Intelligent solar cell panel monitoring and early warning method and device based on Internet of Things

Abstract

The invention discloses an intelligent solar cell panel monitoring and early warning method and device based on the Internet of Things. The method comprises the steps of collecting operation parameters of a solar cell panel in real time through a sensor network; transmitting the collected operating parameters to a cloud server through an Internet of Things module, and storing the operating parameters in a distributed database; based on a preset algorithm model, performing multi-dimensional analysis on the operation parameters, and identifying an abnormal data mode; according to the abnormal data mode, if it is detected that the current or the voltage exceeds a threshold range, the surface image displays damage or environmental parameters are abnormal, and early warning information is triggered; and the early warning information is pushed to operation and maintenance personnel through the mobile terminal, and a fault processing suggestion is generated. According to the technical scheme, real-time monitoring and accurate early warning of the solar cell panel can be realized, the accuracy of fault detection can be improved, the false alarm rate can be reduced, the manual inspection cost can be reduced, and the operation strategy of the cell panel can be optimized, so that the stability and efficiency of a photovoltaic power generation system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of solar panels, and in particular to an intelligent solar panel monitoring and early warning method and device based on the Internet of Things. Background Art

[0002] As a renewable energy source, solar energy has been widely used in the field of photovoltaic power generation. As a core component, the operating status of solar panels directly affects the power generation efficiency and stability of the system. Existing solar panel monitoring mainly relies on a single sensor or regular manual inspections, which has problems such as single monitoring parameters, poor real-time performance, and high maintenance costs. It is difficult to detect faults in time and take effective measures, which affects the reliability and economy of the power generation system.

[0003] In recent years, the development of the Internet of Things, big data analysis, and artificial intelligence technologies have provided new solutions for the intelligent monitoring of solar panels. By integrating multiple sensors and intelligent analysis algorithms, real-time monitoring of the operating status of solar panels can be achieved, and early warnings can be provided before a fault occurs. However, existing technologies still have shortcomings in data transmission, storage, and intelligent analysis, especially in multi-sensor fusion, environmental adaptability, data anomaly detection, and remote operation and maintenance optimization. A complete solution has not yet been formed. How to reduce fault detection errors and how to improve the operating efficiency and maintenance convenience of solar panels have become issues that the industry needs to solve urgently. Summary of the invention

[0004] The present invention provides an intelligent solar panel monitoring and early warning method and device based on the Internet of Things, which is used to realize real-time monitoring and accurate early warning of solar panels by integrating the Internet of Things, multi-sensor monitoring and intelligent analysis technology, improve the accuracy of fault detection, reduce the false alarm rate, reduce the cost of manual inspection, and optimize the operation strategy of the solar panel, thereby improving the stability and efficiency of the photovoltaic power generation system.

[0005] According to a first aspect of the present invention, there is provided a smart solar panel monitoring and early warning method based on the Internet of Things, the smart solar panel monitoring and early warning method based on the Internet of Things comprising:

[0006] collecting operating parameters of the solar panel in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage, and surface image data;

[0007] The collected operating parameters are transmitted to the cloud server through the Internet of Things module and stored in the distributed database;

[0008] Based on a preset algorithm model, the operating parameters are analyzed in multiple dimensions to identify abnormal data patterns;

[0009] According to the abnormal data pattern, if the current or voltage exceeds the threshold range, the surface image shows damage, or the environmental parameters are abnormal, the warning information is triggered;

[0010] The warning information is pushed to the operation and maintenance personnel via mobile terminals, and troubleshooting suggestions are generated.

[0011] In one embodiment, the sensor network comprises:

[0012] Deploy a sun angle tester to track the sun's azimuth and altitude in real time and adjust the direction of the solar panels;

[0013] Install industrial cameras on the surface of the panels to take high-definition images at regular intervals and detect cracks or stains using image recognition algorithms;

[0014] Temperature and wind sensors are integrated on the solar panel bracket to monitor environmental parameters simultaneously.

[0015] In one embodiment, the preset algorithm model includes:

[0016] Establish a current-voltage characteristic curve benchmark library to compare the deviation between real-time data and historical benchmark values;

[0017] Use convolutional neural networks (CNN) to analyze surface images and identify glass breakage or hot spot effects;

[0018] Combined with ambient temperature and wind data, the performance evaluation threshold of the solar panel is dynamically corrected.

[0019] In one embodiment, the triggering warning information includes:

[0020] If the current fluctuation rate exceeds the preset fluctuation range and the duration exceeds the preset time threshold, it is determined to be a circuit fault;

[0021] If the proportion of damaged area in the surface image is greater than the preset area threshold, it is determined to be structural damage;

[0022] If the ambient temperature exceeds the preset temperature threshold or the wind speed is greater than the preset wind speed threshold, an emergency shutdown command is triggered.

[0023] In one embodiment, the fault handling suggestion includes:

[0024] Locate the faulty battery pack based on the abnormal current and generate a replacement or repair work order;

[0025] Mark the damaged location based on the damage image and provide priority suggestions for cleaning or replacing the glass;

[0026] Combined with environmental data, it is recommended to adjust the inclination of the solar panels or start the backup power supply.

[0027] In one embodiment, it further includes:

[0028] Record the processing results of each warning event and the performance recovery data of the solar panels after repair;

[0029] Optimize algorithm parameters through machine learning models to reduce false positive rates and improve detection sensitivity;

[0030] Generate operation and maintenance reports regularly, count failure frequencies and maintenance costs, and optimize inspection strategies.

[0031] According to a second aspect of the present invention, there is provided an intelligent solar panel monitoring and early warning method and device based on the Internet of Things, comprising:

[0032] A collection module, for collecting operating parameters of the solar panel in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage and surface image data;

[0033] A transmission module, used to transmit the collected operating parameters to a cloud server through an Internet of Things module, and store them in a distributed database;

[0034] An analysis module, used to perform multi-dimensional analysis on the operating parameters based on a preset algorithm model to identify abnormal data patterns;

[0035] A detection module is used to trigger an early warning message based on an abnormal data pattern if it detects that the current or voltage exceeds a threshold range, the surface image shows damage, or the environmental parameters are abnormal;

[0036] The processing module is used to push warning information to operation and maintenance personnel through mobile terminals and generate fault handling suggestions.

[0037] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0038] Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned smart solar panel monitoring and early warning methods based on the Internet of Things is implemented.

[0039] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a computer (e.g., a processor in the computer), any of the above-mentioned smart solar panel monitoring and early warning methods based on the Internet of Things is implemented.

[0040] In summary, the present invention provides an intelligent solar panel monitoring and early warning method and device based on the Internet of Things, the method comprising: collecting the operating parameters of the solar panel in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage and surface image data; transmitting the collected operating parameters to the cloud server through the Internet of Things module, and storing them in a distributed database; based on a preset algorithm model, performing multi-dimensional analysis on the operating parameters to identify abnormal data patterns; according to the abnormal data pattern, if it is detected that the current or voltage exceeds the threshold range, the surface image shows damage or the environmental parameters are abnormal, triggering early warning information; pushing the early warning information to the operation and maintenance personnel through the mobile terminal, and generating fault handling suggestions. The technical solution of the present application realizes real-time monitoring and accurate early warning of solar panels by integrating the Internet of Things, multi-sensor monitoring and intelligent analysis technology, which can improve the accuracy of fault detection, reduce the false alarm rate, reduce the cost of manual inspection, and optimize the operation strategy of the solar panel, thereby improving the stability and efficiency of the photovoltaic power generation system.

[0041] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0042] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flow chart of an intelligent solar panel monitoring and early warning method based on the Internet of Things provided by an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a solar panel current and voltage monitoring device provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a solar panel glass damage monitoring device provided by an embodiment of the present invention;

[0047] Figure 4A structural diagram of an intelligent solar panel monitoring and early warning method and device based on the Internet of Things provided by an embodiment of the present invention;

[0048] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present invention.

[0049] Description of reference numerals:

[0050] 1. Solar panels; 2. Junction box; 3. Data transmission system; 4. Current / voltage detector; 5. Energy storage battery and alarm device; 11. Fixed base; 12. Pole; 13. Truss; 14. Temperature / wind test device; 15. Sun angle tester; 16. Fixing device and data transmission device; 17. Rotating device; 18. Industrial camera and lens. DETAILED DESCRIPTION

[0051] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0053] like Figure 1 As shown, the present invention provides an intelligent solar panel monitoring and early warning method based on the Internet of Things, and the intelligent solar panel monitoring and early warning method based on the Internet of Things includes:

[0054] In step S11, operating parameters of the solar panel are collected in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage and surface image data;

[0055] In step S12, the collected operating parameters are transmitted to the cloud server through the Internet of Things module and stored in the distributed database;

[0056] In step S13, based on a preset algorithm model, a multi-dimensional analysis is performed on the operating parameters to identify abnormal data patterns;

[0057] In step S14, according to the abnormal data pattern, if it is detected that the current or voltage exceeds the threshold range, the surface image shows damage, or the environmental parameters are abnormal, a warning message is triggered;

[0058] In step S15, the warning information is pushed to the operation and maintenance personnel via the mobile terminal, and fault handling suggestions are generated.

[0059] In one embodiment, an intelligent solar panel monitoring and early warning system integrated with the Internet of Things is provided. The system collects key parameters such as current, voltage, temperature, wind force, sun angle, and surface image in real time through a multi-sensor network, and combines big data analysis and artificial intelligence algorithms to accurately identify fault modes, trigger early warning information, and provide operation and maintenance suggestions. Various operating parameters of solar panels (such as temperature, wind force, sun angle, current, voltage, and surface image data) are collected in real time through the sensor network, and one or more data can be collected separately or in combination; the collected data is transmitted to a cloud server using an Internet of Things module and stored in a distributed database; based on a preset algorithm model, the transmitted parameters are analyzed in multiple dimensions to identify abnormal data patterns; when abnormal data is detected (for example, current or voltage exceeds a threshold, damage is shown in the image, or environmental parameters are abnormal), an early warning message is triggered; the early warning message is pushed to the operation and maintenance personnel through a mobile terminal, and corresponding fault handling suggestions are generated at the same time.

[0060] A solar angle tester is deployed to track the azimuth and altitude of the sun in real time and adjust the orientation of the solar panels. An industrial camera is installed to take high-definition images on the surface of the solar panel at regular intervals, and cracks or stains are detected through image recognition. Temperature and wind sensors are integrated on the bracket to achieve synchronous monitoring of environmental parameters. A reference library of current-voltage characteristic curves is established to compare the deviation between real-time data and historical reference values. A convolutional neural network (CNN) is used to analyze the collected surface images to identify glass breakage or hot spot effects. Combined with ambient temperature and wind data, the threshold of the solar panel performance evaluation is dynamically corrected. If the current fluctuation rate exceeds the preset range and the duration exceeds the set threshold, it is determined to be a circuit fault; if the damaged area in the surface image exceeds the preset area threshold, it is determined to be structural damage; if the ambient temperature or wind exceeds the preset threshold, an emergency shutdown command is triggered. For example, if the current fluctuation rate exceeds ±15% and lasts for more than 10 minutes, it is judged as a circuit fault; if the damaged area in the surface image accounts for ≥5%, it is judged as structural damage; if the ambient temperature exceeds 80°C or the wind force is ≥10, an emergency shutdown command is triggered. According to the abnormal current situation, the faulty battery pack is located and a replacement or repair work order is generated; based on the image analysis results, the damaged location is marked and priority recommendations for cleaning or replacing the glass are provided; combined with environmental data, it is recommended to adjust the inclination of the battery panel or start the backup power supply. According to the different types of abnormalities detected, targeted maintenance or adjustment suggestions are given to improve the overall operation and maintenance efficiency and safety of the system. Record the processing results of each warning event and the performance recovery data after repair; use machine learning models to optimize algorithm parameters, reduce false alarm rates, and improve detection sensitivity; regularly generate operation and maintenance reports, and count failure frequencies and maintenance costs to optimize inspection strategies. A closed-loop feedback mechanism is formed, which not only realizes real-time monitoring and early warning, but also continuously improves and optimizes the overall performance of the system.

[0061] Data collection and transmission are realized by the sensor network (including temperature, wind force, sun angle, current, voltage and image acquisition), providing a basis for subsequent data analysis. Data analysis is to use preset algorithms (including historical data comparison and CNN image recognition) to perform multi-dimensional analysis on various parameters to detect abnormal conditions. Early warning triggers trigger the early warning mechanism according to specific conditions (such as abnormal current and voltage, damage in the image or abnormal environment). Fault handling suggestions and feedback optimization generate specific fault handling suggestions based on the early warning situation, and optimize the system through recording and machine learning to improve the overall detection effect and operation and maintenance level. Not only pay attention to the real-time and accuracy of monitoring and early warning, but also realize the long-term optimization and economical and efficient operation and maintenance of the system through fault handling suggestions and subsequent feedback mechanisms, thereby improving the safety, reliability and maintenance efficiency of the entire photovoltaic system.

[0062] Solar panel current / voltage monitoring device as attached Figure 2 As shown in FIG. 1 , the device consists of the following parts: The solar panel 1 converts solar energy into DC power and is the core unit of the system energy collection. The junction box 2 is used to realize the signal and power transmission between the internal electrical connection of the solar panel and the external device. The data transmission system 3 is responsible for transmitting the detected current, voltage and other parameters to the cloud or control center in real time. The current / voltage detector 4 monitors the current and voltage data output by the solar panel in real time and timely feedbacks the equipment operation status.

[0063] The main function of the energy storage battery and alarm device is to send out a warning signal when abnormal current or voltage is detected, and at the same time provide backup energy support for short-term fault handling. When working, the solar panel 1 is connected to the data transmission system 3 via the junction box 2, which then cooperates with the current / voltage detector 4 to collect and monitor the electrical output data of the solar panel in real time. The detector transmits the collected data to the control center, such as the PC, for further data analysis and processing. When the system detects that the current or voltage parameters exceed the preset threshold, the energy storage battery and alarm device immediately issue a warning message to remind the operation and maintenance personnel to deal with it in time.

[0064] Solar panel glass damage monitoring device as attached Figure 3 As shown, the device is mainly used to monitor the structural status of the surface of the solar panel. Its main components include: a fixed base 11, a vertical pole 12 and a truss 13, which provide a stable installation platform for the entire monitoring system to ensure that each sensor and detection equipment can still work reliably in harsh environments.

[0065] The temperature / wind force testing device 14 is used to monitor the ambient temperature and wind speed around the solar panel in real time, and to provide reference data for judging the equipment operation status and the impact of the external environment.

[0066] The sun angle tester 15 measures the azimuth and altitude of the sun in real time, and the data are used to dynamically adjust the orientation of the solar panels to optimize light reception and power generation efficiency.

[0067] The fixing device and data transmission device 16 is used to fix each detection module and transmit the collected environment and image data to the control center.

[0068] Rotating device 17 and industrial camera and lens 18 The industrial camera is installed on the pole through the rotating device, which can realize multi-angle shooting and collect high-definition images of the surface of the solar panel. Through the image recognition algorithm, the system can detect abnormal conditions such as glass breakage, cracks or stains.

[0069] When the industrial camera captures anomalies on the surface of the solar panel (such as broken glass) or when the temperature and wind speed data are outside the normal range, the data transmission device 16 will transmit the relevant information to the control center to trigger the early warning mechanism.

[0070] The data center receives real-time data from each monitoring device and stores it in a distributed database.

[0071] The preset algorithm model evaluates the working status of the solar panel by comparing the predicted data at installation with the real-time collected data (including current, voltage, ambient temperature, wind speed and image information). When abnormal data is detected, the system automatically triggers the early warning mechanism and sends early warning information to the operation and maintenance personnel, including the fault type, fault location and corresponding treatment suggestions.

[0072] According to the data fed back by the sun angle tester 15, the system can intelligently adjust the installation angle of the solar panel to ensure that it is always in the best lighting state.

[0073] Use historical data for statistics and analysis to continuously optimize system operation strategies, improve power generation efficiency and ensure system stability.

[0074] The visualization interface provides an intuitive operation interface, showing the real-time operating status, historical data and warning information of the solar panels, which is convenient for monitoring and analysis.

[0075] Supporting remote management and control via the Internet, operation and maintenance personnel can uniformly monitor and maintain multiple solar panel sites through terminal devices at any location.

[0076] The entire technical process is divided into five steps:

[0077] Step 1: Installation of the sun angle monitoring device

[0078] Choose an unobstructed location with a wide field of view to install the sun angle monitoring device to ensure that the sensor can accurately collect sun position and altitude data.

[0079] Install a stable bracket according to design requirements to ensure that the equipment remains stable under adverse weather conditions such as wind and rain.

[0080] Install the sun position tracking sensor on the bracket and adjust it to a suitable angle and height so that it can accurately receive sunlight and measure the sun's angle.

[0081] Connect the sensor to the data processing and control unit through a dedicated line to ensure the accuracy and stability of data transmission.

[0082] Step 2: Installation and commissioning of solar panels

[0083] Fix the solar panel on the bracket according to the installation requirements and adjust it to the best angle to ensure that it is stable and facing the direction of maximum light. After installation, check the installation quality of the solar panel to confirm that the solar cells are intact and the connection lines are correct.

[0084] Start the temperature / wind force testing device 14 to collect temperature data of the working environment of the solar panel and record wind speed data;

[0085] The sun angle tester 15 is started to track the sun position in real time, providing a basis for subsequent solar panel angle adjustment.

[0086] Step 3: Installation and testing of current and voltage detectors

[0087] The current sensor and voltage sensor are installed at the positive and negative output terminals of the solar panel respectively to realize real-time monitoring of current and voltage.

[0088] After confirming that the battery panel is correctly installed, start the current / voltage detector 4 to collect and record relevant electrical parameters. Through the data transmission system 3, the data is transmitted to the PC for analysis and processing.

[0089] Step 4: Solar panel image monitoring and data collection

[0090] The industrial camera and the lens 18 are installed near the solar panel to ensure that the image of the surface of the solar panel can be clearly captured.

[0091] When the solar panels are operating normally, start the industrial camera to take timed photos, focusing on detecting whether there is wear, cracks or other abnormalities on the surface of the panels.

[0092] The collected image data is transmitted to the PC through the data transmission system for subsequent image processing and anomaly detection.

[0093] Step 5: Data comparison and abnormal warning

[0094] The PC compares the received current, voltage and image data with the preset thresholds and standard data to determine the operating status of the equipment.

[0095] When current or voltage data is outside the normal range, it may indicate a circuit fault or reduced panel performance;

[0096] When the image data shows that there are abnormalities such as wear and tear on the surface of the solar panel, it may affect the light absorption efficiency;

[0097] When the system detects any of the above anomalies, it automatically triggers an early warning and pushes fault information and handling suggestions to operation and maintenance personnel through mobile terminals, so as to quickly locate the problem and take corresponding measures.

[0098] In addition, the data center conducts multi-dimensional analysis of the collected parameters, and automatically evaluates the status of the solar panels by combining preset algorithm models (such as comparison and image recognition algorithms based on historical benchmark data). The system not only provides real-time warnings, but also records the processing results and post-repair performance data of each warning event. It continuously optimizes the algorithm parameters through machine learning, reduces the false alarm rate, and improves the detection sensitivity. The system uses the data provided by the sun angle tester to automatically adjust the angle of the solar panel to ensure that the system is always in the best working state. At the same time, through the visual interface and remote management platform, operation and maintenance personnel can centrally monitor and maintain multiple sites at any location, effectively improving the overall operation and maintenance efficiency. The system integrates functions such as real-time data collection, intelligent data analysis, automatic warning and fault feedback, which not only improves the operating safety and power generation efficiency of solar panels, but also provides a scientific basis for subsequent fault handling and system maintenance, with significant economic benefits and application prospects.

[0099] The solar angle monitoring device consists of a solar position tracking sensor and an angle adjustment mechanism. The sensor is responsible for measuring the azimuth and altitude of the sun in the sky, and comprehensively calculates the optimal angle of the sun's current illumination based on parameters such as geographic location, date and time, providing accurate data support for angle adjustment. Automatic angle adjustment is based on the real-time measured solar angle to automatically adjust the angle of the solar panel to ensure that it is always facing the sun, maximize the reception of solar radiation energy, and significantly improve power generation efficiency. For example, at different times of the day, the position of the sun changes continuously, and the device can adjust the angle of the solar panel in time to keep it in the best light-receiving state. Power generation efficiency optimization relies on precise angle adjustment, so that the solar panel can maintain the best state of receiving solar radiation at all times, effectively reducing the energy loss caused by angle deviation, and ensuring the stability and efficiency of power generation efficiency. In the long run, this will help to increase the overall power generation of solar panels and reduce unit power generation costs.

[0100] The solar panel monitoring device integrates current detection, voltage detection devices and image recognition modules. Among them, high-precision current sensors and voltage sensors are used to monitor the current and voltage output of solar panels in real time, intuitively reflecting their working status and power generation performance; the image recognition module uses a high-definition camera to capture the surface image of the solar panel, and through advanced image processing algorithms, accurately identifies traces such as wear and cracks on the glass layer, and realizes real-time monitoring of the physical state of the panel. Electrical performance monitoring can monitor the current and voltage of the solar panel in real time, and can promptly detect electrical faults such as short circuit and open circuit to ensure the stable operation of the entire system. Once abnormal data is detected, the system can quickly issue an alarm to prompt the operation and maintenance personnel to carry out maintenance to avoid further expansion of the fault and ensure the continuity of the power generation system. Physical state monitoring is to monitor the physical state of the surface of the solar panel in real time through image recognition technology, promptly detect problems such as wear and cracks, and prevent performance degradation or damage caused by these problems in advance. This helps to extend the service life of the solar panel, reduce replacement costs, and improve the economy of the system. Data feedback is to transmit the monitored current, voltage and image data to the data processing and control unit in real time, providing a key basis for system optimization and fault warning. The data processing and control unit can analyze and process these data, generate corresponding control instructions or early warning signals, and realize intelligent management of the entire system.

[0101] As the core of the system, the data processing and control unit is equipped with hardware components such as high-performance processors, memory and communication interfaces, and is equipped with dedicated monitoring software and algorithms. Its main responsibility is to receive data collected from each module, perform real-time processing and analysis, and issue adjustment instructions or early warning signals based on the analysis results to achieve intelligent control of the entire system. Data processing and analysis is to accurately judge the working status and potential problems of solar panels by deeply processing and analyzing various types of data such as current, voltage, and images received. For example, by comparing historical data with real-time data, the trend of power generation performance changes of the panels is analyzed to timely discover potential fault hazards. Intelligent control decision-making automatically controls the angle adjustment mechanism to adjust the angle of the solar panel according to the data analysis results to optimize the power generation efficiency; at the same time, when abnormal conditions are found, early warning signals are quickly issued to remind the operation and maintenance personnel to deal with them in time. For example, when cracks are detected on the surface of the panel, the working status of the panel is adjusted in time to avoid further deterioration of the fault. Communication and data management can achieve remote monitoring and management by communicating stably with the remote monitoring center or other systems, uploading monitoring data and early warning information in a timely manner; at the same time, it can effectively manage the locally stored data, provide historical data query and report generation functions, and provide data support for system operation and optimization. Operation and maintenance personnel can understand the operating status of solar panels in real time through the remote monitoring center, formulate reasonable operation and maintenance plans, and improve the management efficiency of the system.

[0102] When there is sufficient sunlight, the battery pack in the auxiliary system stores the excess electricity generated by the solar panels so that it can power the load when there is no sunlight or insufficient sunlight, ensuring the continuous and stable operation of the system. It can ensure that the solar panel monitoring system can still work normally in insufficient light conditions such as at night or on rainy days, and maintain the stability and reliability of the system. For example, on consecutive rainy days, the battery pack can provide power support for the monitoring equipment, ensure the uninterrupted operation of the monitoring system, and promptly discover and deal with possible problems. The inverter converts the direct current generated by the solar panel into alternating current to meet the demand of the load equipment for alternating current, realize the effective conversion and utilization of electric energy, and expand the application scope of the solar panel monitoring system, enabling it to drive more types of load equipment and improve the flexibility and adaptability of the system. For example, some monitoring equipment and angle adjustment mechanisms may require AC power supply. The existence of the inverter enables these devices to work normally and improves the function and performance of the entire system.

[0103] The technical solution in this embodiment integrates the Internet of Things, multi-sensor monitoring and intelligent analysis technology to achieve real-time monitoring and accurate early warning of solar panels, which can improve the accuracy of fault detection, reduce false alarm rate, reduce manual inspection costs, and optimize the panel operation strategy, thereby improving the stability and efficiency of the photovoltaic power generation system.

[0104] In one embodiment, Figure 4 FIG. 1 is a block diagram of a device for monitoring and early warning of an intelligent solar panel based on the Internet of Things according to an exemplary embodiment. Figure 4 As shown, the intelligent solar panel monitoring and early warning method device based on the Internet of Things includes a collection module 41, a transmission module 42, an analysis module 43, a detection module 44 and a processing module 45.

[0105] The acquisition module 41 is used to collect operating parameters of the solar panel in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage and surface image data;

[0106] The transmission module 42 is used to transmit the collected operating parameters to the cloud server through the Internet of Things module and store them in the distributed database;

[0107] The analysis module 43 is used to perform multi-dimensional analysis on the operating parameters based on a preset algorithm model to identify abnormal data patterns;

[0108] The detection module 44 is used to trigger a warning message according to the abnormal data pattern if it is detected that the current or voltage exceeds the threshold range, the surface image shows damage, or the environmental parameters are abnormal;

[0109] The processing module 45 is used to push the warning information to the operation and maintenance personnel through the mobile terminal and generate fault handling suggestions.

[0110] The acquisition module 41, the transmission module 42, the analysis module 43, the detection module 44 and the processing module 45 included in the device block diagram of the intelligent solar panel monitoring and early warning method based on the Internet of Things are controlled to execute the intelligent solar panel monitoring and early warning method based on the Internet of Things described in any of the above embodiments.

[0111] like Figure 5 As shown, the present invention provides an electronic device 500, the electronic device comprising: a communication interface, a processor 501, and a memory 502;

[0112] The memory 502 is used to store program instructions. When the program instructions are executed by the processor 501 that is communicatively connected to the memory 502 through the communication interface, the operating parameters of the solar panel are collected in real time through the sensor network, including any one or more of temperature, wind force, sun angle, current, voltage and surface image data; the collected operating parameters are transmitted to the cloud server through the Internet of Things module and stored in a distributed database; based on a preset algorithm model, the operating parameters are analyzed in multiple dimensions to identify abnormal data patterns; according to the abnormal data pattern, if it is detected that the current or voltage exceeds a threshold range, the surface image shows damage or the environmental parameters are abnormal, an early warning information is triggered; the early warning information is pushed to the operation and maintenance personnel through the mobile terminal, and a fault handling suggestion is generated.

[0113] The present invention provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, operating parameters of a solar panel are collected in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage and surface image data; the collected operating parameters are transmitted to a cloud server through an Internet of Things module and stored in a distributed database; based on a preset algorithm model, the operating parameters are analyzed in multiple dimensions to identify abnormal data patterns; according to the abnormal data pattern, if it is detected that the current or voltage exceeds a threshold range, the surface image shows damage or the environmental parameters are abnormal, an early warning message is triggered; the early warning message is pushed to operation and maintenance personnel through a mobile terminal, and a fault handling suggestion is generated.

[0114] It should be understood that the specific features, operations and details described hereinabove about the method of the present invention may also be similarly applied to the device and system of the present invention, or, vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding parts or units of the device or system of the present invention.

[0115] It should be understood that each module / unit of the device of the present invention can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the computer device in the form of hardware or firmware or independent of the processor, or can be stored in the memory of the computer device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0116] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores computer instructions executable by the processor, and the computer instructions instruct the processor to execute each step of the method of the embodiment of the present invention when executed by the processor. The computer device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities in a broad sense. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The steps of the method of the present invention are executed by the processor.

[0117] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0118] It will be understood by those skilled in the art that the method steps of the present invention can be completed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the steps of the present invention are executed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (through the integration of the Internet of Things, multi-sensor monitoring and intelligent analysis technology, the real-time monitoring and accurate early warning of solar panels can be achieved, the accuracy of fault detection can be improved, the false alarm rate can be reduced, the cost of manual inspection can be reduced, and the operation strategy of the battery panel can be optimized, thereby improving the stability and efficiency of the photovoltaic power generation system PROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0119] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart solar panel monitoring and early warning method based on the Internet of Things, characterized in that: include: collecting operating parameters of the solar panel in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage, and surface image data; The collected operating parameters are transmitted to the cloud server through the Internet of Things module and stored in the distributed database; Based on a preset algorithm model, the operating parameters are analyzed in multiple dimensions to identify abnormal data patterns; According to the abnormal data pattern, if the current or voltage exceeds the threshold range, the surface image shows damage, or the environmental parameters are abnormal, the warning information is triggered; The warning information is pushed to the operation and maintenance personnel via mobile terminals, and troubleshooting suggestions are generated.

2. The method for monitoring and early warning of intelligent solar panels based on the Internet of Things as claimed in claim 1, characterized in that: The sensor network comprises: Deploy a sun angle tester to track the sun's azimuth and altitude in real time and adjust the direction of the solar panels; Install industrial cameras on the surface of the panels to take high-definition images at regular intervals and detect cracks or stains using image recognition algorithms; Temperature and wind sensors are integrated on the solar panel bracket to monitor environmental parameters simultaneously.

3. The method for monitoring and early warning of intelligent solar panels based on the Internet of Things as claimed in claim 1, characterized in that: The preset algorithm model includes: Establish a current-voltage characteristic curve benchmark library to compare the deviation between real-time data and historical benchmark values; Use convolutional neural networks (CNN) to analyze surface images and identify glass breakage or hot spot effects; Combined with ambient temperature and wind data, the performance evaluation threshold of the solar panel is dynamically corrected.

4. The method for monitoring and early warning of intelligent solar panels based on the Internet of Things as claimed in claim 1, characterized in that: The trigger warning information includes: If the current fluctuation rate exceeds the preset fluctuation range and the duration exceeds the preset time threshold, it is determined to be a circuit fault; If the proportion of damaged area in the surface image is greater than the preset area threshold, it is determined to be structural damage; If the ambient temperature exceeds the preset temperature threshold or the wind speed is greater than the preset wind speed threshold, an emergency shutdown command is triggered.

5. The method for monitoring and early warning of intelligent solar panels based on the Internet of Things as claimed in claim 1, characterized in that: The troubleshooting suggestions include: Locate the faulty battery pack based on the abnormal current and generate a replacement or repair work order; Mark the damaged location based on the damage image and provide priority suggestions for cleaning or replacing the glass; Combined with environmental data, it is recommended to adjust the inclination of the solar panels or start the backup power supply.

6. The method for monitoring and early warning of intelligent solar panels based on the Internet of Things as claimed in claim 1, characterized in that: Also includes: Record the processing results of each warning event and the performance recovery data of the solar panels after repair; Optimize algorithm parameters through machine learning models to reduce false positive rates and improve detection sensitivity; Generate operation and maintenance reports regularly, count failure frequencies and maintenance costs, and optimize inspection strategies.

7. An intelligent solar panel monitoring and early warning method and device based on the Internet of Things, characterized in that: include: A collection module, for collecting operating parameters of the solar panel in real time through a sensor network, including any one or more of temperature, wind force, sun angle, current, voltage and surface image data; A transmission module, used to transmit the collected operating parameters to a cloud server through an Internet of Things module, and store them in a distributed database; An analysis module, used to perform multi-dimensional analysis on the operating parameters based on a preset algorithm model to identify abnormal data patterns; A detection module is used to trigger an early warning message based on an abnormal data pattern if it detects that the current or voltage exceeds a threshold range, the surface image shows damage, or the environmental parameters are abnormal; The processing module is used to push warning information to operation and maintenance personnel through mobile terminals and generate fault handling suggestions.

8. The method and device for monitoring and early warning of intelligent solar panels based on the Internet of Things according to claim 7, characterized in that: The acquisition module, the transmission module, the analysis module, the detection module and the processing module are controlled to execute the intelligent solar panel monitoring and early warning method based on the Internet of Things according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the smart solar panel monitoring and early warning method based on the Internet of Things as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the smart solar panel monitoring and early warning method based on the Internet of Things as described in any one of claims 1 to 6.

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