Control method of a self-moving device, device, and storage medium

Abstract

The application belongs to the technical field of automatic control, and particularly relates to a control method and device of a self-moving device and a storage medium, and comprises the following steps: displaying first area information of a working area currently located by the self-moving device through a wearable device; acquiring an interaction signal collected by the wearable device after the first area information is displayed; determining second area information of a working area to be worked from the first area information based on the interaction signal; and controlling the self-moving device to work on the working area to be worked based on the second area information. The method can solve the problems of complex operation process and low control efficiency of the self-moving device controlled in the form of a key, and can improve the control efficiency of the self-moving device.

Description

Technical Field

[0001] This application belongs to the field of automatic control technology, specifically relating to control methods, devices, and storage media for self-moving equipment. Background Technology

[0002] To reduce people's workload, appliance manufacturers have developed various types of self-moving equipment, such as robotic vacuum cleaners and delivery robots, to assist or replace people in completing their work.

[0003] Traditional methods for controlling self-moving devices include controlling the operation of the self-moving device by using virtual buttons on the mobile phone software or physical buttons on the self-moving device itself.

[0004] However, controlling a self-moving device via buttons is a complex process and has low control efficiency. Summary of the Invention

[0005] This application provides a control method, device, and storage medium for self-moving devices, which can solve the problems of complex operation and low control efficiency associated with controlling self-moving devices via buttons. This application provides the following technical solution.

[0006] In a first aspect, a control method for a self-moving device is provided, comprising: displaying first area information of the current working area of ​​the self-moving device through a wearable device; acquiring an interaction signal collected by the wearable device after displaying the first area information; determining second area information of a working area from the first area information based on the interaction signal; and controlling the self-moving device to work on the working area based on the second area information.

[0007] Optionally, the first area information is an area image or area map of the work area, and the step of determining the second area information of the work area from the first area information based on the interaction signal includes: when the interaction signal is a preset interaction signal acting on the first area information, determining the area acting on the interaction signal in the first area information as the second area information.

[0008] Optionally, when the first area information is an area image, the preset interaction signal includes a box selection gesture, which is used to select the area to be worked from the area image; when the first area information is an area map, the preset interaction signal includes a point selection gesture, which is used to select the area to be worked from each map area included in the area map.

[0009] Optionally, before acquiring the interaction signal collected by the wearable device after displaying the first area information, the method further includes: determining a recommended working area in the first area information; and displaying the recommended working area in the first area information by the wearable device using a preset display method, wherein the preset display method is different from the display method of other areas in the first area information besides the recommended working area.

[0010] Optionally, the first area information is a region image of the work area, and determining the recommended work area in the first area information includes: determining the degree of dirtiness of each region location in the work area based on the region image; and determining the region location with a degree of dirtiness greater than a degree threshold as the recommended work area.

[0011] Optionally, the wearable device has a positioning function, the first area information is a regional map of the work area, and determining the recommended work area in the first area information includes: obtaining the positioning location of the wearable device in the regional map determined by the positioning function; and determining the area where the positioning location is located as the recommended work area.

[0012] Optionally, when there are at least two work areas, controlling the self-moving device to work on the work areas based on the second area information includes: determining the work order of at least two work areas; and controlling the self-moving device to work on each work area according to the work order.

[0013] Optionally, determining the working order of at least two work areas includes: determining the working order based on the order in which the interaction signal acts on each work area; or, determining the working order based on the degree of dirtiness of each work area.

[0014] Optionally, after controlling the self-mobile device to work on the area to be worked based on the second area information, the method further includes: acquiring the control signal collected by the wearable device; adjusting the working state of the self-mobile device based on the control signal, wherein different working states correspond to different control signals.

[0015] Optionally, adjusting the working state of the self-moving device based on the control signal includes: controlling the self-moving device to stop working based on the control signal; and / or controlling the self-moving device to move to the next working area based on the control signal; and / or adjusting the working mode of the self-moving device based on the control signal; and / or controlling the self-moving device to perform self-cleaning based on the control signal; and / or controlling the self-moving device to charge based on the control signal.

[0016] In a second aspect, an electronic device is provided, including a processor and a memory; the memory stores a program that is loaded and executed by the processor to implement the control method for the self-moving device as described in the first aspect.

[0017] Thirdly, a computer-readable storage medium is provided, wherein a program is stored therein, which, when executed by a processor, is used to implement the control method for the self-moving device as described in the first aspect.

[0018] The beneficial effects of this application are as follows: A first area information of the current working area of ​​the self-moving device is displayed via a wearable device; interactive signals are acquired by the wearable device after displaying the first area information; second area information of the working area is determined from the first area information based on the interactive signals; and the self-moving device is controlled to work on the working area based on the second area information. This solves the problems of complex operation and low control efficiency associated with controlling a self-moving device via buttons. By determining the second area information of the working area through interactive signals acting on the first area information acquired by the wearable device, and then controlling the self-moving device to work on the working area based on the second area information, the user is no longer required to control the self-moving device via buttons, simplifying the operation process and improving the control efficiency of the self-moving device.

[0019] In addition, when the wearable device displays the first area information, identifying the area where the wearable device is located as the recommended work area and marking it in the displayed first area information can help users quickly determine their current location in the first area information, thereby improving the speed at which users can determine the work area.

[0020] In addition, when the wearable device displays the first area information, the area with a degree of dirtiness greater than the degree threshold is identified as the recommended working area and marked in the displayed first area information. This makes it easier for users to understand the degree of dirtiness in the first area information and improves the speed of identifying the working area.

[0021] In addition, the interaction signals include gesture interaction signals and voice interaction signals, which allows users to choose the interaction method according to their usage habits, thereby improving the user experience.

[0022] In addition, when there are at least two areas to be worked on, determining the working order of the at least two areas in descending order of dirtiness can improve the working efficiency of the self-moving device.

[0023] In addition, when there are at least two areas to be worked, determining the working order of the at least two areas to be worked according to the order in which the interaction signals act on the information of the first area can improve the speed at which the user determines the working order, thereby improving the user experience.

[0024] In addition, during the operation of the self-moving device, the user's control signals can be collected through the wearable device to adjust the working state of the self-moving device, eliminating the need to control the working state of the self-moving device by buttons, simplifying the process of adjusting the working state of the self-moving device, and improving the control efficiency of the self-moving device. Attached Figure Description

[0025] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the structure of a self-moving device control system provided in one embodiment of this application;

[0027] Figure 2 This is a schematic diagram of the structure of a wearable device provided in one embodiment of this application;

[0028] Figure 3 This is a flowchart of a control method for a self-moving device provided in one embodiment of this application;

[0029] Figure 4 This is a schematic diagram of a selection gesture provided in one embodiment of this application;

[0030] Figure 5 This is a schematic diagram of a tap gesture provided in one embodiment of this application;

[0031] Figure 6 This is a block diagram of a self-moving device control device provided in one embodiment of this application;

[0032] Figure 7 This is a block diagram of an electronic device provided in one embodiment of this application. Detailed Implementation

[0033] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. The application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0035] In this application, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used in relation to the direction shown in the accompanying drawings, or in relation to the vertical, perpendicular, or gravitational direction of the component itself; similarly, for ease of understanding and description, "inner" and "outer" refer to the inner and outer contours of each component itself, but the above directional terms are not used to limit this application.

[0036] This application provides a schematic diagram of the structure of a self-moving device control system according to one embodiment. Figure 1 It is known that the system includes at least: a wearable device 110 and a self-moving device 120.

[0037] Wearable device 110 refers to a wearable electronic device that can control self-moving device 120. A communication connection is established between wearable device 110 and self-moving device 120. This communication connection can be established based on a wireless network, a mobile network, or an infrared signal. This embodiment does not limit the type of communication connection between wearable device 110 and self-moving device 120.

[0038] The self-moving device 120 can be a self-moving device such as a floor cleaning robot or a sweeping robot. This embodiment does not limit the type of the self-moving device 120.

[0039] Optionally, the wearable device 110 can be a wrist or leg-supported electronic device, or a head-supported electronic device. This embodiment does not limit the implementation of the wearable device 110.

[0040] For example, taking head-mounted electronic devices as an example, wearable device 110 can be electronic devices such as smart glasses or VR helmets; or, taking wrist-worn electronic devices as an example, wearable device 110 can be a smartwatch that is strapped to the wrist.

[0041] In this embodiment, the wearable device 110 is used to: display first area information of the current working area of ​​the self-mobile device 120; acquire interaction signals collected by the wearable device 110 after displaying the first area information; determine second area information of the working area from the first area information based on the interaction signals; and control the self-mobile device 120 to work in the working area based on the second area information.

[0042] Accordingly, the wearable device 110 has an interactive display screen to display information about the first area.

[0043] Indicatively, for reference Figure 2 The wearable device 110 includes a controller 210 and a display screen 220 connected to the controller 210.

[0044] The controller 210 is used to control the display screen 220 to display the first area information of the working area where the self-moving device 120 is currently located.

[0045] Optionally, the first area information includes an image of the area where the self-moving device 120 is currently located.

[0046] In one example, the region image is an image captured by the wearable device 110. Accordingly, the wearable device 110 also includes an image acquisition component, which can be a camera equipped with a color system (Red Green Blue, RGB) detection function, or a webcam, etc. This embodiment does not limit the implementation of the image acquisition component.

[0047] For example, taking wearable device 110 as smart glasses, the image acquisition component can be a camera suspended in front of the lenses of the smart glasses, or it can be a camera set in the frame of the smart glasses.

[0048] In this embodiment, the number of image acquisition components can be one or at least two. When the number of image acquisition components is at least two, the different image acquisition components may be of the same or different types. This embodiment does not limit the number and implementation method of image acquisition components.

[0049] In another example, the area image may be sent by another device that has established a communication connection with the wearable device 110.

[0050] For example, taking another device as the self-moving device 120, the image acquisition component is installed on the self-moving device 120. The self-moving device 120 uses the image acquisition component to acquire area images and sends the area images to the wearable device 110 through the established communication connection.

[0051] In actual implementation, other devices can also be mobile phones, tablets, or cameras with image capture capabilities. This embodiment does not need to limit the type of other devices.

[0052] Optionally, the first area information includes a map of the area where the self-moving device 120 is currently located.

[0053] The area map can be pre-stored in the wearable device 110 or it can be pre-built by the mobile device 120 moving around in the work area. This embodiment does not limit the implementation method of the area map.

[0054] In addition, when the first area information is an area map, the area map also includes the real-time location of the wearable device 110 in the area map when the wearable device 110 displays the area map.

[0055] Accordingly, the wearable device 110 also includes a positioning component. This positioning component can be an optical positioning component using infrared and laser, an acoustic positioning component using ultrasound and sonar, or a visual positioning component using image processing, etc. This embodiment does not limit the implementation method of the positioning component.

[0056] In actual implementation, the first region information also includes point cloud data. Accordingly, the wearable device 110 includes a lidar sensor or an ultrasonic sensor, etc.

[0057] In other embodiments, the first area information may also include other types of data. Accordingly, the wearable device 110 also includes components for collecting such other types of data, which will not be listed here in this embodiment.

[0058] In this embodiment, after the wearable device 110 displays the first area information, the controller 210 is also used to acquire the interaction signal collected by the wearable device 110, and determine the second area information of the working area from the mobile device 120 based on the interaction signal in the first area information.

[0059] Interaction signals refer to user interactions with the wearable device 110, such as voice interaction signals and gesture interaction signals.

[0060] In this embodiment, the wearable device 110 collects interactive signals through a signal acquisition component.

[0061] Indicatively, for reference Figure 2 The wearable device 110 also includes a signal acquisition component 230. The signal acquisition component 230 is used to acquire interactive signals, and different signal acquisition components 230 acquire different interactive signals.

[0062] In one example, the interaction signal collected by the wearable device 110 is a gesture interaction signal. Accordingly, the signal acquisition component 230 is a gesture acquisition component. This gesture acquisition component includes a gesture sensor, a camera, etc., and this embodiment does not limit the implementation method of the gesture acquisition component.

[0063] The camera is used to collect gesture interaction signals, and the gesture sensor is used to recognize the gesture interaction signals collected by the camera.

[0064] In another example, the interaction signal collected by wearable device 110 is a voice interaction signal.

[0065] Accordingly, the signal acquisition component 230 is a voice acquisition component. This voice acquisition component may include a voice chip and a microphone, etc., and this embodiment does not limit the implementation method of the voice acquisition component.

[0066] The microphone is used to collect voice interaction signals, and the voice chip is used to recognize the voice interaction signals, convert them into text, and determine the working area indicated by the text.

[0067] In addition, the wearable device 110 is also used to collect control signals, which are used to adjust the working state of the self-moving device 120. These control signals include gesture control signals and voice control signals.

[0068] Accordingly, the self-moving device 120 is also used to receive control signals sent by the wearable device 110 and adjust its working state according to the control signals.

[0069] In one example, the self-moving device 120 is in an active state, and stops working after receiving a control signal from the wearable device 110.

[0070] In another example, the self-mobile device 120 is in an operational state. After receiving a control signal from the wearable device 110, the self-mobile device 120 moves to the next work area to work.

[0071] In another example, after receiving a control signal from the wearable device 110, the self-moving device 120 moves to the base station to perform self-cleaning.

[0072] In another example, after receiving a control signal from the wearable device 110, the self-moving device 120 moves to the base station to charge.

[0073] In summary, traditional button-based control of self-moving devices requires locating the button control device or bringing the device close to it to trigger button operations, a complex process that leads to low control efficiency. In this embodiment, however, the self-moving device is controlled by collecting user interaction and control signals through a wearable device. This eliminates the need for a button control device or bringing the device close to it, thus improving control efficiency.

[0074] The control method for the self-moving device provided in this application will be described in detail below.

[0075] This embodiment provides a control method for a self-moving device, such as... Figure 3 As shown, this control method includes at least the following steps:

[0076] Step 301: Display the first area information of the current working area of ​​the self-moving device through the wearable device.

[0077] In this embodiment, the first area information can be an area image or area map of the working area where the self-moving device is currently located.

[0078] Based on this, the wearable device displays information about the first area where the mobile device is currently located, including but not limited to at least one of the following:

[0079] The first method involves the first area information being an image of the work area. Accordingly, displaying the first area information of the work area currently occupied by the wearable device includes: acquiring an image of the work area using an image acquisition component located in the wearable device; and displaying the acquired image using the wearable device.

[0080] In practice, the area image can also be acquired by other devices and sent to the wearable device. This embodiment does not limit the method of acquiring the area image.

[0081] The second type involves the first area information being a map of the work area. Accordingly, displaying the first area information of the work area currently occupied by the wearable device includes: receiving the area map sent by the mobile device; and displaying the received area map via the wearable device.

[0082] In practice, the area map can be sent to the wearable device by other devices, or the area map can be pre-stored in the wearable device. This embodiment does not limit the method of obtaining the area image.

[0083] Step 302: Acquire the interaction signals collected by the wearable device after displaying the information in the first area.

[0084] Interaction signals refer to signals used by the user to interact with the information in the first area. Users of wearable devices can select from the information displayed on the first area of ​​the wearable device using these interaction signals to determine the area to be used from the mobile device.

[0085] Optionally, the interaction signals include gesture interaction signals and / or voice interaction information.

[0086] In addition, to facilitate user selection within the first area information, a recommended working area needs to be identified within that first area information before the wearable device displays it. Marking the recommended working area in the first area information during user selection speed improves the user's selection process and thus enhances the user experience.

[0087] Specifically, before acquiring the interaction signal collected by the wearable device after displaying the first area information, the method further includes: determining the recommended working area in the first area information; and displaying the recommended working area in the first area information by the wearable device using a preset display method.

[0088] The preset display method differs from the display method of other areas in the first area information, excluding the recommended work area; the display methods of each area in other areas can be the same or different.

[0089] In one example, the display mode can be color display. In this case, the display color corresponding to the preset display mode is different from the display color corresponding to the display mode of other areas; the display colors of each area in other areas can be the same or different.

[0090] For example, taking a regional map with the first area information as the work area as an example, the regional map includes a recommended work area and two other areas. When the wearable device displays the first area information, the recommended work area can be displayed in red, and the two other areas can be displayed in green; or, the recommended work area can be displayed in red, and the two other areas can be displayed in green and yellow respectively.

[0091] In another example, the display method can be a selection box display. In this case, the default display method is a selection box of a preset shape, and the shapes of the selection boxes in other areas are different from the preset shape.

[0092] For example, taking the first area information as the working area as an example, the area image includes a recommended working area and two other areas. When the wearable device displays the first area information, the selection box for the recommended working area can be square, and the selection boxes for the two other areas can be triangles or circles, or the two other areas may not have selection boxes displayed.

[0093] In this embodiment, different first region information is used to determine the recommended working region in different ways, specifically including but not limited to at least one of the following methods:

[0094] The first method involves determining the recommended work area based on the degree of dirtiness. In this case, the first area information is the area image of the work area.

[0095] Specifically, determining the recommended working area in the first area information includes at least steps S11 to S12:

[0096] Step S11: Determine the degree of dirtiness in each area of ​​the working area based on the area image.

[0097] The process of determining the degree of dirtiness in each area of ​​the work area based on the regional image includes: identifying the regional image to obtain the degree of dirtiness in each area of ​​the work area.

[0098] In one example, identifying the degree of dirtiness in a work area by analyzing a region image includes: inputting the region image into a pre-trained dirt identification model to obtain the degree of dirtiness at each region location. The dirt identification model is trained using training data on a pre-defined neural network model.

[0099] Optionally, the training data includes sample region images and the degree of dirtiness at each region location in the corresponding sample region images.

[0100] Accordingly, the training process of the dirt recognition model includes: inputting the sample region image into a preset neural network model to obtain the training result; inputting the degree of dirt at each region location in the training result and the corresponding sample region image into a loss function to obtain the loss result; training the neural network model based on the loss result to reduce the difference between the training result and the degree of dirt at each region location in the corresponding sample region image, until the neural network model converges to obtain the dirt recognition model.

[0101] The neural network model can be a convolutional neural network (CNN), a recursive neural network (RNN), or a feedforward neural network (FNN). This embodiment does not limit the implementation method of the neural network model.

[0102] In another example, the process of identifying the degree of dirtiness in each area of ​​the work area by performing target identification on the area image includes: identifying target objects in the area image; obtaining the matching relationship between each target object and a preset area image; searching for the preset area image corresponding to the identified target object in the matching relationship; comparing the area image with the preset area image to determine the dirtiness information of each area in the work area; and determining the degree of dirtiness based on the preset relationship between the dirtiness information and the preset degree of dirtiness.

[0103] The target object can be an obstacle in the region image that indicates the work area. For example, if the work area is a bedroom, the target object can be a bed; if the work area is a living room, the target object can be a sofa. This embodiment does not limit the implementation method of the target object.

[0104] The preset relationship between dirt information and preset dirt level is a correspondence between pre-set dirt information and preset dirt level. Different dirt information corresponds to different preset dirt levels.

[0105] Step S22: Determine the location of the area where the degree of dirtiness is greater than the degree threshold as the recommended working area.

[0106] The degree threshold is a pre-set level of dirtiness.

[0107] In this embodiment, the degree of dirtiness can be numbers, letters, or a combination of numbers and letters. This embodiment does not limit the way the degree of dirtiness is implemented.

[0108] For example, using numbers 1 to 10 to represent the degree of dirtiness, the degree of dirtiness is positively correlated with the size of the number; if the degree threshold is 5, then the area in the region image with a degree of dirtiness greater than 5 is determined as the recommended working area; or, the degree of dirtiness is negatively correlated with the size of the number; if the degree threshold is 5, then the area in the region image with a degree of dirtiness less than 5 is determined as the recommended working area.

[0109] The second method involves determining the recommended work area based on the location of the wearable device. In this case, the first area information is a map of the work area.

[0110] Specifically, determining the recommended working area in the first area information includes at least steps S21 to S22:

[0111] Step S21: Obtain the location of the wearable device in the regional map determined by the positioning function.

[0112] In one example, the wearable device includes a positioning component with positioning capabilities, which outputs the wearable device's location on a regional map.

[0113] Step S22: Determine the area where the location is located as the recommended working area.

[0114] For example, if the location is a bedroom, and the location is determined to be a bedroom by the location function of the wearable device, then the bedroom will be designated as the recommended work area.

[0115] Step 303: Determine the second region information of the region to be worked from the first region information based on the interaction signal.

[0116] Specifically, determining the second region information of the working region from the first region information based on the interaction signal includes: when the interaction signal is a preset interaction signal acting on the first region information, determining the region acting on the interaction signal in the first region information as the second region information.

[0117] In this embodiment, the preset interaction signals include preset gesture interaction signals and preset voice interaction signals. Accordingly, the area affected by the interaction signals in the first area information is determined as the second area information, including but not limited to at least one of the following:

[0118] The first type is a gesture interaction signal, and the gesture interaction signals corresponding to different types of first area information are different.

[0119] Optionally, when the first region information is a region image, the preset interaction signal includes a bounding box gesture. The bounding box gesture is used to select the region to be worked on from the region image.

[0120] For example: Reference Figure 4 Taking wearable devices like smart glasses as an example, images are displayed in the lens area of ​​the smart glasses. Figure 4 The bounding gesture shown is used to select a region in the region image, resulting in a bounding area. This bounding area is then designated as the working area of ​​the self-moving device, and this bounding area is the second region information of the working area.

[0121] Optionally, if the first area information is a regional map, the preset interaction signal includes a selection gesture. The selection gesture is used to select the area to be worked on from the various map areas included in the regional map.

[0122] For example: Reference Figure 5 Taking wearable devices like smart glasses as an example, the smart glasses display a map of the area through the lenses. Figure 5 The gesture shown is used to select a region on the area map, resulting in a selected region. This selected region is then designated as the working area of ​​the self-moving device 120, and this selected region is the second area information of the working area.

[0123] The second type uses voice interaction signals. In this case, the preset interaction signals include preset keywords.

[0124] Accordingly, determining the second region information of the working region from the first region information based on the interaction signal includes: recognizing the voice interaction signal to obtain the interaction text corresponding to the voice interaction signal; matching the interaction text with preset keywords to determine the key interaction text in the interaction text, wherein the key interaction text is used to indicate the second region information of the working region.

[0125] For example, taking the area map with the first area information as the work area as an example, the area map is marked with the area names of each map area, such as bedroom or kitchen; after obtaining the voice interaction signal, the voice interaction signal is recognized to obtain the corresponding interaction text, such as "go clean the bedroom", and the text is matched with keywords. When the key interaction text "bedroom" is recognized in the text, the bedroom is determined as the work area.

[0126] Optionally, the voice interaction information is recognized, including: inputting the voice interaction information into a pre-trained voice recognition model to obtain the interactive text corresponding to the voice interaction information.

[0127] The speech recognition model is obtained by training a pre-defined neural network model using training data.

[0128] Optionally, the training data includes sample voice interaction information and the corresponding sample interaction text.

[0129] Accordingly, the training process of the speech recognition model includes: inputting sample speech information into a preset neural network model to obtain training results; inputting the training results and the sample interaction text corresponding to the sample speech interaction information into a loss function to obtain loss results; training the neural network model based on the loss results to reduce the difference between the training results and the corresponding sample interaction text until the neural network model converges to obtain the speech recognition model.

[0130] The neural network model can be a convolutional neural network (CNN), a recursive neural network (RNN), or a feedforward neural network (FNN). This embodiment does not limit the implementation method of the neural network model.

[0131] Step 304: Control the self-moving device to perform work in the work area based on the second area information.

[0132] Since there may be at least two second-area information in the first-area information when determining the second-area information of the work area from the first-area information, that is, there are at least two work areas in the work area, it is also necessary to control the self-moving device to work on the work areas in the work order.

[0133] Specifically, when there are at least two work areas, controlling the self-moving device to work on the work areas based on the second area information includes at least steps S31 to S32:

[0134] Step S31: Determine the work sequence for at least two work areas.

[0135] Specifically, the working order of at least two work areas is determined, including: determining the working order based on the order in which interactive signals are applied to each work area; or, determining the working order based on the degree of dirtiness of each work area.

[0136] Optionally, the working order is determined based on the order in which the interaction signals act on each working area, including: determining the order in which the interaction signals act on each working area as the working order.

[0137] For example, taking the first working area and the second working area as examples, the interaction signal first acts on the first working area and then acts on the second working area. Therefore, the working order of the self-moving device is the first working area and the second working area.

[0138] In practice, the working order can also be determined by the reverse order of the interaction signals applied to each working area.

[0139] For example, taking the first working area and the second working area as examples, the interaction signal first acts on the first working area and then acts on the second working area. Therefore, the working order of the self-moving device is the second working area and then the first working area.

[0140] Optionally, the work order is determined based on the degree of dirtiness of each work area, including: sorting the work areas from smallest to largest according to their degree of dirtiness to obtain the work order.

[0141] For example, taking the first, second, and third work areas as an example, where the dirtiness of the first work area is greater than that of the second work area, and the dirtiness of the second work area is greater than that of the third work area, then the working order of the self-moving device is the third work area, the second work area, and the first work area.

[0142] Step S32: Control the self-moving device to work on each work area according to the work sequence.

[0143] In addition, during the operation of the self-moving device, in order to facilitate user control of the self-moving device, the working status of the self-moving device can also be controlled through wearable devices.

[0144] The working status of the self-moving device includes its current working area and current working mode.

[0145] Specifically, after the self-moving device is controlled to work in the work area based on the second area information, the process also includes: acquiring control signals collected by the wearable device; adjusting the working state of the self-moving device based on the control signals, with different control signals corresponding to different working states.

[0146] The control signals include gesture control signals and / or voice control signals collected by the wearable device; since the way the wearable device collects control signals is the same as the way the wearable device collects interaction signals, it will not be described in detail here.

[0147] Optionally, adjusting the working state of the self-moving device based on the control signal includes: controlling the self-moving device to stop working based on the control signal; and / or controlling the self-moving device to move to the next working area based on the control signal; and / or adjusting the working mode of the self-moving device based on the control signal; and / or controlling the self-moving device to perform self-cleaning based on the control signal; and / or controlling the self-moving device to charge based on the control signal.

[0148] For example, taking gesture control signals as an example, when the control signal collected by the wearable device is a fist gesture, the device is controlled to stop working; when the control signal collected by the wearable device is a back of hand gesture, the device is controlled to move to the next working area; when the control signal collected by the wearable device is an index finger gesture, the current working mode of the device is adjusted; and when the control signal collected by the wearable device is a thumb gesture, the device is controlled to charge.

[0149] In actual implementation, the correspondence between control signals and working states can be adjusted according to actual needs. This embodiment does not limit the implementation method of control signals.

[0150] In summary, the self-moving device control method provided in this embodiment displays first area information of the current working area of ​​the self-moving device through a wearable device; acquires interaction signals collected by the wearable device after displaying the first area information; determines second area information of the working area from the first area information based on the interaction signals; and controls the self-moving device to work on the working area based on the second area information. This solves the problems of complex operation and low control efficiency associated with controlling a self-moving device via buttons. By determining the second area information of the working area through interaction signals collected by the wearable device acting on the first area information, and then controlling the self-moving device to work on the working area based on the second area information, the method eliminates the need for users to control the self-moving device via buttons, simplifying the operation process and improving the control efficiency of the self-moving device.

[0151] In addition, when the wearable device displays the first area information, identifying the area where the wearable device is located as the recommended work area and marking it in the displayed first area information can help users quickly determine their current location in the first area information, thereby improving the speed at which users can determine the work area.

[0152] In addition, when the wearable device displays the first area information, the area with a degree of dirtiness greater than the degree threshold is identified as the recommended working area and marked in the displayed first area information. This makes it easier for users to understand the degree of dirtiness in the first area information and improves the speed of identifying the working area.

[0153] In addition, the interaction signals include gesture interaction signals and voice interaction signals, which allows users to choose the interaction method according to their usage habits, thereby improving the user experience.

[0154] In addition, when there are at least two areas to be worked on, determining the working order of the at least two areas in descending order of dirtiness can improve the working efficiency of the self-moving device.

[0155] In addition, when there are at least two areas to be worked, determining the working order of the at least two areas to be worked according to the order in which the interaction signals act on the information of the first area can improve the speed at which the user determines the working order, thereby improving the user experience.

[0156] In addition, during the operation of the self-moving device, the user's control signals can be collected through the wearable device to adjust the working state of the self-moving device, eliminating the need to control the working state of the self-moving device by buttons, simplifying the process of adjusting the working state of the self-moving device, and improving the control efficiency of the self-moving device.

[0157] Figure 6 This is a block diagram of a self-moving device control device provided in one embodiment of the present application. The device includes at least the following modules: a region display module 610, a signal acquisition module 620, a region determination module 630, and a work control module 640.

[0158] The area display module 610 is used to display first area information of the current working area of ​​the self-moving device through a wearable device;

[0159] The signal acquisition module 620 is used to acquire the interactive signals collected by the wearable device after displaying the information in the first area;

[0160] The region determination module 630 is used to determine the second region information of the region to be worked from the first region information based on the interactive signal;

[0161] The work control module 640 is used to control the self-moving device to perform work in the work area based on the second area information.

[0162] For relevant details, please refer to the above embodiments.

[0163] It should be noted that the self-moving device control device provided in the above embodiments is only illustrated by the division of the above functional modules when controlling the self-moving device. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the self-moving device control device can be divided into different functional modules to complete all or part of the functions described above. In addition, the self-moving device control device and the self-moving device control method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0164] This embodiment provides an electronic device, such as... Figure 7 As shown, the electronic device can be Figure 1 The self-moving device in the system includes at least a processor 701 and a memory 702.

[0165] Processor 701 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 701 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 701 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 701 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 701 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0166] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 702 are used to store at least one instruction, which is executed by the processor 701 to implement the control method for the self-moving device provided in the method embodiments of this application.

[0167] In some embodiments, the electronic device may also optionally include: a peripheral device interface and at least one peripheral device. The processor 701, memory 702, and peripheral device interface can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface via a bus, signal line, or circuit board. Indicatively, peripheral devices include, but are not limited to: radio frequency circuitry, a touch display screen, audio circuitry, and a power supply.

[0168] Of course, electronic devices may also include fewer or more components, and this embodiment does not limit this.

[0169] Optionally, this application also provides a computer-readable storage medium storing a program that is loaded and executed by a processor to implement the self-moving device control method of the above method embodiments.

[0170] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A control method for a self-moving device, characterized in that, The method includes: The wearable device displays the first area information of the current working area of ​​the self-mobile device, wherein the first area information is an area image or area map of the working area; When the first area information is an area image, target recognition is performed on the area image to obtain the target object, wherein the target object is an obstacle in the area image that can indicate the working area; Obtain the matching relationship between each target object and the preset region image; Search the pre-defined region image corresponding to the identified target object in the matching relationship; The area image is compared with the preset area image to determine the dirt information of each area in the working area; The degree of dirtiness is determined based on the preset relationship between dirtiness information and preset dirtiness level; Areas with a level of dirtiness exceeding a certain threshold are identified as recommended work areas; The recommended work area is displayed in the first area information using a preset display method via the wearable device; Acquire the interaction signals collected by the wearable device after displaying the information in the first area; Based on the interaction signal, the second area information of the area to be worked is determined from the first area information; Based on the second area information, the self-moving device is controlled to perform work on the area to be worked.

2. The method according to claim 1, characterized in that, The step of determining the second region information of the region to be worked from the first region information based on the interaction signal includes: When the interaction signal is a preset interaction signal acting on the first region information, the region acting on the interaction signal in the first region information is determined as the second region information.

3. The method according to claim 2, characterized in that, When the first area information is an area image, the preset interaction signal includes a box selection gesture, which is used to select the area to be worked from the area image; When the first area information is an area map, the preset interaction signal includes a selection gesture, which is used to select the area to be worked from the various map areas included in the area map.

4. The method according to claim 2, characterized in that, The preset display method is different from the display method of other areas in the first area information except for the recommended working area.

5. The method according to claim 4, characterized in that, The wearable device has a positioning function. Before the recommended working area is displayed in the first area information via the wearable device using a preset display method, the method further includes: If the first area information is an area map of the work area, obtain the location of the wearable device in the area map determined by the positioning function; The area where the location is situated is determined as the recommended working area.

6. The method according to claim 1, characterized in that, When the number of work areas is at least two, controlling the self-moving device to work on the work areas based on the second area information includes: Determine the work sequence for at least two areas to be worked on; The self-moving device is controlled to perform work on each work area according to the work sequence.

7. The method according to claim 6, characterized in that, Determining the working order of at least two areas to be worked includes: The working sequence is determined based on the order in which the interactive signals act on each area to be worked. Alternatively, the work sequence can be determined based on the degree of dirtiness of each area to be worked on.

8. The method according to any one of claims 1 to 7, characterized in that, After controlling the self-moving device to work on the area to be worked based on the second area information, the method further includes: Acquire the control signals collected by the wearable device; The operating state of the self-moving device is adjusted based on the control signal, and different control signals are corresponding to different operating states.

9. The method according to claim 8, characterized in that, Adjusting the operating state of the self-moving device based on the control signal includes: Based on the control signal, the self-moving device is controlled to stop working; And / or, based on the control signal, control the self-moving device to move to the next working area; And / or, adjust the operating mode of the self-moving device based on the control signal; And / or, control the self-moving device to perform self-cleaning based on the control signal; And / or, control the self-moving device to charge based on the control signal.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory; the memory stores a program, which is loaded and executed by the processor to implement the control method of the self-moving device as described in any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that, The storage medium stores a program that, when executed by a processor, is used to implement the control method for the self-moving device as described in any one of claims 1 to 9.

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