Lawn mower control method, lawn mower, computer program product, and storage medium

Abstract

Embodiments of the present application provide a lawn mower control method, a lawn mower, a computer program product, and a storage medium. The method comprises: during the mowing process of a lawn mower, performing idling detection on a mowing motor of the lawn mower, wherein the mowing motor is a motor used for controlling rotation of a mowing component of the lawn mower; when idling of the mowing motor is detected, determining a target position area corresponding to an idling time period, wherein the idling time period is a time period during which the mowing motor idles, and the target position area comprises a position area through which the lawn mower passes within the idling time period; and when there is a mowing-missed lawn area in the target position area, controlling the lawn mower to perform additional mowing on the mowing-missed lawn area in the target position area. The present application solves the problem of poor accuracy of missed mowing detection of lawn mowers in lawn mower control methods in the related art, and achieves the effect of improving the accuracy of missed mowing detection of lawn mowers.

Description

Lawn mower control methods, lawn mowers, computer program products and storage media

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411845633.2, filed on December 13, 2024, entitled "Lawnmower Control Method, Lawnmower, Computer Program Product and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of intelligent lawnmowers, specifically to a lawnmower control method, a lawnmower, a computer program product, and a storage medium. Background Technology

[0004] When mowing lawns, intelligent lawnmowers follow a preset path. However, in actual operation, deviations between the intelligent lawnmower's movement trajectory and the preset path may cause missed mowing spots, affecting the mowing effect.

[0005] Therefore, in related technologies, when a lawnmower is performing a mowing task, the location information of the lawnmower is compared with the planned path to determine whether the lawnmower has deviated from the planned path; if the offset between the location of the lawnmower and the planned path is greater than a set threshold, it is determined that there is a missed area, and the missed area needs to be mowed.

[0006] However, while the aforementioned lawnmower control method can identify missed areas, it cannot determine whether any areas were missed along the actual path the lawnmower travels. Therefore, it is evident that the lawnmower control methods in this technology suffer from poor accuracy in detecting missed mowing areas. Summary of the Invention

[0007] This application provides a lawnmower control method, a lawnmower, a computer program product, and a storage medium to at least solve the technical problem of poor accuracy in detecting missed mowing in lawnmower control methods in related technologies.

[0008] According to one aspect of the embodiments of this application, a lawnmower control method is also provided, comprising: during the lawnmower's mowing process, performing start / stop detection on the mowing motor of the lawnmower, wherein the mowing motor is a motor used to control the rotation of the mowing component of the lawnmower; when the mowing motor is detected to have stopped, determining a target location area corresponding to an idle time period, wherein the idle time period is the time period during which the mowing motor stopped, and the target location area includes the location area traversed by the lawnmower during the idle time period; and if there are missed mowing areas in the target location area, controlling the lawnmower to re-mow the missed mowing areas in the target location area.

[0009] According to another aspect of the embodiments of this application, a lawnmower control method is provided, comprising: during the lawnmower's mowing process, detecting idling of the mowing motor of the lawnmower, wherein the mowing motor is a motor used to control the rotation of the mowing component of the lawnmower; when idling of the mowing motor is detected, determining a target location area corresponding to the idling time period, wherein the idling time period is the time period during which the mowing motor idles, and the target location area includes the location area traversed by the lawnmower during the idling time period; and if there are missed mowing areas in the target location area, controlling the lawnmower to re-mow the missed mowing areas in the target location area.

[0010] According to another aspect of the embodiments of this application, a lawnmower is also provided, the lawnmower including a control component, a mowing component, and a mowing motor, the mowing motor being a motor for controlling the rotation of the mowing component; wherein, the control component is used to detect idling of the mowing motor during the mowing process; when idling of the mowing motor is detected, a target location area corresponding to the idling time period is determined, wherein the idling time period is the time period during which the mowing motor idles, and the target location area includes the location area traversed by the lawnmower during the idling time period; if there are missed mowing areas in the target location area, the control component controls the lawnmower to re-mow the missed mowing areas in the target location area.

[0011] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to perform the steps in any of the above method embodiments when it is run.

[0012] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the steps in any of the method embodiments described above.

[0013] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0014] This application employs a method for detecting missed mowing based on the idling or stopping of the mowing motor. During the mowing process, the idling or start-stop detection of the mowing motor, which controls the rotation of the mowing components, is performed. When idling or stopping of the mowing motor is detected, a target location area corresponding to the idling or stopping time period is determined. The idling time period is the time during which the mowing motor idles, and the stopping time period is the time during which the mowing motor stops. The target location area includes the actual location area of ​​the mowing machine corresponding to the idling or stopping time period. If there are missed areas of lawn within the target location area, the lawnmower is controlled to re-mow the missed areas within the target location area. Since the idling or stopping of the mowing motor can indicate whether the mower has mowed the lawn, the area traversed by the mower during the idling or stopping period of the mowing motor is taken as the key area for detecting missed mowing. This allows for timely determination of whether there are any missed mowing areas on the actual path of the mower during its movement, thereby improving the accuracy of missed mowing detection and solving the technical problem of poor accuracy in missed mowing detection in related mower control methods. Attached Figure Description

[0015] Figure 1 is a schematic diagram of an application scenario of an optional lawnmower control method according to an embodiment of this application;

[0016] Figure 2 is a flowchart illustrating an optional lawnmower control method according to an embodiment of this application;

[0017] Figure 3 is a flowchart illustrating another optional lawnmower control method according to an embodiment of this application;

[0018] Figure 4 is a schematic diagram of an optional lawnmower control method according to an embodiment of this application;

[0019] Figure 5 is a schematic diagram of another optional lawnmower control method according to an embodiment of this application;

[0020] Figure 6 is a schematic diagram of another optional lawnmower control method according to an embodiment of this application;

[0021] Figure 7 is a schematic diagram of another optional lawnmower control method according to an embodiment of this application;

[0022] Figure 8 is a schematic diagram of another optional lawnmower control method according to an embodiment of this application;

[0023] Figure 9 is a structural block diagram of an optional lawnmower according to an embodiment of this application;

[0024] Figure 10 is a computer system architecture block diagram of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] According to one aspect of the embodiments of this application, a lawnmower control method is provided. Optionally, in this embodiment, the above-described lawnmower control method can be applied to a hardware environment including a lawnmower 102 and a server 104 as shown in FIG1. ​​As shown in FIG1, the lawnmower 102 can be an intelligent lawnmower equipped with a mowing component. The server 104 is connected to the lawnmower 102 via a network and can be used to provide services to the lawnmower 102 (such as location services). Users can also use their terminal devices to send mowing commands to the lawnmower 102 via the server 104 or directly to control the lawnmower 102 to mow the grass.

[0028] In this embodiment, the network may include, but is not limited to, at least one of the following: a wired network and a wireless network. The wired network may include, but is not limited to, at least one of the following: a wide area network (WAN), a metropolitan area network (MAN), and a local area network (LAN). The wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity) and Bluetooth.

[0029] The lawnmower control method in this embodiment can be executed by the lawnmower 102 alone, or it can be executed by the lawnmower 102 and the server 104 together. In this embodiment, the lawnmower 102 can execute the lawnmower control method by an application running on it.

[0030] Taking the lawnmower control method of this embodiment executed by the lawnmower 102 as an example, Figure 2 is a flowchart of an optional lawnmower control method according to an embodiment of this application. As shown in Figure 2, the process of the above method may include the following steps:

[0031] Step S202: During the mowing process, the mowing motor of the mower is tested for idling. The mowing motor is a motor used to control the rotation of the mowing component of the mower.

[0032] Step S204: When it is detected that the mowing motor is idling, determine the target location area corresponding to the idling time period, wherein the idling time period is the time period during which the mowing motor is idling, and the target location area includes the location area passed by the mowing machine during the idling time period.

[0033] Step S206: If there are missed mowing areas in the target location area, control the lawnmower to mow the missed lawn areas in the target location area.

[0034] Furthermore, as shown in Figure 3, the above method may also include the following steps:

[0035] Step S302: During the mowing process, the start-stop detection of the mowing motor of the mowing machine is performed. The mowing motor is a motor used to control the rotation of the mowing part of the mowing machine.

[0036] Step S304: When it is detected that the mowing motor has stopped, determine the target location area corresponding to the stop time period, wherein the stop time period is the time period during which the mowing motor stops, and the target location area includes the location area traversed by the mowing machine during the stop time period.

[0037] Step S306: If there are missed mowing areas in the target location area, control the lawnmower to mow the missed lawn areas in the target location area.

[0038] In this embodiment, the lawnmower's mowing process refers to the process of mowing within a preset working area. Movement of the lawnmower outside the working area is not included in the "mowing process" as defined in this embodiment. For example, if the lawnmower moves from one enclosed lawn area to another, and a non-grass passage exists between the two enclosed lawn areas, then the movement of the lawnmower within the non-grass passage is not counted in the mowing process. The lawnmower control method in this embodiment can be applied to the field of intelligent devices, specifically to scenarios where the lawnmower performs mowing work on a lawn area. The aforementioned lawnmower can be an intelligent lawnmower, which refers to a device that can autonomously complete lawn mowing without direct human control or operation. It utilizes sensors, navigation systems, and automated control technology to autonomously identify lawn boundaries and plan mowing paths. The lawnmower can be equipped with mowing components, i.e., cutting mechanisms for cutting, also known as cutting devices, such as a blade disc. The mowing components can cut the lawn to complete the mowing task. The above-mentioned mowing components may include, but are not limited to, at least one of the following: rotary mowing components, roller mowing components, reciprocating toothed mowing components, swivel mowing components, and rope mowing components, etc., and may also be other cutting mechanisms capable of completing the mowing task. In this embodiment, the type of mowing component is not limited.

[0039] When mowing lawns, intelligent lawnmowers can cut grass along a preset path. However, in actual operation, deviations in the mower's trajectory may cause missed mowing areas, affecting the mowing effect. To re-mow these missed areas, the relevant technology employs the following re-mowing strategy: comparing the mower's position information with the planned path to determine if the mower has deviated from the planned path; if the deviation between the mower's current position and the planned path exceeds a threshold, a missed area is identified and needs to be re-mowed.

[0040] However, the mowing methods in these technologies all have limitations in accurately detecting missed mowing. For example, if the lawnmower is following the planned path but the mowing mechanism is not activated, these missed mowing detection methods will fail to detect the issue. Furthermore, if the grass is flattened by the mower and cannot be effectively cut, this situation will also go undetected.

[0041] To at least partially solve the above-mentioned technical problems, a method for detecting missed mowing is adopted based on the idling or stopping of the mowing motor (the motor used to control the rotation of the mowing part of the lawnmower). Since the idling or stopping of the mowing motor can reflect whether the lawnmower has cut grass, the area traversed by the lawnmower during the idling or stopping time of the mowing motor is taken as the key area for detecting missed mowing. It can promptly determine whether there are any missed mowing situations on the actual path of the lawnmower while it is moving, which can improve the accuracy of missed mowing location and the timeliness of missed mowing location. Missed areas can be detected while the lawnmower is moving.

[0042] The lawnmower can perform mowing work on a designated lawn area based on user control. This mowing can be performed according to a pre-planned path (i.e., a mowing path). During the mowing process, the mowing motor can be monitored for idling. In addition to the mowing motor and mowing components, the lawnmower may also include a control component. The lawnmower control method in this embodiment can be executed by the control component. Furthermore, the lawnmower may include an idling detection component. Here, idling can affect multiple aspects, such as current and the state of the mowing components. Therefore, different idling detection components can be used depending on the characteristics on which the idling detection is based; moreover, multiple idling detection components can be used to detect a single characteristic. It should be noted that idling in this application is not limited to the lawnmower operating without any load, but also includes the motor operating in a near-no-load state, i.e., a near-idling state.

[0043] A lawnmower motor stopping indicates a malfunction or shutdown in response to a user command. Examples include the mower blades hitting an obstacle, a living object nearby, slippage, or the mower being lifted. When the mower motor stops or shuts down, but the drive motor continues to operate normally, the area the mower passes through will be missed. Therefore, in this embodiment, a target location area corresponding to the stop time period can be determined. The stop time period is the time during which the mower motor stops, and the target location area includes the area traversed by the mower during that stop time period.

[0044] There are many ways for those skilled in the art to test whether a motor is in a stopped state, such as switch signal detection, speed detection, etc., and these are not the focus of this application, so they will not be elaborated here.

[0045] The mowing motor idling indicates that the mowing component has not mowed the grass. There are several possible reasons why the mowing component might not have mowed the grass. For example, the area being cut may not be the intended mowing area; the area may be the intended mowing area, but the grass has fallen over due to lodging or other reasons; or low obstacles such as tiles may have flattened the lawn. Areas that may be missed can occur within the intended mowing area. Therefore, in this embodiment, a target location area corresponding to the idling period can be determined. The idling period is the time during which the mowing motor idles, and the target location area includes the area traversed by the mower during the idling period.

[0046] Optionally, considering that missed mowing areas can only occur within the expected mowing location, if the mowing motor idles but the current location does not include the expected mowing location, then idling is irrelevant to missed mowing, and no missed mowing detection is needed; in this case, the subsequent missed mowing detection steps can be skipped. If the mowing motor idles and the current location includes the expected mowing location, then the target location area can be determined, and the subsequent missed mowing detection steps can be performed. Correspondingly, the target location area can also include the expected mowing location area during the idling period.

[0047] Optionally, in order to reduce the amount of data that needs to be processed, for areas that are not expected to be mowed, such as open areas within the lawn area or non-lawn areas covered by the mower when it turns at the lawn boundary, the idling detection or start-stop detection of the mowing motor can be omitted. Based on the position of the mower and the positional relationship between the mower and the set mowing path, it can be determined which areas are not expected to be mowed.

[0048] To improve the accuracy of missed mowing detection, the expected mowing location area within the target location area can be determined by first identifying the location of the mower during the idle or stopped time periods. Based on the location of the mower during the idle or stopped time periods—usually the center of gravity, the relative position of the mowing component to the mower, and the cutting range of the mowing component—the target cutting range of the mowing component during the idle or stopped time periods can be determined, i.e., the target location area. Then, the intersection of the target cutting range and the expected mowing location area is determined as the expected mowing location area within the target location area.

[0049] The target location area for mowing may include already mowed areas, missed mowing areas, or even empty areas where grass hasn't grown for some reason. For the target location area, it can be determined whether it contains missed mowing areas. There are several ways to determine whether the target location area contains missed mowing areas. For example, the target location area can be matched with recorded mowed areas, identifying the areas within the target location area that are not already mowed as missed mowing areas. Alternatively, the determination can be based on the detection results of other detection devices, such as image acquisition devices like cameras.

[0050] For example, it is possible to record the target location area corresponding to the idling of the lawn mower motor, and determine whether the grass has been trampled or the lawn has been cut before based on the area of ​​the target location area and the surrounding information of the target location area.

[0051] If there are missed areas of lawn within the target area, the lawnmower can be controlled to re-mow these missed areas to ensure the integrity of the mowing and the aesthetics of the lawn. The timing of re-mowing the missed areas can be specified as needed, or flexibly set based on the location of the missed areas. For example, after the mowing motor switches from idle to non-idle operation, the mower can reverse direction to re-mow the missed areas. Alternatively, while moving to the boundary of the area and reversing the mowing process, the mower can re-mow the missed areas when it reaches their vicinity. Yet another example is that after all mowing work is completed (i.e., all areas except the missed areas have been cut), all missed areas can be re-mowed sequentially. This embodiment does not impose any limitations on this approach.

[0052] During re-mowing, the lawnmower can still perform idling or start-stop checks on the mowing motor to prevent any omissions. The re-mowing route can follow a preset path, but only activate the mowing motor in the target area corresponding to the idling or stopping time periods. Alternatively, it can re-plan the path based on the target area corresponding to all idling or stopping time periods and operate according to the newly planned path.

[0053] The embodiments provided in this application detect idling or start / stop of the mower motor during the mowing process. The mower motor is a motor used to control the rotation of the mowing components of the mower. When idling or stopping of the mower motor is detected, the location area traversed by the mower during the idling or stopping time period is determined. The idling time period is the time during which the mower motor idles, and the stopping time period is the time during which the mower motor stops. The target location area includes the location area traversed by the mower during the idling or stopping time period. If there are missed mowing areas in the target location area, the mower is controlled to re-mow the missed mowing areas in the target location area. This solves the technical problem of poor accuracy in detecting missed mowing in related mower control methods and improves the accuracy of missed mowing detection.

[0054] It should be noted that although some embodiments of this application use idling as an example to illustrate the control process of the lawnmower, the idling-related schemes are also applicable to the stopping-related schemes, unless there is any contradiction. For example, both the method of determining whether there are any missed lawn areas in the target location area and the method of controlling the lawnmower to re-cut the missed lawn areas in the target location area are applicable.

[0055] In one exemplary embodiment, detecting idling of the lawnmower motor includes: real-time monitoring of the current of the lawnmower motor; and determining that the lawnmower motor is idling when the monitored real-time current value is less than or equal to a preset current threshold for a duration greater than or equal to a preset duration threshold.

[0056] In this embodiment, the idling detection of the lawnmower motor can be performed based on its current. Compared to other idling detection methods, the current of the lawnmower motor can more intuitively represent the state of the motor, improving both the accuracy and convenience of idling detection. Correspondingly, the current of the lawnmower motor can be monitored in real time, and based on the monitored real-time current, it can be determined whether the lawnmower motor is idling.

[0057] There are several ways to determine whether a lawnmower motor is idling. For example, if the detected real-time current value is less than or equal to a preset current threshold, it can be determined that the lawnmower motor is idling. That is, if the detected real-time current value is less than or equal to the preset current threshold, it can be directly determined that the lawnmower motor is idling. The preset current threshold can be set based on experience or based on the current values ​​obtained from idling and mowing tests of the same model of lawnmower motor. Its value can be 1.5A, 2A, or other values. In this embodiment, the preset current threshold is not limited.

[0058] Optionally, to avoid situations where the monitored real-time current value is less than or equal to a preset current threshold at certain times due to special circumstances, a duration threshold can be set. That is, a preset duration threshold is set such that the lawnmower motor is determined to be idling only when the duration for which the monitored real-time current value is less than or equal to the preset current threshold is greater than or equal to the preset duration threshold. The preset duration threshold can be set based on experience or by analyzing historical lawnmower data—including current data from missed mowing scenarios and current data from normal mowing scenarios—and its value can be 2 seconds, 5 seconds, or other values. In this embodiment, the preset duration threshold is not limited.

[0059] For example, a lawnmower mows grass along a preset path. When the mowing motor is close to idling, its current value is usually low, and idling can be detected based on the current of the mowing motor. Therefore, the current of the mowing motor can be monitored in real time. When the current is less than a current threshold (i.e., a preset current threshold), or when the duration of the current being less than the preset threshold reaches a time threshold (i.e., a preset duration threshold), it indicates that the mowing motor is idling.

[0060] Taking a lithium-ion battery-powered lawnmower as an example, as shown in Figure 4, the current threshold is 2A and the duration threshold is 2 seconds. The real-time current of the mower motor is monitored. If the monitored real-time current is 1A and the duration is 1 second, the monitored real-time current is below the current threshold but the duration has not reached the duration threshold, indicating that the mower motor is not idling. If the monitored real-time current is 1A and the duration is 3 seconds, the monitored real-time current is below the current threshold but the duration has reached the duration threshold, indicating that the mower motor is idling. If the monitored real-time current is 5A, the monitored real-time current is above the current threshold, indicating that the mower motor is idling.

[0061] This embodiment improves the accuracy and convenience of idling detection by using the current of the mower motor to detect idling. It also improves the flexibility and accuracy of idling detection by determining whether the mower motor is idling based on the duration for which the monitored real-time current value reaches a set current threshold.

[0062] In one exemplary embodiment, real-time monitoring of the current of the lawnmower motor includes: acquiring current data from a current sensor using a data acquisition system to monitor the current of the lawnmower motor in real time.

[0063] There are several ways to monitor the current of a lawnmower motor in real time. In this embodiment, a current sensor combined with a data acquisition system can be used to monitor the current of the lawnmower motor in real time. Here, the current sensor can be located on the bus circuit of the lawnmower motor's drive circuit, and the data acquisition system is electrically connected to the current sensor. Through this connection method, the data acquisition system can collect the current sensed by the current sensor, thereby realizing real-time monitoring of the lawnmower motor's current.

[0064] Here, the current sensor can be a Hall sensor, a shunt, etc., which can monitor the current magnitude in the bus circuit of the lawnmower motor's drive circuit in real time. The bus circuit refers to a circuit connecting two or more transmission and transformation circuits, used to collect, distribute, and transmit electrical energy. The data acquisition system can process, store, and transmit the current data; this system can include modules such as analog-to-digital converters, microprocessors, and memory.

[0065] When the lawnmower motor is working, the current of the lawnmower motor can pass through the bus circuit of its drive circuit. The current sensor installed on the bus circuit of the drive circuit can sense the current magnitude in the bus circuit in real time and convert it into an electrical signal. The data acquisition system can acquire the current data (electrical signal) transmitted by the current sensor through the cable or other communication medium, use an analog-to-digital converter to convert the electrical signal into a digital signal, and can also use a microprocessor to process the digital signal, such as filtering, amplification, calibration, etc., to obtain an accurate current value and realize the acquisition of the current data sensed by the current sensor.

[0066] For example, in situations where long-term detection or recording of current data is required, a current sensor (which can be a sampling resistor) and a data acquisition system can be used to collect and analyze the data. The current sensor can collect the current in real time, and when the collected current is less than 10% of the normal mowing current, the mowing motor is considered to be idling.

[0067] In this embodiment, a current sensor is installed on the bus circuit of the lawnmower motor's drive circuit, and a data acquisition system is used to collect the current sensed by the current sensor. This enables the collection of current data sensed by the current sensor, thereby improving the accuracy of real-time current monitoring.

[0068] In one exemplary embodiment, at least one of an ammeter and a multimeter can be used to monitor the current of the lawnmower motor in real time. For scenarios using an ammeter for current monitoring, the ammeter can be directly connected in series with the power cord of the lawnmower motor to measure its operating current in real time. For scenarios using a multimeter for current monitoring, the multimeter can be directly set to the current range and connected in series with the circuit for measurement.

[0069] Correspondingly, the current of the lawn mower motor is monitored in real time, including at least one of the following: using an ammeter to measure the real-time current of the lawn mower motor in real time; or using a multimeter to measure the real-time current of the lawn mower motor in real time.

[0070] Optionally, an ammeter can be connected in series with the lawnmower motor's power cord (when connected in series, the ammeter's polarity is important; ensure current flows in from the "+" terminal and out from the "-" terminal) to measure the current flowing through the lawnmower motor. When using the ammeter, adjust the range knob according to the lawnmower motor's current range to ensure the ammeter's range is greater than the lawnmower motor's maximum operating current, preventing overload damage to the ammeter. Alternatively, calibration can be performed according to the instruction manual to ensure range accuracy.

[0071] Optionally, a multimeter can be connected in series with the power cord of the lawnmower motor. A multimeter is a multifunctional measuring instrument capable of measuring various electrical quantities such as current, voltage, and resistance. In this embodiment, the multimeter can be connected in series with the power cord of the lawnmower motor (the red probe of the multimeter is connected to the break point connected to the positive terminal, and the black probe is connected to the break point connected to the negative terminal), and the rotary switch of the multimeter can be adjusted to an appropriate current range according to the current range of the lawnmower motor. Alternatively, if the current of the lawnmower motor is uncertain, a larger range can be selected initially, and then adjusted based on the measurement results.

[0072] This embodiment allows for real-time monitoring of the lawnmower motor's current using an ammeter or multimeter connected in series with the motor's power cord, improving both the convenience and timeliness of current monitoring.

[0073] In one exemplary embodiment, determining the target location area corresponding to the idling time period includes: determining the location area covered by the lawnmower from the start time of the idling time period to the end time of the idling time period as the target location area.

[0074] The idling of the lawnmower motor is a continuous process. Therefore, during the duration of this idling, the lawnmower may move a certain distance. Consequently, determining the target location area can also be a continuous process, encompassing the area traversed by the lawnmower during the idling period. For example, as shown in Figure 5, idling of the lawnmower motor is detected at time t0, at which point the lawnmower is at position pose. t0 After t0, it was detected that the mowing motor was continuously idling. At time t1, the mower moved to position pose. t1 (Position t1), at this point, the determined target location area is A1. Afterwards, the lawnmower continues to move and performs idling detection of the mowing motor. At time t2, the lawnmower moves to position pose. t2(Position t2), at this time, the determined target location area is A2.

[0075] In this embodiment, to determine the target location area, the area covered by the lawnmower, or the area covered by the mowing component, from the start to the end of the idling period can be defined as the target location area. If the mowing motor is still idling at the current moment, a portion of the target location area can be determined. If the determination of the target location area is performed in real time, rather than waiting until the mowing motor stops idling, the target location area can be updated in real time.

[0076] In this embodiment, the area covered by the lawnmower during the idling period is determined as the range for judging whether there is any missed mowing (i.e., the target area), which can improve the accuracy of missed mowing judgment.

[0077] In an exemplary embodiment, after determining the target location area corresponding to the idling time period, the method further includes: if the target location area does not cross the mowing path of the lawnmower, determining that there is a missed lawn area in the target location area; if the target location area crosses the mowing path of the lawnmower, detecting the lawn state in the target location area based on the area image of the target location area to determine whether there is a missed lawn area in the target location area.

[0078] In this embodiment, the presence of missed mowing areas within the target area where the motor is idling (i.e., the target location area) can be determined based on whether the target area (i.e., the target location area) crosses the mowing path. Optionally, if the target area does not cross the mowing path, it is considered that the lawn in that area has been flattened and missed, and the missed area is mowed. If the target area crosses the mowing path, the lawn in that area has been flattened or has already been mowed, and other tools (e.g., a camera) can be used to assist in the determination.

[0079] After determining the target location area corresponding to the idling time period, it can be determined whether the target location area crosses the mowing path. If the target location area does not cross the mowing path, it indicates that the lawn in the target location area has been trampled, and it can be determined that there are missed mowing areas in the target location area. If the target location area crosses the mowing path, there may be missed mowing areas. In this case, the lawn condition in the target location area can be detected based on the regional image of the target location area to determine whether there are missed mowing areas in the target location area.

[0080] For example, as shown in Figure 6, when the target area for the motor idling is area A, and area A does not cross the mowing path, it is assumed that the lawn in that area has been flattened, resulting in missed mowing. When the target area for the motor idling is area B, and area B does not cross the mowing path, it means that the lawn in that area has been flattened or has already been mowed. This can be determined using camera assistance to assess whether the grass in the target area has been flattened.

[0081] This embodiment determines whether there are missed mowing areas by whether the target area where the motor is idling crosses the mowing path, and combines area images for auxiliary judgment when it is impossible to determine whether there are missed mowing areas, which can improve the accuracy of missed mowing detection.

[0082] In an exemplary embodiment, detecting the state of the lawn within a target location area based on a region image of the target location area includes: inputting each of a plurality of first region images of the target location area into a pre-trained target classification model to obtain a classification result corresponding to each first region image; and detecting the state of the lawn within the target location area based on the classification result corresponding to each first region image.

[0083] In this embodiment, a target classification model can be used to classify the region image of the target location. This target classification model is a three-class classification model for classifying lawn states. The lawn states corresponding to the target classification model include the following three types: normal, mowed, and trampled. Inputting the region image of the target location into the target classification model yields the classification result corresponding to the input region image. This result can be one of the three states: normal, mowed, or trampled, or it can be the probability of each lawn state corresponding to the input region image. For example, a lightweight three-class classification model can be trained using EfficientNet-B0 based on visual images, with the classification results corresponding to the lawn states (grassland states) as normal, mowed, and trampled.

[0084] The target location area image can include multiple first region images, which are region images of the target location area acquired after the lawnmower has reached the first position. Each first region image is input into a pre-trained target classification model to obtain a classification result corresponding to each first region image; based on the classification results corresponding to each first region image, the lawn condition within the target location area is detected. The multiple first region images can all be acquired at the first position, or some can be acquired at the first position and some at one or more subsequent positions; that is, the multiple first region images can be multiple region images acquired at the same position or multiple region images acquired at different positions.

[0085] Here, multiple first-area images can be obtained by using image acquisition components on the lawnmower to capture images of the target location area. The image acquisition component can be a camera, LiDAR, or other image acquisition device, and it can be positioned at any location on the lawnmower, as long as it can acquire an image of the target location area. The acquisition direction of the image acquisition component can be fixed or rotatable. The target location area can be the area covered by the lawnmower during the idle period. Over time, the target location area may change, for example, gradually increasing. Therefore, the target location area corresponding to different locations may not be the same, and the area images acquired at different locations are all area images of the target location area.

[0086] Based on the classification results corresponding to each first region image, the lawn state in the target location area can be detected in the following ways: for cases where the classification result corresponding to each first region image is one of normal state, mowed state, or trampled state, the first region images corresponding to each lawn state can be statistically analyzed, and the lawn state with the most corresponding first region images can be determined as the lawn state in the target location area. Alternatively, other methods can be used to detect the lawn state in the target location area, as long as the rationality of the determined lawn state can be guaranteed.

[0087] For example, when the motor is idling, a camera is triggered to capture multiple frames of images. A trained model is used to infer the results from these multiple frames, and the inference results are then voted on across multiple frames to obtain the lawn state. t1 Record the positioning information of the motor when it is idling. t1 and lawn state t1 (i.e., lawn condition t1).

[0088] In this embodiment, a pre-trained target classification model is used to classify multiple region images of the target area where the motor is idling, and the lawn condition of the target area where the motor is idling is detected based on the classification results corresponding to the multiple region images, which can improve the accuracy of lawn condition detection.

[0089] In an exemplary embodiment, detecting the lawn state within a target location area based on the classification result corresponding to each first region image includes: determining the lawn state corresponding to a first location based on the classification result corresponding to each first region image; and determining the lawn state corresponding to the first location as the lawn state within the target location area.

[0090] In this embodiment, the lawn state corresponding to the first location can be determined based on the classification result corresponding to each first region image. The method for determining the lawn state corresponding to the first location can be similar to that in the previous embodiments, or other methods can be used. After determining the lawn state corresponding to the first location, it can be directly identified as the lawn state within the target location area. This means that the lawn state detected by the lawnmower at a certain location is considered as the lawn state within the entire target location area.

[0091] This embodiment allows the lawn condition determined at a specific location to be directly used as the lawn condition of the target area for motor idling, thereby improving the convenience of lawn condition detection.

[0092] In an exemplary embodiment, detecting the lawn state within a target location region based on the classification result corresponding to each first region image includes: determining the lawn state corresponding to a first location based on the classification result corresponding to each first region image; determining the lawn state within the target location region as normal if the lawn state corresponding to the first location is normal; inputting each of the multiple second region images of the target location region into a target classification model to obtain a classification result corresponding to each second region image if the lawn state corresponding to the first location is not normal; determining the lawn state corresponding to a second location based on the classification result corresponding to each second region image; and determining the lawn state corresponding to the first location as the lawn state within the target location region if the lawn state corresponding to the first location and the lawn state corresponding to the second location are consistent.

[0093] In this embodiment, to improve the accuracy of lawn condition detection, the lawn condition within the target location area can be detected by combining the lawn conditions determined from multiple locations. These multiple locations may include the aforementioned first location. The method for determining the lawn condition corresponding to each first location based on the classification result corresponding to each first region image can be similar to that in the previous embodiments, or other determination methods can be used; this embodiment does not limit this approach.

[0094] If the lawn condition at the first location is determined to be normal, then based on the correlation between distance and location, the lawn condition within the target location area can be preliminarily determined to be normal. In this case, to improve the efficiency of lawn condition determination, the lawn condition within the target location area can be determined to be normal. If the lawn condition at the first location is determined to be mowed or trampled, then multiple second-location images of the target location area can be acquired and analyzed. Combining the analysis results of multiple second-location images, the lawn condition within the target location area can be determined.

[0095] Here, the multiple second-region images are region images of the target location area acquired after the lawnmower reaches the second position. The method of acquiring multiple second-region images after the second position is similar to that in the previous embodiment and will not be repeated here. The second position is located after the first position on the lawnmower's walking path, and the distance between the second and first positions is less than or equal to a preset distance threshold, to ensure that the multiple second-region images and the multiple first-region images are region images acquired from the same area. The preset distance threshold can be set and adjusted based on experience, for example, 0.5 meters, 1 meter, or other values, and is not limited in this embodiment. Here, the lawnmower's walking path is the actual path the lawnmower travels, while the lawnmower's mowing path is the path planned for the lawnmower; there may be some deviation between the two.

[0096] For the acquired multiple second region images, the analysis of each second region image can be performed as follows: Each second region image is input into a target classification model to obtain a classification result corresponding to each second region image. The classification result corresponding to each second region image can be of the same type as the classification result corresponding to the first region image. For example, if the classification result corresponding to the first region image is one of normal state, mowed state, or trampled state, then the classification result corresponding to the second region image is also one of these three states. Similarly, if the classification result corresponding to the first region image represents the probability that the lawn state corresponding to the first region image is each of these lawn states, then the classification result corresponding to the second region image is also the probability that the lawn state corresponding to the second region image is each of these lawn states. Likewise, the lawn state corresponding to the second location can be determined based on the classification result corresponding to each second region image.

[0097] If the lawn condition corresponding to the first position is the same as the lawn condition corresponding to the second position, then the lawn condition corresponding to the first position can be determined as the lawn condition within the target location area. If both the lawn condition corresponding to the first position and the lawn condition corresponding to the second position are mowed, then the lawn condition within the target location area is mowed. If both the lawn condition corresponding to the first position and the lawn condition corresponding to the second position are trampled, then the lawn condition within the target location area is trampled. Here, if the lawn condition within the target location area is trampled, it can be determined that there are unmowed areas within the target location area. Unmowed areas can be identified from the area image of the target location area, or at least one additional mowing can be performed on the target location area to re-mow the unmowed areas within the target location area.

[0098] For example, as shown in Figure 7, when determining the position of the lawnmower... t1 lawn statet1 After that, if the lawnstate t1 After being cut or crushed, when the pose is reached t1 poses in similar positions t2 ;pose t1 and pose t2 If the difference (i.e., the distance between two positions) is less than the specified threshold pose_difference_threshold (i.e., the preset distance threshold), then multiple frames of images are collected, and the inference results are voted on across multiple frames to obtain the lawn state. t2 If the lawn is in lawn state t2 and lawnstate t1 If the conditions are inconsistent, the output result is undetermined; if the lawn conditions are consistent, the output classification result is one of normal, mowed, or trampled; if the output state is normal or mowed, no re-mowing is required; if the output state is trampled, re-mowing is required.

[0099] This embodiment improves the accuracy and efficiency of lawn condition detection by combining regional images of the target area where the motor is idling, collected from multiple locations, to assess the lawn condition within that area.

[0100] In one exemplary embodiment, the method further includes: determining the lawn state within the target location area as a pending state when the lawn state corresponding to the first location and the lawn state corresponding to the second location are inconsistent; sending a status discrimination request to a terminal device bound to the lawnmower, wherein the status discrimination request is used to request the user of the terminal device to discriminate the lawn state within the target location area; and, upon receiving status indication information returned by the terminal device, determining the lawn state indicated by the status indication information as the lawn state within the target location area.

[0101] After comparing the lawn condition corresponding to the first position with the lawn condition corresponding to the second position, if the two are inconsistent, for example, the lawn condition corresponding to the first position is in a trampled state while the lawn condition corresponding to the second position is in a normal state, the lawn condition in the target location area can be determined to be in an undetermined state.

[0102] If the lawn condition within the target location area is undetermined, regional images of the target location area can be acquired at new locations and analyzed. This analysis combines the lawn condition corresponding to the first location with the lawn condition corresponding to the second location to determine the lawn condition within the target location area. However, considering that the lawn condition within the target location area has already been determined by combining the lawn conditions from two locations, it is unlikely that further analysis at new locations will accurately determine the lawn condition.

[0103] In this embodiment, lawn condition determination can be performed through manual intervention. The lawnmower can send a condition determination request to a terminal device (e.g., a smartphone, tablet, etc.) associated with it, requesting the user (usually the lawnmower operator or maintenance personnel) to determine the lawn condition within a target area. The condition determination request may include an image of the target area or other information indicating its location, facilitating the user's determination of the lawn condition. While sending the condition determination request, the lawnmower can either stop moving to allow the user to identify the target area, or continue mowing along the pre-set path, improving mowing efficiency.

[0104] After receiving a status determination request, the terminal device can prompt the user to determine the lawn status within the target area via voice, image, or other means. The user then manually determines the lawn status by viewing the provided area image or other indications, combined with the on-site situation, and sends the determined lawn status to the lawnmower via the terminal device. The terminal device can provide multiple lawn status options for the user to select, or it can provide an information input box or other convenient methods for the user to input the lawn status. After receiving the status indication information from the terminal device, the lawnmower can confirm the lawn status indicated by the status indication information as the lawn status within the target area.

[0105] In this embodiment, when the lawn status in the target area where the motor is idling cannot be determined by combining the lawn status of multiple locations, manual intervention can be requested by sending a status determination request to the terminal device bound to the lawnmower. This ensures the accuracy and efficiency of lawn status determination.

[0106] In an exemplary embodiment, the detection of the lawn state within the target location area based on the classification result corresponding to each first region image includes: determining the lawn state corresponding to a first position based on the classification result corresponding to each first region image; determining the lawn state corresponding to each of a plurality of reference positions after the first position as the lawn mower continues to move; and determining the lawn state that appears most frequently among the lawn states corresponding to the first position and the lawn states corresponding to each reference position as the lawn state within the target location area.

[0107] In this embodiment, to avoid false alarms, the lawn state within the target location area can be determined by combining regional images of the target location area collected from multiple locations. To this end, the lawn state corresponding to the first location can be determined based on the classification result corresponding to each first regional image. Simultaneously, for multiple reference locations after the first location, the lawn state corresponding to each reference location can be determined separately. Among the first location and multiple reference locations, the distance between two adjacent locations is less than or equal to a preset distance threshold. The method for determining the lawn state corresponding to each reference location is the same as or similar to the method for determining the lawn state corresponding to the first location in the aforementioned embodiment, and has already been explained, so it will not be repeated here.

[0108] For the lawn state corresponding to the first location and the lawn state corresponding to each reference location, the frequency of occurrence of each lawn state can be counted separately. That is, the frequency of occurrence of each lawn state among normal state, mowed state, and trampled state can be counted, and the lawn state with the most occurrences can be determined as the lawn state within the target location area. If there are multiple lawn states with the most occurrences, the user can be requested to make a manual judgment in the same or similar manner as in the previous embodiment. Alternatively, the lawn state corresponding to the new location can be determined again, and the statistics and judgment can be performed again. This embodiment does not limit this.

[0109] This embodiment improves the accuracy of lawn condition detection by combining regional images of the target area where the motor is idling, collected from multiple locations, to assess the lawn condition within that area.

[0110] In one exemplary embodiment, the lawn state corresponding to the first location region can be determined using a multi-frame voting mechanism. The multi-frame voting logic for the lawn state can differ depending on the form of the classification result corresponding to each first region image.

[0111] As an optional implementation, for the case where the classification result corresponding to each first region image is used to indicate the confidence (or probability) of each of the three lawn states corresponding to each first region image, the lawn state corresponding to the first position is determined based on the classification result corresponding to each first region image, including: accumulating the confidence corresponding to each lawn state indicated by the classification result corresponding to each first region image to obtain the sum of the confidence of each lawn state and the first position; and determining the lawn state with the largest sum of confidence corresponding to the first position among the three lawn states as the lawn state corresponding to the first position.

[0112] Confidence level describes the accuracy or reliability of an estimate or prediction; it is a range of values. A higher confidence level indicates higher reliability, while a lower confidence level indicates lower reliability. For multiple first region images, the confidence levels corresponding to each lawn state indicated by the classification result for each first region image can be summed to obtain the sum of confidence levels for each lawn state and its corresponding first location. For three lawn states, three sums of confidence levels corresponding to the first location can be calculated. The higher the sum of confidence levels for a lawn state, the greater the probability that the lawn state within the target location area is that lawn state. Therefore, the lawn state with the largest sum of confidence levels corresponding to the first location can be determined as the lawn state corresponding to the first location.

[0113] For example, given n region images, n model inference results are generated. Grassland has three states: normal, mowed (i.e., already cut), and trampled. A softmax operation is performed on the inference results to output the inference. ij (i represents the i-th image, j represents the j-th state), sum the softmax values ​​of the n j-th states to obtain the inference. j As shown in formula (1):

[0114] Select inference j The state index corresponding to the maximum value is output as shown in formula (2): lawnstate t =argmax(inference) j (2)

[0115] The lawnmower's location status can be provided by a SLAM (Simultaneous Localization and Mapping) program.

[0116] As another optional implementation, for the case where the classification result corresponding to each first region image is used to indicate the lawn state with the highest confidence among the three lawn states, the lawn state corresponding to the first position is determined based on the classification result corresponding to each first region image, including: determining the lawn state with the most corresponding first region images among the three lawn states as the lawn state corresponding to the first position.

[0117] Since each first region image corresponds to a lawn state, and each lawn state may correspond to one or more first region images, or may not have a corresponding first region image, the number of first region images corresponding to each lawn state can be determined separately, and the lawn state with the most corresponding first region images among the three lawn states is determined as the lawn state corresponding to the first position.

[0118] Similarly, the method for determining the lawn state corresponding to the second location based on the classification result corresponding to each second region image, and the method for determining the lawn state corresponding to the third location based on the classification result corresponding to each third region image, is similar to the method provided in this embodiment, and will not be described in detail here.

[0119] This embodiment uses different methods to determine the lawn state within the target area where the motor is idling, based on the type of classification result corresponding to the regional image, which can improve the accuracy and flexibility of lawn state detection.

[0120] In an exemplary embodiment, to reduce the impact of non-lawn areas on the recognition results of the target classification model, the lower half of the region image can be selected as the input to the target classification model. For multiple first region images, before inputting each of the multiple first region images of the target location region into the pre-trained target classification model, the method further includes: acquiring multiple first region images acquired by the image acquisition component on the lawnmower when the lawnmower is in a first position; and cropping the lower half of each first region image according to a specified ratio to obtain an updated first region image.

[0121] When the lawnmower is in its first position, it can use image acquisition components such as cameras to acquire multiple first region images of the target location area. The method of acquiring multiple first region images is similar to that in the previous embodiments and will not be repeated here. Before inputting each first region image into the target classification model, image preprocessing can be performed. Image preprocessing can include image cropping. The image cropping operation is performed by cropping the lower half of each first region image. The cropped lower half of the region image can be used as the updated first region image in subsequent processing, thereby focusing on the state of the grass and reducing the influence of non-critical information on lawn state detection, thus improving the model's processing speed and accuracy.

[0122] In this embodiment, the lower half of each first region image can be cropped according to a specified ratio. The specified ratio can be the ratio of the cropped image height to the total image height, or it can be cropped according to the lawn boundary identified in each first region image. Other cropping methods can also be used, but this embodiment does not limit them.

[0123] In this embodiment, by preprocessing the regional image before inputting it into the classification model and cropping the lower half of the image as the regional image input into the classification model, the system can more effectively focus on the state of the ground grass, thereby improving the detection accuracy and efficiency of the classification model.

[0124] In an exemplary embodiment, before inputting each of the multiple first region images of the target location region into a pre-trained target classification model, the method further includes: acquiring multiple first region images acquired by an image acquisition component on a lawnmower when the lawnmower is in a first position; and performing an image enhancement operation on each first region image to obtain an enhanced first region image.

[0125] In this embodiment, multiple first region images can be acquired using the same or similar methods as in the previous embodiments. Before inputting each first region image into the target classification model, image preprocessing can be performed. Image preprocessing may include image enhancement operations, which may include, but are not limited to, at least one of the following: brightness enhancement, contrast enhancement, or Gaussian blurring. The specific image enhancement operation can be selected based on actual environmental conditions and image quality to reduce the impact of ISP (Image Signal Processing) and motion blur on the classification results. The image enhancement operation performed may be an online image enhancement operation.

[0126] Here, brightness enhancement can increase the brightness of a region image when lighting conditions are poor or the image brightness is insufficient, making the details in the region image clearer and easier for the model to recognize; contrast enhancement can enhance the contrast of a region image when the image contrast is low, improving the model's ability to recognize different grass conditions; Gaussian blur can reduce interference when there is too much noise or fine texture in the region image that affects the model's recognition, helping the model focus on key features.

[0127] Optionally, when performing image enhancement and image cropping operations simultaneously, to improve the accuracy of image cropping (for example, when cropping images to identify lawn boundaries), the image enhancement operation can be performed first, followed by the image cropping operation; conversely, to reduce the complexity of preprocessing, the image cropping operation can be performed first, followed by the image enhancement operation. In this embodiment, the execution order of the image enhancement and image cropping operations is not limited.

[0128] In this embodiment, by performing image enhancement operations on the region image before inputting it into the classification model, the impact of ISP and motion blur on the classification results can be reduced, thereby improving the detection accuracy of the classification model.

[0129] In an exemplary embodiment, in this embodiment, when there are missed mowing areas in the target location area and the lawn in the target location area is in a trampled state (i.e., there are lawns that cannot be cut after being trampled), there can be multiple ways to re-mow the missed lawn areas in the target location area. One re-mowing method can be configured for the lawnmower, or multiple re-mowing methods can be configured for the lawnmower. The lawnmower selects a re-mowing method based on the detected environmental information or the positional relationship between the target location area and the mowing path.

[0130] As an optional implementation, if it is determined that the grass has been trampled, the lawnmower can continue mowing along a path opposite to its original direction of travel, as shown in Figure 8. In area A, the mowing method is to use a reverse cutting path. Here, using a reverse cutting path allows the trampled grass to be pushed up before cutting, ensuring a good cutting effect on the lawn. Correspondingly, if there are missed areas of lawn within the target location area, controlling the lawnmower to re-mow the missed areas within the target location area includes: controlling the lawnmower to re-mow the missed areas within the target location area along a first moving path opposite to the direction of the mowing path corresponding to the target location area.

[0131] As another optional implementation, if it is determined that the grass has been trampled, the lawnmower can continue mowing along a path that is at a certain angle to the original direction. Here, using a path at a certain angle to the original direction allows for cutting closer to the missed areas of lawn, ensuring the cutting effect of the lawn. Correspondingly, if there are missed areas of lawn within the target location area, controlling the lawnmower to re-mow the missed areas within the target location area includes: controlling the lawnmower to re-mow the missed areas of lawn within the target location area along a second moving path with a preset angle between the mowing path and the target location area.

[0132] The second movement path used for re-mowing is closer to the missed areas of lawn within the target location area compared to the mowing path corresponding to the target location area. The preset angle can be a pre-specified fixed angle or configured based on the degree of grass flattening; that is, the preset angle is determined based on the correspondence between the angle of grass flattening and the angle between the paths. The preset angle can be equal to 90 degrees, or greater than 90 degrees but less than 180 degrees; that is, between 90 and 180 degrees. This not only ensures the cutting effect of the lawn but also reduces the angle of rotation required by the lawnmower, improving the ease of mowing control.

[0133] This embodiment allows for the use of at least one of a reverse cutting path and a path with a certain angle to the original direction for re-cutting. It can adapt to different re-cutting situations and effectively re-cut areas of lawn that have been missed and are in a trampled state, ensuring the integrity of the mowing operation.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0135] According to another aspect of the embodiments of this application, a lawnmower is also provided, which can be used to implement the lawnmower control method provided in the above embodiments, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0136] Figure 9 is a structural block diagram of an optional lawnmower according to an embodiment of the present application. As shown in Figure 9, the lawnmower includes: a control component 902, a mowing component 904, and a mowing motor 906, wherein the mowing motor 906 is a motor used to control the rotation of the mowing component 904.

[0137] Control unit 902 is used to detect idling of the mowing motor 906 during the mowing process; if idling of the mowing motor 906 is detected, it determines a target location area corresponding to the idling time period, wherein the idling time period is the time during which the mowing motor 906 idles, and the target location area includes the location area traversed by the mowing machine during the idling time period; if there are missed mowing areas in the target location area, it controls the mowing machine to re-mow the missed mowing areas in the target location area; or, during the mowing process, it detects the start / stop of the mowing motor; if the mowing motor stops, it determines a target location area corresponding to the stop time period, wherein the stop time period is the time during which the mowing motor stops, and the target location area includes the location area traversed by the mowing machine during the stop time period; if there are missed mowing areas in the target location area, it controls the mowing machine to re-mow the missed mowing areas in the target location area. Optionally, control unit 902 can be used to execute steps S202 and S206 in the above embodiments.

[0138] The embodiments provided in this application detect idling or start / stop of the mower motor during the mowing process. The mower motor is a motor used to control the rotation of the mowing components of the mower. When idling or stopping of the mower motor is detected, a target location area corresponding to the idling or stopping time period is determined. The idling time period is the time during which the mower motor idles, and the stopping time period is the time during which the mower motor stops. The target location area includes the area traversed by the mower during the idling or stopping time period. If there are missed mowing areas in the target location area, the mower is controlled to re-mow the missed areas. This solves the technical problem of poor accuracy in detecting missed mowing areas in related mower control methods and improves the accuracy of missed mowing detection.

[0139] In an exemplary embodiment, the control unit 902 is further configured to: monitor the current of the lawn mower motor in real time; and determine that the lawn mower motor is idling if the monitored real-time current value is less than or equal to a preset current threshold for a duration greater than or equal to a preset duration threshold.

[0140] In one exemplary embodiment, the lawnmower also includes a data acquisition system and a current sensor, the current sensor being located on the bus circuit of the lawnmower motor's drive circuit, and the data acquisition system being electrically connected to the current sensor. The control unit 902 is further configured to use the data acquisition system to acquire current data from the current sensor for real-time monitoring of the lawnmower motor's current.

[0141] In one exemplary embodiment, the lawnmower further includes at least one of the following: an ammeter connected in series with the power supply line of the lawnmower motor; or a multimeter connected in series with the power supply line of the lawnmower motor and set to the current range. The control unit 902 is also configured to perform at least one of the following: measuring the real-time current of the lawnmower motor in real time using the ammeter; or measuring the real-time current of the lawnmower motor in real time using the multimeter.

[0142] In one exemplary embodiment, the control component 902 is further configured to determine the area covered by the lawnmower from the start time of the idling period to the end time of the idling period as the target location area.

[0143] In an exemplary embodiment, the control unit 902 is further configured to: after determining the target location area corresponding to the idling time period, if the target location area does not cross the mowing path of the lawnmower, determine that there is a missed lawn area in the target location area; if the target location area crosses the mowing path of the lawnmower, detect the lawn state in the target location area based on the area image of the target location area to determine whether there is a missed lawn area in the target location area.

[0144] In an exemplary embodiment, the control unit 902 is further configured to: input each of the plurality of first region images of the target location region into a pre-trained target classification model to obtain a classification result corresponding to each first region image, wherein the plurality of first region images are region images of the target location region collected after the lawnmower is in the first position, and the target classification model is a three-class classification model for classifying lawn state, and the classification result of the target classification model includes the following three lawn states: normal state, mowed state, and trampled state;

[0145] Based on the classification results corresponding to each first region image, the lawn status within the target location area is detected. The lawn status is normal, indicating that there are uncut lawn areas within the target location area; the lawn status is trampled, indicating that there are uncut lawn areas within the target location area; and the lawn status is cut, indicating that there are no uncut lawn areas within the target location area.

[0146] In an exemplary embodiment, the control unit 902 is further configured to: determine the lawn state corresponding to the first location based on the classification result corresponding to each first region image; and determine the lawn state corresponding to the first location as the lawn state within the target location area.

[0147] In an exemplary embodiment, the control unit 902 is further configured to: determine the lawn state corresponding to a first position based on the classification result corresponding to each first region image; determine the lawn state within the target position area as normal if the lawn state corresponding to the first position is normal; input each of the plurality of second region images of the target position area into a target classification model to obtain a classification result corresponding to each second region image if the lawn state corresponding to the first position is not normal, wherein the plurality of second region images are region images of the target position area collected after the lawnmower is in a second position, the second position is located after the first position on the lawnmower's walking path and the distance between the second position and the first position is less than or equal to a preset distance threshold; determine the lawn state corresponding to the second position based on the classification result corresponding to each second region image; and determine the lawn state corresponding to the first position as the lawn state within the target position area if the lawn state corresponding to the first position and the lawn state corresponding to the second position are consistent.

[0148] In an exemplary embodiment, the control unit 902 is further configured to: determine the lawn state within the target location area as a pending state when the lawn state corresponding to the first position and the lawn state corresponding to the second position are inconsistent; send a status discrimination request to a terminal device bound to the lawnmower, wherein the status discrimination request is used to request the user of the terminal device to discriminate the lawn state within the target location area; and, upon receiving status indication information returned by the terminal device, determine the lawn state indicated by the status indication information as the lawn state within the target location area.

[0149] In an exemplary embodiment, the control unit 902 is further configured to: determine the lawn state corresponding to the first position based on the classification result corresponding to each first region image; determine the lawn state corresponding to each of a plurality of reference positions after the first position as the lawn mower continues to move; and determine the lawn state that appears most frequently among the lawn states corresponding to the first position and the lawn states corresponding to each reference position as the lawn state within the target position area.

[0150] In one exemplary embodiment, the control unit 902 is further configured to perform one of the following: when the classification result corresponding to each first region image is used to indicate the confidence level of each of the three lawn states corresponding to each first region image, the confidence level corresponding to each lawn state indicated by the classification result corresponding to each first region image is accumulated to obtain the sum of confidence levels of each lawn state and the first position; the lawn state with the largest sum of confidence levels corresponding to the first position among the three lawn states is determined as the lawn state corresponding to the first position; or when the classification result corresponding to each first region image is used to indicate the lawn state with the highest confidence among the three lawn states, the lawn state with the largest number of corresponding first region images among the three lawn states is determined as the lawn state corresponding to the first position.

[0151] In an exemplary embodiment, the control unit 902 is further configured to: acquire multiple first region images acquired by the image acquisition unit on the lawnmower when the lawnmower is in a first position before inputting each of the multiple first region images of the target location region into the pre-trained target classification model; and crop the lower half of each first region image according to a specified ratio to obtain an updated first region image, wherein the specified ratio is the proportion of the cropped image height to the total image height.

[0152] In an exemplary embodiment, the control unit 902 is further configured to: acquire multiple first region images acquired by the image acquisition unit on the lawnmower when the lawnmower is in a first position before inputting each of the multiple first region images of the target location region into the pre-trained target classification model; perform an image enhancement operation on each first region image to obtain an enhanced first region image, wherein the image enhancement operation includes at least one of the following: brightness enhancement operation, contrast enhancement operation, or Gaussian blur operation.

[0153] In an exemplary embodiment, the control unit 902 is further configured to: control the lawnmower to re-mow the missed lawn area in the target location area along a first moving path opposite to the direction of the mowing path corresponding to the target location area when there is a missed lawn area in the target location area and the lawn in the target location area is in a trampled state; or, control the lawnmower to re-mow the missed lawn area in the target location area along a second moving path with an angle of a preset angle between the mowing path corresponding to the target location area and the lawn mowing path corresponding to the target location area, wherein the second moving path is closer to the missed lawn area in the target location area than the mowing path corresponding to the target location area.

[0154] In one exemplary embodiment, the control component 902 is further configured to control a preset angle greater than or equal to 90 degrees, or a preset angle greater than 90 degrees and less than 180 degrees.

[0155] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0156] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0157] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0158] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is configured to perform the steps of any of the method embodiments described above via the computer program. In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0159] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0160] According to another aspect of the embodiments of this application, a computer program product is also provided, comprising a computer program / instructions containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit 1001, it performs various functions provided in the embodiments of this application. The sequence numbers of the embodiments of this application above are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0161] Figure 10 schematically illustrates a computer system architecture block diagram for an electronic device implementing embodiments of the present application. As shown in Figure 10, the computer system 1000 includes a CPU (Central Processing Unit) 1001, which can perform various appropriate actions and processes according to a program stored in ROM 1002 or a program loaded from storage portion 1008 into RAM 1003. Various programs and data required for system operation are also stored in the random access memory 1003. The CPU 1001, ROM 1002, and random access memory 1003 are interconnected via a bus 1004. An I / O (Input / Output) interface 1005 is also connected to the bus 1004.

[0162] The following components are connected to I / O interface 1005: input section 1006 including keyboard, mouse, etc.; output section 1007 including CRT (Cathode Ray Tube), LCD (Liquid Crystal Display), etc., and speakers, etc.; storage section 1008 including hard disk, etc.; and communication section 1009 including network interface card, modem, etc. Communication section 1009 performs communication processing via a network such as the Internet. Drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0163] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit 1001, it performs various functions defined in the system of this application.

[0164] It should be noted that the computer system 1000 of the electronic device shown in Figure 10 is only an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0165] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0166] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A lawnmower control method, wherein, The method includes: During the mowing process, the start-stop detection of the mowing motor of the mowing machine is performed. The mowing motor is a motor used to control the rotation of the mowing component of the mowing machine. If the lawnmower motor stops, a target location area corresponding to the stop time period is determined, wherein the stop time period is the time period during which the lawnmower motor stops, and the target location area includes the location area traversed by the lawnmower during the stop time period. If there are missed mowing areas in the target location area, the lawnmower is controlled to re-mow the missed lawn areas in the target location area.

2. A lawnmower control method, wherein, The method includes: During the mowing process, the mowing motor of the mowing machine is tested for idling. The mowing motor is a motor used to control the rotation of the mowing component of the mowing machine. When it is detected that the mowing motor is idling, a target location area corresponding to the idling time period is determined, wherein the idling time period is the time period during which the mowing motor is idling, and the target location area includes the location area traversed by the mowing machine during the idling time period. If there are missed mowing areas in the target location area, the lawnmower is controlled to re-mow the missed lawn areas in the target location area.

3. The method according to claim 2, wherein, The step of performing an idle test on the mowing motor of the lawnmower includes: The current of the lawnmower motor is monitored in real time; If the monitored real-time current value is less than or equal to a preset current threshold for a duration greater than or equal to a preset duration threshold, it is determined that the lawnmower motor is idling.

4. The method according to claim 3, wherein, The real-time monitoring of the current of the lawnmower motor includes: A data acquisition system is used to acquire current data from a current sensor to monitor the current of the lawnmower motor in real time. The current sensor is located on the bus circuit of the drive circuit of the lawnmower motor, and the data acquisition system is electrically connected to the current sensor.

5. The method according to claim 3, wherein, The real-time monitoring of the current of the lawnmower motor includes at least one of the following: The real-time current of the lawnmower motor is measured using an ammeter, wherein the ammeter is connected in series with the power supply line of the lawnmower motor; or The real-time current of the lawnmower motor is measured using a multimeter connected in series with the power line of the lawnmower motor and the multimeter is set to the current range.

6. The method according to claim 2, wherein, The determination of the target location area corresponding to the idling time period includes: The target location area is defined as the area covered by the lawnmower from the start time of the idling period to the end time of the idling period.

7. The method according to claim 2, wherein, After determining the target location area corresponding to the idling time period, the method further includes: If the target location area does not cross the mowing path of the lawnmower, it is determined that there are unmowed areas of lawn within the target location area; When the target location area crosses the mowing path of the lawnmower, the lawn condition within the target location area is detected based on the area image of the target location area to determine whether there are any missed mowing areas within the target location area.

8. The method according to claim 7, wherein, The detection of the lawn condition within the target location area based on the region image of the target location area includes: Each of the multiple first region images of the target location area is input into a pre-trained target classification model to obtain a classification result corresponding to each first region image. The multiple first region images are region images of the target location area collected after the lawnmower is in the first position. The target classification model is a three-class classification model for classifying lawn conditions. The classification result of the target classification model includes the following three lawn conditions: normal condition, mowed condition, and trampled condition. Based on the classification results corresponding to each first region image, the lawn state within the target location area is detected. The lawn state is normal, indicating that there are uncut lawn areas within the target location area; the lawn state is trampled, indicating that there are uncut lawn areas within the target location area; and the lawn state is cut, indicating that there are no uncut lawn areas within the target location area.

9. The method according to claim 8, wherein, The detection of the lawn state within the target location area based on the classification result corresponding to each first region image includes: Based on the classification results corresponding to each first region image, the state of the lawn corresponding to the first location is determined; The lawn condition corresponding to the first location is determined as the lawn condition within the target location area.

10. The method according to claim 8, wherein, The detection of the lawn state within the target location area based on the classification result corresponding to each first region image includes: Based on the classification results corresponding to each first region image, the state of the lawn corresponding to the first location is determined; If the lawn condition corresponding to the first location is normal, the lawn condition within the target location area is determined to be normal. If the lawn condition corresponding to the first position is not normal, each of the multiple second region images of the target position area is input into the target classification model to obtain the classification result corresponding to each second region image. The multiple second region images are region images of the target position area collected after the lawnmower is in the second position. The second position is located after the first position on the lawnmower's walking path and the distance between the second position and the first position is less than or equal to a preset distance threshold. Based on the classification results corresponding to each second region image, the state of the lawn corresponding to the second location is determined; If the lawn condition corresponding to the first position is consistent with the lawn condition corresponding to the second position, the lawn condition corresponding to the first position is determined as the lawn condition within the target location area.

11. The method according to claim 10, wherein, The method further includes: If the lawn condition corresponding to the first position is inconsistent with the lawn condition corresponding to the second position, the lawn condition within the target location area is determined to be a pending state. A status determination request is sent to a terminal device bound to the lawnmower, wherein the status determination request is used to request the user of the terminal device to determine the status of the lawn in the target location area; Upon receiving status indication information returned by the terminal device, the lawn status indicated by the status indication information is determined as the lawn status within the target location area.

12. The method according to claim 8, wherein, The detection of the lawn state within the target location area based on the classification result corresponding to each first region image includes: Based on the classification results corresponding to each first region image, the state of the lawn corresponding to the first location is determined; As the lawnmower continues to move, the state of the lawn is determined for each of a plurality of reference positions following the first position. The lawn state that appears most frequently among the lawn states corresponding to the first location and the lawn states corresponding to each reference location is determined as the lawn state within the target location area.

13. The method according to any one of claims 9 to 12, wherein, Determining the lawn state corresponding to the first location based on the classification result corresponding to each first region image includes one of the following: When the classification result corresponding to each first region image is used to indicate the confidence level of each lawn state among the three lawn states, the confidence levels corresponding to each lawn state indicated by the classification result corresponding to each first region image are accumulated to obtain the sum of the confidence levels of each lawn state and the first position; the lawn state with the largest sum of confidence levels corresponding to the first position among the three lawn states is determined as the lawn state corresponding to the first position. or When the classification result corresponding to each first region image is used to indicate the lawn state with the highest confidence among the three lawn states, the lawn state with the most corresponding first region images among the three lawn states is determined as the lawn state corresponding to the first position.

14. The method according to claim 8, wherein, Before inputting each of the multiple first region images of the target location region into the pre-trained target classification model, the method further includes: The image acquisition unit on the lawnmower acquires images of the plurality of first regions when the lawnmower is in the first position; Each first region image is cropped according to a specified ratio to obtain an updated first region image, wherein the specified ratio is the proportion of the cropped image height to the total image height.

15. The method according to claim 8, wherein, Before inputting each of the multiple first region images of the target location region into the pre-trained target classification model, the method further includes: The image acquisition unit on the lawnmower acquires images of the plurality of first regions when the lawnmower is in the first position; An image enhancement operation is performed on each of the first region images to obtain an enhanced image of each of the first region images, wherein the image enhancement operation includes at least one of the following: brightness enhancement operation, contrast enhancement operation, or Gaussian blur operation.

16. The method according to claim 1 or 2, wherein, In the event that there are missed mowing areas within the target location area, controlling the lawnmower to re-mow the missed areas within the target location area includes: If there are missed mowing areas within the target location area, and the lawn within the target location area is in a trampled state, the lawnmower is controlled to re-mow the missed lawn areas within the target location area along a first moving path opposite to the mowing path direction corresponding to the target location area; or... The lawnmower is controlled to re-mow missed lawn areas within the target location area along a second moving path with an angle of a preset angle between the mowing path and the target location area. The second moving path is closer to the missed lawn areas within the target location area than the mowing path corresponding to the target location area.

17. The method according to claim 16, wherein, The preset angle is equal to 90 degrees, or the preset angle is greater than 90 degrees and less than 180 degrees.

18. A lawnmower, wherein, The lawnmower includes a control unit, a mowing unit, and a mowing motor, wherein the mowing motor is a motor used to control the rotation of the mowing unit; wherein, The control component is used to detect idling of the mowing motor during the mowing process; when idling of the mowing motor is detected, it determines a target location area corresponding to the idling time period, wherein the idling time period is the time period during which the mowing motor idles, and the target location area includes the location area traversed by the mowing machine during the idling time period; if there are missed mowing areas in the target location area, it controls the mowing machine to re-mow the missed mowing areas in the target location area.

19. A lawnmower, wherein, The lawnmower includes a control unit, a mowing unit, and a mowing motor, wherein the mowing motor is a motor used to control the rotation of the mowing unit; wherein, The control component is used to detect the start and stop of the mowing motor during the mowing process; when the mowing motor stops, it determines a target location area corresponding to the stop time period, wherein the stop time period is the time period during which the mowing motor stops, and the target location area includes the location area traversed by the mowing machine during the stop time period; if there are missed mowing areas in the target location area, it controls the mowing machine to re-mow the missed mowing areas in the target location area.

20. A computer program product comprising a computer program / instructions, wherein, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 17.

21. A computer-readable storage medium, wherein, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 17.

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