Photovoltaic monitoring system and method

Abstract

The invention relates to the technical field of photovoltaic monitoring, and provides a photovoltaic monitoring system and method, and the system comprises a data collection module which is used for collecting the operation parameters of a photovoltaic module; and the analysis control module is used for predicting the working state of the photovoltaic module according to the operation parameters of the photovoltaic module and the historical operation parameters of the photovoltaic module, and performing early warning under the condition that the working state of the photovoltaic module is predicted to be faulty. Prediction and early warning are carried out before faults occur, so that operators can handle and intervene in time, occurrence and further expansion of the faults are avoided, potential equipment faults can be found in time, production halt time can be shortened, power generation efficiency can be improved, and operation and maintenance cost can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of photovoltaic monitoring technology, and in particular, to a photovoltaic monitoring system and method. Background Art

[0002] Photovoltaic power generation, as a clean and renewable energy technology, plays an increasingly important role in the world's energy structure today. It uses solar panels to directly convert sunlight into electrical energy, with the advantages of no noise, no emissions, and simple maintenance. It plays a significant role in reducing greenhouse gas emissions and mitigating global climate change. With the increasing global awareness of sustainable development and environmental protection, photovoltaic power generation is rapidly becoming a key force in replacing traditional fossil energy and promoting energy transformation. The efficient operation and long-term stability of photovoltaic power generation systems need to rely on advanced monitoring and management technologies.

[0003] In the related art, equipment maintenance of a photovoltaic power station is usually carried out after a fault occurs, and after a fault occurs, the equipment needs to be shut down for equipment maintenance. During the shutdown period, power generation cannot be performed, which affects the power generation of the photovoltaic power station. Summary of the invention

[0004] The purpose of the present disclosure is to provide a photovoltaic monitoring system and method to solve the problems in the related art.

[0005] In order to achieve the above object, according to a first aspect of an embodiment of the present disclosure, a photovoltaic monitoring system is provided, the photovoltaic monitoring system comprising: A data acquisition module, used to collect operating parameters of photovoltaic modules; The analysis control module is used to predict the working state of the photovoltaic assembly according to the operating parameters of the photovoltaic assembly and the historical operating parameters of the photovoltaic assembly, and to issue an early warning when a working state failure of the photovoltaic assembly is predicted.

[0006] Optionally, the photovoltaic monitoring system further includes: The video monitoring module is used to monitor the video images of the photovoltaic power station and to provide fire warning and dust accumulation warning according to the video images of the photovoltaic power station.

[0007] Optionally, the video images of the photovoltaic power station include video images of a photovoltaic module area, video images of an inverter area, video images of a box-type substation area, video images of a primary equipment warehouse, and video images of a secondary equipment warehouse.

[0008] Optionally, the video surveillance module is also used to provide fire warning based on the video images of the photovoltaic power station through computer vision and brain-like technology, combined with photovoltaic fire monitoring application scenarios.

[0009] Optionally, the video monitoring module is also used to provide dust accumulation warning based on the video images of the photovoltaic power station through computer vision and brain-like technology, combined with dust accumulation defect application scenarios.

[0010] Optionally, the video monitoring module is also used to provide dust accumulation warning based on the video images and environmental monitoring data of the photovoltaic power station through computer vision and brain-like technology, combined with dust accumulation defect application scenarios.

[0011] Optionally, the photovoltaic monitoring system further includes: The power generation prediction module is used to predict the future power generation of the photovoltaic power station based on weather forecast information and historical power generation information of the photovoltaic power station.

[0012] Optionally, the photovoltaic monitoring system further includes: A remote monitoring module is connected to each photovoltaic power station and is used to adjust the power generation strategy of each photovoltaic power station according to the future power generation of each photovoltaic power station.

[0013] Optionally, the remote monitoring module is further used to adjust the operating parameters of the corresponding photovoltaic power station according to the power generation strategy of each photovoltaic power station to control the power generation of the photovoltaic power station.

[0014] According to a second aspect of an embodiment of the present disclosure, a photovoltaic monitoring method is provided, the photovoltaic monitoring method comprising: Collect the operating parameters of photovoltaic modules; The working state of the photovoltaic assembly is predicted according to the operating parameters of the photovoltaic assembly and the historical operating parameters of the photovoltaic assembly, and an early warning is issued when a working state failure of the photovoltaic assembly is predicted.

[0015] By adopting the above technical solution, the operating parameters of the photovoltaic module are collected through the data acquisition module, and then the working state of the photovoltaic module is predicted according to the operating parameters of the photovoltaic module and the historical operating parameters of the photovoltaic module through the analysis control module, and an early warning is issued when a working state failure of the photovoltaic module is predicted. Prediction and early warning are carried out before the failure occurs, so that the operating personnel can handle and intervene in time to avoid the occurrence and further expansion of the failure, and the potential equipment failure is discovered in time, which can reduce the downtime, improve the power generation efficiency, and reduce the operation and maintenance cost.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 is a block diagram of a photovoltaic monitoring system according to an exemplary embodiment.

[0018] Figure 2 The figure is a flow chart of a photovoltaic monitoring method according to an exemplary embodiment.

[0019] Figure 3 is a block diagram of another photovoltaic monitoring system according to an exemplary embodiment.

[0020] Figure 4 The flowchart of another photovoltaic monitoring method is shown according to an exemplary embodiment. DETAILED DESCRIPTION

[0021] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0022] In the description below, words such as “first” and “second” are only used for the purpose of distinguishing the description and should not be understood as indicating or implying relative importance or order.

[0023] Photovoltaic power generation, as a clean and renewable energy technology, plays an increasingly important role in the world's energy structure today. It uses solar panels to directly convert sunlight into electrical energy, with the advantages of no noise, no emissions, and simple maintenance. It plays a significant role in reducing greenhouse gas emissions and mitigating global climate change. With the increasing global awareness of sustainable development and environmental protection, photovoltaic power generation is rapidly becoming a key force in replacing traditional fossil energy and promoting energy transformation. The efficient operation and long-term stability of photovoltaic power generation systems need to rely on advanced monitoring and management technologies.

[0024] In the related art, equipment maintenance of a photovoltaic power station is usually carried out after a fault occurs, and after a fault occurs, the equipment needs to be shut down for equipment maintenance. During the shutdown period, power generation cannot be performed, which affects the power generation of the photovoltaic power station.

[0025] In order to solve the above technical problems, the operating parameters of the photovoltaic modules are collected through the data acquisition module, and then the working state of the photovoltaic modules is predicted by the analysis and control module according to the operating parameters of the photovoltaic modules and the historical operating parameters of the photovoltaic modules, and an early warning is issued when a failure in the working state of the photovoltaic modules is predicted. Prediction and early warning are carried out before the failure occurs, so that the operating personnel can handle and intervene in time to avoid the occurrence and further expansion of the failure, and the potential equipment failure is discovered in time, which can reduce the downtime, improve the power generation efficiency, and reduce the operation and maintenance costs.

[0026] Figure 1 is a block diagram of a photovoltaic monitoring system according to an exemplary embodiment. Figure 1 The photovoltaic monitoring system may include a data acquisition module and an analysis and control module.

[0027] The data acquisition module is used to collect the operating parameters of the photovoltaic components.

[0028] The analysis control module is used to predict the working state of the photovoltaic module based on the operating parameters of the photovoltaic module and the historical operating parameters of the photovoltaic module, and to issue an early warning when a working state failure of the photovoltaic module is predicted.

[0029] The operating parameters of the photovoltaic module may be, but are not limited to, the temperature, voltage, current, etc. of the photovoltaic module.

[0030] The historical operating parameters of the PV modules are analyzed to obtain the fault reference operating parameters of the PV modules before the fault occurs.

[0031] The operating parameters of the PV module are compared with the fault reference operating parameters. When the operating parameters of the PV module are consistent with the fault reference operating parameters, it indicates that the PV module may be about to fail and there is a risk of failure. In this case, the working state of the PV module is predicted to be faulty. When the operating parameters of the PV module are inconsistent with the fault reference operating parameters, it is predicted that the working state of the PV module is normal.

[0032] When a PV module failure is predicted, an early warning can be issued through sound, light, voice, etc. to remind the operator that the PV module may be about to fail, so that the operator can deal with and intervene in time to avoid the occurrence and further expansion of the failure. Timely detection of potential equipment failures can reduce downtime, improve power generation efficiency, and reduce operation and maintenance costs.

[0033] In a possible implementation, the photovoltaic monitoring system may further include: The video monitoring module is used to monitor the video images of the photovoltaic power station and to provide fire warning and dust accumulation warning based on the video images of the photovoltaic power station.

[0034] The video images of the photovoltaic power station can include the video images of the photovoltaic module area, the video images of the inverter area, the video images of the box-type substation area, the video images of the primary equipment warehouse, and the video images of the secondary equipment warehouse. By installing cameras at different locations in the photovoltaic power station, video images of different areas can be obtained.

[0035] The video monitoring module is used to issue fire warnings based on the video images of the photovoltaic module area, the inverter area, the box-type substation area, the primary equipment warehouse, and the secondary equipment warehouse.

[0036] The video surveillance module is also used to provide fire warnings based on video images of the photovoltaic power station through computer vision and brain-like technology, combined with photovoltaic fire monitoring application scenarios.

[0037] Computer vision technology can be used to analyze video images of photovoltaic power plants to identify and monitor the photovoltaic module area, inverter area, box-type substation area, and the primary and secondary equipment warehouses. Brain-like computing technology is used to simulate the way the human brain processes information, improving the ability to understand and analyze complex scenes.

[0038] The video images of the photovoltaic power station can be preprocessed first, including denoising, contrast enhancement, image segmentation, etc., to improve the accuracy of subsequent analysis, and computer vision technology can be used to extract fire-related features from the video images, such as the shape, color, movement trajectory of the smoke, and the brightness and flickering frequency of the flame. Brain-like computing technology is applied to simulate the way the human brain processes information, and combined with machine learning and deep learning algorithms to learn photovoltaic fire monitoring application scenarios, so that it can identify typical fire patterns in photovoltaic power stations, and then perform intelligent analysis and pattern recognition on the extracted features. Through the trained model, fire signs in the image, such as smoke and flames, can be identified to achieve early identification of fire. Once signs of fire are detected, an early warning is issued immediately.

[0039] In other embodiments, multi-dimensional judgment may be performed through other sensors, such as temperature sensors, smoke concentration sensors, etc., to improve the accuracy of fire warning and reduce the false alarm rate.

[0040] In a possible implementation, the video monitoring module is used to issue a dust accumulation warning based on video images of the photovoltaic module area.

[0041] The video surveillance module is also used to provide dust accumulation warning based on the video images of the photovoltaic power station through computer vision and brain-like technology, combined with dust accumulation defect application scenarios.

[0042] The video images of the photovoltaic power station can be preprocessed first, including denoising, contrast enhancement, image segmentation, etc., to improve the accuracy of subsequent analysis, and computer vision technology can be used to extract dust-related features from the image, such as color changes, texture features, shape information, etc. in the dust-covered area. Machine learning or deep learning algorithms, such as convolutional neural networks, are used to train the model to identify the characteristics of dust accumulation. Labeled dust images can be used as training data. Brain-like technology is applied to simulate the way the human brain processes visual information and enhance the ability to recognize dust features. Brain-like algorithms can improve the autonomous intelligence and robustness of the system. The trained model is deployed in the video monitoring module to analyze the video images of the photovoltaic power station. Once the dust accumulation reaches the preset threshold, an early warning is issued immediately.

[0043] In a possible implementation, the video monitoring module is also used to provide dust accumulation warning based on the video images and environmental monitoring data of the photovoltaic power station through computer vision and brain-like technology, combined with dust accumulation defect application scenarios.

[0044] Environmental monitoring data may include, but is not limited to, temperature, humidity, rainfall, wind speed, etc.

[0045] The video images of the photovoltaic power station can be preprocessed first, including denoising, contrast enhancement, image segmentation, etc., to improve the accuracy of subsequent analysis, and computer vision technology can be used to extract dust-related features from the image, such as color changes, texture features, shape information, etc. in the dust-covered area. Machine learning or deep learning algorithms, such as convolutional neural networks, are used to train the model to identify the characteristics of dust accumulation. Labeled dust images can be used as training data. Brain-like technology is applied to simulate the way the human brain processes visual information and enhance the ability to recognize dust features. Brain-like algorithms can improve the autonomous intelligence and robustness of the system. The trained model is deployed in the video monitoring module to analyze the video images of the photovoltaic power station. Once the dust accumulation reaches the preset threshold and the environmental monitoring data is within the preset range, an early warning is issued.

[0046] Multi-dimensional judgments are made through temperature, humidity, rainfall, wind speed, etc. to further improve the accuracy of dust accumulation warning.

[0047] In a possible implementation, the photovoltaic monitoring system may further include: The power generation prediction module is used to predict the future power generation of the photovoltaic power station based on weather forecast information and the historical power generation information of the photovoltaic power station.

[0048] The historical power generation information includes the historical power generation and its corresponding historical meteorological data.

[0049] The historical power generation data of photovoltaic power stations, including power generation, operating status of photovoltaic panels, etc., weather forecast information, including meteorological data such as solar radiation intensity, temperature, cloud cover, wind speed, etc., analyze the impact of meteorological conditions on photovoltaic power generation performance, identify key meteorological variables such as solar radiation, temperature, etc., and analyze the relationship between them and photovoltaic power generation. Select a suitable prediction model based on the analysis results. Common prediction models include statistical models (such as regression analysis, time series analysis), physical models (based on the physical properties of photovoltaic cells), machine learning models (such as neural networks, support vector machines), etc. Use historical data to train the selected model. For example, if a neural network model is selected, the network weights need to be trained through historical meteorological data and historical power generation data, and the weather forecast information is input into the trained model to predict the future power generation of the photovoltaic power station.

[0050] Optionally, the photovoltaic monitoring system may further include: The remote monitoring module is connected to each photovoltaic power station and is used to adjust the power generation strategy of each photovoltaic power station according to the future power generation of each photovoltaic power station.

[0051] Each photovoltaic power station includes a data acquisition module, an analysis and control module, a video monitoring module and a power generation prediction module. The remote supervision module is connected to each photovoltaic power station and adjusts the power generation strategy of each photovoltaic power station according to the future power generation of each photovoltaic power station.

[0052] For example, on days when high sunshine hours are predicted, power generation can be increased; on days when more cloud cover is predicted, power generation can be reduced or equipment maintenance can be scheduled. In this way, the photovoltaic monitoring system can effectively integrate historical power generation data and weather forecast information to provide accurate power generation forecasts and decision support for power station operators.

[0053] Optionally, the remote monitoring module is further used to adjust the operating parameters of the corresponding photovoltaic power station according to the power generation strategy of each photovoltaic power station to control the power generation of the photovoltaic power station.

[0054] Based on the same inventive concept, please refer to Figure 2 This embodiment also provides a photovoltaic monitoring method, the photovoltaic monitoring method comprising: Step S1, collecting operating parameters of photovoltaic modules; Step S2, predicting the working state of the photovoltaic module according to the operating parameters of the photovoltaic module and the historical operating parameters of the photovoltaic module, and issuing an early warning when a working state failure of the photovoltaic module is predicted.

[0055] In one embodiment, see Figure 3 The photovoltaic monitoring system can include: data acquisition and protection module, video imaging monitoring module, background big data analysis module, artificial intelligence technology module, remote monitoring and control module, and energy dispatching management module.

[0056] 1. Data acquisition and protection module, using the IV curve scanning function of the photovoltaic inverter, can continuously monitor the temperature, voltage, current and other operating parameters of the photovoltaic module. Through the collection and transmission of real-time data, operators can quickly understand the status of the equipment, including whether there are problems such as overheating and leakage. This helps to detect potential equipment failures in time and reduce downtime. Other equipment sensors can also detect abnormal conditions in the operation of the equipment and send out early warning signals through the system. Once abnormalities such as degradation of photovoltaic module performance and unstable current are found, an alarm will be automatically triggered to remind operators to deal with them in time, thereby avoiding further expansion of the fault. The acquisition module can not only monitor equipment parameters in real time, but also collect a large amount of data. After being transmitted to the background big data analysis module, these data can be used for analysis and optimization. By analyzing historical data, the long-term performance trend of photovoltaic modules can be revealed, providing a basis for formulating power generation plans and maintenance strategies. The application of the data acquisition module makes the monitoring of equipment status more accurate and timely. This helps to predict and intervene before a fault occurs, thereby reducing downtime, improving power generation efficiency and reducing maintenance costs.

[0057] 2. Video image monitoring module: The video image monitoring module realizes the perception, identification and alarm functions of fire hazards in the photovoltaic module area, inverter area, box transformer and primary and secondary warehouses. In order to monitor the risks in the production and operation process of photovoltaic power stations, this module uses computer vision and brain-like computing technology, combined with typical application scenarios of photovoltaic fire monitoring and photovoltaic module dust accumulation defects, and predicts and warns the degree of dust accumulation of photovoltaic modules through the photovoltaic power station power generation big data comparative analysis calculation model, environmental PM2.5 trend prediction big data algorithm model and brain-like image model, so as to effectively monitor the fire hazards and dust accumulation defects of the entire photovoltaic power station site in real time, and realize advanced artificial intelligence technology and big data applications.

[0058] 3. Backstage big data analysis module. The big data analysis module can integrate historical power generation data, weather forecasts and other information, and use complex algorithm models for analysis and prediction. This allows power station operators to accurately predict future power generation, thereby formulating more reasonable power generation plans, adjusting power generation strategies to adapt to different weather changes, and maximizing power generation efficiency. Big data analysis technology helps monitor the operating status and performance of equipment. When equipment is abnormal, potential failure modes can be identified by analyzing historical data and issuing early warnings. This enables maintenance personnel to take preventive measures before a failure occurs, reducing downtime and losses. If a failure occurs, the big data analysis module can also provide reference solutions to operation and maintenance personnel based on the failure phenomenon to assist operation and maintenance personnel in analyzing the cause of the failure.

[0059] 4. Artificial intelligence technology module. The artificial intelligence technology module plays a key role in equipment failure prediction, maintenance management, power generation efficiency improvement and power station revenue improvement. Artificial intelligence technology simulates human intelligent behavior and thinking process to realize the analysis, learning and decision-making of massive data, providing a new method for the operation and management of photovoltaic power generation. Artificial intelligence technology can identify the patterns and characteristics of equipment failures by analyzing historical data, thereby predicting possible failures. Using machine learning algorithms, artificial intelligence can identify abnormal patterns related to equipment failures and provide maintenance personnel with clues to take early action, thereby avoiding serious failures and reducing the risk of shutdowns. Artificial intelligence technology can formulate more accurate maintenance plans. By analyzing the use of equipment, performance data and environmental factors, artificial intelligence can formulate customized maintenance plans for operation and maintenance personnel, reduce unnecessary maintenance costs and improve maintenance efficiency. Artificial intelligence can analyze the operation of the power generation system based on real-time data and make timely adjustments based on factors such as weather and light. This helps to optimize the working state of the photovoltaic power generation system, so that it is always in the best power generation state and improves power generation efficiency.

[0060] 5. Remote monitoring and control module. Operators can remotely adjust the operating parameters of photovoltaic equipment, control power generation, and even perform switch operations through the photovoltaic monitoring system. This enables operators to achieve refined management of power stations without being on site. Remote monitoring and control technology reduces the number of times operators need to actually go to the site for monitoring and operation, thereby saving labor costs. Even in different locations, operators can monitor and manage multiple photovoltaic power stations in real time, improving operation and maintenance efficiency. Remote monitoring systems usually use secure encryption technology to ensure the security of data transmission. This plays an important role in protecting sensitive data and preventing unauthorized access, and improves the safety and reliability of photovoltaic power stations.

[0061] 6. Energy dispatch management module. Through the energy dispatch management module, operators can understand important parameters such as power generation, power consumption and photovoltaic grid-connected power of the power station in real time, so as to better manage the energy dispatch of the photovoltaic power station. It can help identify the peak and trough periods of energy consumption, so as to formulate a more reasonable energy consumption plan. By reasonably allocating energy use time and optimizing power consumption strategies, power stations can reduce energy consumption costs and waste. Operators can monitor the power load in real time. According to the changes in power load, the power output strategy can be flexibly adjusted. The energy dispatch management module can formulate smarter and more flexible energy purchase and sales strategies based on market prices, power demand and other factors. By analyzing the data, operators can charge energy storage when the power market price is low, discharge energy storage when the price is high, and send out all photovoltaic power to increase power station income.

[0062] Based on the above photovoltaic monitoring system, please refer to Figure 4 The data acquisition and protection module collects data from the equipment in the photovoltaic area. The protection module protects the equipment in the photovoltaic area. The collected data is transmitted to the background big data analysis module and the artificial intelligence technology module for real-time data monitoring and analysis. The video image monitoring module transmits the image data collected on-site in the photovoltaic area to the artificial intelligence technology module for computer vision and brain-like computing analysis of the data, and performs fault alarm and fault prediction.

[0063] In the event of a fault alarm, the system alarm informs the operation and maintenance personnel, and the equipment protection trips. Big data provides reference solutions for the protection solution, and the operation and maintenance personnel handle the fault. In the case of fault prediction, the fault is analyzed and the operation and maintenance personnel are informed of the warning. The operation and maintenance personnel handle the fault in advance to prevent further expansion of the problem. Energy dispatch control regulates and dispatches energy to maximize economic efficiency.

[0064] The data is real-time and timely, with strong data analysis capabilities, strong scalability and flexibility, quick remote monitoring and maintenance, reducing operation and maintenance costs, improving the power generation of the photovoltaic system, and increasing the project investment return rate.

[0065] Using artificial intelligence and big data to analyze photovoltaic monitoring data, energy scheduling management, and video monitoring analysis can reduce the time and cost of photovoltaic operation and maintenance personnel in troubleshooting, improve the personnel's ability to accurately judge faults, predict potential fault risks, improve the rationality of energy scheduling, and increase project investment returns.

[0066] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0067] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0068] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A photovoltaic monitoring system, characterized in that: The photovoltaic monitoring system comprises: A data acquisition module, used to collect operating parameters of photovoltaic modules; The analysis control module is used to predict the working state of the photovoltaic assembly according to the operating parameters of the photovoltaic assembly and the historical operating parameters of the photovoltaic assembly, and to issue an early warning when a working state failure of the photovoltaic assembly is predicted.

2. The photovoltaic monitoring system according to claim 1, characterized in that: The photovoltaic monitoring system also includes: The video monitoring module is used to monitor the video images of the photovoltaic power station and to provide fire warning and dust accumulation warning according to the video images of the photovoltaic power station.

3. The photovoltaic monitoring system according to claim 2, characterized in that: The video images of the photovoltaic power station include video images of the photovoltaic module area, video images of the inverter area, video images of the box-type substation area, video images of the primary equipment warehouse, and video images of the secondary equipment warehouse.

4. The photovoltaic monitoring system according to claim 2, characterized in that: The video monitoring module is also used to provide fire warnings based on the video images of the photovoltaic power station through computer vision and brain-like technology, combined with photovoltaic fire monitoring application scenarios.

5. The photovoltaic monitoring system according to claim 2, characterized in that: The video monitoring module is also used to provide dust accumulation warning based on the video images of the photovoltaic power station through computer vision and brain-like technology, combined with dust accumulation defect application scenarios.

6. The photovoltaic monitoring system according to claim 2, characterized in that: The video monitoring module is also used to provide dust accumulation warning based on the video images and environmental monitoring data of the photovoltaic power station through computer vision and brain-like technology, combined with dust accumulation defect application scenarios.

7. The photovoltaic monitoring system according to claim 1, characterized in that: The photovoltaic monitoring system also includes: The power generation prediction module is used to predict the future power generation of the photovoltaic power station based on weather forecast information and historical power generation information of the photovoltaic power station.

8. The photovoltaic monitoring system according to claim 7, characterized in that: The photovoltaic monitoring system also includes: A remote monitoring module is connected to each photovoltaic power station and is used to adjust the power generation strategy of each photovoltaic power station according to the future power generation of each photovoltaic power station.

9. The photovoltaic monitoring system according to claim 8, characterized in that: The remote monitoring module is also used to adjust the operating parameters of the corresponding photovoltaic power station according to the power generation strategy of each photovoltaic power station to control the power generation of the photovoltaic power station.

10. A photovoltaic monitoring method, characterized in that: The photovoltaic monitoring method comprises: Collect the operating parameters of photovoltaic modules; The working state of the photovoltaic assembly is predicted according to the operating parameters of the photovoltaic assembly and the historical operating parameters of the photovoltaic assembly, and an early warning is issued when a working state failure of the photovoltaic assembly is predicted.

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