Photovoltaic cleaning robot control method, system and electronic device

Abstract

The application discloses a photovoltaic cleaning robot control method and system and electronic equipment. The method comprises the following steps: a state monitoring device is used to collect the running state data of the photovoltaic cleaning robot; an ultrasonic sensor is used to collect the distance data of the photovoltaic cleaning robot from the edge of the photovoltaic panel or the obstacle; a camera is used to collect the target image data of the photovoltaic panel; a laser radar is used to collect the point cloud data on the photovoltaic panel; a main control device is used to generate a control instruction based on the running state data, the distance data, the target image data and the point cloud data sent by a single-chip microcomputer, and send the control instruction to the single-chip microcomputer; and the single-chip microcomputer is further used to control the photovoltaic cleaning robot to perform a cleaning task for the photovoltaic panel based on the control instruction. The application solves the technical problem that the data collected by the control system in the related art is not comprehensive when the photovoltaic cleaning robot is controlled, which leads to inaccurate control and further leads to poor cleaning effect of the photovoltaic panel.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot control, and in particular, to a photovoltaic cleaning robot control method and system and electronic device. BACKGROUND

[0002] In recent years, the new energy industry has developed rapidly, especially the photovoltaic industry, which has gradually become popular in application, and the use of photovoltaic panels has increased. With the widespread application of photovoltaic power generation, the problem of dust, leaves and other debris accumulation on photovoltaic panels has gradually emerged. These contaminants can significantly reduce the energy conversion efficiency of photovoltaic panels, so the demand for photovoltaic panel cleaning is growing. In this context, the development and application of photovoltaic panel cleaning robots have begun to emerge. They use automation technology to efficiently clean large areas of photovoltaic panels, reducing manual labor and water usage. Currently, some photovoltaic cleaning solutions use mechanical arms, tracked robots and other technologies to clean without damaging the surface of the photovoltaic panel. At the same time, many cleaning robots are equipped with intelligent navigation systems that can automatically identify dirty areas and develop cleaning plans.

[0003] However, the photovoltaic panel cleaning technology in the related art still faces some challenges. The cleaning method in the related art is often inefficient or inconvenient. For example, the mechanical arm solution not only has a large volume, but also has high difficulty and cost in implementing in photovoltaic power stations with limited space. Small mechanical cleaning devices usually cannot achieve automatic navigation, resulting in incomplete data collection when controlling the photovoltaic cleaning robot, and the need for a large amount of manpower, water or high energy consumption, thereby resulting in poor photovoltaic panel cleaning effect.

[0004] To address the above problems, no effective solutions have been proposed so far. SUMMARY

[0005] The embodiments of the present application provide a photovoltaic cleaning robot control method, system and electronic device to at least solve the technical problem that the data collected by the control system in the related art when controlling the photovoltaic cleaning robot is not comprehensive, resulting in inaccurate control and poor photovoltaic panel cleaning effect.

[0006] According to an aspect of some embodiments of the present application, there is provided a photovoltaic cleaning robot control system, comprising: a state monitoring device configured to collect operation state data of a photovoltaic cleaning robot; an ultrasonic sensor configured to collect distance data of the photovoltaic cleaning robot from an edge of a photovoltaic panel or an obstacle; a camera configured to collect target image data of the photovoltaic panel; a laser radar configured to collect point cloud data on the photovoltaic panel; a single-chip microcomputer connected with the state monitoring device and the ultrasonic sensor, configured to receive the operation state data from the state monitoring device and the distance data from the ultrasonic sensor, and send the operation state data and the distance data to a master control device; the master control device connected with the single-chip microcomputer, configured to generate a control instruction based on the operation state data, the distance data, the target image data and the point cloud data, and send the control instruction to the single-chip microcomputer, wherein the control instruction at least includes a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot; and the single-chip microcomputer is further configured to control the photovoltaic cleaning robot to perform a cleaning task for the photovoltaic panel based on the control instruction.

[0007] According to another aspect of some embodiments of the present application, there is also provided a photovoltaic cleaning robot control method applied to any one of the control systems, the method comprising: obtaining operation state data of a photovoltaic cleaning robot, distance data of the photovoltaic cleaning robot from an edge of a photovoltaic panel or an obstacle, target image data of the photovoltaic panel, and point cloud data on the photovoltaic panel; determining a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot based on the operation state data, the distance data, the target image data and the point cloud data; generating a control instruction based on the motion control strategy and the cleaning strategy; and controlling the photovoltaic cleaning robot to act based on the control instruction.

[0008] According to another aspect of some embodiments of the present application, there is also provided an electronic device comprising one or more processors and a memory, the memory being configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any one of the photovoltaic cleaning robot control methods.

[0009] In the embodiment of the present application, by setting the photovoltaic cleaning robot control system includes a state monitoring device for collecting the running state data of the photovoltaic cleaning robot; an ultrasonic sensor for collecting the distance data of the photovoltaic cleaning robot from the edge of the photovoltaic panel or the obstacle; a camera for collecting the target image data of the photovoltaic panel; a laser radar for collecting the point cloud data on the photovoltaic panel; a single-chip microcomputer connected with the state monitoring device and the ultrasonic sensor, for receiving the running state data from the state monitoring device and the distance data from the ultrasonic sensor, and sending the running state data and the distance data to the host device; the host device is connected with the single-chip microcomputer, for generating a control instruction based on the running state data, the distance data, the target image data and the point cloud data, and sending the control instruction to the single-chip microcomputer, wherein the control instruction at least includes the motion control strategy and the cleaning strategy of the photovoltaic cleaning robot; the single-chip microcomputer is further used for controlling the photovoltaic cleaning robot to perform the cleaning task for the photovoltaic panel based on the control instruction, which achieves the purpose of setting the control system integrated with the state monitoring device, the ultrasonic sensor, the camera, the laser radar, the single-chip microcomputer and the host device, based on more comprehensive collected data, and accurately controlling the photovoltaic cleaning robot through the cooperation of the single-chip microcomputer and the host device, so as to realize the technical effect of improving the control accuracy of the photovoltaic cleaning robot and the cleaning effect of the photovoltaic panel, and further solve the technical problem that the data collected by the control system in the related art is not comprehensive when controlling the photovoltaic cleaning robot, which leads to inaccurate control and further leads to poor cleaning effect of the photovoltaic panel. BRIEF DESCRIPTION OF DRAWINGS

[0010] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0011] Figure 1 is a structural schematic diagram of a photovoltaic cleaning robot control system according to an embodiment of the present application;

[0012] Figure 2 is a structural schematic diagram of an optional battery temperature detection circuit according to an embodiment of the present application;

[0013] Figure 3 is a structural schematic diagram of an optional battery voltage detection circuit according to an embodiment of the present application;

[0014] Figure 4 is a structural schematic diagram of an optional brush current detection circuit according to an embodiment of the present application;

[0015] Figure 5is a structural schematic diagram of an optional photovoltaic cleaning robot control system according to an embodiment of the present application;

[0016] Figure 6 is a flow chart of a photovoltaic cleaning robot control method according to an embodiment of the present application;

[0017] Figure 7 is a structural schematic diagram of a photovoltaic cleaning robot control device according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely below in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0019] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] According to an embodiment of the present application, a system embodiment of photovoltaic cleaning robot control is provided, Figure 1 is a structural schematic diagram of a photovoltaic cleaning robot control system according to an embodiment of the present application, as Figure 1 shown, the above-mentioned photovoltaic cleaning robot control system comprises a state monitoring device 10, an ultrasonic sensor 11, a camera 12, a laser radar 13, a single-chip microcomputer 14, a main control device 15, wherein:

[0021] The state monitoring device 10 is used for collecting the running state data of the photovoltaic cleaning robot;

[0022] The ultrasonic sensor 11 is used for collecting the distance data of the photovoltaic cleaning robot from the edge of the photovoltaic panel or the obstacle;

[0023] The camera 12 is used for collecting the target image data of the photovoltaic panel;

[0024] a laser radar 13 for collecting point cloud data on the photovoltaic panel;

[0025] a single-chip microcomputer 14 connected with the state monitoring device 10 and the ultrasonic sensor 11, for receiving the running state data from the state monitoring device 10 and the distance data from the ultrasonic sensor 11, and sending the running state data and the distance data to a master control device 15;

[0026] the master control device 15 connected with the single-chip microcomputer 14, for generating a control instruction based on the running state data, the distance data, the target image data and the point cloud data, and sending the control instruction to the single-chip microcomputer 14, wherein the control instruction at least includes a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot;

[0027] the single-chip microcomputer 14 is further configured to control the photovoltaic cleaning robot to perform a cleaning task for the photovoltaic panel based on the control instruction.

[0028] In this embodiment, the state monitoring device 10 collects the running state data of the robot, such as the battery status, motor working condition, etc., to ensure the safe and efficient operation of the robot. The ultrasonic sensor 11 collects distance data to help the robot perceive the distance between itself and the edge of the photovoltaic panel or other obstacles, achieving accurate navigation and collision avoidance. The camera 12 and the laser radar 13 respectively acquire image and point cloud data, providing detailed information of the surface of the photovoltaic panel, including the cleaning status and possible damage, to provide the basis for intelligent decision-making of the master control device 15. The single-chip microcomputer 14, as the core of the lower computer, receives the data of the state monitoring device 10 and the ultrasonic sensor 11, and transmits these data to the master control device 15 (such as a cloud server) through a communication module. The master control device 15 generates a control instruction based on the received data, in combination with the target image and point cloud data, to achieve remote monitoring and intelligent control. The control instruction includes the motion control strategy and the cleaning strategy of the robot, which enables the robot to adjust the cleaning method and path according to the actual situation of the photovoltaic panel, improving the cleaning effect and efficiency. The single-chip microcomputer 14 automatically controls the motion and cleaning operation of the robot according to the received control instruction, reducing manual intervention and improving the automation level of the operation. The control system also designs a safety protection mechanism, such as locking the motor immediately when the sensor or vision detects an anomaly, and moving the robot to a safe position through the unlock button of the remote control under the condition of human safety assurance, to ensure that the robot can operate safely in any situation and avoid damage to equipment and personnel. Through real-time monitoring and intelligent analysis, the master control device 15 can adjust the cleaning strategy of the robot, including the cleaning sequence, intensity and range, to achieve the best cleaning effect. The combination of the laser radar 13 and the camera 12 enables the robot to recognize complex structures and minor damages on the photovoltaic panel, improving the accuracy and reliability of the cleaning operation. Through precise control of the cleaning operation, the control system can reduce the use of water resources and energy consumption.

[0029] In summary, the control system can achieve comprehensive monitoring and remote intelligent control of the photovoltaic cleaning robot by integrating various sensors and intelligent control technologies, significantly improving cleaning efficiency, effectiveness and safety, while also embodying the design concepts of automation, intelligence and environmental protection, playing an important role in promoting the cleaning management of the photovoltaic industry.

[0030] In an alternative embodiment, the state monitoring device 10 comprises: a battery temperature detection circuit for collecting the battery temperature of the photovoltaic cleaning robot; a battery voltage detection circuit for collecting the battery voltage of the photovoltaic cleaning robot; a roller brush current detection circuit for collecting the roller brush current of the roller brush motor on the photovoltaic cleaning robot; wherein the operating state data comprises the battery temperature, the battery voltage and the roller brush current.

[0031] Optionally, the battery temperature detection circuit can continuously monitor the temperature of the battery to ensure that the battery operates within a safe temperature range, avoiding overheating that can cause battery performance degradation or safety issues. Real-time monitoring of the battery temperature is crucial for the long-term stable operation of the cleaning robot. The battery voltage detection circuit can continuously detect the voltage state of the battery through a voltage sensor, which helps to prevent overcharging or overdischarging of the battery, prolongs the service life of the battery, and maintains the continuous working ability of the cleaning robot. The setting of the voltage detection circuit can ensure that the battery supplies power in the best state, avoiding interruption of cleaning work due to battery problems. The roller brush current detection circuit can use a current sensor to monitor the current of the roller brush motor in real time, ensuring that the roller brush operates normally during cleaning, and avoiding damage to the motor due to locked rotor or other faults. Monitoring of the motor current is crucial for ensuring efficient execution of cleaning tasks and maintenance of the equipment.

[0032] The operating state data includes battery temperature, battery voltage and roller brush current. These data are key indicators for evaluating the operating health status of the photovoltaic cleaning robot, which are collected and transmitted to the single-chip microcomputer 14, and then sent to the host device 15 by the single-chip microcomputer 14 through serial communication. The host device 15 can understand the working status of the cleaning robot in real time by analyzing these data, and timely discover potential problems such as battery overheating or abnormal voltage, roller brush motor operation, etc., so as to adjust the working strategy or take maintenance measures. Through the setting of the battery temperature detection circuit, the battery voltage detection circuit and the roller brush current detection circuit, the control system can comprehensively monitor the key operating parameters of the cleaning robot during the operation process, ensure the safe operation of the equipment, and provide decision basis for the host device 15 to optimize the motion and cleaning strategy of the cleaning robot, improve the cleaning efficiency and effectiveness, reduce resource waste, and ensure the smooth progress of the cleaning work. These circuits and data collection methods together constitute an intelligent, efficient and safe photovoltaic cleaning robot lower computer control system.

[0033] Optionally,Figure 2 is a schematic diagram of an optional battery temperature detection circuit structure according to an embodiment of the application, as shown in Figure 2 The circuit is a temperature sensor circuit, which uses a 3.3V constant voltage source with model number REF3033AIDBZR. The temperature sensor is a thermal resistance type temperature sensor probe PT100, which is a thermistor that can convert temperature signals into resistance values. PT100 and resistors (R64, R65, R68) together form a bridge, and all of these resistors are high-precision resistors with one-thousandth precision. A reference source is generated by REF3033AIDBZR to improve the precision of sampling. The function of this bridge is to convert resistance values into electrical signals through voltage division, which can effectively convert small resistance changes into the required electrical signals. For example, the measurement range of this circuit is between -120°C and 200°C, which can be modified by modifying the resistance value of R68. The temperature sensor circuit also includes a differential isolation amplifier U31, which has a fixed 8x gain. The output voltage range is 0-3.3V, and 105 is the capacitance value, i.e. 1 microfarad (uf), which functions to suppress noise on the power supply, thereby improving the accuracy of the voltage output. U31 receives signals from the temperature sensor and processes them. Two differential signals output by U31 are connected to the pins of two 12-bit ADCs of the single-chip microcomputer (to convert analog signals into digital signals), and the current temperature can be obtained through processing. The circuit also includes resistors (R64, R65, R68), capacitors, and other components, such as J9 port, GND port, and GND_I port. The design purpose of the entire circuit is to accurately convert temperature signals from the temperature sensor into digital signals within the measurement range of -120°C to 200°C.

[0034] Optionally, Figure 3 is a schematic diagram of an optional battery voltage detection circuit structure according to an embodiment of the application, as shown in Figure 3As shown, in this battery voltage detection circuit, the battery voltage (voltage range 23V-30V) is input to the isolation amplifier U3 and then divided by resistors. The dividing resistors are high-precision resistors with a voltage divider value of 0.1%, reducing the battery voltage range of 23V-30V by a factor of 148 for easier acquisition. The divided voltage is then fed to the operational amplifier OPA2188AIDR via the J2 interface. This operational amplifier acts as a voltage follower or buffer to reduce load effects and maintain the stability of the input voltage. Simultaneously, a filter circuit consisting of resistor R6 and capacitor C14 filters the input voltage to remove high-frequency noise and interference. The output terminal VOUTP of the operational amplifier OPA2188AIDR provides a stable voltage signal that reflects the battery voltage. This output signal can be used for subsequent microcontroller sampling to convert the battery voltage into a digital signal for further processing or monitoring. Furthermore, this battery voltage detection circuit also includes components or parameters such as the isolation amplifier AMC12006DWVR and a fixed 8x gain, which can be used to improve the circuit's isolation performance or gain. Finally, the entire circuit is powered by a 5V power supply, and all components and assemblies need to be able to operate normally under a 5V power supply voltage.

[0035] Optional, Figure 4 This is a schematic diagram of an optional brush current detection circuit according to an embodiment of the present invention, as shown below. Figure 4 As shown, the brush motor current detection circuit is mainly based on a Hall effect current sensor (TMCS1108A2UQDR), which is used to detect the current flowing through the small motor. The circuit also includes a power supply (3V3) to provide power to the entire circuit; the power lines of the brush motor flow into the input terminals (IN+ and IN-) of the current sensor (U2) via terminal J1, with the current direction from IN+ to IN- considered positive. This current sensor is highly integrated, simplifying the peripheral circuitry. It converts the current signal into a voltage signal at a fixed ratio and outputs it from the output terminal (VOUT), exhibiting high linearity. This voltage signal can be sampled by a subsequent microcontroller and converted into a digital signal for further processing or monitoring. The entire circuit, through the electrically isolated Hall effect current sensor, can achieve accurate detection of the small motor current, featuring high precision and good isolation.

[0036] In an optional embodiment, the condition monitoring device 10 further includes: a pressure sensor disposed at the outlet of the liquid valve spray device of the photovoltaic cleaning robot, for collecting pressure data of the output pipe of the liquid valve spray device; and a flow meter disposed at a predetermined position of the liquid valve of the liquid valve spray device, for collecting the liquid flow rate through the liquid valve; wherein, the condition monitoring device 10 further includes pressure data and liquid flow rate.

[0037] Optionally, the pressure sensor is installed at the outlet of the liquid valve spraying device, which functions to collect real-time pressure data in the output pipeline. Pressure data is crucial for evaluating the performance of the spraying device, which helps to ensure that the liquid can be sprayed on the photovoltaic panel at an appropriate pressure, neither too high to cause water waste and possible damage to the photovoltaic panel, nor too low to affect the cleaning effect. The monitoring results of the pressure sensor are transmitted to the host device 15 through the single-chip microcomputer 14 as part of the photovoltaic cleaning robot control system, to adjust the working state of the spraying device in real time, to achieve the best cleaning effect. The flow meter is installed at a predetermined position of the liquid valve of the liquid valve spraying device, for accurate measurement of the liquid flow through the liquid valve. The monitoring of flow data helps to optimize the use of water resources in the cleaning process, avoiding excessive spraying or insufficient spraying. By monitoring the flow in real time, the control system can adjust the liquid flow of the spraying device according to the actual situation of the photovoltaic panel and the cleaning needs, to achieve efficient and environmentally friendly cleaning operations. By setting the state monitoring device 10, which not only includes the battery temperature detection circuit, the battery voltage detection circuit and the brush current detection circuit, but also further includes the pressure sensor and the flow meter. These sensors collectively collect key operating state data of the photovoltaic cleaning robot, including battery status, brush motor operation, pressure and flow of the spraying device, etc., to provide comprehensive information support for the intelligent control of the robot.

[0038] In summary, by integrating additional sensors (pressure sensor and flow meter), the control system can more comprehensively monitor and control key parameters in the cleaning process, thereby improving cleaning efficiency, reducing resource consumption, and ensuring the safe and stable operation of the equipment. By integrating these data into the control system, the host device 15 can more accurately generate cleaning strategies and motion control strategies, ensuring that the cleaning operation can be efficiently and accurately performed in any environment, and is environmentally friendly.

[0039] In an optional embodiment, the control system further comprises a manual control device, wherein the manual control device is connected with the single-chip microcomputer 14 and is used to send remote control instructions to the single-chip microcomputer 14, wherein the remote control instructions include at least one of the following: motion control strategy and cleaning strategy of the photovoltaic cleaning robot; the single-chip microcomputer 14 is further used to control the photovoltaic cleaning robot to act based on the remote control instructions.

[0040] Optionally, a manual control device, i.e. a remote controller handle, is wirelessly connected with the single-chip microcomputer 14 in the control system, providing a means for users to directly intervene in the robot's movement and cleaning strategy. The remote controller has the function of sending remote control instructions, which include the movement control strategy and cleaning strategy of the photovoltaic cleaning robot. This means that users can manually control the cleaning path, speed, and start and stop of the cleaning operation of the photovoltaic cleaning robot through the remote controller. This manual control mode provides a supplement to the automatic mode, increasing the flexibility of operation. During the working process of the photovoltaic cleaning robot, if special or complex situations are encountered, such as obstacles that cannot be recognized by the automatic navigation system or specific cleaning needs, users can send remote control instructions through the remote controller to directly adjust the robot's movement and cleaning strategy. The sending of remote control instructions provides users with the ability to directly control the cleaning process, ensuring that the robot can adapt to various working environments and needs. When the single-chip microcomputer 14 receives the remote control instructions, it will analyze the instruction content and adjust the robot's actions according to the remote control instructions. This can but is not limited to include adjusting the robot's movement direction, speed, and controlling the start and stop of cleaning devices such as roller brush motors and hydraulic spraying devices. The control response mechanism of the single-chip microcomputer 14 can ensure that in the manual control mode, user instructions can be accurately executed, so that the photovoltaic cleaning robot can clean according to the user's requirements.

[0041] In summary, the introduction of the manual control device provides the photovoltaic cleaning robot control system with a dual control mode: automatic navigation control and manual remote control. In automatic mode, the robot works according to the control instructions of the host device 15; when necessary, users can switch to manual mode and directly control the robot through the remote controller. This design not only increases the flexibility of the control system, but also provides the ability to manually intervene in complex or special situations, ensuring the efficiency and safety of cleaning operations. At the same time, the connection between the remote controller and the single-chip microcomputer 14 allows users to monitor the robot's running state in real time and adjust its working mode as necessary, providing a more comprehensive and intelligent control scheme for the operation of the photovoltaic cleaning robot.

[0042] In an optional embodiment, the control system further comprises a cooling fan, wherein the cooling fan is connected with the single-chip microcomputer 14 and is used to start when receiving the cooling start instruction issued by the single-chip microcomputer 14 and stop when receiving the cooling stop instruction issued by the single-chip microcomputer 14; the single-chip microcomputer 14 is further used to generate the cooling start instruction and the cooling stop instruction based on the temperature data collected by the monitoring device.

[0043] Optionally, the heat dissipation fan is an important component of the control system for active heat dissipation, connected with the single-chip microcomputer 14 to respond to the heat dissipation start instruction and the heat dissipation stop instruction issued by the single-chip microcomputer 14. During the operation of the photovoltaic cleaning robot, the motor and electronic components may generate a large amount of heat, especially the key components such as the battery and the single-chip microcomputer 14, and high temperature may affect their performance and service life, and even cause the control system to fail. The battery temperature detection circuit in the state monitoring device 10 is responsible for continuously monitoring the temperature of the battery to ensure that the battery operates within a safe temperature range. At the same time, the entire system may also contain other temperature sensors for monitoring the temperature of key electronic components. These temperature data will be received and processed by the single-chip microcomputer 14 as the basis for generating heat dissipation instructions. Based on the received temperature data, the single-chip microcomputer 14 can automatically generate heat dissipation start instructions and heat dissipation stop instructions. For example, when the temperature of the battery or other key components reaches the preset threshold, the single-chip microcomputer 14 will trigger the heat dissipation start instruction to start the heat dissipation fan to reduce the temperature and protect the system from overheating damage. When the temperature drops to a safe range, the single-chip microcomputer 14 will send a heat dissipation stop instruction to control the heat dissipation fan to turn off and save energy. Through this dynamic temperature management mechanism, the control system can automatically adjust the operating state of the heat dissipation fan according to real-time temperature data to ensure that the photovoltaic cleaning robot maintains good working condition and equipment safety in complex environments and long-time operation. This design can avoid the problem of excessive heat dissipation or insufficient heat dissipation, optimize the energy consumption and efficiency of the equipment, and prolong the service life of the battery and key components.

[0044] In an optional embodiment, the control system further comprises a light supplement lamp, wherein the light supplement lamp is connected with the single-chip microcomputer 14 and is used to turn on when receiving the light supplement start instruction issued by the single-chip microcomputer 14 and turn off when receiving the light supplement stop instruction issued by the single-chip microcomputer 14.

[0045] Optionally, the light supplement lamp, as a component of the control system, is connected with the single-chip microcomputer 14, and is mainly used to improve the imaging quality of the camera 12 under insufficient light conditions. When the robot works in a relatively dark environment, such as in the evening, early morning, or overcast days, the light supplement lamp can provide additional illumination to ensure that the camera 12 can clearly capture the state of the photovoltaic panel, which is crucial for automatic navigation and improvement of cleaning efficiency. The turning on and off of the light supplement lamp is automatically controlled by the single-chip microcomputer 14 according to the environmental light conditions and the cleaning operation requirements. When the single-chip microcomputer 14 detects that the light intensity is lower than the preset threshold value, or when the cleaning operation requires clearer images, it will send a light supplement start instruction to the light supplement lamp to turn it on. Conversely, when the light conditions are met or the cleaning operation is completed, the single-chip microcomputer 14 will send a light supplement stop instruction to turn off the light supplement lamp. This intelligent control mechanism not only ensures the visual needs of the cleaning operation, but also saves energy and avoids unnecessary power consumption. The connection of the light supplement lamp with the single-chip microcomputer 14 can reflect the close integration and coordinated work of the components in the control system. During the operation of the cleaning robot, the single-chip microcomputer 14 decides the turning on and off of the light supplement lamp according to the received various sensor data (such as the data of the light intensity sensor), which can ensure the smooth progress of the cleaning operation and the efficient operation of the system.

[0046] Based on the above embodiments and optional embodiments, the present application proposes an optional photovoltaic cleaning robot control system implementation, Figure 5 is a structural schematic diagram of an optional photovoltaic cleaning robot control system according to an embodiment of the present application, as Figure 5 shown, the control system includes a lower computer, an automatic navigation system, a sensor module, a battery, a remote controller, a cleaning device, and a motion device to fully meet the needs of photovoltaic panel cleaning, wherein,

[0047] The sensor module includes a state monitoring device 10 and an ultrasonic sensor 11, wherein the state monitoring device 10 includes:

[0048] The battery temperature detection circuit is used to monitor the battery temperature in real time by using a temperature sensor, to ensure that it is always within a safe range and prevent failure caused by overheating.

[0049] The battery voltage detection circuit is used to monitor the battery voltage state by using a voltage sensor to prevent overcharging or overdischarging of the battery and protect the battery life.

[0050] The roll brush motor current detection circuit monitors the current of the roll brush motor in real time through a current sensor to ensure the normal work of the roll brush and avoid damage caused by locked rotor.

[0051] The ultrasonic sensor 11 is used for edge detection, auxiliary navigation, and positioning of the robot.

[0052] The lower computer includes an isolated power supply step-down circuit, a single-chip microcomputer 14, an analog-to-digital converter (ADC) module, a communication module, and a 24V digital output module, wherein:

[0053] The isolated power supply step-down circuit provides a stable power supply for the control system to ensure the normal operation of each module.

[0054] The single-chip microcomputer 14 (such as an STM32RCT6 single-chip microcomputer 14) serves as the core control unit of the control system, responsible for data processing and execution of control instructions.

[0055] The ADC module is used to convert analog signals into digital signals for processing and analysis.

[0056] The communication module is used for remote data transmission with the upper computer through serial communication, RS485 communication for data interaction with the motor drive to control or read the motor parameters, supports the ModBus protocol, and reserves two-way RS232 communication and CAN communication.

[0057] The 24V digital output module is used to provide 24V power supply for external devices (such as a rolling brush motor, a hydraulic valve, a camera 12 light supplement lamp, a cooling fan, etc.).

[0058] The cleaning device includes a rolling brush and a hydraulic spraying device, wherein:

[0059] The rolling brush is used for cleaning work and has high cleaning efficiency, which can effectively remove dirt on the photovoltaic panel.

[0060] The hydraulic spraying device is used to provide liquid for cleaning to enhance the cleaning effect and achieve more comprehensive cleaning.

[0061] The automatic navigation system includes a camera 12 light supplement lamp, a camera 12, and a laser radar 13, wherein:

[0062] The camera 12 light supplement lamp is used to improve the accuracy of camera 12 recognition and ensure the stable operation of the device in complex environments.

[0063] The camera 12 is used to record the cleaning state and damage state of the photovoltaic panel while the photovoltaic robot is working, and transmit the video stream to the upper computer (i.e., the main control device 15, such as a cloud server) for data analysis.

[0064] The laser radar 13 is used for environment perception and obstacle detection to assist in realizing the automatic navigation function.

[0065] The motion device (i.e., the driving motor) includes a motor drive module and a motor, wherein:

[0066] The motor driving module supports MODBUS communication protocol based on RS485 to interact with the control system.

[0067] The maximum power of the motor reaches 400 watts, meeting the power demand of robot movement and cleaning.

[0068] The battery is used to provide 24V output voltage with a capacity of 30AH, ensuring the continuous power supply of the control system under long-time operation.

[0069] The remote controller is used to provide convenient manual control operation for the user.

[0070] The photovoltaic cleaning robot has two control modes: remote controller control and automatic navigation control. By default, it is in the automatic navigation mode, and when the remote controller is not turned on, the automatic navigation is realized by the upper computer combined with the laser radar 13. The upper computer controls the lower computer through real-time data transmission, thereby adjusting the rotation of the motor. At the same time, the upper computer records the state of the photovoltaic panel by using the camera 12, so as to monitor the cleaning effect and damage in real time.

[0071] When the remote controller is turned on and switched to the corresponding mode, the motor movement is controlled by the lower computer analyzing the PWM signal output by the remote controller receiver. If the sensor module detects any alarm signal, the control system will immediately lock the motor, and only when the two back keys of the remote controller are pressed at the same time, the motor can be unlocked, so that the remote controller can control the robot to move to a safe position, ensuring the absolute safety of operation.

[0072] It should be noted that in the present embodiment, through the integration of multiple sensors, combined with the battery temperature, voltage, current and ultrasonic sensor 11, comprehensive state monitoring is realized to ensure safe operation of the equipment. The STM32RCT6 single-chip microcomputer 14 is used as the core control unit, which has high data processing capability and supports multiple communication protocols (such as RS485 and ModBus), realizing flexible data interaction. The robot has two modes of automatic navigation and remote control, which can be flexibly switched according to user needs, improving the operation convenience. The cleaning state and damage of the photovoltaic panel are recorded by the camera 12, and the video stream is transmitted to the cloud, which is convenient for remote monitoring and data analysis. A motor locking mechanism is designed to ensure that the motor stops running immediately when the sensor alarms, and only under certain conditions can the motor be unlocked, ensuring the safety of operation. The lower computer modules and external interfaces are isolated to ensure the safe and stable operation of the equipment. The integrated roller brush motor and hydraulic spraying device improve the cleaning efficiency and ensure the cleaning effect of the photovoltaic panel. Through modular design, the modules are connected through standard interfaces, which is convenient for maintenance and upgrading, and enhances the scalability of the system.

[0073] In the present embodiment, the photovoltaic panel cleaning robot control system based on the single-chip microcomputer 14 can at least achieve the following technical effects:

[0074] 1) Yield improvement: By efficient automatic navigation and cleaning functions, the photovoltaic cleaning robot can complete the cleaning task of photovoltaic panels in a shorter time, thereby improving the overall yield of cleaning work. 2) Quality improvement: The real-time monitoring function of multiple sensors can ensure the comprehensive evaluation of the state of the photovoltaic panel during the cleaning process, thereby improving the quality of the cleaning effect and reducing the occurrence of uncleaned areas. 3) Precision improvement: The introduction of edge detection function can ensure that the robot avoids collision and damage to the photovoltaic panel during the cleaning process, improving the precision and safety of the operation. 4) Efficiency improvement: Multi-sensor integration and intelligent control system make the cleaning process more automated, reduce human intervention, improve work efficiency, and have more advantages than traditional manual cleaning methods. 5) Energy consumption reduction: The use of high-efficiency motors and tracked motion devices optimizes energy consumption, reduces energy consumption, and enables efficient operation in the inclined state of photovoltaic panels. 6) Raw material saving: By accurately controlling the usage amount and cleaning frequency of the spraying device, resource waste can be effectively reduced, and water resource consumption can be saved. 7) Simple operation: The control system also integrates remote control operation and automatic navigation functions, making user operation more convenient and reducing the technical threshold, requiring less for the operator. 8) Maintenance convenience: Modular design makes the system easy to maintain and upgrade, reducing equipment maintenance costs and extending the service life of the equipment. In summary, the control system can optimize photovoltaic panel cleaning operations by improving yield, quality, precision, and efficiency, while reducing energy consumption and raw material usage, and has an important role in promoting the cleaning management of the photovoltaic industry.

[0075] It should be noted that the specific structure of the photovoltaic cleaning robot control system shown in the present application Figure 1 and Figure 5 may have more or less structure than the photovoltaic cleaning robot control system shown in Figure 1 and Figure 5 .

[0076] According to the embodiment of the present application, a photovoltaic cleaning robot control method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in any of the aforementioned photovoltaic cleaning robot control systems, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0077] Figure 6 is a flowchart of the photovoltaic cleaning robot control method according to the embodiment of the present application, which is applied to any of the aforementioned control systems, such as Figure 6 , the method comprises the following steps:

[0078] In step S602, the running state data of the photovoltaic cleaning robot, the distance data of the photovoltaic cleaning robot from the edge of the photovoltaic panel or an obstacle, the target image data of the photovoltaic panel, and the point cloud data on the photovoltaic panel are obtained.

[0079] Optionally, the execution subject of steps S102 to S108 can be a master control device (such as a server) in the photovoltaic cleaning robot control system.

[0080] Optionally, the running state data includes but is not limited to battery state (temperature, voltage), working current of the brush motor, and real-time health status of the robot. Through continuous monitoring and collection of these data by the sensor module, the single-chip microcomputer can understand the running state of the robot in real time, which is crucial for preventing equipment failure, maintaining battery safety, and ensuring normal operation of the brush motor. The distance data provided by the ultrasonic sensor helps the robot perceive its distance from the edge of the photovoltaic panel or surrounding obstacles. This data is crucial for edge detection and automatic navigation, ensuring that the robot does not collide with the photovoltaic panel or other objects during cleaning, improving the safety and efficiency of the cleaning process. The target image data refers to real-time images of the photovoltaic panel captured by the camera. These image data are not only used to record the cleaning process, but also can be analyzed through images to identify the dirty areas and cleaning degree of the photovoltaic panel, helping to develop more accurate cleaning strategies. Real-time transmission and analysis of image data enable the remote host computer to obtain visual feedback of the cleaning process, further optimizing the control strategy. The point cloud data is generated by the laser radar, which provides three-dimensional position information of the robot's surrounding environment, including the irregularities of the photovoltaic panel surface and the specific location of obstacles. These data are crucial for the automatic navigation system, helping the robot to build an environmental map and plan the optimal cleaning path, avoiding getting lost or colliding in complex terrain.

[0081] In combination with these data, the control method can comprehensively evaluate the running state of the robot, the surrounding environment, and the cleaning demand, and generate dynamic motion control strategies and cleaning strategies. For example, when the single-chip microcomputer detects that the battery temperature is too high or the voltage is too low, it may adjust the cleaning strategy to reduce the cleaning frequency or suspend the cleaning operation to protect the battery. At the same time, according to the distance data and the point cloud data, the single-chip microcomputer can plan the path of the robot to avoid collision, and adjust the use of cleaning equipment according to the target image data to ensure the accuracy of the cleaning area and maximize the cleaning effect. The above methods embody the characteristics of intelligence and automation, which can significantly improve the working efficiency and operation safety of the photovoltaic cleaning robot.

[0082] In step S604, the motion control strategy and the cleaning strategy of the photovoltaic cleaning robot are determined based on the running state data, the distance data, the target image data, and the point cloud data.

[0083] Optionally, all the data of the above-mentioned running state data, distance data, target image data and point cloud data are integrated to generate motion control strategies and cleaning strategies. The motion control strategies can include but are not limited to the moving direction, speed and path planning of the robot; the cleaning strategies can include but are not limited to the start, stop, cleaning mode (such as strong cleaning or gentle cleaning), cleaning sequence, and the rotation speed of the roller brush motor and the opening of the liquid valve of the liquid valve spraying device of the cleaning equipment. Based on these strategies, specific control instructions can be generated for subsequent generation of specific control instructions to command the photovoltaic cleaning robot to perform cleaning tasks according to the established strategies, while ensuring its safety and efficiency.

[0084] In an optional embodiment, based on the foreign matter identification result and the target positioning data, the motion control strategy and the cleaning strategy are determined, including: determining the rotation speed of the driving motor of the photovoltaic cleaning robot, and the rotation speed of the roller brush motor and the opening of the liquid valve of the liquid valve spraying device according to the foreign matter identification result; determining the motion control strategy based on the target positioning data and the rotation speed of the driving motor; determining the cleaning strategy based on the rotation speed of the roller brush motor and the opening of the liquid valve of the liquid valve spraying device.

[0085] Optionally, first, according to the results obtained by the foreign matter recognition model, analyze the type, volume, and coverage area of the foreign matter on the photovoltaic panel. These information is crucial for the development of cleaning strategies, as different foreign matters may require different cleaning intensity and methods. For example, large-volume and strongly adhering foreign matter can be thoroughly removed by higher roller brush motor speed and larger liquid valve spraying opening; while light dirt with wide coverage area can be cleaned by adjusting the working mode of the roller brush and spraying device, taking into account the cleaning effect and resource conservation. Based on the foreign matter recognition results, according to the cleaning needs of the photovoltaic panel and the current position of the robot, determine the speed of the drive motor. If there are more foreign matters or more detailed cleaning is needed in a certain area, the speed of the drive motor can be slowed down to allow the robot to stay in that area for a longer time to ensure thorough cleaning. Conversely, if the identified foreign matter is less or the cleaning demand is not great, the speed of the drive motor can be increased to speed up the movement of the robot and improve the cleaning efficiency. The speed of the roller brush motor and the opening of the liquid valve spraying device can also be determined according to the nature of the foreign matter and the size of the cleaning area. This involves adjusting the working intensity of the cleaning equipment to adapt to different cleaning tasks. For example, for large-volume and strongly adhering foreign matter, higher roller brush motor speed and larger liquid valve opening are required to increase the cleaning intensity; while for light dust, lower roller brush motor speed and moderate liquid valve opening can be used to save water and electricity. The motion control strategy of the photovoltaic cleaning robot is based on the target positioning data and the determined speed of the drive motor, according to the three-dimensional terrain information constructed from the point cloud data, combined with edge detection and obstacle avoidance algorithms, to plan the shortest path and optimal speed for the robot from the current position to the next cleaning point. This includes adjusting the direction and speed of the robot to ensure that it can move efficiently along the planned route while avoiding colliding with the edge of the photovoltaic panel or other obstacles. The cleaning strategy is based on the parameter adjustment of the roller brush motor and the liquid valve spraying device, according to the foreign matter recognition results, to develop appropriate cleaning strategies for different cleaning areas and foreign matter types, such as roller brush speed, spraying intensity, and range. This ensures that each area of the photovoltaic panel is cleaned with appropriate intensity, while reducing unnecessary energy and water consumption. Finally, according to the determined motion control strategy and cleaning strategy, generate control instructions, such as adjusting the parameters of the drive motor, roller brush motor, and liquid valve spraying device. These instructions are sent to the corresponding actuators, such as the motor drive module and cleaning equipment control module, to accurately control the actions of the photovoltaic cleaning robot and ensure that it completes the cleaning task according to the established strategy.

[0086] Through the embodiment, intelligent decision-making based on data can be realized, ensuring that the photovoltaic cleaning robot can automatically adjust its movement and cleaning strategy according to real-time environment and cleaning requirements, and realizing efficient and accurate cleaning operation. The strategy formulation process fully embodies the close integration and cooperation between the control system in data processing, strategy formulation and action execution, which is a key innovation point in the photovoltaic cleaning robot technology.

[0087] In step S606, control instructions are generated based on the movement control strategy and the cleaning strategy.

[0088] Optionally, the control instructions are sent to the single-chip microcomputer based on the movement control strategy and the cleaning strategy, and the single-chip microcomputer generates corresponding motor control instructions based on the control instructions to command the driving motor to adjust the direction and speed of the robot, so as to safely and efficiently complete the movement.

[0089] In an optional embodiment, the control instructions are generated based on the running state data, the distance data, the target image data and the point cloud data, including: detecting whether the running state data is within a predetermined running range, wherein the running state data at least includes: the battery temperature and the battery voltage of the photovoltaic cleaning robot, the brush motor current of the brush motor on the photovoltaic cleaning robot, the pressure data of the output pipeline of the liquid valve spraying device, and the liquid flow of the liquid valve of the liquid valve spraying device; in the case that the running state data is within the predetermined running range, foreign matter identification is performed on the photovoltaic panel based on the target image data to obtain a foreign matter identification result, wherein the foreign matter identification result includes the volume and the coverage area of the foreign matter; the target positioning data of the photovoltaic cleaning robot is determined based on the distance data and the point cloud data; the movement control strategy and the cleaning strategy are determined based on the foreign matter identification result and the target positioning data.

[0090] Optionally, first detect whether the running state data is within the predetermined running range. These data include battery temperature and voltage, current of the brush motor, output pipe pressure and liquid flow of the liquid valve spraying device, etc. If these data exceed the normal range, such as battery overheating or voltage too low, or brush motor current abnormal (may indicate blockage or wear), or spraying device pressure and flow abnormal, corresponding safety instructions will be generated immediately, such as suspending cleaning operation, starting cooling fan or adjusting spraying parameters, to ensure the safe operation of the equipment. When the photovoltaic cleaning robot running state is confirmed to be normal, the target image data captured by the camera will be used for foreign matter identification. Through image processing algorithms, foreign matter on the photovoltaic panel, such as dust, leaves, bird droppings, etc., can be identified, and the volume and coverage area of the foreign matter can be calculated. This information is crucial for developing cleaning strategies, which can help determine which areas need to be cleaned, as well as the intensity and method of cleaning (such as whether to use the spraying device, adjust the speed of the brush, etc.). The distance data provided by the laser radar and ultrasonic sensor, as well as the point cloud data generated by the laser radar, are used to determine the precise position of the photovoltaic cleaning robot and the detailed information of the surrounding environment. Using these data, an environment map is constructed to determine the target positioning data of the robot, i.e. the specific location on the photovoltaic panel that the robot needs to reach, thereby ensuring that the robot can move along the optimal path, avoiding collision with edges or obstacles, and accurately finding the area that needs to be cleaned. Combining the foreign matter identification results and target positioning data, the system can determine the motion control strategy and cleaning strategy. The motion control strategy includes how the robot moves to reach the cleaning target, such as selecting the path, adjusting the moving speed, etc.; the cleaning strategy involves how the cleaning equipment works, such as adjusting the water spray amount of the spraying device, the rotation speed of the brush motor, and the cleaning sequence, etc. Through precise control of these strategies, the system can ensure that the photovoltaic cleaning robot efficiently and accurately completes the cleaning task.

[0091] Optionally, based on the above strategies, the single-chip microcomputer generates specific control instructions, including but not limited to starting the brush motor, adjusting the spraying device parameters, controlling the driving motor direction and speed, and starting the cooling fan or light supplement lamp when necessary. These instructions are sent to the corresponding execution modules, such as motor drive module, cleaning equipment control module, etc., and the actuators perform actual actions according to the instructions, ensuring that the photovoltaic cleaning robot performs cleaning work according to the formulated strategies, thereby realizing fine management and control of the photovoltaic cleaning robot operation.

[0092] In an optional embodiment, when the running state data is within the predetermined running range, foreign matter identification is performed on the photovoltaic panel based on the target image data to obtain a foreign matter identification result, including: when the running state data is within the predetermined running range, based on the target image data, a foreign matter identification model is used to obtain a foreign matter identification result, wherein the foreign matter identification model is obtained through machine learning based on multiple sets of image data, and the multiple sets of image data correspond to the volume and coverage area of foreign matter respectively.

[0093] Optionally, before foreign matter identification, it is verified whether the running state data is within the predetermined running range. This is a basic step to ensure the safety and normal operation of the robot. If the data is abnormal, appropriate safety measures will be taken, such as pausing cleaning or starting a cooling mechanism, etc. The foreign matter identification model is obtained through machine learning technology using multiple sets of training image data and their corresponding foreign matter volume and coverage area. These training data include images of various common foreign matters on photovoltaic panels, such as dust, leaves, bird droppings, etc., as well as volume and coverage area information of these foreign matters. Through machine learning algorithms (such as deep learning convolutional neural networks, support vector machines, etc.), the model can learn the characteristics of foreign matter and its appearance in images, thereby having the ability to identify foreign matter in new images. After confirming that the running state is normal, further analysis is performed on the target image data captured by the photovoltaic cleaning robot camera. These data contain real-time images of the photovoltaic panel surface, which are pre-processed (such as denoising, contrast enhancement, etc.) and then input into the foreign matter identification model. The foreign matter identification model takes the target image data as input and outputs the foreign matter identification result. These results include the volume and coverage area of foreign matter on the photovoltaic panel, helping to determine the area that needs to be cleaned and its severity. For example, larger volume foreign matter may require more intensive cleaning, while wide coverage area dirt may require a wider cleaning range. Based on the identified volume and coverage area of foreign matter, the system can adjust the cleaning strategy, such as adjusting the speed of the roller brush motor, the water spray amount of the spraying device, and deciding whether to use high pressure or gentle cleaning mode. For example, if the volume and coverage area of foreign matter are large, increase the speed of the roller brush motor and increase the water spray amount of the spraying device; if the volume and coverage area of foreign matter are small, reduce the speed of the roller brush motor and reduce the water spray amount of the spraying device. This can ensure the pertinence and efficiency of the photovoltaic panel cleaning process, avoiding resource waste and potential damage to the photovoltaic panel.

[0094] Optionally, the foreign matter identification model can be continuously trained and optimized to improve its recognition accuracy. As the photovoltaic cleaning robot is used, more real scene data will be collected, which can be used for retraining of the model, making it better adapt to various complex photovoltaic panel surface conditions and improving the identification and cleaning capabilities.

[0095] In this embodiment, by using machine learning technology for foreign matter recognition, the photovoltaic cleaning robot control system can implement a more intelligent and accurate cleaning strategy, which not only improves cleaning efficiency, but also ensures the comprehensiveness and pertinence of cleaning, effectively deals with the challenges in photovoltaic panel cleaning, and provides key technical support for the automation and intelligentization of photovoltaic cleaning.

[0096] In an optional embodiment, based on the distance data and the point cloud data, the target positioning data of the photovoltaic cleaning robot is determined, including: based on the distance data, detecting whether the distance between the photovoltaic cleaning robot and the edge of the photovoltaic panel is greater than a predetermined distance; in the case that the distance between the photovoltaic cleaning robot and the edge of the photovoltaic panel is greater than the predetermined distance, based on the point cloud data, the target positioning data of the photovoltaic cleaning robot is determined.

[0097] Optionally, the distance data provided by the ultrasonic sensor and the laser radar is used to monitor the distance between the photovoltaic cleaning robot and the edge of the photovoltaic panel in real time, to ensure that the robot does not exceed the range or collide with the photovoltaic panel during cleaning. If the distance between the robot and the edge is less than a predetermined minimum safety distance, an instruction to slow down or stop is immediately generated to prevent the robot from exceeding the range of the photovoltaic panel or colliding with the edge, to ensure the safety of the cleaning process. The distance between the robot and the edge of the photovoltaic panel is continuously checked to see if it is greater than a preset safety distance, which takes into account the size of the photovoltaic panel, the size of the cleaning robot, and possible errors that may occur during cleaning. If the detected distance is greater than the preset safety value, the system considers that the robot is within a safe cleaning range and can continue to the next step. Once the distance to the edge is confirmed to be safe, the point cloud data generated by the laser radar is further used to construct a three-dimensional model of the environment around the robot. The point cloud data includes not only the distance to the edge, but also the structural information of the surface of the photovoltaic panel and the location of any obstacles. Based on these data, the single-chip microcomputer can accurately calculate the position of the robot relative to the photovoltaic panel within its cleaning range, which is called target positioning data. The target positioning data includes the precise coordinates and direction of the robot, as well as the topographic features of the surface of the photovoltaic panel, and is a key basis for planning the cleaning path and actions of the robot. By integrating edge distance detection and point cloud data analysis, the target positioning data of the photovoltaic cleaning robot can be determined, including the current coordinates of the robot on the photovoltaic panel and the topographic features of the photovoltaic panel. Based on these data, the control system can intelligently plan the cleaning path of the robot to avoid collisions and omissions, ensuring that the robot can accurately cover every area that needs to be cleaned during the cleaning process. Finally, based on the target positioning data, precise control instructions can be generated, including the direction, speed and path of the robot's movement, as well as the start and adjustment instructions of the cleaning device. These instructions ensure that the photovoltaic cleaning robot can efficiently and safely complete the cleaning task according to the established strategy.

[0098] In the above manner, through accurate perception and control of the robot's position, the automation and safety of the cleaning process can be ensured. This positioning method based on distance and point cloud data, combined with the instant feedback of ultrasonic sensors and the high-precision scanning of laser radars, provides a solid foundation for the intelligent control of photovoltaic cleaning robots. Through real-time position adjustment and obstacle avoidance, the robot can accurately perform cleaning tasks while avoiding damage to the photovoltaic panels or the risk of collision, thereby significantly improving cleaning efficiency and safety.

[0099] In an alternative embodiment, based on the distance data and the point cloud data, the target positioning data of the photovoltaic cleaning robot is determined, including: based on the distance data, the point cloud data and the target image data, the photovoltaic cleaning robot is positioned respectively to obtain three sets of positioning data; based on the three sets of positioning data, the target positioning data is determined.

[0100] Optionally, the ultrasonic sensor and the laser radar continuously monitor the distance between the robot and the edge of the photovoltaic panel, providing real-time position information. The point cloud data generated by the laser radar contains three-dimensional information of the environment around the robot, including the undulations of the photovoltaic panel surface and the positions of obstacles. The photovoltaic panel surface image captured by the camera provides visual positioning information, helping the robot to identify specific cleaning areas and obstacles. Preliminary positioning is performed using distance data to ensure safe movement of the robot within the photovoltaic panel cleaning range, avoiding collisions with the edge or obstacles. Point cloud data provides more detailed three-dimensional positioning, including the robot's precise coordinates and orientation relative to the photovoltaic panel, as well as detailed perception of environmental obstacles for more complex path planning. Target image data is used for visual positioning, determining the robot's specific position on the photovoltaic panel, as well as identifying cleaning targets and possible obstacles on the photovoltaic panel. The positioning information obtained from the above three data sources is fused and processed, and the three sets of positioning data are compared and analyzed through algorithms to improve the accuracy and reliability of positioning. For example, a comprehensive positioning result can be obtained by weighted averaging or optimal selection, combining the immediacy of distance data, the accuracy of point cloud data and the intuitiveness of image data. Finally, a most accurate and reliable target positioning data is determined based on the three sets of positioning data. This data not only includes the coordinates of the robot, but also its orientation, posture and topographic features of the photovoltaic panel, providing comprehensive information support for the robot's path planning and motion control.

[0101] Optionally, based on the determined target positioning data, the motion control strategy and cleaning strategy can be further optimized. For example, the positioning data can be used to adjust the speed and path of the robot's movement to ensure it reaches the cleaning area accurately; at the same time, it can also adjust the working parameters of the cleaning equipment according to the topographic features, such as the speed of the roller brush, the water spray amount of the spraying device, etc., to adapt to different cleaning needs.

[0102] Based on the multi-source data positioning method in this embodiment, the photovoltaic cleaning robot control system can achieve more intelligent and accurate positioning and control, which not only improves the efficiency and accuracy of cleaning operation, but also enhances the adaptability and safety of the robot. The comprehensive use of multiple sensors and data fusion technology provide strong technical support for the autonomous navigation and operation of the robot in complex environments, which is a major innovation in this technical solution.

[0103] In an alternative embodiment, based on the three sets of positioning data, the target positioning data is determined, including: performing weighted calculation on the three sets of positioning data to obtain the target positioning data; or calculating the position difference between the three sets of positioning data, and determining at least two sets of positioning data with a position difference less than a preset difference threshold from the three sets of positioning data; and determining the target positioning data based on the at least two sets of positioning data.

[0104] Optionally, the determination of the target positioning data is realized by two data fusion methods, namely weighted calculation and position difference comparison. These two methods aim to integrate the data of different sensors to improve the accuracy and reliability of positioning. In the method of weighted calculation, different weights are assigned to each type of positioning data according to their accuracy and reliability. For example, laser radar point cloud data usually provides high-precision three-dimensional positioning information and may be assigned a higher weight; while ultrasonic sensor data is effective within a short distance, but may be less accurate in long distances or complex environments, and therefore may be assigned a lower weight. The three sets of positioning data are weighted calculated to integrate their information. Weighted calculation can use linear combination or more complex mathematical models such as least squares or Kalman filtering to fuse data. The final target positioning data will be more comprehensive and accurate, and can better reflect the actual position and direction of the robot on the photovoltaic panel.

[0105] Optionally, if the position difference comparison method is adopted, the position differences between the ultrasonic sensor data, the lidar point cloud data, and the host positioning data are calculated respectively. A preset difference threshold is set to judge the consistency of the positioning data. If the position difference between the two sets of positioning data is less than the preset threshold, it indicates that their positioning results are relatively consistent and can be considered reliable. From the three sets of positioning data, at least two sets of data with a position difference less than the preset threshold are selected, and then the final target positioning data is determined based on the two or more sets of data. This is also a data fusion strategy, by comparing and screening, the most consistent positioning data is selected, thereby improving the accuracy and robustness of positioning. Whether weighted calculation or position difference comparison is adopted, the determination of the target positioning data is to ensure the accurate positioning of the robot during cleaning. The positioning data includes the coordinates, direction of the robot, and the topographic features of the photovoltaic panel surface, which is an important basis for planning the motion path and cleaning strategy. After determining the target positioning data, the system can generate accurate control instructions to guide the robot to move on the photovoltaic panel, avoid obstacles, and ensure comprehensive coverage of the cleaning area, improving cleaning efficiency.

[0106] It should be noted that in the process of data fusion, the safety and efficiency of the data are given priority. For example, in sensitive areas such as the edge of the photovoltaic panel, more reliance may be placed on ultrasonic sensor data, as it can provide immediate feedback at close range, helping to approach the edge more safely; in open areas, lidar point cloud data may be given higher weight to achieve more efficient long-distance positioning and navigation. Through the above data fusion method, the control system of the photovoltaic cleaning robot can effectively integrate the data of multiple sensors, improve the accuracy and reliability of positioning, and provide a solid foundation for the intelligent cleaning operation of the robot. This precise positioning capability helps to improve cleaning efficiency while ensuring the safety of operation, which is an important innovation point in photovoltaic cleaning robot technology.

[0107] Step S608, based on the control instructions, control the photovoltaic cleaning robot to act.

[0108] Optionally, based on the generated control instructions, the photovoltaic cleaning robot will perform corresponding actions, including adjusting the speed of the roller brush motor, controlling the water spray amount of the hydraulic spraying device, driving the motor to move according to the planned path, and starting the cooling fan or light supplement lamp when necessary. Through the above methods, the photovoltaic cleaning robot can flexibly cope with various cleaning scenarios and ensure that the photovoltaic panel is thoroughly and effectively cleaned.

[0109] Through the steps S102 to S108, the control system integrated with the state monitoring device, the ultrasonic sensor, the camera, the laser radar, the single-chip microcomputer and the master control device can be set, the photovoltaic cleaning robot is accurately controlled based on more comprehensive collected data, and the technical effects of improving the control accuracy of the photovoltaic cleaning robot and the cleaning effect of the photovoltaic panel are realized, so as to solve the technical problems that the data collected by the control system in the related art is not comprehensive when the photovoltaic cleaning robot is controlled, the control is inaccurate, and the cleaning effect of the photovoltaic panel is poor.

[0110] In the embodiment, a photovoltaic cleaning robot control device is also provided, which is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" "device" can be a combination of software and / or hardware that realizes a predetermined function. Although the device described in the following embodiments is preferably realized in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0111] According to the embodiment of the present application, a device embodiment for implementing the above-mentioned photovoltaic cleaning robot control method is also provided, Figure 7 is a structural schematic diagram of a photovoltaic cleaning robot control device according to an embodiment of the present application, as Figure 7 shown, the above-mentioned photovoltaic cleaning robot control device comprises a data acquisition module 700, a strategy determination module 702, an instruction generation module 704 and a control module 706, wherein:

[0112] The data acquisition module 700 is used to acquire the running state data of the photovoltaic cleaning robot, the distance data of the photovoltaic cleaning robot from the edge of the photovoltaic panel or the obstacle, the target image data of the photovoltaic panel and the point cloud data on the photovoltaic panel.

[0113] The strategy determination module 702 is connected to the data acquisition module 700 and is used to determine the motion control strategy and the cleaning strategy of the photovoltaic cleaning robot based on the running state data, the distance data, the target image data and the point cloud data.

[0114] The instruction generation module 704 is connected to the strategy determination module 702 and is used to generate the control instruction based on the motion control strategy and the cleaning strategy.

[0115] The control module 706 is connected to the instruction generation module 704 and is used to control the action of the photovoltaic cleaning robot based on the control instruction.

[0116] It should be noted that the above-mentioned modules can be realized by software or hardware, for example, for the latter, the above-mentioned modules can be located in the same processor, or the above-mentioned modules are located in different processors in any combination.

[0117] It should be noted that the above data acquisition module 700, strategy determination module 702, instruction generation module 704, control module 706 correspond to steps S602 to S608 in the embodiment, and the above modules have the same instances and application scenarios as the corresponding steps, but are not limited to the above disclosed embodiments. It should be noted that the above modules can be run in a computer terminal as part of the device.

[0118] It should be noted that the optional or preferred embodiments of the present embodiment can refer to the related description in the embodiment, which will not be repeated here.

[0119] The above photovoltaic cleaning robot control device can also include a processor and a memory, and the above data acquisition module 700, strategy determination module 702, instruction generation module 704, control module 706, etc. are stored in the memory as program modules, and the above program modules stored in the memory are executed by the processor to realize the corresponding functions.

[0120] The processor includes a core, and the core retrieves the corresponding program module from the memory. The above core can be set to one or more. The memory can include a non-persistent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0121] According to the embodiments of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in the present embodiment, the above non-volatile storage medium includes a stored program, wherein the above program controls the device where the above non-volatile storage medium is located to execute the above any one photovoltaic cleaning robot control method when the above program is running.

[0122] Optionally, in the present embodiment, the above non-volatile storage medium can be located in any one of a computer terminal group in a computer network, or in any one of a mobile terminal group, and the above non-volatile storage medium includes a stored program.

[0123] Optionally, the program controls the device where the non-volatile storage medium is located to perform the following functions when the program is running: acquiring the running state data of the photovoltaic cleaning robot, the distance data of the photovoltaic cleaning robot from the edge of the photovoltaic panel or the obstacle, the target image data of the photovoltaic panel, and the point cloud data on the photovoltaic panel; based on the running state data, the distance data, the target image data and the point cloud data, determining the motion control strategy and the cleaning strategy of the photovoltaic cleaning robot; based on the motion control strategy and the cleaning strategy, generating control instructions; and based on the control instructions, controlling the photovoltaic cleaning robot to act.

[0124] According to the embodiments of the present application, an embodiment of a processor is further provided. Optionally, in the embodiment, the processor is used to run a program, and the program performs any of the above photovoltaic cleaning robot control methods when running.

[0125] According to the embodiments of the present application, an embodiment of a computer program product is further provided, which is adapted to perform the steps of initializing any of the above photovoltaic cleaning robot control methods when executed on a data processing device.

[0126] The embodiments of the present application provide an electronic device, which comprises a processor, a memory, and a program stored in the memory and capable of running on the processor, and the processor implements the steps of any of the above photovoltaic cleaning robot control methods when running the program.

[0127] The sequence of the above embodiments of the present application is only for description, and does not represent the advantages or disadvantages of the embodiments.

[0128] In the above embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0129] In the several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the above device embodiments are only illustrative, and the division of the above modules can be a logical function division, and there can be another division manner in actual implementation, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed modules can be indirect coupling or communication connection through some interfaces, modules, electrical or other forms.

[0130] The modules described as separate components can or can not be physically separate, and the components shown as modules can or can not be physical modules, that is, they can be located in one place, or they can be distributed to multiple modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0131] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically, or two or more modules can be integrated into one module. The integrated module can be realized in the form of hardware or in the form of a software functional module.

[0132] If the above-mentioned integrated modules are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable nonvolatile storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The aforementioned non-volatile storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0133] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A photovoltaic cleaning robot control method, characterized by, The application is applied to a photovoltaic cleaning robot control system, and the control system comprises: a state monitoring device configured to collect operation state data of the photovoltaic cleaning robot; an ultrasonic sensor configured to collect distance data of the photovoltaic cleaning robot from an edge of a photovoltaic panel or an obstacle; a camera configured to collect target image data of the photovoltaic panel; a laser radar configured to collect point cloud data on the photovoltaic panel; a single-chip microcomputer connected with the state monitoring device and the ultrasonic sensor, configured to receive the operation state data from the state monitoring device and the distance data from the ultrasonic sensor, and send the operation state data and the distance data to a master control device; the master control device connected with the single-chip microcomputer, configured to generate a control instruction based on the operation state data, the distance data, the target image data and the point cloud data, and send the control instruction to the single-chip microcomputer, wherein the control instruction at least comprises a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot; and the single-chip microcomputer is further configured to control the photovoltaic cleaning robot to perform a cleaning task for the photovoltaic panel based on the control instruction. The method comprises: acquiring operation state data of the photovoltaic cleaning robot, distance data of the photovoltaic cleaning robot from an edge of a photovoltaic panel or an obstacle, target image data of the photovoltaic panel, and point cloud data on the photovoltaic panel; determining a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot based on the operation state data, the distance data, the target image data and the point cloud data; generating a control instruction based on the motion control strategy and the cleaning strategy; determining target positioning data of the photovoltaic cleaning robot based on the distance data and the point cloud data, comprising: positioning the photovoltaic cleaning robot based on the distance data, the point cloud data and the target image data to obtain three groups of positioning data; calculating position differences between the three groups of positioning data, and determining at least two groups of positioning data with a position difference less than a preset difference threshold from the three groups of positioning data; and determining the target positioning data based on the at least two groups of positioning data; controlling the photovoltaic cleaning robot to act based on the control instruction.

2. The method of claim 1, wherein, The generating of the control instruction based on the operation state data, the distance data, the target image data and the point cloud data comprises: detecting whether the operation state data is within a predetermined operation range, wherein the operation state data at least comprises battery temperature and battery voltage of the photovoltaic cleaning robot, brush motor current of a brush motor on the photovoltaic cleaning robot, pressure data of an output pipeline of a liquid valve spraying device, and liquid flow of a liquid valve of the liquid valve spraying device; in a case where the operation state data is within the predetermined operation range, identifying foreign matter on the photovoltaic panel based on the target image data to obtain a foreign matter identification result, wherein the foreign matter identification result comprises volume and coverage area of the foreign matter; and The motion control strategy and the cleaning strategy are determined based on the foreign matter identification result and the target positioning data.

3. The method of claim 2, wherein, The foreign matter identification is performed on the photovoltaic panel based on the target image data to obtain a foreign matter identification result, in a case where the operation state data is within the predetermined operation range. In a case where the operation state data is within the predetermined operation range, a foreign matter identification model is used to obtain the foreign matter identification result based on the target image data, wherein the foreign matter identification model is obtained through machine learning based on a plurality of groups of image data, and the plurality of groups of image data respectively correspond to volumes and coverage areas of foreign matters.

4. The method of claim 2, wherein, The target positioning data of the photovoltaic cleaning robot is determined based on the distance data and the point cloud data, including: It is detected whether the distance between the photovoltaic cleaning robot and the edge of the photovoltaic panel is greater than a predetermined distance based on the distance data; In a case where the distance between the photovoltaic cleaning robot and the edge of the photovoltaic panel is greater than the predetermined distance, the target positioning data of the photovoltaic cleaning robot is determined based on the point cloud data.

5. The method of claim 2, wherein, The motion control strategy and the cleaning strategy are determined based on the foreign matter identification result and the target positioning data, including: The speed of a drive motor of the photovoltaic cleaning robot, and the speed of a roller brush motor and the opening degree of a liquid valve of a liquid valve spraying device are determined according to the foreign matter identification result; The motion control strategy is determined based on the target positioning data and the speed of the drive motor; The cleaning strategy is determined based on the speed of the roller brush motor and the opening degree of the liquid valve of the liquid valve spraying device.

6. An electronic device, comprising: The photovoltaic cleaning robot control method of any one of claims 1 to 5 is implemented by one or more processors and a memory storing one or more programs, wherein the one or more programs are executed by the one or more processors to cause the one or more processors to implement the photovoltaic cleaning robot control method.

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