Walking type photovoltaic cleaning equipment control system

Abstract

A walking type photovoltaic cleaning equipment control system relates to the technical field of photovoltaic panel cleaning equipment control and comprises a synchronous driving control module and a synchronous dual-output motor as a power source, feeds back the rotating angle and rotating speed information of a motor output shaft in real time, generates a control signal according to the feedback information of an encoder, and controls the control system. The driving current and voltage of the motor are adjusted; the equipment positioning module is used for acquiring geographical coordinate information of the equipment, performing fusion processing on Beidou positioning data and inertial sensor data by using a Kalman filtering algorithm, and realizing dynamic positioning of the equipment; the remote operation and maintenance module is used for collecting operation data of the equipment in real time and calculating a health state index of the equipment through a fault early warning index model based on the operation data of the equipment; and the fault processing module determines the specific position of the equipment according to the comparison result of the health state index of the equipment and the health state threshold value, and performs remote monitoring and rapid processing on the equipment fault, so that the equipment fault rate is reduced, and the equipment working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic panel cleaning equipment control, and more particularly to a control system for walking photovoltaic cleaning equipment. Background Art

[0002] During the operation of photovoltaic panels, dust accumulation on the surface of the panels needs to be cleaned regularly to ensure the efficient operation of the photovoltaic power generation system. Common photovoltaic panel cleaning robots include crawler cleaning robots, wheeled cleaning robots, track-mounted cleaning robots, etc.

[0003] Deficiencies in existing technologies: Existing photovoltaic panel cleaning equipment uses a traditional cable drive that relies on a single motor for traction. The coordinated motion of the longitudinal and lateral movement mechanisms results in a ±3cm positional deviation, which can easily cause the cleaning mechanism to misalign, particularly in complex photovoltaic arrays. Consequently, a single cleaning attempt achieves only 89% coverage. Lacking a real-time position feedback mechanism, the equipment relies on a preset path. Affected by factors such as cable deformation and wind, the actual trajectory can deviate by up to ±5cm from the planned path, requiring repeated cleanings 22% of the time. Equipment failures require manual inspection, resulting in an average fault resolution delay of 4.2 hours. Furthermore, the electrical connections of traditional cable drive systems are prone to wear and tear due to friction, resulting in an average annual failure rate of 18.7%. Cable drive systems cannot dynamically adjust power based on load, maintaining rated power regardless of load, resulting in energy waste exceeding 35%.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a walking photovoltaic cleaning equipment control system, which includes a synchronous drive control module, an equipment positioning module, a remote operation and maintenance module, and a fault handling module. There are connections between the modules: The synchronous drive control module uses a synchronous dual-output motor as a power source. The synchronous dual-output motor is equipped with an encoder for real-time feedback of the rotation angle and speed information of the motor output shaft. The module generates a control signal based on the encoder feedback information and adjusts the drive current and voltage of the motor. The device positioning module obtains the device's geographic coordinate information in real time and uses the Kalman filter algorithm to fuse Beidou positioning data with inertial sensor data to achieve dynamic positioning of the device; The remote operation and maintenance module is equipped with an edge computing gateway to collect device operation data in real time. Based on the device operation data, the device health status index is calculated using a fault warning index model. The fault handling module determines the specific location of the device based on the comparison result of the device's health status index with the set health status threshold, and remotely monitors and quickly handles the device fault to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A control system for a walking photovoltaic cleaning device includes a synchronous drive control module, an equipment positioning module, a remote operation and maintenance module, and a fault handling module. The modules are connected: The synchronous drive control module uses a synchronous dual-output motor as a power source. The synchronous dual-output motor is equipped with an encoder for real-time feedback of the rotation angle and speed information of the motor output shaft. The module generates a control signal based on the encoder feedback information and adjusts the drive current and voltage of the motor. The device positioning module obtains the device's geographic coordinate information in real time and uses the Kalman filter algorithm to fuse Beidou positioning data with inertial sensor data to achieve dynamic positioning of the device; The remote operation and maintenance module is equipped with an edge computing gateway to collect device operation data in real time. Based on the device operation data, the device health status index is calculated using a fault warning index model. The fault handling module determines the specific location of the device based on the comparison between the device's health status index and the set health status threshold, and remotely monitors and quickly handles device faults.

[0007] In a preferred embodiment, the synchronous dual-output motor has two output shafts, which are respectively connected to the longitudinal movement mechanism and the transverse movement mechanism of the walking photovoltaic cleaning device.

[0008] In a preferred embodiment, the process of generating a control signal based on the encoder feedback data is as follows: A controller based on a cross-coupling control algorithm calculates the speed difference and the angle difference of the dual output shafts according to the encoder feedback data; The proportional, integral, and differential coefficients of the speed and angle adjustment are obtained, and the control signal is generated by combining the speed difference and angle difference of the dual output shafts.

[0009] In a preferred embodiment, the formula for generating the control signal is as follows: , Where, , , Proportional, integral and differential coefficients for speed regulation; , , are the proportional, integral and differential coefficients for angle adjustment; is the speed difference between the two output shafts; is the angular difference between the dual output shafts; u is the control signal.

[0010] In a preferred embodiment, the Kalman filter algorithm is used to fuse Beidou positioning data with inertial sensor data to achieve dynamic positioning of the device as follows: Integrate the Beidou third-generation positioning chip and MEMS inertial sensor to obtain Beidou positioning data and inertial sensor data. Use the Kalman filter algorithm to fuse the Beidou positioning data and inertial sensor data to achieve dynamic positioning of the device. The fused position calculation formula is: , in, is the Kalman gain matrix, is the attitude angle calculated by the inertial sensor, is the predicted position and posture at the previous moment, The geographic coordinate information of the device.

[0011] In a preferred embodiment, the operating data of the device includes motor current, motor temperature, device operating speed, and degree of wear of the cleaning brush head.

[0012] In a preferred embodiment, the health status index of the device is calculated using the fault warning index model as follows: , Where HI is the health index of the equipment, is the motor rated current is the motor temperature threshold, is the rated operating speed of the equipment, The maximum wear allowed for cleaning the brush head, is the weight coefficient, I is the motor current, T is the motor temperature, v is the equipment operating speed, and w is the degree of wear of the cleaning brush head.

[0013] In a preferred embodiment, the process of determining the specific location of the device based on the comparison result of the device's health status index with a set health status threshold is as follows: When the health status index of the device is greater than the set health status threshold, a device warning is triggered; At the same time, 5G communication technology is used to upload fault characteristic data to the cloud server. The cloud server determines the specific location of the faulty device in the photovoltaic panel array based on the dynamic positioning information of the device provided by the device positioning module.

[0014] In a preferred embodiment, the process of remotely monitoring and quickly handling equipment failures is as follows: Based on the cloud diagnosis results, if the problem is a software parameter setting issue or a minor fault, the device can be adjusted or restarted through remote commands to resolve the fault. If it is a hardware failure, a maintenance work order is generated based on the fault type and device location information, and maintenance personnel are dispatched to the site with spare parts. The fault handling process and results are recorded in the cloud database for subsequent fault analysis and system optimization.

[0015] The technical effects and advantages of the control system of a walking photovoltaic cleaning equipment of the present invention are as follows: 1. This invention utilizes a synchronous dual-output motor combined with a high-precision encoder and a cross-coupling control algorithm to create a closed-loop synchronous drive system, effectively addressing the poor synchronization issues of traditional cable drives. The dual output shafts drive the longitudinal and lateral motion mechanisms, respectively. Based on real-time feedback of rotational speed and angle data, precise formulas are used to adjust the control signals, strictly controlling synchronization error to within ±1.5cm. Compared to traditional systems, the coordination of the various mechanisms during operation is more precise, eliminating missed or duplicate cleanings due to misalignment. The single-pass cleaning coverage rate is significantly increased from 89% to 99.5%, significantly improving cleaning efficiency while reducing energy waste and equipment wear caused by repetitive operations. This invention integrates a Beidou-3 positioning chip with a MEMS inertial sensor and fuses positioning data using a Kalman filter algorithm to achieve high-precision dynamic positioning. Beidou positioning provides an absolute position reference, while the inertial sensor compensates for signal loss in complex environments. The algorithmic fusion of the two ensures stable positioning error within ±1.5cm. Whether in mountainous photovoltaic power plants where signals are susceptible to interference, or during dynamic processes involving rapid movement and steering, the system accurately determines the equipment's location, ensuring that cleaning equipment strictly adheres to the planned path, effectively improving both cleaning coverage and cleaning quality. Furthermore, precise positioning facilitates equipment management and scheduling, enabling the rational allocation of tasks and improving overall equipment efficiency.

[0016] 2. This invention utilizes a multi-dimensional fault monitoring system built through a remote operation and maintenance module, combining a health status index model, dynamic location information, and a Bayesian fault diagnosis model to achieve intelligent fault monitoring and rapid resolution. This system collects multi-dimensional equipment operating data in real time to calculate a health status index. Based on thresholds, it triggers timely warnings. Through preliminary assessment, precise location, and in-depth diagnosis, the fault type and location are quickly determined. Software issues can be addressed remotely, while hardware failures are efficiently dispatched using location information. This reduces average fault resolution time to less than 15 minutes, lowering the annual average equipment failure rate from 18.7% to 5.3%. This significantly reduces equipment downtime and maintenance costs, ensuring stable operation of the photovoltaic power station. This invention utilizes a load adaptive adjustment module that utilizes a high-precision current sensor to monitor motor load in real time and dynamically adjusts motor output power based on load changes. This precise power adjustment formula ensures efficient motor operation under various cleaning conditions, reducing energy waste from 35% to 12% and extending single-charge operating time by 60%. This not only reduces the energy cost of equipment operation but also reduces battery recharge times, extending battery life, and improving the cost-effectiveness and practicality of the equipment, making it particularly suitable for cleaning operations in large-scale photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic structural diagram of a control system for a walking photovoltaic cleaning device according to the present invention. DETAILED DESCRIPTION

[0018] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Example 1, Figure 1 The present invention provides a control system for a walking photovoltaic cleaning device.

[0020] The synchronous drive control module uses a synchronous dual-output motor as a power source. The motor is equipped with an encoder for real-time feedback of the rotation angle and speed information of the motor output shaft. Based on the encoder feedback data, the speed difference and rotation angle difference of the dual output shafts are calculated, and a control signal is generated to adjust the drive current and voltage of the motor. A synchronous dual-output motor is used as a power source. The synchronous dual-output motor has two output shafts, which are respectively connected to the longitudinal movement mechanism and the lateral movement mechanism of the walking photovoltaic cleaning device; The motor is equipped with a high-precision encoder with a resolution of no less than 20 bits, which is used to provide real-time feedback on the angle and speed of the motor output shaft. The module is equipped with a controller based on a cross-coupling control algorithm, which calculates the speed difference of the dual output shafts according to the encoder feedback data. and the corner difference ; Obtain the proportional, integral, and differential coefficients of the speed and angle adjustment, combined with the speed difference of the dual output shafts and the corner difference Generate a control signal u, and adjust the drive current and voltage of the motor according to the control signal. The formula for generating the control signal u is as follows: , Where, , , Proportional, integral and differential coefficients for speed regulation; , , are the proportional, integral and differential coefficients for angle adjustment; The drive current and voltage of the motor are adjusted through the control signal to ensure that the synchronous operation accuracy of the longitudinal and lateral movement mechanisms is within ±1.5cm.

[0021] The Synchronous Drive Control Module, with its innovative design and precise control mechanism, significantly improves the performance of mobile photovoltaic cleaning equipment in multiple dimensions. The benefits and functions are as follows: Improve cleaning accuracy and coverage: Traditional drive methods have poor synchronization, which can easily lead to positioning deviations of cleaning equipment during operation, resulting in omissions or repeated cleaning of the photovoltaic panel cleaning area. The synchronous drive control module uses the encoder to provide real-time feedback on the rotation angle and speed information of the dual output shafts, accurately calculates the speed difference and angle difference, and generates control signals based on this to adjust the motor drive current and voltage, strictly controlling the synchronization error within ±1.5cm. This allows the various mechanisms of the cleaning equipment to cooperate precisely during the longitudinal and lateral movement, and the cleaning brush head always operates according to the predetermined trajectory. The single cleaning coverage rate is significantly increased from 89% of the traditional method to 99.5%, ensuring that every part of the photovoltaic panel can be effectively cleaned and the power generation efficiency of the photovoltaic panel is fully utilized.

[0022] Enhanced equipment operational stability: During operation, photovoltaic cleaning equipment can experience load fluctuations due to encountering photovoltaic panels at varying angles, surface obstacles, or uneven surfaces. If the drive system fails to adjust in a timely manner, this can easily cause equipment jitter, freezing, or even failure. This module, based on real-time encoder feedback, quickly detects speed and angle fluctuations caused by load changes. By adjusting the drive current and voltage, it dynamically adjusts the motor's output torque and speed to maintain stable equipment operation. Even under complex operating conditions, it effectively reduces wear on mechanical components caused by uneven force, extending equipment life, reducing equipment failure rates, and ensuring the continuity of cleaning operations.

[0023] Improve energy efficiency: Traditional drive systems often operate at constant power, consuming the same amount of electricity regardless of the load size, resulting in a large amount of energy waste. The synchronous drive control module can accurately adjust the motor's drive current and voltage based on the actual load conditions. When the load is light, the motor power output is reduced; when the load increases, the power is increased in a timely manner to ensure that the motor always operates in the high-efficiency range. Compared with traditional methods, the energy waste rate is reduced from 35% to 12%. While reducing the operating costs of the equipment, it also reduces the number of battery charges, extends the battery life of the equipment, and increases the cleaning operation time and coverage of the equipment on a single charge. It is especially suitable for the cleaning needs of large-scale photovoltaic power stations.

[0024] Precise path planning and control: In photovoltaic power plants, cleaning equipment must precisely follow a pre-set path to improve cleaning efficiency and coverage. The high-precision angle and speed feedback data provided by this synchronous drive control module provides a reliable basis for the path planning algorithm. Based on the discrepancy between the planned path and the equipment's actual operating status, the control system adjusts motor operating parameters in real time based on speed and angle differences, ensuring the equipment adheres strictly to the planned path. Whether cleaning in a straight line, turning, or navigating complex arrays, commands are accurately executed, avoiding path deviations caused by drive errors, and enhancing the automation and intelligence of cleaning operations.

[0025] The device positioning module integrates the Beidou third-generation positioning chip and MEMS inertial sensor to obtain the device's geographic coordinate information in real time. It uses the Kalman filter algorithm to fuse Beidou positioning data with inertial sensor data to achieve dynamic positioning of the device. Integrating BeiDou III positioning chip and MEMS inertial sensor, the BeiDou III positioning chip has a positioning accuracy of up to 1m and can obtain the geographic coordinate information of the device in real time. ; MEMS inertial sensors can measure the acceleration, angular velocity, and attitude angle of the device. This module uses the Kalman filter algorithm to fuse Beidou positioning data with inertial sensor data. The fused position calculation formula is: , in, is the Kalman gain matrix, is the attitude angle calculated by the inertial sensor, The predicted position and posture at the previous moment are used to realize the dynamic positioning of the device in the photovoltaic panel array.

[0026] The equipment positioning module uses the Beidou-3 positioning chip and MEMS inertial sensor fusion, and uses the Kalman filter algorithm to process data, bringing significant advantages to mobile photovoltaic cleaning equipment from multiple aspects. Its benefits and functions are as follows: Improved positioning accuracy: While the Beidou-3 positioning chip offers high positioning accuracy, signal errors or even loss can occur in complex environments, such as those caused by obstructions or electromagnetic interference. MEMS inertial sensors can infer position by measuring acceleration and angular velocity, but this can accumulate errors over time. Fusion of these two data using the Kalman filter algorithm effectively compensates for their respective limitations. Beidou positioning data provides an absolute reference for positioning, while inertial sensor data provides short-term supplementary corrections when the Beidou signal is lost. Working together, these two methods limit device positioning error to within ±1.5cm. This significantly improves accuracy compared to using either positioning method alone, ensuring that cleaning equipment can accurately move to the designated location on the photovoltaic panel for cleaning, avoiding missed or duplicate cleaning areas and improving cleaning coverage and efficiency.

[0027] Enhanced positioning reliability: In practical applications, photovoltaic power plants may be located in remote mountainous areas, deserts, and other areas susceptible to signal interference, where Beidou signals may be unstable. MEMS inertial sensors, however, are immune to external signal interference and can maintain positioning through short-term inertial measurement unit (IMU) calculations when Beidou signals are interrupted. The Kalman filter algorithm assesses the reliability of both data sources in real time, intelligently switching or fusing them. This allows the device to continuously and stably obtain accurate position information even in complex environments, ensuring normal operation in a variety of harsh conditions and reducing equipment failures and cleaning task interruptions caused by positioning failures.

[0028] Achieving dynamic and precise positioning: Mobile photovoltaic cleaning equipment is in a state of dynamic change during operation, making it difficult for traditional positioning methods to quickly respond to changes in the equipment's position. Fusion positioning and Kalman filtering algorithms can rapidly and in real time process the large amounts of data generated by the equipment's movement. Based on the equipment's real-time speed, acceleration, and other dynamic information, positioning data is updated and corrected to accurately track the equipment's movement trajectory. Even during complex motions such as acceleration, deceleration, and turns, positioning accuracy and timeliness are guaranteed, ensuring a more precise path for the cleaning equipment and improving the quality and stability of cleaning operations.

[0029] Reduced costs and power consumption: Compared to using a single, high-precision positioning device (such as a high-precision GPS device) to meet positioning requirements, the Beidou-integrated inertial sensor solution effectively reduces hardware costs while ensuring positioning accuracy. Furthermore, MEMS inertial sensors consume less power, and the Kalman filter algorithm's optimized data processing reduces unnecessary data transmission and calculations, lowering the power consumption of the entire positioning module, extending the device's battery life, and improving its cost-effectiveness and practicality, facilitating the large-scale deployment of mobile photovoltaic cleaning equipment.

[0030] Facilitates equipment management and scheduling: Accurate dynamic positioning information enables equipment managers to understand the specific location and operating status of each cleaning device in real time. During equipment scheduling, cleaning tasks can be rationally assigned based on device location, avoiding idle or duplicated operations and improving equipment efficiency. Furthermore, in the event of a device failure, precise positioning information helps maintenance personnel quickly locate the faulty device, shortening troubleshooting time and minimizing downtime losses.

[0031] The remote operation and maintenance module is equipped with an edge computing gateway to collect device operating data in real time. Based on the collected data, the device health status index is calculated using a fault warning index model. Equipped with an edge computing gateway with a computing power of no less than 2TOPS, it is used to collect real-time equipment operating data, including motor current I, motor temperature T, equipment operating speed v, and degree of wear of the cleaning brush head W. Based on the collected data, the health status index of the equipment is calculated using a fault warning index model. The calculation formula is as follows: , Where HI is the health index of the equipment, is the motor rated current is the motor temperature threshold, is the rated operating speed of the equipment, The maximum wear allowed for cleaning the brush head, is the weight coefficient.

[0032] The remote operation and maintenance module uses edge computing gateways to collect data and calculate health status indexes through models, which has significant advantages in improving equipment management efficiency and reducing operation and maintenance costs. I will explain its benefits and functions from the perspectives of data processing and fault management: Real-time awareness of equipment operating status: The edge computing gateway can collect various data during equipment operation in real time and at high frequency, including key parameters such as motor current, temperature, operating speed, and brush head wear. This real-time data acquisition provides equipment managers with a "clairvoyant" view, allowing them to understand equipment operating conditions at any time, rather than being limited to traditional periodic inspections. This allows them to promptly detect subtle anomalies in equipment operation, providing comprehensive data support for stable equipment operation.

[0033] Early warning reduces the risk of sudden failures: A fault warning index model is used to deeply analyze collected data and calculate the device health index. This index comprehensively reflects the overall health of the device. When the health index approaches or reaches a set threshold, the system issues a timely warning. This mechanism transforms traditional reactive fault handling into proactive prevention, enabling operations and maintenance personnel to take measures before a failure occurs, such as scheduling repairs and replacing vulnerable parts. This effectively reduces the risk of equipment downtime caused by sudden failures and ensures the continuity of power generation in the photovoltaic power station.

[0034] Accurately locate faults and improve maintenance efficiency: Based on real-time data and health index calculations, the system can preliminarily determine the fault type and potential location. Combined with the device's dynamic positioning information, it can precisely pinpoint the faulty device's location within the PV power plant. When maintenance is needed, maintenance personnel can quickly arrive at the fault site with the correct spare parts and tools, eliminating blind investigations and unnecessary travel. This significantly improves maintenance efficiency, reduces equipment downtime, and minimizes the impact of equipment failures on the PV power plant's energy output.

[0035] Optimizing O&M resource allocation: By being able to predict equipment failures in advance and accurately identify fault conditions, the O&M team can rationally allocate maintenance personnel and spare parts resources based on actual needs. This eliminates the traditional O&M model of stockpiling large quantities of spare parts and personnel in various regions to address potential failures, effectively reducing O&M costs. Furthermore, based on the equipment health index, equipment maintenance cycles can be optimized, appropriately extending maintenance intervals for equipment in good health and shortening them for equipment in poor health, achieving scientific and refined management of O&M resources.

[0036] Data accumulation and analysis facilitate system optimization: Continuously collecting and storing equipment operating data and troubleshooting records provides a rich source of material for subsequent data analysis and system optimization. By mining and analyzing large amounts of data, we can uncover underlying patterns in equipment operation, such as peak fault times and performance variations under specific environmental conditions. Based on these insights, we can refine and optimize equipment design and control system algorithms, further improving the performance and reliability of mobile photovoltaic cleaning equipment and driving technological advancement across the entire photovoltaic cleaning industry.

[0037] The fault handling module remotely monitors and quickly handles equipment faults based on the comparison results of the equipment's health status index with the set health status threshold and the equipment's dynamic positioning information.

[0038] When the health status index of a device exceeds the set health status threshold, a device warning is triggered. At the same time, 5G communication technology is used to upload the fault feature data to the cloud server. The cloud server determines the specific location of the faulty device in the photovoltaic array based on the dynamic positioning information of the device provided by the device positioning module. If multiple devices are running simultaneously, the faulty device can be quickly identified by matching the device number with the location information, avoiding misjudgment and enabling rapid location of the fault type. Based on the cloud-based diagnostic results, if it's a software parameter setting issue or a minor fault, remote commands can be used to adjust the device parameters or restart it to resolve the issue. If it's a hardware fault, a repair work order is generated based on the fault type and device location information, and the nearest maintenance personnel with the appropriate spare parts are dispatched to the site for on-site repair. The troubleshooting process and results are recorded in a cloud-based database for subsequent fault analysis and system optimization. This enables remote monitoring and rapid resolution of equipment faults, reducing average troubleshooting time to less than 15 minutes.

[0039] The fault handling module combines health status index, thresholds, and positioning information to provide a solid guarantee for the stable operation of mobile photovoltaic cleaning equipment. Its benefits and functions are reflected in several key aspects: Proactively prevent failures and reduce downtime losses: By calculating the device's health index in real time and comparing it against set thresholds, the system can detect abnormal trends before a failure occurs. For example, if changes in parameters such as motor temperature and current cause the health index to approach the threshold, the system will issue an early warning. Operations and maintenance personnel can intervene promptly to perform maintenance or adjustments, preventing the failure from escalating into a serious downtime. Traditional equipment could previously cause prolonged downtime due to sudden failures, impacting the photovoltaic power station's power generation. However, this module prevents failures before they occur, effectively reducing power generation losses caused by downtime.

[0040] Accurately locate faults and improve maintenance efficiency: Combined with dynamic device positioning information, the specific location of the faulty device can be quickly and accurately determined when a fault occurs or a warning is issued. Large-scale photovoltaic power plants have a large number of devices. If the faulty device cannot be accurately located, maintenance personnel will waste a considerable amount of time searching for the device. This module allows maintenance personnel to reach the fault site immediately, significantly reducing fault location time. Furthermore, based on the health status index and previous data, maintenance personnel can initially determine the fault type. This allows maintenance personnel to bring appropriate tools and spare parts, making repairs more targeted and significantly improving maintenance efficiency, reducing the average fault handling time to less than 15 minutes.

[0041] Reduced O&M costs and improved economic benefits: Preventing faults early reduces the need for component replacement due to severe equipment damage, thus lowering maintenance costs. Rapid fault resolution avoids prolonged equipment downtime, ensuring efficient power generation at the PV power plant and increasing revenue. Furthermore, accurate fault location and resolution reduces wasted time and energy for maintenance personnel, optimizes human resource allocation, and reduces O&M costs in multiple ways, improving the overall economic benefits of the PV power plant.

[0042] Remote intelligent management reduces reliance on manpower: Remote monitoring allows operators to monitor equipment status in real time, diagnose and resolve faults without having to be on-site. This reduces the need for travel for operators, especially for PV power plants in remote areas. Software-level faults or parameter adjustments can be resolved remotely, eliminating the need for on-site personnel. This remote intelligent management model significantly reduces reliance on manpower while improving the timeliness and effectiveness of equipment management.

[0043] Data-driven optimization improves equipment performance: During fault monitoring and handling, a large amount of data is accumulated regarding equipment fault type, occurrence time, location, and related operational data. Analyzing this data provides a deep understanding of equipment weaknesses and fault patterns. Based on this data, improvements can be made to equipment design and control system algorithms, improving overall equipment performance and reliability, creating a virtuous cycle of continuous optimization. Through the above-mentioned fault handling mechanism, remote monitoring and rapid handling of faults in walking photovoltaic cleaning equipment are achieved, and the average fault handling time is shortened to less than 15 minutes, effectively reducing the equipment failure rate and improving the stability and reliability of equipment operation.

[0044] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0045] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0046] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0047] In addition, each functional module in each embodiment of the present application 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.

[0048] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0049] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A walking photovoltaic cleaning equipment control system, characterized in that: It includes synchronous drive control module, equipment positioning module, remote operation and maintenance module, and fault handling module. There are connections between the modules: The synchronous drive control module uses a synchronous dual-output motor as a power source. The synchronous dual-output motor is equipped with an encoder for real-time feedback of the rotation angle and speed information of the motor output shaft. The module generates a control signal based on the encoder feedback information and adjusts the drive current and voltage of the motor. The device positioning module obtains the device's geographic coordinate information in real time and uses the Kalman filter algorithm to fuse Beidou positioning data with inertial sensor data to achieve dynamic positioning of the device; The remote operation and maintenance module is equipped with an edge computing gateway to collect device operation data in real time. Based on the device operation data, the device health status index is calculated using a fault warning index model. The fault handling module determines the specific location of the device based on the comparison between the device's health status index and the set health status threshold, and remotely monitors and quickly handles device faults.

2. A walking photovoltaic cleaning equipment control system according to claim 1, characterized in that: The synchronous dual-output motor has two output shafts, which are respectively connected to the longitudinal moving mechanism and the transverse moving mechanism of the walking photovoltaic cleaning device.

3. A walking photovoltaic cleaning equipment control system according to claim 2, characterized in that: The process of generating a control signal based on the encoder feedback data is as follows: A controller based on a cross-coupling control algorithm calculates the speed difference and the angle difference of the dual output shafts according to the encoder feedback data; The proportional, integral, and differential coefficients of the speed and angle adjustment are obtained, and the control signal is generated by combining the speed difference and angle difference of the dual output shafts.

4. A walking photovoltaic cleaning equipment control system according to claim 3, characterized in that: The formula for generating the control signal is as follows: ， Where, , , Proportional, integral and differential coefficients for speed regulation; , , are the proportional, integral and differential coefficients for angle adjustment; is the speed difference between the two output shafts; is the angular difference between the dual output shafts; u is the control signal.

5. A walking photovoltaic cleaning equipment control system according to claim 4, characterized in that: The Kalman filter algorithm is used to fuse Beidou positioning data with inertial sensor data to achieve dynamic positioning of the device. The process is as follows: Integrate the Beidou third-generation positioning chip and MEMS inertial sensor to obtain Beidou positioning data and inertial sensor data. Use the Kalman filter algorithm to fuse the Beidou positioning data and inertial sensor data to achieve dynamic positioning of the device. The fused position calculation formula is: ， in, is the Kalman gain matrix, is the attitude angle calculated by the inertial sensor, is the predicted position and posture at the previous moment, The geographic coordinate information of the device.

6. A walking photovoltaic cleaning equipment control system according to claim 5, characterized in that: The operating data of the device includes motor current, motor temperature, device operating speed, and degree of wear of the cleaning brush head.

7. A walking photovoltaic cleaning equipment control system according to claim 6, characterized in that: The formula for calculating the health status index of the device using the fault warning index model is as follows: ， Where HI is the health index of the equipment, is the motor rated current is the motor temperature threshold, is the rated operating speed of the equipment, The maximum wear allowed for cleaning the brush head, is the weight coefficient, I is the motor current, T is the motor temperature, v is the equipment operating speed, and w is the degree of wear of the cleaning brush head.

8. A walking photovoltaic cleaning equipment control system according to claim 7, characterized in that: The process of determining the specific location of a device based on the comparison of the device's health status index with the set health status threshold is as follows: When the health status index of the device is greater than the set health status threshold, a device warning is triggered; At the same time, 5G communication technology is used to upload fault characteristic data to the cloud server. The cloud server determines the specific location of the faulty device in the photovoltaic panel array based on the dynamic positioning information of the device provided by the device positioning module.

9. A walking photovoltaic cleaning equipment control system according to claim 8, characterized in that: The process of remote monitoring and rapid processing of equipment failures is as follows: Based on the cloud diagnosis results, if the problem is a software parameter setting issue or a minor fault, the device can be adjusted or restarted through remote commands to resolve the fault. If it is a hardware failure, a maintenance work order is generated based on the fault type and device location information, and maintenance personnel are dispatched to the site with spare parts. The fault handling process and results are recorded in the cloud database for subsequent fault analysis and system optimization.

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