Photovoltaic cleaning robot control method and system and electronic equipment

Abstract

The invention discloses a photovoltaic cleaning robot control method and system and electronic equipment. The method comprises the steps that state monitoring equipment is used for collecting running state data of the photovoltaic cleaning robot; the ultrasonic sensor is used for collecting distance data between the photovoltaic cleaning robot and the edge of the photovoltaic panel or an obstacle; the camera is used for collecting target image data of the photovoltaic panel; the laser radar is used for collecting point cloud data on the photovoltaic panel; the main control equipment is used for generating a control instruction based on the operation state data, the distance data, the target image data and the point cloud data sent by the single chip microcomputer, and sending the control instruction to the single chip microcomputer; the single-chip microcomputer is further used for controlling the photovoltaic cleaning robot to execute a cleaning task for the photovoltaic panel based on the control instruction. According to the invention, the technical problem of poor cleaning effect of the photovoltaic panel due to inaccurate control caused by incomplete data collected by a control system during control of the photovoltaic cleaning robot in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and in particular, to a photovoltaic cleaning robot control method, system and electronic equipment. Background Art

[0002] The new energy industry has developed rapidly in recent years, especially the photovoltaic industry, whose application has gradually become popular, and the use of photovoltaic panels has continued to increase. With the widespread application of photovoltaic power generation, the accumulation of dust, fallen leaves and other debris on photovoltaic panels has gradually emerged. These dirt will significantly reduce the energy conversion efficiency of photovoltaic panels, so the demand for photovoltaic panel cleaning is growing. Against this background, the research and development and application of photovoltaic panel cleaning robots have begun to rise. They use automation technology to efficiently clean large areas of photovoltaic panels, reduce the burden of manual labor, and reduce the use of water sources. At present, some photovoltaic cleaning solutions use technologies such as robotic arms and crawler robots to clean without damaging the surface of photovoltaic panels. At the same time, many cleaning robots are also equipped with intelligent navigation systems that can automatically identify dirty areas and formulate cleaning plans.

[0003] However, the photovoltaic panel cleaning technology in the related art still faces some challenges, and the cleaning methods in the related art are often inefficient or inconvenient. For example, the robotic arm solution is not only large in size, but also difficult and costly to implement in photovoltaic power stations with limited space. Small mechanical cleaning equipment usually cannot achieve automatic navigation, resulting in incomplete data collected when controlling photovoltaic cleaning robots. At the same time, it requires a lot of manpower, water or high energy consumption, which leads to poor photovoltaic panel cleaning effects.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a photovoltaic cleaning robot control method, system and electronic device to at least solve the technical problem in the related art that the control system collects incomplete data when controlling the photovoltaic cleaning robot, resulting in inaccurate control and poor photovoltaic panel cleaning effect.

[0006] According to one aspect of an embodiment of the present invention, a photovoltaic cleaning robot control system is provided, comprising: a state monitoring device for collecting operating state data of the photovoltaic cleaning robot; an ultrasonic sensor for collecting distance data of the photovoltaic cleaning robot from the edge or obstacle of a photovoltaic panel; a camera for collecting target image data of the photovoltaic panel; a laser radar for collecting point cloud data on the photovoltaic panel; a single-chip microcomputer connected to the state monitoring device and the ultrasonic sensor, for receiving the operating state data from the state monitoring device and the distance data from the ultrasonic sensor, and sending the operating state data and the distance data to a main control device; the main control device is connected to the single-chip microcomputer, for generating control instructions based on the operating state data, the distance data, the target image data and the point cloud data, and sending the control instructions to the single-chip microcomputer, wherein the control instructions at least include: a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot; the single-chip microcomputer is also used to control the photovoltaic cleaning robot to perform cleaning tasks for the photovoltaic panel based on the control instructions.

[0007] According to another aspect of an embodiment of the present invention, a photovoltaic cleaning robot control method is also provided, which is applied to any one of the control systems described above, and the method includes: acquiring operating status data of the photovoltaic cleaning robot, distance data of the photovoltaic cleaning robot from the edge or obstacle of a photovoltaic panel, 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 operating status data, the distance data, the target image data, and the point cloud data; generating control instructions based on the motion control strategy and the cleaning strategy; and controlling the action of the photovoltaic cleaning robot based on the control instructions.

[0008] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the photovoltaic cleaning robot control methods described.

[0009] In an embodiment of the present invention, a photovoltaic cleaning robot control system is provided, including a state monitoring device for collecting operating state data of the photovoltaic cleaning robot; an ultrasonic sensor for collecting distance data of the photovoltaic cleaning robot from the edge or obstacle of the photovoltaic panel; a camera for collecting target image data of the photovoltaic panel; a laser radar for collecting point cloud data on the photovoltaic panel; a single-chip microcomputer connected to the state monitoring device and the ultrasonic sensor, for receiving the operating state data from the state monitoring device and the distance data from the ultrasonic sensor, and sending the operating state data and the distance data to a main control device; the main control device is connected to the single-chip microcomputer, for generating a Control instructions are sent to the single-chip microcomputer, wherein the control instructions at least include: the motion control strategy and cleaning strategy of the photovoltaic cleaning robot; the single-chip microcomputer is also used to control the photovoltaic cleaning robot to perform the cleaning task for the photovoltaic panel based on the control instructions, thereby achieving the purpose of setting a control system integrating status monitoring equipment, ultrasonic sensors, cameras, lidars, single-chip microcomputers, and main control equipment, and accurately controlling the photovoltaic cleaning robot through the cooperation of the single-chip microcomputer and the main control equipment based on more comprehensive collected data, thereby achieving the technical effect of improving the control accuracy of the photovoltaic cleaning robot and the cleaning effect of the photovoltaic panels, thereby solving the technical problem in the related technology that the control system collects incomplete data when controlling the photovoltaic cleaning robot, resulting in inaccurate control, and then resulting in poor cleaning effect of the photovoltaic panels. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

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

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

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

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

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

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

[0017] Figure 7 Schematic diagram of the structure of a photovoltaic cleaning robot control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

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

[0021] A state monitoring device 10, used to collect operating state data of the photovoltaic cleaning robot;

[0022] The ultrasonic sensor 11 is used to collect distance data between the photovoltaic cleaning robot and the edge of the photovoltaic panel or obstacles;

[0023] Camera 12, used to collect target image data of photovoltaic panels;

[0024] A laser radar 13, used to collect point cloud data on the photovoltaic panel;

[0025] The single chip computer 14 is connected to the state monitoring device 10 and the ultrasonic sensor 11, and is used to receive the operation state data from the state monitoring device 10 and the distance data from the ultrasonic sensor 11, and send the operation state data and the distance data to the main control device 15;

[0026] The main control device 15 is connected to the single-chip computer 14 and is used to generate control instructions based on the operation status data, distance data, target image data and point cloud data, and send the control instructions to the single-chip computer 14, wherein the control instructions at least include: motion control strategy and cleaning strategy of the photovoltaic cleaning robot;

[0027] The single chip microcomputer 14 is also used to control the photovoltaic cleaning robot to perform cleaning tasks for the photovoltaic panels based on control instructions.

[0028] In this embodiment, the state monitoring device 10 collects the running state data of the robot, such as the battery state, the working condition of the motor, etc., to ensure the safe and efficient operation of the robot. The ultrasonic sensor 11 collects distance data to help the robot sense the distance between itself and the edge of the photovoltaic panel or other obstacles, achieve accurate navigation and avoid collision. The camera 12 and the laser radar 13 respectively obtain image and point cloud data, provide detailed information on the surface of the photovoltaic panel, including the cleaning state and possible damage, and provide a basis for the intelligent decision-making of the main 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 main control device 15 (such as a cloud server) through the communication module. The main control device 15 generates control instructions based on the received data, combined with the target image and point cloud data, to achieve remote monitoring and intelligent control. The control instructions include the robot's motion control strategy and cleaning strategy, which enables the robot to adjust the cleaning method and path according to the actual situation of the photovoltaic panel, and improve the cleaning effect and efficiency. The single-chip microcomputer 14 automatically controls the robot's movement and cleaning operations according to the received control instructions, reduces manual intervention, and improves the level of operation automation. The control system is also designed with safety protection mechanisms, such as immediately locking the motor when an abnormality is detected by the sensor or vision, and moving the robot to a safe position by using the unlock button of the remote control under the condition of ensuring safety, so as to ensure that the robot can operate safely under any circumstances and avoid damage to equipment and personnel. Through real-time monitoring and intelligent analysis, the main control device 15 can adjust the robot's cleaning strategy, including cleaning sequence, strength and range, to achieve the best cleaning effect. The combined use of LiDAR 13 and camera 12 enables the robot to identify complex structures and minor damage on photovoltaic panels, improving the accuracy and reliability of cleaning operations. By precisely controlling the cleaning operation, the control system can reduce the use of water resources and energy consumption.

[0029] In summary, the control system can realize comprehensive monitoring and remote intelligent control of photovoltaic cleaning robots by integrating multiple sensors and intelligent control technologies, significantly improving cleaning efficiency, effectiveness and safety. At the same time, it also reflects the design concept of automation, intelligence and environmental protection, and plays an important role in promoting cleaning management in the photovoltaic industry.

[0030] In an optional embodiment, the status monitoring device 10 includes: 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 status data includes battery temperature, battery voltage and 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 and avoid overheating that may cause battery performance degradation or safety issues. Real-time monitoring of battery temperature is essential 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 prevent overcharging or over-discharging of the battery, extend the battery life, and maintain the continuous working ability of the cleaning robot. The setting of the voltage detection circuit can ensure that the battery is powered in the best condition to avoid interrupting the cleaning operation 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 to ensure that the roller brush operates normally during the cleaning process and avoid motor damage caused by jamming or other faults. Monitoring of motor current is essential to ensure efficient execution of cleaning tasks and maintenance of equipment.

[0032] The operating status data includes battery temperature, battery voltage and roller brush current. These data are key indicators for evaluating the operating health of the photovoltaic cleaning robot. They are collected and transmitted to the single-chip microcomputer 14, and then sent to the main control device 15 by the single-chip microcomputer 14 through serial communication. The main control device 15 can understand the working status of the cleaning robot in real time by analyzing these data, and promptly discover potential problems, such as battery overheating or voltage abnormality, abnormal operation of the roller brush motor, 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 a decision-making basis for the main control device 15 to optimize the movement and cleaning strategy of the cleaning robot, improve the cleaning efficiency and effect, reduce resource waste, and ensure the smooth progress of the cleaning operation. These circuits and data acquisition methods together constitute an intelligent, efficient and safe photovoltaic cleaning robot lower computer control system.

[0033] Optional, Figure 2 is a schematic diagram of an optional battery temperature detection circuit structure according to an embodiment of the present invention, such as Figure 2 As shown, this circuit is a temperature sensor circuit, which uses a 3.3V constant voltage source of model REF3033AIDBZR. The temperature sensor is a thermal resistor 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. These resistors are high-precision resistors with an accuracy of one thousandth. REF3033AIDBZR is used to generate a reference source to improve the sampling accuracy. The function of this bridge is to convert the resistance value into an electrical signal through voltage division. It can effectively convert a resistor with a small change into the required electrical signal. For example, the measurement range can be modified by modifying the resistance value of R68. The temperature measurement range of this circuit is between -120℃ and 200℃. The temperature sensor circuit also includes a differential isolation amplifier U31, which has a fixed gain of 8 times. The output voltage range is 0-3.3V, and 105 is the capacitance value, which is 1 microfarad (uf). Its function is to suppress the noise on the power supply, thereby improving the accuracy of the voltage output. U31 receives the signal from the temperature sensor and processes it. The two differential signals output by U31 are connected to the two 12-bit ADC pins of the microcontroller (to convert the analog signal into a digital signal), 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 entire circuit is designed to accurately convert the temperature signal from the temperature sensor into a digital signal within the measurement range of -120℃ to 200℃.

[0034] Optional, Figure 3 is a schematic diagram of an optional battery voltage detection circuit structure according to an embodiment of the present invention, such as Figure 3As shown, first, in the battery voltage detection circuit, the voltage of the battery (voltage range is 23V-30V) is input to the isolation amplifier U3 and then divided by a resistor, wherein the voltage dividing resistor is a one thousandth high-precision resistor, and the battery voltage in the range of 23V-30V is reduced by 148 times for collection. The divided voltage is sent to the operational amplifier OPA2188AIDR through the J2 interface. This operational amplifier is used as a voltage follower or buffer to reduce the load effect and maintain the stability of the input voltage. At the same time, the filter circuit composed 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, which can reflect the voltage of the battery. This output signal can be used for subsequent single-chip microcomputer sampling to convert the battery voltage into a digital signal for further processing or monitoring. In addition, the battery voltage detection circuit also includes components or parameters such as the isolation amplifier AMC12006DWVR and a fixed 8-fold gain, which can be used to improve the isolation performance or gain of the circuit. 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 supply voltage.

[0035] Optional, Figure 4 is a schematic diagram of an optional roller brush current detection circuit structure according to an embodiment of the present invention, such as Figure 4 As shown in the figure, the roller brush current detection circuit is mainly composed of a Hall effect current sensor (TMCS1108A2UQDR) as the core, which is used to detect the current flowing through the small motor. The circuit also includes a power supply (3V3) to provide power for the entire circuit; the power line of the roller brush motor flows into the input terminals (IN+ and IN-) of the current sensor (U2) through terminal J1, and the current direction from IN+ to IN- is considered positive polarity. The current sensor is highly integrated and simplifies the peripheral circuit. It converts the current signal into a voltage signal at a fixed ratio and outputs it from the output terminal (VOUT), which has high linearity. This voltage signal can be sampled by the subsequent single-chip microcomputer and converted into a digital signal for further processing or monitoring. The entire circuit can realize accurate detection of the current of the small motor through the electrically isolated Hall effect current sensor, which has the characteristics of high precision and good isolation.

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

[0037] Optionally, the pressure sensor is arranged at the outlet of the liquid valve spray device, and its function is to collect the pressure data in the output pipeline in real time. Pressure data is crucial for evaluating the performance of the spray device. It helps 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 result of the pressure sensor is transmitted to the main control device 15 through the single-chip microcomputer 14 as part of the photovoltaic cleaning robot control system to adjust the working state of the spray 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 spray device to accurately measure the liquid flow through the liquid valve. Monitoring of flow data helps to optimize the use of water resources in the cleaning process and avoid excessive spraying or insufficient spraying. By real-time monitoring of the flow, the control system can adjust the liquid flow of the spray device according to the actual situation and cleaning requirements of the photovoltaic panel to achieve efficient and environmentally friendly cleaning operations. By setting the state monitoring device 10, it not only includes a battery temperature detection circuit, a battery voltage detection circuit and a roller brush current detection circuit, but also further includes a pressure sensor and a flow meter. These sensors jointly collect key operating status data of the photovoltaic cleaning robot, including battery status, roller brush motor operation, spray device pressure and flow, etc., providing comprehensive information support for the robot's intelligent control.

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

[0039] In an optional embodiment, the control system also includes: a manual control device, wherein the manual control device is connected to 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 also used to control the movement of the photovoltaic cleaning robot based on the remote control instructions.

[0040] Optionally, a manual control device, i.e., a remote control handle, is wirelessly connected to the single-chip microcomputer 14 in the control system, providing a means for the user to directly intervene in the robot's motion and cleaning strategy. The remote control has the function of sending remote control instructions, which include the motion control strategy and cleaning strategy of the photovoltaic cleaning robot. This means that the user can manually control the cleaning path, speed, and start and stop of the cleaning operation of the photovoltaic cleaning robot through the remote control. This manual control mode provides a supplement to the automatic mode and increases the flexibility of operation. During the operation of the photovoltaic cleaning robot, if special or complex situations are encountered, such as obstacles that the automatic navigation system cannot identify or specific cleaning needs, the user can send remote control instructions through the remote control to directly adjust the robot's motion and cleaning strategy. The sending of remote control instructions provides users with direct control capabilities over 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 instruction, it will parse the instruction content and adjust the robot's movements according to the remote control instruction. This may include, but is not limited to, adjusting the robot's motion direction and speed, and controlling the start and stop of cleaning devices (such as roller brush motors and hydraulic spray devices). This control response mechanism of the single chip microcomputer 14 can ensure that the user's instructions can be accurately executed in the manual control mode, so that the photovoltaic cleaning robot can perform cleaning work according to the user's requirements.

[0041] In summary, the introduction of the manual control device provides a dual control mode for the photovoltaic cleaning robot control system: automatic navigation control and manual remote control. In the automatic mode, the robot works according to the control instructions of the main control device 15; when necessary, the user can switch to the manual mode and control the robot directly through the remote control. This design not only increases the flexibility of the control system, but also provides the ability to perform manual intervention in complex or special situations, ensuring the efficiency and safety of the cleaning operation. At the same time, the connection between the remote control and the single-chip microcomputer 14 enables the user to monitor the operating status of the robot in real time and quickly adjust its working mode when necessary, which can provide a more comprehensive and intelligent control solution for the operation of the photovoltaic cleaning robot.

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

[0043] Optionally, the cooling fan is an important component for active heat dissipation in the control system, and is connected to 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 lot of heat, especially key components such as batteries and single-chip microcomputers 14. Excessive temperature may affect their performance and life, and even cause control system failure. 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 include 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 a basis for generating heat dissipation instructions. The single-chip microcomputer 14 can automatically generate heat dissipation start instructions and heat dissipation stop instructions based on the received temperature data. For example, when the temperature of the battery or other key components reaches a preset threshold, the single-chip microcomputer 14 will trigger the heat dissipation start instruction, so that the heat dissipation fan starts, thereby reducing the temperature and protecting 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 status of the cooling fan according to real-time temperature data, ensuring that the photovoltaic cleaning robot maintains good working condition and equipment safety in complex environments and long-term operations. This design can avoid the problem of excessive or insufficient heat dissipation, optimize the energy consumption and efficiency of the equipment, and extend the service life of batteries and key components.

[0044] In an optional embodiment, the control system also includes: a fill light, wherein the fill light is connected to the single-chip microcomputer 14, and is used to turn on when receiving a fill light start command issued by the single-chip microcomputer 14, and to turn off when receiving a fill light stop command issued by the single-chip microcomputer 14.

[0045] Optionally, the fill light is connected to the single-chip microcomputer 14 as a component of the control system, and is mainly used to improve the imaging quality of the camera 12 under insufficient light conditions. When the robot works in a dark environment, such as in the evening, early morning or cloudy, the fill light can provide additional lighting to ensure that the camera 12 can clearly capture the state of the photovoltaic panel, which is crucial for automatic navigation and the improvement of cleaning efficiency. The opening and closing of the fill light is automatically controlled by the single-chip microcomputer 14 according to the ambient light conditions and the requirements of the cleaning operation. When the single-chip microcomputer 14 detects that the light intensity is lower than the preset threshold, or when a clearer image is required for the cleaning operation, it will send a fill light start instruction to the fill light and turn on the fill light. On the contrary, when the lighting conditions are met or the cleaning operation is completed, the single-chip microcomputer 14 will send a fill light stop instruction to turn off the fill light. This intelligent control mechanism can not only ensure the visual requirements of the cleaning operation, but also save energy and avoid unnecessary power consumption. The connection between the fill light and the single-chip microcomputer 14 can reflect the close integration and coordinated work of the various components in the control system. During the operation of the cleaning robot, the single chip microcomputer 14 determines whether to turn on or off the fill light according to various sensor data received (such as data from the light intensity sensor), thereby ensuring 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 invention proposes an optional implementation mode of a photovoltaic cleaning robot control system. Figure 5 is a schematic structural diagram of an optional photovoltaic cleaning robot control system according to an embodiment of the present invention, such as Figure 5 As 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 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 status using a voltage sensor to prevent the battery from being overcharged or over-discharged and to protect the battery life.

[0050] The roller brush motor current detection circuit monitors the current of the roller brush motor in real time through the current sensor to ensure the normal operation of the roller brush and avoid damage caused by stalling.

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

[0052] The lower computer includes an isolation 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 the STM32RCT6 single chip microcomputer 14) serves as the core control unit of the control system and is responsible for data processing and execution of control instructions.

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

[0056] The communication module is used to realize remote data transmission with the host computer through serial communication, and realize data interaction with the motor drive through RS485 communication to control or read the parameters of the motor. It supports ModBus protocol and reserves two channels of RS232 communication and CAN communication.

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

[0058] The cleaning equipment includes a roller brush and a hydraulic spray device, including:

[0059] The roller brush is used for cleaning operations and has high efficiency in cleaning and can effectively remove dirt from photovoltaic panels.

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

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

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

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

[0064] The laser radar 13 is used for environmental perception and obstacle detection, and assists in realizing the automatic navigation function.

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

[0066] The motor drive module supports the MODBUS communication protocol based on RS485 to exchange data with the control system.

[0067] The maximum power of the motor reaches 400 watts, which meets the power requirements of the robot's movement and cleaning.

[0068] The battery is used to provide 24V output voltage with a capacity of 30AH to ensure continuous power supply for the control system during long-term operation.

[0069] Remote controller, used to provide users with convenient manual control operations.

[0070] The photovoltaic cleaning robot has two control modes: remote control and automatic navigation control. By default, it is in automatic navigation mode. When the remote control is not turned on, automatic navigation is achieved by the host computer in combination with the laser radar 13. The host computer controls the lower computer through real-time transmitted data to adjust the rotation of the motor. At the same time, the host computer uses the camera 12 to record the status of the photovoltaic panel in order to monitor the cleaning effect and damage in real time.

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

[0072] It should be noted that, in this embodiment, through multi-sensor integration, combined with battery temperature, voltage, current and ultrasonic sensor 11, comprehensive state monitoring is achieved to ensure the safe operation of the equipment. STM32RCT6 single-chip microcomputer 14 is used as the core control unit, with efficient data processing capabilities, supporting multiple communication protocols (such as RS485 and ModBus), and flexible data interaction is achieved. The robot has two modes of automatic navigation and remote control, which can be flexibly switched according to user needs to improve the convenience of operation. The cleaning state and damage of the photovoltaic panel are recorded by camera 12, and the video stream is transmitted to the cloud for remote monitoring and data analysis. The motor locking mechanism is designed to ensure that the motor is stopped immediately when the sensor alarms, and it can only be unlocked under specific conditions to ensure safe operation. The isolation measures are taken for each module of the lower computer and the external interface to ensure the safe and stable operation of the equipment. The integrated roller brush motor and hydraulic spray device improve the cleaning efficiency and ensure the cleaning effect of the photovoltaic panel. Through modular design, each module is connected through a standard interface, which is convenient for maintenance and upgrading, and enhances the scalability of the system.

[0073] In this 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) Improved productivity: Through 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 productivity of cleaning work. 2) Improved quality: The real-time monitoring function of a variety of sensors can ensure a comprehensive evaluation of the status of photovoltaic panels during the cleaning process, thereby improving the quality of the cleaning effect and reducing the appearance of uncleaned areas. 3) Improved accuracy: The introduction of edge detection function can ensure that the robot avoids collision and damage to photovoltaic panels during the cleaning process, improving the accuracy and safety of operation. 4) Improved efficiency: Multi-sensor integration and intelligent control system make the cleaning process more automated, reduce human intervention, and improve work efficiency, which is more advantageous than traditional manual cleaning methods. 5) Reduced energy consumption: The use of high-efficiency motors and crawler motion devices optimizes energy consumption, reduces energy consumption, and achieves efficient operation when the photovoltaic panels are tilted. 6) Saving of raw materials: By accurately controlling the use of the spray device and the cleaning frequency, it can effectively reduce resource waste and save water consumption. 7) Ease of operation: The control system also integrates remote control and automatic navigation functions, making user operation easier, lowering the technical threshold, and requiring lower operators. 8) Easy maintenance: The 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 effectively optimize the cleaning operation of photovoltaic panels by improving productivity, quality, accuracy and efficiency while reducing energy consumption and raw material usage, and plays an important role in promoting the cleaning management of the photovoltaic industry.

[0075] It should be noted that in this application Figure 1 and Figure 5 The specific structure of the photovoltaic cleaning robot control system shown in the figure is only for reference. In specific applications, the photovoltaic cleaning robot control system in this application can be compared to Figure 1 and Figure 5 The photovoltaic cleaning robot control system shown has more or less structure.

[0076] According to an embodiment of the present invention, a method embodiment of a photovoltaic cleaning robot control is provided. It should be noted that the steps shown in the flowchart in 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 flow chart of a photovoltaic cleaning robot control method according to an embodiment of the present invention, the method is applied to any of the aforementioned control systems, such as Figure 6 As shown, the method comprises the following steps:

[0078] Step S602, obtaining the running status data of the photovoltaic cleaning robot, the distance data between the photovoltaic cleaning robot and the edge or obstacle of the photovoltaic panel, the target image data of the photovoltaic panel, and the point cloud data on the photovoltaic panel;

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

[0080] Optionally, the operating status data includes but is not limited to the battery status (temperature, voltage), the operating current of the roller brush motor, and the real-time health status of the robot. By continuously monitoring and collecting this data through the sensor module, the microcontroller can understand the operating status of the robot in real time, which is essential for preventing equipment failures, maintaining battery safety, and ensuring the normal operation of the roller brush motor. The distance data provided by the ultrasonic sensor helps the robot sense its distance from the edge of the photovoltaic panel or surrounding obstacles. This data is essential for edge detection and automatic navigation functions, ensuring that the robot does not collide with the photovoltaic panel or other objects during the cleaning process, improving the safety and efficiency of the cleaning process. The target image data refers to the real-time image of the photovoltaic panel captured by the camera. These image data are not only used to record the cleaning process, but also to identify the dirty areas and cleanliness of the photovoltaic panel through image analysis, helping to formulate more accurate cleaning strategies. The real-time transmission and analysis of image data enables the remote host computer to obtain visual feedback of the cleaning process and further optimize the control strategy. The point cloud data is generated by the lidar, which provides three-dimensional position information of the robot's surrounding environment, including irregularities on the surface of the photovoltaic panel and the specific location of obstacles. This data is crucial for autonomous navigation systems, helping robots build environmental maps, plan optimal cleaning paths, and avoid getting lost or colliding in complex terrain.

[0081] Combined with these data, the control method can comprehensively evaluate the robot's operating status, surrounding environment, and cleaning needs, and generate dynamic motion control strategies and cleaning strategies. For example, when the MCU detects that the battery temperature is too high or the voltage is too low, it may adjust the cleaning strategy, reduce the cleaning frequency, or suspend the cleaning operation to protect the battery. At the same time, based on the distance data and point cloud data, the MCU can plan the robot's path to avoid collisions, and adjust the use of cleaning equipment based on the target image data to ensure the accuracy of the cleaning area and maximize the cleaning effect. The above methods reflect the characteristics of intelligence and automation, and can significantly improve the work efficiency and operational safety of photovoltaic cleaning robots.

[0082] Step S604, determining a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot based on the operating status data, the distance data, the target image data, and the point cloud data;

[0083] Optionally, the motion control strategy and cleaning strategy are generated by integrating all the above-mentioned operating status data, distance data, target image data and point cloud data. The motion control strategy may include but is not limited to the robot's moving direction, speed and path planning; the cleaning strategy may include but is not limited to the start, stop, cleaning mode (such as strong cleaning or gentle cleaning), cleaning sequence, and the speed of the roller brush motor and the liquid valve opening of the liquid valve spray device. Based on these strategies, specific control instructions can be generated in the future to command the photovoltaic cleaning robot to perform cleaning tasks according to the established strategy while ensuring its safety and efficiency.

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

[0085] Optionally, firstly, the type, volume and coverage area of ​​foreign matter on the photovoltaic panel are analyzed according to the results obtained by the foreign matter recognition model. This information is crucial for the formulation of cleaning strategies, because different foreign matter may require different cleaning strengths and methods. For example, foreign matter with large volume and strong adhesion can be completely removed by a higher roller brush motor speed and a larger liquid valve spray opening; while dirt with a wide coverage area but slight dirt can be removed by adjusting the working mode of the roller brush and the spray device to balance the cleaning effect and resource conservation. Based on the foreign matter recognition results, the speed of the drive motor is determined according to the cleaning requirements of the photovoltaic panel and the current position of the robot. If there are more foreign matter in a certain area or more detailed cleaning is required, the speed of the drive motor can be slowed down to allow the robot to stay in the area for a longer time to ensure thorough cleaning. On the contrary, if fewer foreign matter are identified or the cleaning requirements are not large, the speed of the drive motor can be increased to speed up the movement speed of the robot and improve the cleaning efficiency. The speed of the roller brush motor and the liquid valve opening of the liquid valve spray 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 foreign objects with large volume and strong adhesion, a higher roller brush motor speed and a larger liquid valve opening are required to increase the cleaning force; while for light dust, a lower roller brush motor speed and a moderate liquid valve opening can be used to save water resources and electricity. The motion control strategy of the photovoltaic cleaning robot is based on the target positioning data and the determined drive motor speed. According to the three-dimensional terrain information constructed by the point cloud data, combined with the edge detection and obstacle avoidance algorithm, the shortest path and optimal speed of the robot from the current position to the next cleaning point are planned. This includes adjusting the direction and speed of the robot to ensure that the robot can move efficiently along the planned route while avoiding collisions with the edges of the photovoltaic panels or other obstacles. The cleaning strategy is based on the parameter adjustment of the roller brush motor and the liquid valve spray device. According to the foreign object recognition results, corresponding cleaning strategies are formulated for different cleaning areas and foreign object types, such as the speed of the roller brush, the strength and range of the spray. This ensures that each area of ​​the photovoltaic panel can be cleaned with appropriate intensity while reducing unnecessary energy and water consumption. Finally, according to the determined motion control strategy and cleaning strategy, control instructions are generated, such as adjusting the parameters of the drive motor, roller brush motor and liquid valve spray device. These instructions are sent to corresponding actuators, such as motor drive modules and cleaning equipment control modules, to achieve precise control of the actions of the photovoltaic cleaning robot and ensure that it completes the cleaning task according to the established strategy.

[0086] Through this embodiment, intelligent decision-making based on data can be achieved, ensuring that the photovoltaic cleaning robot can automatically adjust its movement and cleaning strategy according to the real-time environment and cleaning needs, and achieve efficient and accurate cleaning operations. This strategy formulation process fully reflects the close integration and coordination between the control system data processing, strategy formulation and action execution, which is a key innovation in photovoltaic cleaning robot technology.

[0087] Step S606, generating a control instruction based on the motion control strategy and the cleaning strategy;

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

[0089] In an optional embodiment, a control instruction is generated based on the operating status data, distance data, target image data and point cloud data, including: detecting whether the operating status data is within a predetermined operating range, wherein the operating status data at least includes: the battery temperature and battery voltage of the photovoltaic cleaning robot, the roller brush current of the roller brush motor on the photovoltaic cleaning robot, the pressure data of the output pipeline of the liquid valve spray device, and the liquid flow rate of the liquid valve of the liquid valve spray device; when the operating status data is within the predetermined operating range, foreign object recognition is performed on the photovoltaic panel based on the target image data to obtain a foreign object recognition result, wherein the foreign object recognition result includes the volume and coverage area of ​​the foreign object; based on the distance data and the point cloud data, the target positioning data of the photovoltaic cleaning robot is determined; based on the foreign object recognition result and the target positioning data, the motion control strategy and the cleaning strategy are determined.

[0090] Optionally, first check whether the operating status data is within the predetermined operating range. These data include battery temperature and voltage, roller brush motor current, output pipe pressure and liquid flow of the liquid valve spray device, etc. If these data are beyond the normal range, such as battery overheating or low voltage, or roller brush motor current abnormality (which may indicate blockage or wear), or spray device pressure and flow abnormality, corresponding safety instructions will be generated immediately, such as pausing the cleaning operation, starting the cooling fan or adjusting the spray parameters to ensure the safe operation of the equipment. After confirming that the photovoltaic cleaning robot is operating normally, the target image data captured by the camera will be used for foreign object recognition. It can be, but is not limited to, through image processing algorithms to identify foreign objects on photovoltaic panels, such as dust, leaves, bird droppings, etc., and calculate the volume and coverage area of ​​the foreign objects. This information is crucial for formulating cleaning strategies, which can help determine which areas need to be cleaned, as well as the intensity and method of cleaning (for example, whether a spray device needs to be used, roller brush speed adjustment, etc.). The distance data provided by the LiDAR and ultrasonic sensors, as well as the point cloud data generated by the LiDAR, are used to determine the precise location of the photovoltaic cleaning robot and detailed information about the surrounding environment. These data are used to build an environmental map and determine the robot's target positioning data, that is, the specific location on the photovoltaic panel that the robot needs to reach, thereby ensuring that the robot can move along the optimal path, avoid collisions with edges or obstacles, and accurately find the area that needs to be cleaned. Combining the results of foreign body recognition 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 a path, adjusting the moving speed, etc. The cleaning strategy involves how the cleaning equipment works, such as adjusting the amount of water sprayed by the spray device, the speed of the roller brush motor, and the cleaning sequence. Through the precise control of these strategies, the system can ensure that the photovoltaic cleaning robot completes the cleaning task efficiently and accurately.

[0091] Optionally, based on the above strategy, the single chip microcomputer generates specific control instructions, including but not limited to starting the roller brush motor, adjusting the parameters of the spray device, controlling the driving direction and speed of the drive motor, and starting the cooling fan or fill light when necessary. These instructions are sent to the corresponding execution modules, such as the motor drive module, the cleaning equipment control module, etc. The actuator performs actual actions according to the instructions to ensure that the photovoltaic cleaning robot performs cleaning operations according to the formulated strategy, thereby realizing refined management and control of the photovoltaic cleaning robot operation.

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

[0093] Optionally, before performing foreign body recognition, verify whether the operating status data is within the predetermined operating range. This is a basic step to ensure the safety and normal operation of the robot. If the data is abnormal, corresponding safety measures will be taken, such as suspending cleaning or starting a heat dissipation mechanism. The foreign body recognition model is obtained by machine learning technology using multiple sets of training image data and their corresponding foreign body volumes and coverage areas. These training data include images of various common foreign bodies on photovoltaic panels, such as dust, leaves, bird droppings, etc., as well as the volume and coverage area information of these foreign bodies. Through machine learning algorithms (such as deep learning convolutional neural networks, support vector machines, etc.), the model can learn the characteristics of foreign bodies and their performance in images, so as to have the ability to recognize foreign bodies in new images. When the operating status is confirmed to be normal, the target image data captured by the camera of the photovoltaic cleaning robot is further analyzed. These data contain real-time images of the surface of the photovoltaic panel, which are input into the foreign body recognition model after image preprocessing (such as denoising, contrast enhancement, etc.). The foreign body recognition model takes the target image data as input and outputs the foreign body recognition results. These results include the volume and coverage area of ​​foreign bodies on the photovoltaic panel, which help determine the area that needs to be cleaned and its severity. For example, larger foreign objects may require stronger cleaning force, while dirt covering a wide area may require a wider cleaning range. Based on the identified foreign object volume and coverage area, the system can adjust the cleaning strategy, such as adjusting the speed of the roller brush motor, the amount of water sprayed by the spray device, and deciding whether to use high pressure or gentle cleaning mode. For example, if the volume and coverage area of ​​the foreign object are large, increase the speed of the roller brush motor and increase the amount of water sprayed by the spray device; if the volume and coverage area of ​​the foreign object are small, reduce the speed of the roller brush motor and reduce the amount of water sprayed by the spray device. This ensures the pertinence and efficiency of the photovoltaic panel cleaning process, avoiding waste of resources and potential damage to the photovoltaic panels.

[0094] Optionally, the foreign body recognition 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 to retrain the model so that it can better adapt to various complex photovoltaic panel surface conditions and improve its recognition and cleaning capabilities.

[0095] In this embodiment, by adopting machine learning technology to identify foreign objects, the photovoltaic cleaning robot control system can realize more intelligent and accurate cleaning strategy formulation, which can not only improve the cleaning efficiency, but also ensure the comprehensiveness and pertinence of cleaning, effectively respond to the challenges in photovoltaic panel cleaning, and provide key technical support for realizing the automation and intelligence of photovoltaic cleaning.

[0096] In an optional embodiment, the target positioning data of the photovoltaic cleaning robot is determined based on the distance data and the point cloud data, 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; when the distance between the photovoltaic cleaning robot and the edge of the photovoltaic panel is greater than the predetermined distance, determining the target positioning data of the photovoltaic cleaning robot based on the point cloud data.

[0097] Optionally, the distance data provided by the ultrasonic sensor and the lidar are 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 cross the boundary or collide with the photovoltaic panel during the cleaning process. If the distance between the robot and the edge is less than the predetermined minimum safety distance, a deceleration or stop instruction 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 robot is continuously checked to see if the distance between the robot and the edge of the photovoltaic panel is greater than a preset safety distance, which takes into account the size of the photovoltaic panel, the size of the cleaning robot, and the errors that may occur during the cleaning process. 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 proceed to the next step. Once the distance to the edge is confirmed to be safe, the point cloud data generated by the lidar is further used to construct a three-dimensional model of the robot's surroundings. The point cloud data includes not only the distance to the edge, but also the structural information on the surface of the photovoltaic panel and the location of any obstacles. Based on this data, the microcontroller 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 and the terrain features on the surface of the photovoltaic panel, which is the key basis for planning the robot's cleaning path and action. Through the analysis of integrated edge distance detection and point cloud data, 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 terrain characteristics of the photovoltaic panel. Based on this data, the control system can intelligently plan the robot's cleaning path 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-up and adjustment instructions of the cleaning equipment. These instructions ensure that the photovoltaic cleaning robot can complete the cleaning operation efficiently and safely according to the established strategy.

[0098] In the above methods, the automation and safety of the cleaning process can be ensured by accurately sensing and controlling the robot's position. This positioning method based on distance and point cloud data, combined with the instant feedback of ultrasonic sensors and high-precision scanning of lidar, 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 photovoltaic panels or the risk of collision with itself, thereby significantly improving cleaning efficiency and safety.

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

[0100] Optionally, ultrasonic sensors and lidar 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 lidar contains three-dimensional information about the robot's surroundings, including the undulations of the photovoltaic panel surface and the location of obstacles. The surface image of the photovoltaic panel captured by the camera provides visual positioning information to help the robot identify specific cleaning areas and obstacles. The distance data is used for preliminary positioning to ensure the safe movement of the robot within the cleaning range of the photovoltaic panel and avoid collisions with the edge or obstacles. The point cloud data provides more refined three-dimensional positioning, including the precise coordinates and orientation of the robot relative to the photovoltaic panel, as well as detailed perception of environmental obstacles, for more complex path planning. The target image data is used for visual positioning, determining the specific position of the robot on the photovoltaic panel through image recognition, and 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 by algorithms to improve the accuracy and reliability of positioning. For example, a comprehensive positioning result can be obtained by combining the immediacy of distance data, the accuracy of point cloud data, and the intuitiveness of image data through methods such as weighted average or optimal selection. Finally, the most accurate and reliable target positioning data is determined based on the three sets of positioning data. This data not only includes the robot's coordinate position, but also its orientation, posture, and the terrain characteristics 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 to ensure that it reaches the area that needs to be cleaned accurately; at the same time, the working parameters of the cleaning equipment can be adjusted according to the terrain characteristics, such as the speed of the roller brush and the amount of water sprayed by the spray device, to meet 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 operations, but also enhances the robot's adaptability and safety. The comprehensive use of multiple sensors and data fusion technology provide strong technical support for the robot's autonomous navigation and operation in complex environments, which is a major innovation in this technical solution.

[0103] In an optional embodiment, the target positioning data is determined based on three sets of positioning data, 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 from the three sets of positioning data whose position difference is less than a preset difference threshold; and determining the target positioning data based on at least two sets of positioning data.

[0104] Optionally, the determination of the target positioning data is achieved through two data fusion methods, namely weighted calculation and position difference comparison. These two methods are intended to integrate data from different sensors to improve the accuracy and reliability of positioning. In the weighted calculation method, different weights are assigned to each type of positioning data according to their accuracy and reliability. For example, lidar point cloud data usually provides high-precision three-dimensional positioning information and may be assigned a higher weight; while ultrasonic sensor data is effective at short distances, but may have lower accuracy at long distances or in complex environments, so it may be assigned a lower weight. The three sets of positioning data are weighted to combine their information. Weighted calculations can use linear combinations or more complex mathematical models, such as least squares or Kalman filtering, to fuse data. The resulting 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 difference between the ultrasonic sensor data, the laser radar point cloud data and the upper computer positioning data is 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 means that their positioning results are relatively consistent and can be considered reliable. At least two sets of data with a position difference less than the preset threshold are selected from the three sets of positioning data, and then the final target positioning data is determined based on these two or more sets of data. This is also a data fusion strategy. By comparing and screening, the most consistent positioning data is selected to improve the accuracy and robustness of positioning. Regardless of whether weighted calculation or position difference comparison is adopted, the determination of target positioning data is to ensure the precise positioning of the robot during the cleaning process. The positioning data includes the coordinates and direction of the robot and the terrain 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 precise control instructions to guide the robot to move on the photovoltaic panel, avoid obstacles, and ensure full coverage of the cleaning area to improve cleaning efficiency.

[0106] It should be noted that in the process of data fusion, data security and efficiency will be given priority. For example, in sensitive areas such as the edge of a photovoltaic panel, ultrasonic sensor data may be more relied upon because it can provide immediate close-range feedback and help approach the edge more safely; in open areas, lidar point cloud data may be given a 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 data from multiple sensors, improve positioning accuracy and reliability, and provide a solid foundation for the robot's intelligent cleaning operations. This precise positioning capability helps to improve cleaning efficiency while ensuring the safety of operation, and is an important innovation in photovoltaic cleaning robot technology.

[0107] Step S608, controlling the actions of the photovoltaic cleaning robot based on the control instructions.

[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 volume of the hydraulic spray device, driving the motor to move along the planned path, and starting the cooling fan or fill light when necessary. In this way, the photovoltaic cleaning robot can flexibly respond to various cleaning scenarios to ensure that the photovoltaic panels are fully and effectively cleaned.

[0109] Through the above steps S102 to S108, it is possible to set up a control system that integrates status monitoring equipment, ultrasonic sensors, cameras, lidars, single-chip microcomputers, and main control equipment, and accurately control the photovoltaic cleaning robot based on more comprehensive collected data, thereby achieving the technical effect of improving the control accuracy of the photovoltaic cleaning robot and the cleaning effect of the photovoltaic panels, and further solving the technical problem in the related technology that the control system in the photovoltaic cleaning robot controls the photovoltaic cleaning robot and collects incomplete data, resulting in inaccurate control, and then leading to poor cleaning effect of the photovoltaic panels.

[0110] In this embodiment, a photovoltaic cleaning robot control device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0111] According to an embodiment of the present invention, there is also provided an apparatus embodiment for implementing the above photovoltaic cleaning robot control method. Figure 7 is a schematic structural diagram of a photovoltaic cleaning robot control device according to an embodiment of the present invention. Figure 7 As shown, the 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 operation status data of the photovoltaic cleaning robot, the distance data between the photovoltaic cleaning robot and the edge or obstacle of the photovoltaic panel, the target image data of the photovoltaic panel, and the point cloud data on the photovoltaic panel;

[0113] A strategy determination module 702, connected to the data acquisition module 700, is used to determine the motion control strategy and cleaning strategy of the photovoltaic cleaning robot based on the operating status data, distance data, target image data and point cloud data;

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

[0116] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be 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, and control module 706 correspond to steps S602 to S608 in the embodiment, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above 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 implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

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

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

[0121] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any of the photovoltaic cleaning robot control methods.

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

[0123] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: obtain the operating status data of the photovoltaic cleaning robot, the distance data between the photovoltaic cleaning robot and the edge or obstacle of the photovoltaic panel, the target image data of the photovoltaic panel, and the point cloud data on the photovoltaic panel; based on the operating status data, distance data, target image data and point cloud data, determine the motion control strategy and cleaning strategy of the photovoltaic cleaning robot; based on the motion control strategy and cleaning strategy, generate control instructions; based on the control instructions, control the movement of the photovoltaic cleaning robot.

[0124] According to an embodiment of the present application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein when the program is run, any one of the photovoltaic cleaning robot control methods is executed.

[0125] According to an embodiment of the present application, an embodiment of a computer program product is also provided. When executed on a data processing device, it is suitable for executing a program that initializes any of the above-mentioned photovoltaic cleaning robot control method steps.

[0126] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein the processor implements any one of the above-mentioned steps of the photovoltaic cleaning robot control method when executing the program.

[0127] The above sequence of the embodiments of the present invention is for description only and does not represent the superiority or inferiority of the embodiments.

[0128] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above modules can be a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

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

[0131] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0132] If the above-mentioned integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution 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 enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0133] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A photovoltaic cleaning robot control system, characterized in that: include: Status monitoring equipment, used to collect operating status data of the photovoltaic cleaning robot; An ultrasonic sensor, used to collect distance data between the photovoltaic cleaning robot and the edge of the photovoltaic panel or obstacles; A camera, used to collect target image data of the photovoltaic panel; A laser radar, used to collect point cloud data on the photovoltaic panel; A single chip microcomputer, connected to the state monitoring device and the ultrasonic sensor, for receiving the operation state data from the state monitoring device and the distance data from the ultrasonic sensor, and sending the operation state data and the distance data to a main control device; The main control device is connected to the single-chip microcomputer and is used to generate a control instruction based on the running status 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; The single chip microcomputer is also used to control the photovoltaic cleaning robot to perform cleaning tasks for the photovoltaic panel based on the control instructions.

2. The control system according to claim 1, characterized in that: The condition monitoring device comprises: A battery temperature detection circuit, used to collect the battery temperature of the photovoltaic cleaning robot; A battery voltage detection circuit, used to collect the battery voltage of the photovoltaic cleaning robot; A roller brush current detection circuit, used for collecting the roller brush current of the roller brush motor on the photovoltaic cleaning robot; The operating status data includes the battery temperature, the battery voltage and the roller brush current.

3. The control system according to claim 2, characterized in that: The condition monitoring device also includes: A pressure sensor is disposed at the outlet of the liquid valve spray device of the photovoltaic cleaning robot and is used to collect pressure data of an output pipeline of the liquid valve spray device; a flow meter, disposed at a predetermined position of the liquid valve of the liquid valve spraying device, for collecting the liquid flow passing through the liquid valve; Wherein, the state monitoring device also includes the pressure data and the liquid flow rate.

4. The control system according to claim 2, characterized in that: The control system further comprises: a manual control device, wherein: The manual control device is connected to the single-chip microcomputer and is used to send remote control instructions to the single-chip microcomputer, wherein the remote control instructions include at least one of the following: the motion control strategy and the cleaning strategy of the photovoltaic cleaning robot; The single chip microcomputer is also used to control the action of the photovoltaic cleaning robot based on the remote control instruction.

5. The control system according to claim 2, characterized in that: The control system further includes: a cooling fan, wherein: The cooling fan is connected to the single-chip microcomputer, and is used to be turned on when receiving a cooling start instruction issued by the single-chip microcomputer, and turned off when receiving a cooling stop instruction issued by the single-chip microcomputer; The single chip microcomputer is also used to generate the heat dissipation start instruction and the heat dissipation stop instruction based on the temperature data collected by the monitoring device.

6. The control system according to claim 2, characterized in that: The control system further includes: a fill light, wherein: The fill light is connected to the single chip microcomputer, and is used to be turned on when a fill light start instruction sent by the single chip microcomputer is received, and turned off when a fill light stop instruction sent by the single chip microcomputer is received.

7. A photovoltaic cleaning robot control method, characterized in that: Applied to the control system according to any one of claims 1 to 6, the method comprising: Acquire the running status data of the photovoltaic cleaning robot, the distance data of the photovoltaic cleaning robot from the edge or obstacle of the photovoltaic panel, the target image data of the photovoltaic panel, and the point cloud data on the photovoltaic panel; Determining a motion control strategy and a cleaning strategy of the photovoltaic cleaning robot based on the operating status data, the distance data, the target image data, and the point cloud data; Based on the motion control strategy and the cleaning strategy, generating a control instruction; Based on the control instruction, the photovoltaic cleaning robot is controlled to move.

8. The method according to claim 7, characterized in that The generating a control instruction based on the running status data, the distance data, the target image data and the point cloud data comprises: Detecting whether the operating status data is within a predetermined operating range, wherein the operating status data at least includes: a battery temperature and a battery voltage of the photovoltaic cleaning robot, a roller brush current of a roller brush motor on the photovoltaic cleaning robot, pressure data of an output pipeline of a liquid valve spraying device, and a liquid flow rate of a liquid valve of the liquid valve spraying device; When the operating state data is within the predetermined operating range, performing foreign object recognition on the photovoltaic panel based on the target image data to obtain a foreign object recognition result, wherein the foreign object recognition result includes a volume and a coverage area of ​​the foreign object; Determining target positioning data of the photovoltaic cleaning robot based on the distance data and the point cloud data; The motion control strategy and the cleaning strategy are determined based on the foreign object recognition result and the target positioning data.

9. The method according to claim 8, characterized in that When the operating state data is within the predetermined operating range, performing foreign object recognition on the photovoltaic panel based on the target image data to obtain a foreign object recognition result includes: When the operating status data is within the predetermined operating range, a foreign object recognition model is used based on the target image data to obtain the foreign object recognition result, wherein the foreign object recognition model is based on multiple sets of image data, and the multiple sets of image data respectively correspond to the volume and coverage area of ​​the foreign object, and are obtained through machine learning.

10. The method according to claim 8, characterized in that The step of determining target positioning data of the photovoltaic cleaning robot based on the distance data and the point cloud data includes: 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 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.

11. The method according to claim 8, characterized in that The step of determining target positioning data of the photovoltaic cleaning robot based on the distance data and the point cloud data includes: 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.

12. The method according to claim 11, characterized in that The determining the target positioning data based on the three sets of positioning data includes: Performing weighted calculation on the three sets of positioning data to obtain the target positioning data; or Calculate the position difference between the three sets of positioning data, and determine at least two sets of positioning data from the three sets of positioning data whose position difference is less than a preset difference threshold; and determine the target positioning data based on the at least two sets of positioning data.

13. The method according to claim 8, characterized in that The determining the motion control strategy and the cleaning strategy based on the foreign body identification result and the target positioning data includes: According to the foreign body recognition result, determining the rotation speed of the driving motor of the photovoltaic cleaning robot, the rotation speed of the roller brush motor, and the liquid valve opening of the liquid valve spraying device; Determining the motion control strategy based on the target positioning data and the rotation speed of the drive motor; The cleaning strategy is determined based on the rotation speed of the roller brush motor and the opening of the liquid valve of the liquid valve spraying device.

14. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the photovoltaic cleaning robot control method described in any one of claims 7 to 13.

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