Photovoltaic station monitoring system and method based on environment characteristics and data characteristics

Abstract

The invention discloses a photovoltaic field station monitoring system and method based on environmental characteristics and data characteristics. The system comprises a local acquisition monitoring base station, a communication network and a monitoring center. The in-situ acquisition monitoring base station is connected and communicated with the monitoring center through a communication network; the camera module collects an image of the photovoltaic module, the image is transmitted to the monitoring center through the communication module, various contours in the image of the photovoltaic module are obtained, existing problems are recognized, and an alarm is given through the alarm; and by analyzing the operation data of the photovoltaic module equipment collected by the sensor, problems are identified, and an alarm is given through the alarm. Through linkage of the algorithm platform, the Internet of Things platform and the video monitoring platform, internal operation data and environmental data of key equipment of the photovoltaic station are taken as main factors for algorithm selection, corresponding algorithms are automatically scheduled, the problem of adaptive reconstruction cost of the current algorithm under different environments and internal abnormal conditions of the station is solved, the manual configuration cost is reduced, and the efficiency of the system is improved. And intelligent operation and maintenance inspection is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic energy station safety, and relates to a photovoltaic station monitoring system and method, and specifically to a photovoltaic station monitoring system and method based on environmental characteristics and data characteristics. Background Art

[0002] Video surveillance is a traditional video surveillance alarm system for photovoltaic stations and is widely used. With the development of 5G networks and cloud service technologies, the combination of video surveillance and algorithm services has enabled video surveillance to have computing capabilities and cloud control, enabling video surveillance to have remote control, data collection, analysis and early warning capabilities. In the intelligent operation and maintenance inspection of photovoltaic stations, video surveillance is mainly combined with cloud control, target detection algorithms, IoT device linkage and other technologies to achieve algorithm-based target monitoring and recognition.

[0003] The Chinese invention patent "Video monitoring method, device, storage medium and system based on scene linkage" (publication number: CN117671915A) proposes a method and system for priority scheduling and control of robots in a station based on scene information from an alarm camera and target time difference calculation.

[0004] China's invention patent, an intelligent video surveillance method and system based on AI algorithm and 5G message (publication number: CN117615098A), proposes to push the corresponding algorithm to the camera on the AI ​​algorithm platform for alarm analysis, and when an alarm occurs, link the 5G message channel to send messages and real-time monitoring video.

[0005] China's invention patent A distribution station scene linkage system and method based on content recognition (publication number: CN113269034A) proposes a configuration method for a distribution station scene linkage system to achieve the overall linkage of the Internet of Things management platform, video monitoring platform, distribution station artificial intelligence gateway, environmental monitoring terminal, environmental linkage equipment, security linkage equipment, equipment status monitoring, NVR and video surveillance cameras. The AI ​​algorithm will be used to identify the content in the video, integrate the environmental monitoring data such as video, sound, temperature, humidity, and harmful gases, automatically link and control equipment such as air conditioners, lights, fans, and alarms, and can intelligently adjust the station environment and give timely alarms for abnormal conditions.

[0006] The Chinese invention patent algorithm management method and system (publication number: CN117641113A) proposes to obtain the target algorithm through the algorithm cloud platform, thereby obtaining the target monitoring information that matches the target monitoring function.

[0007] In the above-mentioned existing technologies, due to the insufficient linkage of IoT devices in the station and the insufficient comprehensive and three-dimensional information collection, the application of traditional video surveillance alarm systems in photovoltaic stations is out of touch with actual operation and maintenance, and efficient and automated operation is not achieved.

[0008] The video algorithm is deployed on the cloud server. In the scenario of intelligent inspection and operation and maintenance of the station, it is mainly linked with the on-site alarm system, environmental perception equipment, and equipment status monitoring. The algorithm scheduling rules are driven by the video surveillance algorithm. There is no information feedback and optimization mechanism. The algorithm management platform is out of touch with the business scenario, resulting in poor practicality and difficulty in improving the actual operation and maintenance efficiency. Summary of the invention

[0009] In order to solve the above technical problems, the present invention provides a photovoltaic station monitoring system and method based on environmental characteristics and data characteristics.

[0010] The technical solution adopted by the method of the present invention is: a photovoltaic field station monitoring system based on environmental characteristics and data characteristics, including an on-site acquisition monitoring base station, a communication network and a monitoring center; the on-site acquisition monitoring base station is connected and communicated with the monitoring center through the communication network;

[0011] The local data acquisition and monitoring base station comprises a power supply module (1), a power supply voltage detection module (2), an ARM local diagnosis module (3), a memory (4), a real-time clock (5), a communication module (6), a digital-to-analog converter (7), a temperature sensor (8), a humidity sensor (9), a sensor (10) and a camera module (12);

[0012] The power supply voltage detection module (2), memory (4), real-time clock (5), communication module (6), and digital-to-analog converter (7) are all integrated on the ARM local diagnosis module (3); the power supply module (1) is connected to the ARM local diagnosis module (3) and is used to provide power for the local data acquisition and monitoring base station; the photovoltaic components are all provided with temperature sensors (8), humidity sensors (9), and sensors (10), which are connected to the (5) via the digital-to-analog converter (7); the camera module (12) is provided at the front end of the photovoltaic component and is connected to the (4).

[0013] Preferably, the power module (1) consists of a solar panel and a battery.

[0014] Preferably, the communication module (6) is a 5G wireless communication module.

[0015] Preferably, the sensor (10) is used to collect photovoltaic module equipment operating data.

[0016] Preferably, the system is also provided with an alarm (11).

[0017] The technical solution adopted by the method of the present invention is: a photovoltaic station monitoring method based on environmental characteristics and data characteristics, wherein the camera module (12) collects photovoltaic component images, transmits them to the monitoring center through the communication module (6), obtains various contours in the photovoltaic component images, identifies existing problems, and alarms through the alarm (11); and identifies problems by analyzing photovoltaic component equipment operation data collected by the sensor (10), and alarms through the alarm (11).

[0018] Preferably, various contours in the photovoltaic module image are obtained through a contour extraction network;

[0019] The contour extraction network includes a contour map database, an image preprocessing layer, a contour map random extraction layer, a water mark feature extraction layer and a local information enhancement layer;

[0020] The contour map database includes a plurality of contour maps; the image to be processed is divided into a series of N×N image blocks through the image preprocessing layer, and then these image blocks are adjusted to T p ∈R M×D The image block Token of the shape, where N is the preset value, M = N 2 , D represents the dimension of the image block Token, and R represents the real number domain; the contour map random extraction layer is used to randomly extract C from the contour map database l Contours The image block Token and the contour map T l After splicing, the feature extraction layer is input to obtain the information expressing the image block. and express contour features Said and The contour extraction result is obtained after the local information enhancement layer

[0021] Preferably, the local information enhancement layer first Resize to N×N size and then project through a 1×1 convolutional layer to R represents the field of real numbers; finally, Channel adjustment and global maximum pooling are performed to emphasize the most salient features within the block. Implement global average pooling to obtain the contour extraction result

[0022]

[0023] Among them, GMP() represents the global maximum pooling operation, and GAP() represents the global average pooling operation.

[0024] Compared with the prior art, the beneficial effects of the present invention include:

[0025] (1) The present invention integrates the equipment operation parameter data and environmental monitoring data of the monitoring device to provide scenario-based configuration for the algorithm scheduling of the video monitoring algorithm platform, builds a multi-dimensional linkage framework system for intelligent inspection and operation and maintenance of photovoltaic stations, and realizes targeted monitoring and early warning of the operation and maintenance inspection of key equipment in photovoltaic stations.

[0026] (2) The present invention uses the linkage of the algorithm platform, the Internet of Things platform, and the video monitoring platform to take the internal operating data and environmental data of the key equipment of the photovoltaic station as the main factors for algorithm selection, automatically schedule the corresponding algorithm, solve the current algorithm adaptation and transformation costs in different station environments and internal abnormal conditions, reduce manual configuration costs, and realize intelligent operation and maintenance inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The technical solution of the present invention is further described below using embodiments and specific implementation methods. In addition, in the process of describing the technical solution, some drawings are also used. For those skilled in the art, other drawings and the intention of the present invention can also be obtained based on these drawings without paying creative work.

[0028] Figure 1 is a system principle diagram of an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of an on-site data collection and monitoring base station according to an embodiment of the present invention;

[0030] Figure 3 Schematic diagram of the method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to facilitate ordinary technicians in the field to understand and implement the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0032] Please see Figure 1 and Figure 2 , This embodiment provides a photovoltaic field station monitoring system based on environmental characteristics and data characteristics, including an on-site acquisition monitoring base station, a communication network and a monitoring center; the on-site acquisition monitoring base station is connected to the monitoring center for communication through the communication network;

[0033] The local acquisition monitoring base station includes a power module 1, a power supply voltage detection module 2, an ARM local diagnosis module 3, a memory 4, a real-time clock 5, a communication module 6, a digital-to-analog converter 7, a temperature sensor 8, a humidity sensor 9, a sensor 10 and a camera module 12;

[0034] The power supply voltage detection module 2, memory 4, real-time clock 5, communication module 6, and digital-to-analog converter 7 are all integrated on the ARM local diagnosis module 3; the power supply module 1 is connected to the ARM local diagnosis module 3 to provide power for the local data acquisition and monitoring base station; the temperature sensor 8, humidity sensor 9, and sensor 10 are all arranged on the photovoltaic module and connected to the 5 through the digital-to-analog converter 7; the camera module 12 is arranged at the front end of the photovoltaic module and connected to the 4.

[0035] In one embodiment, the power module 1 is composed of a solar panel and a battery.

[0036] In one embodiment, the communication module 6 is a 5G wireless communication module. You can choose Hongdian 5GDTU, which supports 5G RedCap, 5G LAN, 5G SA / NSA networking, and is backward compatible with 4G / 3G network standards, making networking more convenient; in terms of interfaces, it meets the interface requirements of gigabit network port, serial port, wireless GPS (reserved), etc., among which the gigabit LAN port supports 10 / 100 / 1000Mbps adaptation. Under actual measurement, the online power consumption is as low as 40mA@12V DC, and the maximum rate is greater than 500Mbps. The product is small in size, light in weight, and has better adaptability. It can be used for miniaturization and embedded 5G application scenarios in traditional and emerging industries.

[0037] In one embodiment, the sensor 10 is used to collect photovoltaic module equipment operation data.

[0038] In one embodiment, the system is further provided with an alarm 11. Combined with the equipment fault analysis algorithm, the equipment fault classification is confirmed, and according to different classification levels, remote control instructions are issued to the video monitoring platform and on-site operation and maintenance personnel are notified in a timely manner to realize remote automatic zooming in, zooming out, focusing, moving, recording and other operations, and realize the linkage shooting of multiple monitoring videos.

[0039] Please see Figure 3 The present embodiment provides a photovoltaic station monitoring method based on environmental characteristics and data characteristics. The camera module 12 collects the photovoltaic component image, transmits it to the monitoring center through the communication module 6, obtains various contours in the photovoltaic component image, identifies the existing problems, and alarms through the alarm 11; and analyzes the photovoltaic component equipment operation data collected by the sensor (10), identifies the problems, and alarms through the alarm (11). Further final fault analysis and classification are performed.

[0040] In one embodiment, various contours in the photovoltaic module image are obtained through a contour extraction network;

[0041] The contour extraction network includes a contour map database, an image preprocessing layer, a contour map random extraction layer, a water mark feature extraction layer and a local information enhancement layer;

[0042] The contour map database includes a plurality of contour maps; the image to be processed is passed through the image preprocessing layer to be equally divided into a series of image blocks of N×N size, and then these image blocks are adjusted to T p ∈R M×D The image block Token of the shape, where N is the preset value, M = N 2 , D represents the dimension of the image block Token, and R represents the real number domain; the contour map random extraction layer is used to randomly extract C from the contour map database l Contours The image block Token and the contour map T l After splicing, the feature extraction layer is input to obtain the information expressing the image block. and express contour features Said and The contour extraction result is obtained after the local information enhancement layer

[0043] The local information enhancement layer first Resize to N×N size and then project through a 1×1 convolutional layer to R represents the domain of real numbers; finally, Channel adjustment and global maximum pooling are performed to emphasize the most salient features within the block. Implement global average pooling to obtain the contour extraction result

[0044]

[0045] Among them, GMP represents the global maximum pooling operation, and GAP represents the global average pooling operation.

[0046] The present invention collects abnormal data based on on-site environmental monitoring data and equipment operation, sends corresponding image recognition algorithms to the monitoring videos of abnormal equipment areas, and confirms abnormal performance of target equipment through algorithm analysis of real-time video portraits. Combined with the equipment fault analysis algorithm, the equipment fault classification is confirmed, and according to different classification levels, remote control instructions are sent to the video monitoring platform and on-site operation and maintenance personnel are notified in a timely manner to realize remote automatic zooming in, zooming out, focusing, moving, recording and other operations, and realize the linkage shooting of multiple monitoring videos.

[0047] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0048] It should be understood that the embodiments described above are part of the embodiments of the present invention, rather than all of the embodiments. In addition, the technical features in the various embodiments or single embodiments provided by the present invention can be combined with each other arbitrarily to form a feasible technical solution. Such combination is not restricted by the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0049] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of patent protection of the present invention. Under the enlightenment of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested for the present invention shall be based on the attached claims.

Claims

1. A photovoltaic station monitoring system based on environmental characteristics and data characteristics, characterized by: It includes an on-site collection and monitoring base station, a communication network and a monitoring center; the on-site collection and monitoring base station is connected and communicated with the monitoring center through the communication network; The local data acquisition and monitoring base station comprises a power supply module (1), a power supply voltage detection module (2), an ARM local diagnosis module (3), a memory (4), a real-time clock (5), a communication module (6), a digital-to-analog converter (7), a temperature sensor (8), a humidity sensor (9), a sensor (10) and a camera module (12); The power supply voltage detection module (2), memory (4), real-time clock (5), communication module (6), and digital-to-analog converter (7) are all integrated on the ARM local diagnosis module (3); the power supply module (1) is connected to the ARM local diagnosis module (3) and is used to provide power for the local data acquisition and monitoring base station; the photovoltaic components are all provided with temperature sensors (8), humidity sensors (9), and sensors (10), which are connected to the (5) via the digital-to-analog converter (7); the camera module (12) is provided at the front end of the photovoltaic component and is connected to the (4).

2. The photovoltaic station monitoring system based on environmental characteristics and data characteristics according to claim 1 is characterized in that: The power module (1) is composed of a solar cell panel and a storage battery.

3. The photovoltaic station monitoring system based on environmental characteristics and data characteristics according to claim 1 is characterized in that: The communication module (6) is a 5G wireless communication module.

4. The photovoltaic station monitoring system based on environmental characteristics and data characteristics according to claim 1 is characterized in that: The sensor (10) is used to collect photovoltaic module equipment operation data.

5. The photovoltaic station monitoring system based on environmental characteristics and data characteristics according to any one of claims 1 to 4, characterized in that: The system is also provided with an alarm (11).

6. A photovoltaic station monitoring method based on environmental characteristics and data characteristics, applied to the system according to any one of claims 1 to 5; characterized in that: The camera module (12) collects images of the photovoltaic components, transmits them to the monitoring center via the communication module (6), obtains various contours in the images of the photovoltaic components, identifies existing problems, and issues an alarm via the alarm (11); and identifies problems by analyzing the photovoltaic component equipment operation data collected by the sensor (10), and issues an alarm via the alarm (11).

7. The photovoltaic station monitoring method based on environmental characteristics and data characteristics according to claim 6 is characterized in that: Various contours in the photovoltaic module image are obtained through the contour extraction network; The contour extraction network includes a contour map database, an image preprocessing layer, a contour map random extraction layer, a water mark feature extraction layer and a local information enhancement layer; The contour map database includes a plurality of contour maps; the image to be processed is passed through the image preprocessing layer to be equally divided into a series of image blocks of N×N size, and then these image blocks are adjusted to T p ∈R M×D The image block Token of the shape, where N is the preset value, M = N 2 , D represents the dimension of the image block Token, and R represents the real number domain; the contour map random extraction layer is used to randomly extract C from the contour map database l Contours The image block Token and the contour map T l After splicing, the feature extraction layer is input to obtain the information expressing the image block. and express contour features Said and The contour extraction result is obtained after the local information enhancement layer 8. The photovoltaic station monitoring method based on environmental characteristics and data characteristics according to claim 6 is characterized in that: The local information enhancement layer first Resize to N×N size and then project through a 1×1 convolutional layer to R represents the domain of real numbers; finally, Channel adjustment and global maximum pooling are performed to emphasize the most salient features within the block. Implement global average pooling to obtain the contour extraction result Among them, GMP( ) represents the global maximum pooling operation, and GAP( ) represents the global average pooling operation.

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