Patent Description:
A self-moving device is a type of robot that uses sensors to sense the ambient environment and the status of the self-moving device, understands and determines the complex environment, makes decisions and plans on this basis, and implements object-oriented movement, to accomplish a specific work task. The self-moving device can receive instructions inputted by a user to operate and can automatically operate according to a running program. The self-moving device may be used indoors or outdoors and applied to industry or homes. The self-moving device may replace security guards in patrols or replace people in floor cleaning. The self-moving device may further be used for family companionship and office work assistance, and is, for example, a vacuum cleaning robot or an autonomous lawn mower.

<CIT> discloses a robot recognition system.

<CIT> discloses a delegation of object and pose detection.

The self-moving device may recognize an object based on images shot by a camera apparatus disposed on the self-moving device, to perform an action based on the recognized object, for example, avoiding the obstacle and moving along the object. However, during actual application, no recognition technology can ensure perfect accuracy. Facing the complex real environment, a mobile robot may perform an incorrect action due to misrecognition and recognition miss.

To eliminate deficiencies above, the problem to be solved in embodiments of the present invention is to provide an automatic working system that accurately recognizes a working environment around a self-moving device in time.

The foregoing objectives, technical solutions, and beneficial effects of embodiments of the present invention can be achieved by using the following accompanying drawings:.

<FIG> is a schematic diagram of an automatic working system <NUM> according to an embodiment of the present invention. As shown in <FIG>, the automatic working system includes a self-moving device and a server. In this embodiment, the self-moving device is an autonomous lawn mower <NUM>. In another embodiment, the self-moving device may also be an unattended device including an automatic cleaning device, an automatic irrigation device, and an automatic snowplow. The automatic working system further includes a server <NUM>, capable of communicating with an autonomous lawn mower <NUM>, and providing functions such as storage and computing. The automatic working system <NUM> further includes a charging station <NUM>, configured to recharge the autonomous lawn mower <NUM>.

<FIG> is a schematic structural diagram of the autonomous lawn mower <NUM> according to an embodiment of the present invention. As shown in <FIG>, in this embodiment, the autonomous lawn mower <NUM> includes: a housing <NUM>; a movement module <NUM>, mounted in the housing <NUM>, and driving the autonomous lawn mower <NUM> to move; and a task execution module <NUM>, mounted at the bottom of the housing <NUM>, and including a cutting component to perform cutting work. The autonomous lawn mower <NUM> further includes an energy module, supplying energy to the autonomous lawn mower <NUM> to move and work. In this embodiment, the energy module is a battery module <NUM>. The autonomous lawn mower <NUM> further includes a control module <NUM>, electrically connected to the movement module <NUM>, the task execution module <NUM>, and the energy module. The control module <NUM> controls the movement module <NUM> to drive the autonomous lawn mower <NUM> to move and controls the task execution module <NUM> to perform a work task. The autonomous lawn mower <NUM> further includes an image detection module <NUM>, mounted in the housing <NUM>, and detecting images in a surrounding area of the autonomous lawn mower <NUM> and outputting environmental image. The autonomous lawn mower <NUM> further includes a first recognition module <NUM>, receiving the environmental image outputted by the image detection module <NUM>. The first recognition module <NUM> recognizes a specific object in the image and generates a first recognition signal. The control module <NUM> controls the movement module <NUM> based on the first recognition signal, and controls an action of the self-moving device <NUM> according to different working scenarios. The autonomous lawn mower <NUM> further includes a first communication module <NUM>, communicatively connected to the server <NUM>.

<FIG> is a schematic diagram of a working system according to an embodiment of the present invention. As shown in <FIG>, in this embodiment, the server is a server based on a cloud architecture. In another embodiment, the server <NUM> may be alternatively a physical server such as a single server, a server cluster or a distributed server. The autonomous lawn mower <NUM> may communicate with user equipment <NUM> by using the first communication module <NUM> or in another communication manner to implement secondary monitoring on the autonomous lawn mower <NUM>. The server <NUM> may further communicate with the user equipment <NUM>, and directly transmit an operation result of the server <NUM> to a user.

<FIG> is a schematic structural diagram of a server <NUM> according to an embodiment of the present invention. As shown in <FIG>, in this embodiment, the server <NUM> includes a second communication module <NUM>, communicatively connected to the first communication module <NUM>, and receiving the environmental image or other signals sent by the first communication module <NUM>. The server <NUM> further includes a second recognition module <NUM>. The function of the second recognition module <NUM> is basically the same as that of the first recognition module <NUM>. The second recognition module <NUM> recognizes the specific object in the image based on the environmental image, generates a second recognition signal, and sends the second recognition signal to the autonomous lawn mower <NUM> by using the second communication module <NUM>. Differences lie in that the operation speed of the second recognition module <NUM> is faster than that of the first recognition module <NUM>. Therefore, the second recognition module <NUM> can invoke a more complex image recognition algorithm and provide a recognition result with faster speed and higher precision.

In an embodiment, the image detection module <NUM> includes a camera apparatus. The self-moving device <NUM> performs related operations according to the image shot by the camera apparatus. In another embodiment, there may be a plurality of camera apparatuses. In another embodiment, the camera apparatus can shoot static images at different moments at specific time intervals. In another embodiment, the camera apparatus can shoot a video. Because the video is formed by image frames, through continuous or discontinuous acquisition of the obtained image frames in the video, a frame of image may be selected as an image. In this embodiment, the image detection module <NUM> can directly output an original image. In another embodiment, the image detection module <NUM> further includes a compression device, a clipping device or the like, preliminarily processing the original image and outputting a processed image.

In an embodiment, the first communication module <NUM> includes a <NUM> communication module. Compared with the <NUM> technology, the user bandwidth may be up to <NUM> Gbps (<NUM> Mbps) in a <NUM> design standard, which is <NUM> times the user bandwidth in current <NUM>, and has a more uniform data transfer rate, lower latency, and a lower unit cost. Currently, the average latency of the state-of-the-art <NUM> LTE is about <NUM> milliseconds, and the average latency of a conventional wired network is about <NUM> milliseconds to <NUM> milliseconds. However, the target latency of <NUM> is within <NUM> milliseconds. A design objective of the target latency of <NUM> is one fiftieth that of <NUM>, and is much less than that of a current conventional wired network.

In an embodiment, the first communication module <NUM> includes large-scale input/output antenna units. A quantity of input antennas in the large-scale input/output units is greater than or equal to <NUM>, and a quantity of output antennas is greater than or equal to <NUM>. In an embodiment, the quantity of the input antennas of the large-scale input/output antenna units is <NUM>, and the quantity of the output antennas is <NUM>. Generally, a <NUM>-receive <NUM>-transmit base station and a <NUM>-receive <NUM>-transmit antenna equipment support <NUM>*<NUM> MIMO. In this embodiment, a <NUM>-transmit <NUM>-receive self-moving device of the base station performs <NUM>-transmit <NUM>-receive, and has four concurrent data flows, that is, <NUM>*<NUM> MIMO, of which the speed doubles that of <NUM>*<NUM> MIMO. A layout form of the antennas in the large-scale input/output units is an antenna array. The antennas receive and send signals independently and ensure a correlation that is low enough between each other. In addition, each antenna has an absolutely isolated data flow, thereby receiving and sending the data flow efficiently.

In an embodiment, the first recognition module <NUM> includes a first storage unit and a first operation unit. The first storage unit has a first deep learning model. Based on the environmental image outputted by the image detection module <NUM>, the first operation unit invokes the first deep learning model to perform an operation and output the first recognition signal. The success rate of image recognition is affected by two maj or factors: first, the design of a recognition algorithm model; and second, the scale of a recognition algorithm training set (a quantity of images used for training and recognizing the model. The increase in the complexity of the recognition algorithm model, for example, the increase in the quantity of layers of convolution and the increase in the quantity of nodes of a neural network, facilitates the increase in the recognition accuracy. However, the increase in the complexity of the algorithm model may lead to a higher requirement on the operation capability. When the training set scale is larger, the training effect is better, and the trained recognition algorithm has a higher success rate. To improve the success rate, the training set needs to be as large as possible in the practical engineering application. However, when costs and time are taken into consideration, the training set scale in the first recognition module <NUM> of the autonomous lawn mower <NUM> is relatively small. To support the requirement of a deep algorithm, the first deep model needs to be optimized. For example, the quantity of layers of convolution or the quantity of neural nodes needs to be controlled. Because the image recognition requires massive computing, when the device is not supported by a powerful server, restricted by hardware and software resources of the autonomous lawn mower <NUM>, the first recognition module <NUM> is prone to recognition errors, slow recognition speed and even freezes. In this embodiment, the second recognition module <NUM> receives the environmental image, and invokes the second deep learning model stored in the second recognition module <NUM> to perform an operation to input the second recognition signal. It may be understood that an operation scale of the second deep learning model is larger than that of the first deep learning model, and the algorithm model is more complex than the first deep learning model.

In an embodiment, to ensure a sufficient success rate of recognition, the first recognition module <NUM> includes a central processing unit (CPU), and further includes a graphics processing unit (GPU) or a digital signal processing unit (DSP), to provide a sufficient floating-point arithmetic capability. In another embodiment, the first recognition module <NUM> includes a CPU, and further includes a dedicated neural network processing unit (NPU), directly providing neural network operation with optimized operation support.

In an embodiment, the first recognition module <NUM> includes software programs of image recognition methods such as the image recognition algorithm based on the neural network and the image recognition algorithm based on wavelet moments, to process, analyze, and recognize the taken images and further obtain an object region of a corresponding object category in the image. The object region may be represented by features such as the grayscale of an object and the contour of the object. For example, a method for representing the object region by using the contour of the object includes obtaining the recognized object region by using a contour extraction method. The contour extraction method includes, but is not limited to, methods such as a binary method, a grayscale method, and a canny operator method. Next, the object having an object category label labeled in advance and the object region in the image, correspondence of content, features, structures, relationships, textures and grayscales between the object and the image, similarity and consistency are analyzed to seek a similar image target, so that the object region in the image corresponds to the object category labeled in advance. In an embodiment, the first recognition module <NUM> includes a neural network model (for example, a CNN) that is obtained by training in advance, and the first recognition module <NUM> recognizes the object region corresponding to each of the object categories in the image by performing the neural network model.

In an embodiment, the specific object recognized by the first recognition module <NUM> includes an obstacle. For the autonomous lawn mower <NUM>, categories of the obstacles include, but are not limited to, a charging station, a flower bed, a tree, another garden tool, and a pet. When the autonomous lawn mower <NUM> is working normally and the first recognition module <NUM> recognizes an obstacle, the control module <NUM> may perform obstacle avoidance according to the first recognition signal. Specifically, the autonomous lawn mower <NUM> may pause, reverse or steer. In an embodiment, the first recognition module <NUM> may recognize categories of different obstacles. The control module <NUM> performs obstacle avoidance according to the different categories of the obstacles. For example, the obstacle includes a flower bed and a tree. To reach the edge of the obstacle to cut grass, the control module <NUM> reduces the execution standards of the obstacle avoidance. For example, the control module <NUM> only controls the movement module <NUM> to slow down. For a pet and a human body, the control module <NUM> may increase the execution standards of the obstacle avoidance. For example, the control module <NUM> controls the movement module <NUM> to reverse and controls the task execution module <NUM> to stop at the same time, to ensure that the pet and the human body are unharmed, thereby improving the safety of operating the autonomous lawn mower <NUM>.

In an embodiment, the specific object recognized by the first recognition module <NUM> includes a boundary of the working area. The working area of the autonomous lawn mower <NUM> is a lawn, and the boundary of the working area is a non-lawn area. The working area and is distinguished from a non-working area by using the lawn and the non-lawn area.

In an embodiment, the first recognition module <NUM> recognizes a lawn/a non-lawn area in an image. The control module <NUM> controls the autonomous lawn mower <NUM> to move on the lawn. When detecting that a non-lawn area appears in the image or in a position of the image, the control module <NUM> controls the movement module <NUM> to reverse or steer. Specifically, a mounting position of the image detection module <NUM> determines an image recognized by the first recognition module <NUM>, and correspondingly affects a control manner of the control module <NUM>. For example, the image detection module <NUM> detects the ground in front of the autonomous lawn mower <NUM>. If the ground position detected by the image detection module <NUM> is relatively close to the autonomous lawn mower <NUM>, the control module <NUM> needs to respond to the first recognition signal outputted by the first recognition module <NUM> faster, and controls the movement module <NUM> to reverse and steer. If the ground position detected by the image detection module <NUM> is relatively away from the autonomous lawn mower <NUM>, after receiving the first recognition signal, the control module <NUM> may determine whether to control the movement module <NUM> to move forward or to control the movement module <NUM> to change a movement direction.

In an embodiment, the first recognition module <NUM> recognizes a lawn/a non-lawn area in the image. The control module <NUM> determines a position of the autonomous lawn mower <NUM> relative to the boundary of the working area through the first recognition signal, and may control the movement module <NUM> to move along the boundary of the working area, so that the autonomous lawn mower <NUM> reaches the boundary of the working area to cut grass, or returns along the boundary of the working area. Specifically, the control module <NUM> may control the autonomous lawn mower <NUM> to move along an inner boundary of the working area, or control the autonomous lawn mower <NUM> to be partially located inside the working area and partially located outside the working area, or control the autonomous lawn mower <NUM> to move along an outer boundary of the working area.

In an embodiment, the specific object recognized by the first recognition module <NUM> includes a charging station <NUM>. When the autonomous lawn mower <NUM> is in a return mode, the control module <NUM> controls the movement direction of the movement module <NUM> according to the charging station <NUM> recognized by the first recognition module <NUM>, to enable the self-moving device <NUM> to move toward the charging station <NUM>, thereby implementing the return of the self-moving device <NUM> to the charging station <NUM>. In another embodiment, the first recognition module <NUM> can recognize the charging station <NUM>, and can also recognize a marker on the charging station <NUM> or a docking terminal of the charging station <NUM>. The control module <NUM> controls the movement direction of the movement module <NUM> according to the docking terminal of the charging station <NUM> recognized by the first recognition module <NUM>, to enable the self-moving device <NUM> to be docked to the charging station <NUM>.

In an embodiment, normally, the first communication module <NUM> communicates with the second communication module <NUM> normally. When the autonomous lawn mower <NUM> is working, the image detection module <NUM> acquires images and outputs an environmental image. The first recognition module <NUM> performs recognition based on the environmental image and outputs a first recognition signal. The control module <NUM> performs control based on the first recognition signal. However, due to the restrictions of the operation capability and recognition accuracy of the first recognition module <NUM>, a number of recognition errors may occur or the recognition speed may be excessively slow. For some objects, because there are differences in factors such as the angle of view, environment, light, and deformation, recognition may fail. Therefore, in this embodiment, one preset condition is set for the first recognition module <NUM>. If the first recognition signal meets the preset condition, the control module <NUM> performs control based on the first recognition signal. If the first recognition signal does not meet the preset condition, the environmental image is sent to the server <NUM> and is recognized by the second recognition module <NUM>.

In an embodiment, the operation time of the first recognition module <NUM> is counted. The control module <NUM> includes a timer. When the image detection module <NUM> sends the environmental image to the first recognition module <NUM>, the timer starts timing. If the image detection module <NUM> has not sent the first recognition signal yet when the time of the timer exceeds a first preset time, the control module <NUM> controls the first communication module <NUM> to send the environmental image to the second communication module <NUM>. The second recognition signal is recognized and outputted by the second recognition module <NUM>, and is sent by the second communication module <NUM> to the first communication module <NUM>. The control module <NUM> performs control according to the second recognition signal. Therefore, delayed response caused by restrictions of hardware or software of the first recognition module <NUM> is avoided.

In an embodiment, to prevent danger caused when the autonomous lawn mower <NUM> fails to recognize a target object in time, if the first recognition signal does not meet the preset condition, the control module <NUM> controls the autonomous lawn mower <NUM> to enter a safe working mode. In the safe working mode, a working manner of the autonomous lawn mower <NUM> may be set according to a practical requirement. The movement module <NUM> may be controlled to reduce the movement speed or stop moving, the movement module <NUM> may also be controlled to reverse or steer, and the task execution module <NUM> may also be controlled to stop working.

In an embodiment, when the autonomous lawn mower <NUM> is working, the image detection module <NUM> acquires images and outputs an environmental image. The first recognition module <NUM> performs recognition based on the environmental image and outputs the first recognition signal. At the same time, the first communication module <NUM> sends the environmental image to the second communication module <NUM>, and receives the second recognition signal outputted by the second recognition module <NUM>. The control module <NUM> may first select the second recognition signal to control the autonomous lawn mower <NUM> to improve the recognition accuracy of the environmental image. Because the autonomous lawn mower <NUM> and the server <NUM> are connected in a wireless communication manner, there is a possibility of disconnection. In an embodiment, the control module <NUM> starts timing from a moment at which the first communication module <NUM> sends the environmental image. If the second recognition signal is not received within a second preset time, the control module <NUM> performs control based on the first recognition signal. In another embodiment, a confidence level of the first recognition signal outputted by the first recognition module <NUM> is calculated. If the confidence level is greater than a second preset value, the control module <NUM> performs control according to the first recognition signal. In another embodiment, according to the hardware capability and the software complexity of the autonomous lawn mower <NUM>, parameters including the receiving time of the second recognition signal, the confidence level of the first recognition signal, and the operation time of the first recognition signal may all be taken into consideration, thereby ensuring the accuracy of recognition based on that the control module <NUM> can respond to the environmental image in time.

In an embodiment, when the state of charge of the battery module <NUM> is less than a preset state of charge, the first communication module <NUM> is controlled to stop working, thereby reducing power consumption. Because the first communication module <NUM> has a high transmission rate, the power consumption of the first communication module <NUM> is greater than that of a conventional communication module. When the first communication module <NUM> stops working, the success rate that the autonomous lawn mower <NUM> returns to the charging station <NUM> for charging can be improved, and the battery module <NUM> can also be protected, thereby preventing the service life of the battery module <NUM> from being affected due to exhaustion of power.

In an embodiment, as shown in <FIG>, in some scenarios, some users may want to avoid sending image information to a server for the sake of privacy. Based on the above, before the control module <NUM> sends the environmental image to the server <NUM>, user authorization needs to be obtained. If the environmental image is authorized by a user, the control module <NUM> may control the first communication module <NUM> to send the environmental image to the server <NUM>. If the environmental image is not in an authorization range, the control module <NUM> cannot control the first communication module <NUM> to send the environmental image to the server <NUM>. In this embodiment, the first communication module <NUM> is communicatively connected to the user equipment <NUM>. When the autonomous lawn mower <NUM> starts to work, the control module <NUM> sends an authorization request to the user equipment <NUM>. The first communication module <NUM> can send the environmental image only after the control module <NUM> needs to receive an authorization signal sent by the user equipment <NUM>. In another embodiment, the authorization signal can include authorization for a specific time or a specific scenario. The control module <NUM> sends the environmental image according to the authorization range of the authorization signal.

<FIG> is a schematic structural diagram of a server <NUM> according to an embodiment of the present invention. As shown in <FIG>, in an embodiment, the server <NUM> includes a software update module <NUM>, training software programs of the first recognition module based on the environmental image received by the second communication module <NUM> and the first recognition signal. Training methods include, but are not limited to, adjusting intrinsic parameters of the software programs and configuration information of the software programs. In this embodiment, the first recognition module <NUM> invokes a deep learning model. A training process of deep learning needs to be supported by massive data and maintains relatively high flexibility. The server <NUM> has powerful computing resources and can effectively extract corresponding training parameters. For example, the software programs include network structures and connection manners of the neural network model. A back-propagation algorithm is used to train the parameters in the neural network model, so that the accuracy of the neural network model is improved and the software update module <NUM> may encapsulate the parameters in the trained neural network model into an update data packet.

The update data packet may include a service pack applied to the software and a data packet required for software update. For example, in an embodiment, a first software program or a second software program includes the network structure and the connection manner of the neural network model. Correspondingly, the update data packet includes the parameters in the corresponding neural network. For example, in a case that the first software program or the second software program is executed by using a CNN, the update data packet obtained by training and updating the first software program or the second software program includes related parameters such as weight parameters and offset parameters in the corresponding CNN.

In an embodiment, the first recognition signal and the second recognition signal corresponding to the same environmental image are compared, so that the level of a first recognition algorithm model is evaluated in a relatively objective manner, particularly in a case that recognition accuracy for an object or recognition accuracy for an object in a specific scenario is relatively low. The comparison can be accomplished at the end of the autonomous lawn mower <NUM> or accomplished on the server <NUM>. The server <NUM> may perform training for a comparison result, and a manufacturer may be reminded to perform optimization, thereby improving the first recognition algorithm model.

In an embodiment, the self-moving device includes a processing unit, a positioning module and a communication module. The processing unit is arranged inside the self-moving device and electrically connected to the positioning module and the communication module. The positioning module includes a satellite signal receiving apparatus for receiving a satellite signal. The communication module is connected to a base station to receive information of the base station. The processing unit parses a position reference signal according to the received information transmitted by the communication module and combines the received satellite signal to calculate position coordinates of the self-moving device (that is, performing positioning on the self-moving device).

In the foregoing implementation, the satellite signal receiving apparatus includes an antenna (In an embodiment, arranged outside the self-moving device), and a data processing module (disposed on a high-precision positioning board card), arranged in the self-moving device, and electrically connected to the processing unit.

The communication module is compatible with a mobile network (for example, a <NUM> mobile network and a <NUM> mobile network), and is electrically connected to the processing unit. In an embodiment, the communication module is arranged in the self-moving device in a pluggable manner. During running, the communication module is connected to a nearby mobile <NUM> base station, receives information transmitted by the base station, and feeds back the information to the processing unit. Communication cards such as a standard SIM card, a micro SIM card, and a nano SIM card are disposed in the communication module. In an implementation, the communication module is mounted in the self-moving device in a pluggable manner. In an implementation, the communication module is integrated in the processing unit. In this case, the communication module is disposed in the processing unit according to a specific rule without an extra standard SIM card, micro SIM card or nano SIM card, thereby reducing the volume of the processing unit and improving the stability.

The processing unit parses the reference signal in the information based on a specified reading algorithm according to the received information transmitted by the communication module, uses the reference signal as a differential signal, and combines the signal transmitted by the satellite signal receiving apparatus to calculate the (current) position coordinates of the self-moving device (that is, to perform the positioning on the self-moving device). Therefore, the requirements of the self-moving device are adequately satisfied.

The base station is a <NUM>-based mobile base station. The base station functions as a radio transceiver station that transfers information between a mobile communication switch center and a mobile phone terminal and has a (GPS-based) positioning and timing function. In this case, usually, the base station further includes a high-precision satellite antenna, and the antenna receives satellite information by using a dual-frequency mode (different from a conventional single-frequency mode).

In an embodiment, the self-moving device includes a body, a satellite signal, a receiving apparatus, and a communication module.

The satellite signal receiving apparatus includes an antenna used for receiving the satellite signal and a data processing module arranged in the body to receive and process the satellite signal.

The communication module is electrically connected to the data processing module, to receive the information transmitted by the base station (<NUM> base station) and transmit the information to the data processing module. The data processing module receives the information transmitted by the base station and parses a correction and combines the satellite signal received by the antenna to calculate the position coordinates (that is, current position information) of the self-moving device. The processing unit of the self-moving device controls the self-moving device according to the position coordinates to move.

In an implementation, the antenna of the satellite signal receiving apparatus is mounted in the self-moving device in a pluggable manner. Therefore, in an idle state, the antenna can be removed to facilitate the storage of the self-moving device.

In an implementation, the data processing module is disposed on the high-precision positioning board card and electrically connected to the processing unit. Components of the processing unit are arranged on a circuit board through a layout.

In an implementation, the high-precision positioning board card is integrated in the processing unit. The components of the processing unit are arranged on a circuit board through the layout.

In an implementation, the self-moving device has an external port, configured to mount the communication module. If the communication module needs to be replaced, a user can buy and mount a matching module.

In an implementation, the data processing module (disposed on the high-precision positioning board card) supports dual-antenna input, and supports three-system (BDS B <NUM>/B2, GPS L1/L2, and GLONASS G1/G2) dual-frequency signals. During data exchange, the data processing module supports data in formats such as the standards NMEA-<NUM> GPGGA, GPGGARTK, GPGSV, GPGLL, GPGSA, GPGST, GPHDT, GPRMC, GPVTG, GPZDA, and the like; CMR (GPS) CMROBS, CMRREF, RTCM2. X RTCM1, RTCM3, RTCM9, RTCM1819, RTCM31, RTCM59; RTCM3. <NUM><NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, RTCM3. <NUM> MSM4 & MSM5 <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>.

The connection between the self-moving device and a single <NUM> base station is used as an example for description. The self-moving device uses the satellite signal received by the satellite signal receiving apparatus thereof, and uses the communication module that is connected to the base station and exchanges information with the base station, to obtain position information of the mobile base station, and uses the message as a differential correction to calculate the position coordinates (the current position information) of the self-moving device. Therefore, in this embodiment, it is no necessary to establish a base station as a reference station, thereby greatly reducing system costs.

To resolve the foregoing problem, it is proposed that a signal network having a larger coverage area than that of CORS is a cellular communication base station network.

During practical application, a plurality of <NUM> mobile base stations are arranged in a region, and include a series of cellular base stations. The cellular base stations divide the entire communication region into cellular cells. A cellular wireless networking manner is used to connect the terminal and the network equipment by a radio channel.

In an embodiment, a plurality of mobile base stations are established in a region. For example, there are usually three or more base stations. The base stations include a first reference base station, a second reference base station, a third reference base station and a data processing center. A particular distance (for example, <NUM> kilometers to <NUM> kilometers) is kept between the reference base stations. The data processing center integrates data of an entire network of the reference base stations by combining a network RTK algorithm, to perform an operation to simulate a virtual base station (VRS) (<<NUM>) near the self-moving device and calculate a more precise reference signal (a differential correction), thereby implementing the high-precision positioning of the self-moving device. In this implementation, the data processing center exchanges information with a self-moving device <NUM>. A <NUM>-based mobile network is used for a transmission mode between the reference base stations in a mobile base station group and the self-moving device. In this implementation, the arrangement of the self-moving device is the same as that in the solution described in <FIG>. The first reference base station, the second reference base station, the third reference base station, and the second reference base station are all <NUM> base stations.

In a design of the self-moving device, the self-moving device is provided with at least one battery pack. The battery pack may be disposed inside the self-moving device. For example, one battery pack is used. The battery pack is disposed as close as possible to the center of gravity of the self-moving device, to improve the stability during working. When two battery packs are used (the two battery packs may be electrically connected in series or electrically connected in parallel), the battery packs are disposed as close as possible to a central region of the self-moving device (from the perspective of projecting the tool to the ground from the top) to improve stability during working.

In a design of the satellite signal receiving apparatus, the satellite signal receiving apparatus includes an antenna, configured to receive a satellite signal, and a data processing module (the data processing module is disposed on a high-precision positioning board card) is arranged in the self-moving device to receive the signal received by the antenna, and combines a reference signal transmitted by the communication module to perform positioning on current position coordinates of the self-moving device.

The antenna is disposed outside the self-moving device and fixed to the self-moving device. In a design of the satellite antenna, the satellite antenna is attached to the surface of a housing of the self-moving device in the form of a patch.

In a design of the connection module, the communication module is compatible with a mobile network (for example, a <NUM> network or a <NUM> network). During running, the communication module is connected to a nearby base station, receives information transmitted by the base station, and feeds back the information to the processing unit. In an embodiment, communication cards such as a standard SIM card, a micro SIM card, and a nano SIM card are disposed in the communication module. In an embodiment, the communication module is mounted in the self-moving device or integrated in the processing unit in a pluggable manner.

In a design of the mobile base station, the 5Gbase station functions as a radio transceiver station that transfers information between a mobile communication switch center and a mobile phone terminal and has a (GPS-based) positioning and timing function. In this case, usually, the base station includes a high-precision satellite antenna, and the antenna receives satellite information by using a dual-frequency mode (different from a conventional single-frequency mode).

In a design of the mobile base station group, a region includes a plurality of <NUM> mobile base stations. A reference station that runs continuously is formed by the base stations in a networking manner.

In a design of the battery pack, a maximum voltage of the battery pack may be <NUM> V, <NUM> V, <NUM> V, <NUM> V, <NUM> V, and <NUM> V A specific voltage depends on an application scenario of the self-moving device. This is not limited herein. A battery chip inside the battery pack may be a lithium-based battery, a fuel cell or the like.

In the foregoing implementations, the self-moving device may be a lawn mower, a cleaning machine, a snowplow or the like. In an embodiment, the lawn mower, the cleaning machine, and the snowplow have a function of planning a path automatically.

Claim 1:
An automatic working system, the automatic working system comprises a self-moving device (<NUM>), the self-moving device (<NUM>) moving and working in a working area, comprising:
an image detection module (<NUM>), detecting an environment around the self-moving device (<NUM>) to generate an environmental image;
a first recognition module (<NUM>), recognizing a specific object in the image based on the environmental image to generate a first recognition signal;
a first communication module (<NUM>), selectively sending the environmental image and/or the first recognition signal to the server and receiving a second recognition signal corresponding to the environmental image; and
a control module(<NUM>), controlling an action of the self-moving device (<NUM>) based on the first recognition signal and/or the second recognition signal, wherein when the control module(<NUM>) determines that the first recognition signal does not meet a preset condition, the control module controls the first communication module(<NUM>) to send the environmental image to the server and receive the second recognition signal, wherein the preset condition comprises that the first recognition signal is generated within a first preset time.