing equipment and acquire mowing parameters, which include at least one of lawn status data of the target mowing area, environmental perception parameters, and preset lawn parameters. A mowing strategy determination module is used to determine a mowing strategy based on the mowing parameters, wherein the mowing strategy includes at least one of mowing time, blade control parameters, and mowing path. The mowing control module is used to control the mowing equipment to mow the grass in the target mowing area according to the mowing strategy.

13. A lawnmower, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 11.

Images