Photovoltaic power station fire treatment method, device and equipment and storage medium

Abstract

The invention discloses a photovoltaic power station fire treatment method, device and equipment and a storage medium. The method comprises the following steps: monitoring a key position of a photovoltaic power station in real time through a multi-mode image processing technology, and collecting any one or more of a thermal image, an infrared image and a visible light image; analyzing the acquired image data through a convolutional neural network to identify flames, smog and abnormal high-temperature points; when a fire disaster or abnormal high temperature is monitored, an emergency power-off program of the photovoltaic power station is automatically triggered, and a high-voltage direct current area is isolated; through intelligent fire extinguishing equipment including an unmanned aerial vehicle or a robot, a non-conductive fire extinguishing agent is used at a fire source position for fire extinguishing. According to the technical scheme, rapid identification and accurate fire extinguishing of the photovoltaic power station fire are realized, the electric shock risk can be effectively reduced, the fire response efficiency and safety are improved, continuous operation of the photovoltaic power station is ensured, and the fire loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method, device, equipment and storage medium for handling fire in a photovoltaic power station. Background Art

[0002] Photovoltaic power stations, as one of the main forms of clean energy generation, have been widely used in recent years. Photovoltaic systems convert solar energy into electrical energy and convert direct current into alternating current through inverters for use by power grids or users. Due to its renewable and environmentally friendly characteristics, the installed capacity of photovoltaic power stations continues to increase worldwide. However, with the expansion of the scale of photovoltaic power stations and the popularization of their applications, safety issues have gradually attracted attention, especially when fires occur, the high-voltage direct current of photovoltaic systems poses a huge challenge. How to properly solve the above problems has become an urgent issue for the industry. Summary of the invention

[0003] The present invention provides a photovoltaic power station fire handling method, device, equipment and storage medium, which are used to effectively reduce the risk of electric shock, improve the efficiency and safety of fire response, ensure the continuous operation of the photovoltaic power station, and reduce fire losses.

[0004] According to a first aspect of the present invention, a method for handling a fire in a photovoltaic power station is provided, the method comprising:

[0005] Real-time monitoring of key locations in PV power plants through multimodal image processing technology, collecting any one or more of thermal, infrared and visible light images;

[0006] The collected image data is analyzed through convolutional neural networks to identify flames, smoke and abnormally high temperature points, thereby determining the location of the fire source;

[0007] When a fire or abnormally high temperature is detected, the emergency power-off procedure of the photovoltaic power station is automatically triggered to isolate the high-voltage direct current area;

[0008] Fires are extinguished at the source using non-conductive fire extinguishing agents using intelligent fire-fighting equipment, including drones or robots.

[0009] In one embodiment, it further includes:

[0010] After collecting any one or more of the thermal imaging image, the infrared image and the visible light image, a fusion process is performed on at least two of the thermal imaging image, the infrared image and the visible light image.

[0011] In one embodiment, the automatic triggering of the emergency power-off procedure of the photovoltaic power station includes:

[0012] When the emergency power-off procedure is automatically executed, it is analyzed that the fire source is located in the area of ​​the photovoltaic power station;

[0013] Disconnect power to the area to limit the spread of fire and reduce the risk of electric shock.

[0014] In one embodiment, the method of extinguishing a fire at a fire source location using a non-conductive fire extinguishing agent includes:

[0015] Analyze the amount of extinguishing agent required to extinguish the fire through image processing;

[0016] Dynamically adjust the spray flow rate of the fire extinguishing agent according to the progress of fire extinguishing.

[0017] In one embodiment, it further includes:

[0018] Using augmented reality devices to show firefighters real-time fire location and high-voltage area identification;

[0019] When firefighters approach a high-voltage area, the augmented reality device sends a warning signal and provides a suggested fire-fighting path.

[0020] In one embodiment, it further includes:

[0021] After the fire is extinguished, all image data is automatically archived;

[0022] Based on archived image data, fire detection models are retrained through deep learning techniques to improve the accuracy of future fire identification.

[0023] According to a second aspect of the present invention, there is provided a photovoltaic power station fire handling device, comprising:

[0024] A monitoring module, used to monitor key locations of a photovoltaic power station in real time by using multimodal image processing technology, collecting any one or more of thermal imaging, infrared images and visible light images;

[0025] An analysis module is used to analyze the collected image data through a convolutional neural network to identify flames, smoke, and abnormally high temperature points, thereby determining the location of the fire source;

[0026] Isolation module, used to automatically trigger the emergency power-off procedure of the photovoltaic power station and isolate the high-voltage direct current area when a fire or abnormally high temperature is detected;

[0027] The fire extinguishing module is used to extinguish the fire at the fire source location using a non-conductive fire extinguishing agent through intelligent fire extinguishing equipment, including drones or robots.

[0028] In one embodiment, the monitoring module, the analyzing module, the isolating module and the fire extinguishing module are controlled to execute any one of the above-mentioned photovoltaic power station fire handling methods.

[0029] 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;

[0030] 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 photovoltaic power station fire handling methods is implemented.

[0031] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having computer program instructions stored thereon, which implement any of the above-mentioned photovoltaic power station fire handling methods when executed by a computer (e.g., a processor in the computer).

[0032] In summary, the present invention provides a method and device for handling fires in a photovoltaic power station, the method comprising: real-time monitoring of key locations of the photovoltaic power station through multimodal image processing technology, collecting any one or more of thermal imaging, infrared images and visible light images; analyzing the collected image data through a convolutional neural network to identify flames, smoke and abnormally high temperature points, thereby determining the location of the fire source; when a fire or abnormally high temperature is detected, the emergency power-off procedure of the photovoltaic power station is automatically triggered to isolate the high-voltage direct current area; and using intelligent fire-fighting equipment, including drones or robots, to extinguish the fire at the fire source using non-conductive fire-extinguishing agents. The technical solution of the present application realizes rapid identification and precise fire-fighting of photovoltaic power station fires by integrating multimodal image processing, convolutional neural network analysis, automatic power-off, intelligent fire-fighting equipment and augmented reality technology, which can effectively reduce the risk of electric shock, improve the efficiency and safety of fire response, ensure the continuous operation of the photovoltaic power station, and reduce fire losses.

[0033] 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, claims, and drawings.

[0034] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] 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.

[0036] Figure 1 A flow chart of a photovoltaic power station fire handling method provided by an embodiment of the present invention;

[0037] Figure 2 A structural diagram of a photovoltaic power station fire handling device provided by an embodiment of the present invention;

[0038] Figure 3 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] 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.

[0040] 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.

[0041] like Figure 1 As shown, the present invention provides a method for handling fires in a photovoltaic power station, the method for handling fires in a photovoltaic power station comprising:

[0042] In step S11, the key positions of the photovoltaic power station are monitored in real time by multimodal image processing technology, and any one or more of thermal images, infrared images and visible light images are collected;

[0043] In step S12, the collected image data is analyzed by a convolutional neural network to identify flames, smoke and abnormally high temperature points, thereby determining the location of the fire source;

[0044] In step S13, when a fire or abnormally high temperature is detected, the emergency power-off procedure of the photovoltaic power station is automatically triggered to isolate the high-voltage direct current area;

[0045] In step S14, a non-conductive fire extinguishing agent is used to extinguish the fire at the fire source by using intelligent fire extinguishing equipment, including a drone or a robot.

[0046] In one embodiment, the traditional fire handling method of photovoltaic power station usually faces the following problems. Risk of high voltage direct current. The voltage of photovoltaic power station is usually between 600V and 1000V, or even higher. When the cable is aged, the joint fails, or the cable insulation fails due to external factors, it is very easy to cause a DC arc, which in turn causes a fire. Due to the particularity of the DC arc, ordinary fire extinguishing methods are difficult to work. In addition, when a fire occurs, there are still hundreds or even thousands of volts of DC high voltage in the system. There is a great risk of electric shock in the traditional fire extinguishing process, which makes it impossible for firefighters to immediately carry out rescue. Usually, they can only wait until the sun goes down or the photovoltaic power station is completely burned before they can deal with it. Fire detection and response delay. Most of the current fire monitoring systems are based on smoke detectors or temperature sensors. These devices are often difficult to detect fire hazards in photovoltaic systems in a timely manner, especially when electrical faults have just occurred, delaying the best emergency response time. In addition, the traditional system lacks effective real-time monitoring means, and it is difficult to conduct comprehensive and real-time monitoring and analysis of key areas of photovoltaic power stations. Fire extinguishing is difficult. The vast area and complex electrical system structure of photovoltaic power stations make it difficult to quickly locate and control the fire source after a fire occurs. Since power stations are usually outdoors, weather conditions can also affect the development of fires and firefighting operations. Traditional fire-fighting equipment, such as water or foam fire extinguishers, may not be able to effectively extinguish fires without damaging electrical equipment, and may even increase the risk of electric shock due to the conductivity of water. Lacking intelligence and automation, existing photovoltaic power station fire handling systems mostly rely on manual operations and lack intelligent and automated fire-fighting equipment. Even if there is a certain degree of automation, it is usually limited to simple power-off operations, and it is impossible to dynamically adjust the fire-fighting strategy according to the real-time situation, affecting the efficiency and effect of fire-fighting. The embodiments in this application can quickly identify the source of fire, effectively isolate high-voltage direct current, and extinguish fires through intelligent and automated equipment when a fire occurs in a photovoltaic power station. Technical solutions. By combining multimodal image processing technology, convolutional neural network analysis, intelligent fire-fighting equipment (such as drones, robots) and augmented reality equipment, a comprehensive photovoltaic power station fire response method and device is provided to improve the safety and efficiency of fire handling, which helps to significantly reduce the losses caused by photovoltaic power station fires, protect personnel safety, and promote the sustainable development of the photovoltaic industry. Based on advanced technologies such as multimodal image processing, convolutional neural network (CNN) analysis, and intelligent fire-fighting equipment, early detection of fires, automated response, and firefighter assistance are achieved, significantly improving the safety and response efficiency of photovoltaic power stations when fires occur.

[0047] Photovoltaic power stations face safety risks such as fires due to their wide application and complex system structure. Fires in photovoltaic power stations are usually caused by electrical faults (such as cable aging, loose joints, overload, etc.), and due to the large scale of power stations and the wide distribution of equipment, traditional fire monitoring methods are difficult to detect and locate the fire source in time. Use multimodal image processing technology to monitor the key locations of photovoltaic power stations in real time.

[0048] Multimodal image processing technology refers to the use of different types of image data (such as thermal imaging, infrared images, visible light images, etc.) to comprehensively analyze and monitor environmental conditions. Thermal imaging images can detect the temperature distribution on the surface of an object, infrared images can penetrate smoke to observe flames, and visible light images provide detailed environmental information. These image data can complement each other, enabling the monitoring system to work effectively under various environmental conditions. Through an adaptive multimodal image processing system, the most appropriate imaging technology can be automatically selected according to real-time environmental conditions (such as light intensity, weather conditions, day or night). For example, in strong light during the day, the system can rely mainly on thermal imaging and infrared images; at night or in low light conditions, the importance of visible light images will increase. It can not only improve the comprehensiveness of monitoring, but also significantly improve the accuracy of fire source detection.

[0049] In the fire response of photovoltaic power plants, quickly and accurately identifying the location of the fire source is the key to timely response and control of the fire. Traditional fire monitoring systems rely on simple temperature threshold alarms or smoke detectors, which are often unable to cope with complex environmental changes and are prone to false alarms or missed alarms. Convolutional neural networks (CNNs) are used to analyze the collected image data to identify flames, smoke, and abnormally high temperature points to determine the location of the fire source. CNN is a deep learning model that is particularly suitable for image recognition tasks. It gradually extracts the feature information of the image through convolutional layers, pooling layers, and fully connected layers, and can automatically learn patterns and features in the image. CNN is used to analyze multimodal image data, identify early signs of fire, and accurately locate the fire source. To further improve the accuracy of fire source identification, other deep learning algorithms, such as recurrent neural networks (RNNs) or long short-term memory networks (LSTMs), can be combined to process continuous image data. These algorithms can capture the dynamic changes of the fire source and predict the possible development direction of the fire. In addition, by introducing generative adversarial networks (GANs), the system can simulate possible fire scenarios, thereby training the model in advance to cope with complex situations in actual fires. This fire source identification system, which combines multiple deep learning algorithms, can more effectively detect and locate fire sources in various complex scenarios.

[0050] Fires in photovoltaic power plants are often accompanied by the presence of high-voltage direct current, which increases the complexity and danger of fires. If the power is not disconnected in time, the high-voltage current may aggravate the fire and greatly hinder the fire-fighting work. Therefore, the system needs to automatically trigger the emergency power-off procedure of the photovoltaic power plant when a fire or abnormally high temperature is detected to isolate the high-voltage direct current area and ensure the safety of the fire-fighting operation. In the automatic power-off procedure, the system analyzes the location of the fire source and automatically disconnects the power supply in the area where the fire source is located according to the electrical layout of the power station. This can effectively limit the spread of the fire and reduce the risk of electric shock, while retaining the normal power supply of other unaffected areas and reducing economic losses. In order to achieve more intelligent power-off control, a partition management system can be developed. The system can flexibly adjust the power-off range according to the real-time location of the fire source and the development of the fire. For example, the system can predict the spread path of the fire and disconnect the power area that may be affected in advance. At the same time, the system can also adjust the power network inside the photovoltaic power station to ensure that important equipment or areas can continue to be powered, thereby minimizing the impact of the fire on the entire power station.

[0051] After locating the fire source and isolating the high-voltage area, the next step is to perform fire extinguishing operations. Traditional fire extinguishing methods (such as handheld fire extinguishers or fire hoses) pose a greater risk in the high-voltage environment of a photovoltaic power station. Introducing intelligent fire extinguishing equipment, such as drones or robots, uses non-conductive fire extinguishing agents to extinguish fires at the fire source. Non-conductive fire extinguishing agents can avoid the risk of electric shock during the fire extinguishing process and ensure the safety of fire extinguishing work. Intelligent fire extinguishing equipment can not only accurately locate the fire source for fire extinguishing, but also analyze the changes in the fire source in real time through image processing technology. The spraying amount and flow rate of the fire extinguishing agent can be dynamically adjusted according to the progress of the fire extinguishing to improve the efficiency of fire extinguishing. For example, in the early stage of a large fire, the system can increase the spraying intensity of the fire extinguishing agent, and after the fire is gradually controlled, the system can reduce the spraying amount to save fire extinguishing agent and reduce the impact on the environment. Intelligent fire extinguishing equipment can also achieve collaborative work. Multiple drones or robots can share real-time data through wireless networks and assign different fire extinguishing tasks. For example, one group of drones is responsible for controlling the fire source, and another group is responsible for protecting surrounding equipment from the fire. In addition, intelligent fire-fighting equipment can automatically adjust fire-fighting strategies according to changes in the external environment (such as wind speed, wind direction, temperature, etc.) to ensure the effectiveness of fire-fighting operations.

[0052] In order to ensure the safety of firefighters in firefighting operations in high-pressure environments, augmented reality (AR) technology has been introduced. By wearing AR devices, firefighters can display key information such as the location of the fire source and the identification of high-pressure areas in real time in their field of vision. The intuitive display method can help firefighters quickly understand the fire situation and make correct decisions. When firefighters approach the high-pressure area, the AR device will issue a warning signal and provide a recommended firefighting path. It not only improves the safety of operations, but also helps firefighters find the most effective firefighting path faster in complex fires and shortens the response time. AR devices can further integrate more intelligent functions. For example, the system can update the 3D model of the fire scene in real time according to the development trend of the fire, and display the possible spread path of the fire in the AR interface. In addition, AR devices can integrate control interfaces with intelligent firefighting equipment, allowing firefighters to directly command drones or robots to perform firefighting operations through the AR system. This integration can greatly improve the efficiency and flexibility of on-site operations.

[0053] After the fire is extinguished, the system automatically archives all image data, providing a resource foundation for future improvements to the system and model. By analyzing this data, the cause of the fire, the evolution of the fire source, and the effectiveness of the fire extinguishing operation can be identified. The archived data will be used to retrain the fire detection model through deep learning technology. Every fire is unique, and its image and data features are of great value to the training and optimization of the model. Through continuous retraining, the model can continuously learn and adapt to new fire scenarios, improving the accuracy and response speed of future fire identification. Data archiving and analysis can be used not only for model retraining, but also for fire prevention. For example, by analyzing historical data, the system can find that the frequency of fires in certain specific parts or equipment is high, so as to take preventive measures in advance and reduce potential fire risks. In addition, the archived data can also be used to develop a virtual reality (VR) fire training system to help firefighters conduct fire response drills in a virtual environment and improve their actual operation capabilities.

[0054] In addition, the image processing strategy can be dynamically adjusted according to the environment and lighting conditions to ensure the best monitoring effect under different conditions. According to the location of the fire source and the development of the fire, the power outage range can be dynamically adjusted, and the impact on the overall operation of the photovoltaic power station can be minimized by reconfiguring the power network. Through wireless networks and intelligent algorithms, multiple intelligent fire-fighting equipment can work together and divide the work to achieve comprehensive control of the fire. AR devices are not only used to display fire scene information, but can also be integrated with the control system of intelligent fire-fighting equipment, allowing firefighters to directly command the fire-fighting equipment to operate through the AR system. Through the archiving and analysis of fire data, the system can continuously optimize the fire detection model and predict and prevent possible future fire risks.

[0055] The technical solution in this embodiment achieves rapid identification and precise fire extinguishing of photovoltaic power station fires by integrating multimodal image processing, convolutional neural network analysis, automatic power off, intelligent fire extinguishing equipment and augmented reality technology, which can effectively reduce the risk of electric shock, improve the efficiency and safety of fire response, ensure the continuous operation of photovoltaic power stations, and reduce fire losses. By combining advanced image processing technology with intelligent fire extinguishing methods, the efficiency and safety of photovoltaic power station fire response are significantly improved. Not only can it effectively deal with fires in photovoltaic power stations, it also has a high degree of scalability and can be continuously optimized and upgraded with the advancement of technology, providing strong guarantees for the safe operation of photovoltaic power stations.

[0056] In one embodiment, Figure 2 FIG. 1 is a block diagram of a photovoltaic power station fire handling device according to an exemplary embodiment. Figure 2 As shown, the photovoltaic power station fire handling device includes a monitoring module 21, an analysis module 22, an isolation module 23 and a fire extinguishing module 23.

[0057] The monitoring module 21 is used to monitor the key positions of the photovoltaic power station in real time by multimodal image processing technology, and collect any one or more of thermal imaging, infrared images and visible light images;

[0058] The analysis module 22 is used to analyze the collected image data through a convolutional neural network to identify flames, smoke and abnormally high temperature points, thereby determining the location of the fire source;

[0059] The isolation module 23 is used to automatically trigger the emergency power-off procedure of the photovoltaic power station and isolate the high-voltage direct current area when a fire or abnormally high temperature is detected;

[0060] The fire extinguishing module 24 is used to extinguish a fire at the fire source using a non-conductive fire extinguishing agent through intelligent fire extinguishing equipment, including a drone or a robot.

[0061] The monitoring module 21, the analyzing module 22, the isolating module 23 and the fire extinguishing module 24 included in the photovoltaic power station fire handling device block diagram are controlled to execute the photovoltaic power station fire handling method described in any of the above embodiments.

[0062] like Figure 3 As shown, the present invention provides an electronic device 300, the electronic device includes: a communication interface, a processor 301, and a memory 302;

[0063] Among them, the memory 302 is used to store program instructions. When the program instructions are executed by the processor 301 that is communicatively connected to the memory 302 through the communication interface, the key positions of the photovoltaic power station are monitored in real time through multimodal image processing technology, and any one or more of thermal imaging, infrared images and visible light images are collected; the collected image data are analyzed through a convolutional neural network to identify flames, smoke and abnormally high temperature points, so as to determine the location of the fire source; when a fire or abnormally high temperature is detected, the emergency power-off procedure of the photovoltaic power station is automatically triggered to isolate the high-voltage direct current area; and a non-conductive fire extinguishing agent is used to extinguish the fire at the fire source through intelligent fire extinguishing equipment, including a drone or a robot.

[0064] The present invention provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the key positions of a photovoltaic power station are monitored in real time through multimodal image processing technology, and any one or more of thermal imaging, infrared images and visible light images are collected; the collected image data are analyzed through a convolutional neural network to identify flames, smoke and abnormally high temperature points, so as to determine the location of the fire source; when a fire or abnormally high temperature is monitored, the emergency power-off procedure of the photovoltaic power station is automatically triggered to isolate the high-voltage direct current area; and a non-conductive fire extinguishing agent is used to extinguish the fire at the fire source through intelligent fire extinguishing equipment, including a drone or a robot.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] It can be understood by a person 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 in this article 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 integrated multimodal image processing, convolutional neural network analysis, automatic power off, intelligent fire extinguishing equipment and augmented reality technology, the rapid identification and accurate fire extinguishing of photovoltaic power station fires are realized, which can effectively reduce the risk of electric shock, improve the efficiency and safety of fire response, ensure the continuous operation of photovoltaic power stations, and reduce fire losses 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.

[0070] 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.

[0071] 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 method for handling fire in a photovoltaic power station, characterized in that: include: Real-time monitoring of key locations in photovoltaic power plants through multimodal image processing technology, collecting any one or more of thermal, infrared and visible light images; The collected image data is analyzed through convolutional neural networks to identify flames, smoke and abnormally high temperature points, thereby determining the location of the fire source; When a fire or abnormally high temperature is detected, the emergency power-off procedure of the photovoltaic power station is automatically triggered to isolate the high-voltage direct current area; Fires are extinguished at the source using non-conductive fire extinguishing agents using intelligent fire-fighting equipment, including drones or robots.

2. The photovoltaic power station fire handling method according to claim 1, characterized in that: Also includes: After collecting any one or more of the thermal imaging image, the infrared image and the visible light image, a fusion process is performed on at least two of the thermal imaging image, the infrared image and the visible light image.

3. The method for handling fire in a photovoltaic power station according to claim 1, characterized in that: The automatic triggering of the emergency power-off procedure of the photovoltaic power station includes: When the emergency power-off procedure is automatically executed, it is analyzed that the fire source is located in the area of ​​the photovoltaic power station; Disconnect power to the area to limit the spread of fire and reduce the risk of electric shock.

4. The photovoltaic power station fire handling method according to claim 1, characterized in that: The fire extinguishing method of using a non-conductive fire extinguishing agent at the fire source location includes: Analyze the amount of extinguishing agent required to extinguish the fire through image processing; Dynamically adjust the spray flow rate of the fire extinguishing agent according to the progress of fire extinguishing.

5. The method for handling fire in a photovoltaic power station according to claim 1, characterized in that: Also includes: Using augmented reality devices to show firefighters real-time fire location and high-voltage area identification; When firefighters approach a high-voltage area, the augmented reality device sends a warning signal and provides a suggested fire-fighting path.

6. The photovoltaic power station fire handling method according to claim 1, characterized in that: Also includes: After the fire is extinguished, all image data is automatically archived; Based on archived image data, fire detection models are retrained through deep learning techniques to improve the accuracy of future fire identification.

7. A photovoltaic power station fire handling device, characterized in that: include: A monitoring module, used to monitor key locations of the photovoltaic power station in real time through multimodal image processing technology, collecting any one or more of thermal imaging, infrared images and visible light images; An analysis module is used to analyze the collected image data through a convolutional neural network to identify flames, smoke, and abnormally high temperature points, thereby determining the location of the fire source; Isolation module, used to automatically trigger the emergency power-off procedure of the photovoltaic power station and isolate the high-voltage direct current area when a fire or abnormally high temperature is detected; The fire extinguishing module is used to extinguish the fire using a non-conductive fire extinguishing agent at the fire source location through intelligent fire extinguishing equipment, including drones or robots.

8. The photovoltaic power station fire handling device according to claim 7, characterized in that: The monitoring module, the analyzing module, the isolating module and the fire extinguishing module are controlled to execute the photovoltaic power station fire handling method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; 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 photovoltaic power station fire handling method according to 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 photovoltaic power station fire handling method according to any one of claims 1 to 6.

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