CN116054405A - Double-light monitoring analysis system of photovoltaic power station based on computer vision and machine learning - Google Patents
Double-light monitoring analysis system of photovoltaic power station based on computer vision and machine learning Download PDFInfo
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Abstract
The invention discloses a photovoltaic power station double-light monitoring analysis system based on computer vision and machine learning, which comprises: the image acquisition module is used for acquiring image information of the photovoltaic equipment; the image transmission module is connected with the image acquisition module and used for transmitting the image information; the local server module is connected with the image transmission module and is used for storing and processing the image information; the cloud platform module is connected with the local server module and used for analyzing the processed image information and displaying the analysis result. The monitoring analysis system can carry out real-time remote monitoring on power station equipment through double-light equipment, and the intelligent level of unmanned inspection operation and maintenance of the photovoltaic power station is improved by utilizing AI functions such as computer vision, deep machine learning and the like.
Description
Technical Field
The invention belongs to the field of new energy power generation equipment monitoring, and particularly relates to a photovoltaic power station double-light monitoring analysis system based on computer vision and machine learning.
Background
Since the 21 st century, the power industry develops rapidly, and along with the proposal of double carbon, new energy power generation, especially photovoltaic power generation, is further promoted, the social economic development and the dependence degree of people life on electricity are higher and higher, the importance of power safety production is more and more outstanding, and safe and reliable power supply is directly related to economic development and social life order.
And upgrading and reforming the power distribution network to promote the construction of the intelligent power grid. The construction of the modern power grid is safe, reliable, economical, efficient and advanced in technology, meets the intelligent requirement of the power system and supports the construction of the efficient and intelligent power system. The power safety production supervision and management method indicates that the power safety production work should adhere to the guidelines of 'safety first, foredefense as main and comprehensive treatment', prevent and reduce power accidents, and ensure the safe and stable operation and reliable power supply of a power system.
The photovoltaic power equipment is generally divided into a photovoltaic area and a transformer substation according to the position of the photovoltaic area, wherein the photovoltaic area mainly comprises a photovoltaic module, an inverter and a box-type transformer, and the transformer substation mainly comprises a power transformer, a high-low voltage power distribution cabinet, a transformer, a capacitor, a lightning arrester, a circuit breaker, an isolating switch, an insulator, a cable, a sleeve and the like. The abnormal and fault phenomena of the photovoltaic power station are mostly caused by the thermal effect of the electric power, and if measures are not taken in time, safety accidents are probably caused. Therefore, it is important to strengthen equipment inspection and ensure normal operation of power equipment of the photovoltaic power station.
The abnormality and the fault of the power equipment of the photovoltaic power station are mostly related to the abnormality of the temperature, and the existing infrared temperature measurement and data analysis work is mainly carried out manually. The power station operator can check and know the running condition of the equipment through regular inspection, such as observing whether the appearance of the equipment is abnormal, whether the temperature of the equipment which is in contact with the equipment is normal, detecting the temperature of the equipment by using a handheld thermal infrared imager, and the like, so as to judge whether the running condition of the equipment is normal. However, the method is more limited, and is difficult to collect important abnormal data completely in time simply by means of regular maintenance and detection of operation and maintenance personnel, so that detection dead zones exist in box-type transformers and power distribution cabinets which are inconvenient to measure temperature, in addition, manual detection is difficult and high in cost, real-time uninterrupted detection cannot be achieved, if the abnormal temperature phenomenon cannot be found in time, equipment failure or safety accidents can be caused, and even normal operation of a power grid power supply system can be seriously affected.
In addition, the existing infrared temperature measurement monitoring systems are only used for purely monitoring the infrared temperature of equipment of a single power station in real time, a monitoring camera does not have a constant-light shooting function, cannot remotely access and know the real state of the equipment in real time, does not analyze the acquired image, cannot form the early warning analysis judging capability of the equipment, still needs to be manually carried out, has low intelligent degree, and has limited monitoring effect on the photovoltaic power station.
Therefore, the power system needs an online intelligent temperature measurement early warning system to realize online real-time temperature detection and real-time early warning analysis of key equipment.
Disclosure of Invention
In order to solve the problem that the existing monitoring effect is limited, the invention provides a photovoltaic power station double-light monitoring analysis system based on computer vision and machine learning. The local monitoring platform is deployed through each power station, the power station is monitored in real time, the cloud platform is deployed at a headquarter, the data uploaded by each power station local monitoring platform are subjected to distributed scheduling analysis, the monitoring condition of a single power station is known in time, meanwhile, video image data monitored by all the power stations can be summarized, and the intelligent level of unmanned inspection operation and maintenance of the photovoltaic power station is improved by utilizing AI functions such as computer vision, deep machine learning and the like.
In order to achieve the above object, the present invention provides the following solutions: a photovoltaic power plant dual light monitoring analysis system based on computer vision and machine learning, comprising:
the image acquisition module is used for acquiring image information of the photovoltaic equipment;
the image transmission module is connected with the image acquisition module and used for transmitting the image information;
the local server module is connected with the image transmission module and is used for storing and processing the image information;
the cloud platform module is connected with the local server module and used for analyzing the processed image information and displaying the analysis result.
Preferably, the image acquisition module comprises an infrared thermal imaging unit and a constant light shooting unit;
the infrared thermal imaging unit is used for acquiring infrared image information by detecting the intensity of infrared thermal radiation;
the constant light shooting unit is used for collecting constant light image information of the photovoltaic equipment.
Preferably, the infrared thermal imaging unit comprises a detection unit, a conversion unit and a processing unit;
the detection unit is used for detecting infrared heat radiation;
the conversion unit is used for converting the detected infrared thermal radiation signals into electric signals;
the processing unit is used for amplifying the electric signal.
Preferably, the image transmission module comprises a wired transmission unit and a wireless transmission unit;
the wired transmission unit is used for transmitting the image information through a transmission line;
the wireless transmission unit is used for transmitting the image information through the shared network.
Preferably, the transmission line comprises more than five twisted pairs and optical fibers, and an optical-to-electrical converter is added to perform optical-to-electrical signal conversion when the optical fibers are adopted for transmission.
Preferably, the local server module comprises a storage unit, an image management unit and an image analysis unit;
the storage unit is used for storing the image information;
the image management unit is used for classifying the image information according to the address of the acquisition module;
the image analysis unit is used for synchronously mapping the infrared image and the temperature value onto the constant light image to obtain a mapping chart.
Preferably, the cloud platform module comprises a user management unit, an analysis unit, an alarm unit, a storage unit, a query management unit and a real-time display unit;
the user management unit is used for managing access rights;
the analysis unit is used for carrying out image contrast analysis on the mapping graph based on computer vision, outputting image change contrast by adopting a hash algorithm, and generating a historical temperature curve;
the alarming unit is used for alarming when the photovoltaic equipment and the analysis system run abnormally;
the query management unit is used for querying the image information in real-time operation;
the real-time display unit is used for displaying the image information in running in real time;
the storage unit is used for storing the mapping chart, the infrared image, the constant light image and the historical temperature curve.
Preferably, the alarm unit comprises a first alarm unit, a second alarm unit and a third alarm unit;
the first alarm unit is used for setting an alarm threshold value, and when the image change contrast or the temperature is higher than the alarm threshold value, the alarm unit gives an alarm;
the second alarm unit is used for giving an alarm when the image analysis is abnormal;
the third alarm unit is used for giving an alarm when the video transmission signal is abnormal.
The invention discloses the following technical effects:
according to the invention, the local monitoring platform is deployed through each power station, the power station is monitored in real time, the cloud platform is deployed at the headquarter, the data uploaded by the local monitoring platform of each power station is subjected to distributed scheduling analysis, the monitoring condition of a single power station is known in time, meanwhile, the video image data monitored by all the power stations can be summarized, and the intelligent level of unmanned inspection operation and maintenance of the photovoltaic power station is improved by utilizing AI functions such as computer vision, deep machine learning and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a dual optical monitoring analysis system according to an embodiment of the present invention;
FIG. 2 is a block diagram of a local server module according to an embodiment of the present invention;
fig. 3 is a diagram of a cloud platform module according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in FIG. 1, the invention provides a photovoltaic power station double-light monitoring analysis system based on computer vision and machine learning. The system comprises an image acquisition module, an image transmission module, a local server module and a cloud platform module.
The image acquisition module adopts an intelligent double-light holder thermal imager, the visible light part adopts a 300-kilowatt high-definition network core, 30-time high-definition optical zoom is supported, and more details are observed. The thermal imaging adopts a 400 multiplied by 300/640 multiplied by 512 temperature-measuring type network thermal imager module, the temperature-measuring response time is less than 30ms, and the temperature-measuring precision is up to +/-2 ℃. The thermal imager supports 20 temperature measuring points/temperature measuring lines/temperature measuring areas, the temperature measuring areas can be associated with preset positions, different preset positions correspond to different temperature measuring areas, and the requirement of large-scale multipoint simultaneous temperature measurement of projects is met. The infrared thermal imaging camera collects video information of the photovoltaic equipment to be monitored in real time, all objects with the temperature higher than absolute zero (-273.15 ℃) can continuously emit infrared thermal radiation, and the radiation energy is related to the temperature of the surface of the object: the higher the surface temperature, the stronger the infrared heat radiation. The thermal infrared imager converts the temperature signal into an electric signal by detecting infrared thermal radiation emitted by an object, and then sends the electric signal into an image transmission unit after amplification and processing. The constant light camera collects video information of the required monitoring photovoltaic equipment in real time, converts the video information into an electric signal, and sends the electric signal to the image transmission unit after amplification processing.
The image transmission module transmission network adopts a wired or wireless monitoring private network to transmit, the distance is 80 meters, video and temperature information acquired by a front-end camera can be transmitted to a machine room or a monitoring room through more than five types of twisted pair lines, more than 80 meters and the part monitored inside equipment can not utilize a communication cable, a 4G wireless router can be additionally arranged on a camera side, a router is additionally arranged on a server side, communication is performed by utilizing a shared network, a relay can be additionally arranged in the middle or optical fiber transmission can be used, and the front-end monitoring equipment in the station can be accessed and managed inside a transformer substation. And acquiring front-end video, temperature and alarm data of each power station. For equipment such as a photovoltaic area, which is provided with buildings and mountain shielding, the equipment is inconvenient to wire, the sharing network communication is difficult to realize, the mode of additionally installing a wireless bridge can be adopted for communication, a camera is firstly connected with a switch, and the switch is connected with the wireless bridge to transmit communication through the network bridge. The transmission of the video stream must bring a large amount of occupied bandwidth, and reasonable calculation is required to be made for the occupied network bandwidth, so that network congestion is not caused, and network resources are saved. The following are specific bandwidth requirements for network transmission construction: 1. the access bandwidth from each front-end thermal imaging system to each management platform should be not less than 4Mbps.2. The network bandwidth of each level of system is allocated according to the transmission requirement bandwidth of the video image, the bandwidth is not smaller than the bandwidth of the concurrent transmission multi-channel video code stream, and partial redundancy is reserved at the same time, so that the real-time performance of data transmission is ensured. 3. Under the condition of the specified network transmission performance, the transmission delay of the communication protocol of each level of monitoring user terminals accessing the local monitoring system through the management platform is less than 500ms, and the delay of accessing the monitoring resources is less than 2s.
The local server module is shown in fig. 2, wherein the storage part adopts a high-performance special network hard disk storage server, and can store front-end video stream and temperature data. The storage server supports a quick configuration function and a disk grouping storage strategy, has stable and reliable performance, and ensures the safety of data. The method comprises the steps of supporting access of network cameras conforming to the onvif, supporting an access platform of national standard GB28181 protocol, CGI and SDK modes, supporting self-adaptive hybrid access of H.265/H.264 cameras, supporting preview, storage and playback of 4K high-definition network videos, supporting HDMI and VGA interfaces, supporting thermal imaging historical temperature inquiry, generating a temperature curve, supporting various alarm accesses and linkages such as I/O alarm, thermal imager temperature and the like, supporting 8 SATA interfaces, supporting multilevel user authority management, refining user authority management to channel authority, and supporting P2P mobile phone monitoring. And the position and the temperature value of the high-temperature point found by thermal imaging detection are mapped onto the visible light image through a special image processing algorithm aiming at the problem that the thermal image is not easy to identify, so that the corresponding position of the high-temperature point can be conveniently and rapidly found for confirmation. The function of binding the preset positions with the temperature measuring areas is realized by matching with the omnibearing cradle head, different preset positions correspond to different temperature measuring areas, and the system automatically completes the multipoint temperature measurement and alarm of a plurality of preset positions, thereby realizing the automatic detection of equipment targets in a large range and different scenes of the transformer substation.
The cloud platform uses a cloud server, the structure of the cloud platform is shown in fig. 3, a cloud platform unit uses a distributed coordination technology to construct the cloud platform server, and a storage module comprises an object data storage (effective storage of video and image unstructured data), a relational data storage (effective storage of power station image analysis data) and a time sequence data storage (effective storage of real-time operation change data). The analysis module is mainly used for analyzing the image and video of the power station, and comprises computer vision contrast analysis of the running image of the power station equipment and machine learning abnormality analysis of the running image of the power station equipment. The alarm module comprises equipment real-time temperature abnormality alarm, image analysis abnormality alarm and video transmission signal abnormality alarm. The query management module queries real-time operation monitoring pictures of each power station. The real-time display module is used for displaying real-time operation monitoring pictures of all power stations in real time. The user management module belongs to server access authority management, supports multi-level user authority management, different departments and different management personnel can allocate different viewing and management authorities, and a system manager has the highest authority and can allocate and manage authorities of all subordinate power station users; the lower-level power station user can use the assigned functions to check and configure the equipment with assigned rights, and can not check and manage other equipment.
The cloud platform unit uses a distributed coordination technology to construct a cloud platform server, and the storage module comprises an object data storage (video and image unstructured data effective storage), a relational data storage (power station image analysis data effective storage) and a time sequence data storage (real-time operation change data effective storage). The analysis module is mainly used for analyzing the image and video of the power station, and comprises computer vision contrast analysis of the running image of the power station equipment and machine learning abnormality analysis of the running image of the power station equipment. The alarm module comprises equipment real-time temperature abnormality alarm, image analysis abnormality alarm and video transmission signal abnormality alarm. The query management module queries real-time operation monitoring pictures of each power station. The real-time display module is used for displaying real-time operation monitoring pictures of all power stations in real time. The user management module belongs to server access authority management, supports multi-level user authority management, different departments and different management personnel can allocate different viewing and management authorities, and a system manager has the highest authority and can allocate and manage authorities of all subordinate power station users; the lower-level power station user can use the assigned functions to check and configure the equipment with assigned rights, and can not check and manage other equipment.
The cloud platform is implemented using a distributed coordination technique. The cloud platform data acquisition system firstly transmits the double-light video data to the platform and quickly persists; the real-time engine receives subscribed data from the message cluster and performs operations such as cleaning, analysis, summarization and the like on the video data; the offline computing module regularly analyzes historical data formed by video images and the like according to the scheduling plan; data mining is performed using machine learning and computer vision analysis. The data display layer displays the video and data analysis results of the monitoring system in an intuitive interface.
The cloud platform function can be divided into a cloud platform online function and a cloud platform offline function. Cloud platform online function: real-time infrared video and constant light video of the photovoltaic power station can be checked in real time on the cloud platform, multi-picture segmentation is supported, and simultaneous watching of multiple pictures is met; the picture switching interval time can be flexibly set, and the picture interval time can be adjusted; the manual image grabbing function is supported, and a user can store some key conditions while monitoring. And (5) storing the transient image of the constant-light camera at fixed time, and storing the transient image into an album. When the infrared thermal imaging camera monitors temperature abnormality, an alarm popup window is supported, and information such as alarm time, alarm equipment, alarm type, preset position, area, temperature and the like is displayed in the popup window. Through warning bullet window suggestion, when reaching real-time warning purpose, still can help electric power maintainer to fix a position temperature anomaly point fast. Cloud platform offline function: the method comprises the steps of capturing stored transient images of a camera at intervals, performing image contrast analysis by using computer vision, outputting image change contrast by using a hash algorithm, setting a threshold in programming, receiving contrast result signals by a cloud platform when the contrast of the transient images before and after equipment exceeds the threshold, sending out alarm signals, and displaying information such as alarm time, alarm equipment, alarm type, preset position, area, temperature and the like in the alarm window. The warning pop-up window prompts, so that the purpose of real-time warning and reminding is achieved, and meanwhile, power maintenance personnel can be helped to quickly locate abnormal points; in addition, the machine learning module can access the transient image storage album according to the transient image storage time sequence, store the transient image storage album in the machine learning module data center to form a comparison prototype, compare and analyze the transient images acquired by the same cameras in the database, detect the output data spread and output the difference part, and summarize the reasons for the change of the monitoring images of the power station equipment. The comparison prototype can be used for preserving a database after comparison is completed, the data volume of the database is increased, the comparison sample is increased continuously, the machine learning speed and accuracy are accelerated, a power station equipment AI analysis library is finally formed, and the new power station equipment running condition real-time picture is sent to the machine learning module for analysis, and then the equipment running condition is judged.
Various temperature measurement alarm prompts. If the temperature of a part of power equipment is increased rapidly after abnormality occurs, if the abnormal temperature is not found and processed in time, the equipment can be finally caused to malfunction or cause safety accidents, and even the normal operation of a power grid power supply system is seriously influenced. The system supports continuous online monitoring of the temperature condition of the power equipment for 7X 24 hours, once the temperature of the equipment is abnormal, an alarm can be timely found and triggered, related personnel are reminded to carry out alarm processing, accidents are avoided, and the normal operation of the system is ensured. When temperature measurement alarm occurs, the system supports linkage of local and remote I/O alarm output, such as I/O alarm output can be externally connected with an audible and visual alarm to remind relevant personnel of a monitoring center to carry out alarm processing. The client supports an alarm popup window, and information such as alarm time, alarm equipment, alarm type, preset position, area, temperature and the like is displayed in the popup window. Through warning bullet window suggestion, when reaching real-time warning purpose, still can help electric power maintainer to fix a position temperature anomaly point fast.
When the timing snapshot platform receives the temperature measurement alarm signal of the front end, the single IP camera visible light and the thermal imaging channel simultaneously snapshot pictures and store the pictures to the local, and the pictures are overlapped with temperature value information, so that quick looking up and analyzing alarm conditions are facilitated, and materials are provided for machine learning.
The historical temperature of the equipment can be retrieved according to the conditions of the equipment, time, temperature type and the like, information of the equipment, time, preset position, area, temperature and the like is displayed, and historical temperature change of the power equipment is inquired.
Real-time/historical temperature profile. The real-time temperature change curves of a plurality of temperature measuring areas of the thermal imager are supported to be checked, and the current temperature change trend of the power equipment is intuitively reflected. And a historical temperature curve is generated according to the historical temperature data, and the historical temperature change condition of the power equipment is intuitively displayed in a graph mode. When the power equipment is in normal operation, the temperature should keep a stable curve, and if the temperature curve shows an ascending or descending trend, an abnormality may occur.
Intelligent analysis background. And each substation storage server is accessed to the monitoring cloud platform again, so that remote access and control are realized. The cloud platform unit platform can perform unified management on equipment such as a thermal imager and a storage server, supports centralized management, distributed deployment and multi-user remote access, has the functions of user management, equipment management, server management, thermal imaging history temperature inquiry, alarm management, equipment configuration, real-time video, video playback, AI analysis and the like, and meets the requirements in various application scenes.
The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the design spirit of the present invention.
Claims (8)
1. A photovoltaic power station double-light monitoring analysis system based on computer vision and machine learning is characterized by comprising:
the image acquisition module is used for acquiring image information of the photovoltaic equipment;
the image transmission module is connected with the image acquisition module and used for transmitting the image information;
the local server module is connected with the image transmission module and is used for storing and processing the image information;
the cloud platform module is connected with the local server module and used for analyzing the processed image information and displaying the analysis result.
2. The computer vision and machine learning based dual light monitoring and analysis system of a photovoltaic power plant of claim 1, wherein,
the image acquisition module comprises an infrared thermal imaging unit and a constant light shooting unit;
the infrared thermal imaging unit is used for acquiring infrared image information by detecting the intensity of infrared thermal radiation;
the constant light shooting unit is used for collecting constant light image information of the photovoltaic equipment.
3. The computer vision and machine learning based dual light monitoring and analysis system of a photovoltaic power plant of claim 2, wherein,
the infrared thermal imaging unit comprises a detection unit, a conversion unit and a processing unit;
the detection unit is used for detecting infrared heat radiation;
the conversion unit is used for converting the detected infrared thermal radiation signals into electric signals;
the processing unit is used for amplifying the electric signal.
4. The computer vision and machine learning based dual light monitoring and analysis system of a photovoltaic power plant of claim 1, wherein,
the image transmission module comprises a wired transmission unit and a wireless transmission unit;
the wired transmission unit is used for transmitting the image information through a transmission line;
the wireless transmission unit is used for transmitting the image information through the shared network.
5. The computer vision and machine learning based dual light monitoring and analysis system for a photovoltaic power plant of claim 4, wherein,
the transmission line comprises more than five twisted pairs and optical fibers, and an optical-to-electrical converter is added to convert optical-to-electrical signals when the optical fibers are adopted for transmission.
6. The computer vision and machine learning based dual light monitoring and analysis system of a photovoltaic power plant of claim 2, wherein,
the local server module comprises a storage unit, an image management unit and an image analysis unit;
the storage unit is used for storing the image information;
the image management unit is used for classifying the image information according to the address of the acquisition module;
the image analysis unit is used for synchronously mapping the infrared image and the temperature value onto the constant light image to obtain a mapping chart.
7. The computer vision and machine learning based dual light monitoring and analysis system of a photovoltaic power plant of claim 6, wherein,
the cloud platform module comprises a user management unit, an analysis unit, an alarm unit, a storage unit, a query management unit and a real-time display unit;
the user management unit is used for managing access rights;
the analysis unit is used for carrying out image contrast analysis on the mapping graph based on computer vision, outputting image change contrast by adopting a hash algorithm, and generating a historical temperature curve;
the alarming unit is used for alarming when the photovoltaic equipment and the analysis system run abnormally;
the query management unit is used for querying the image information in real-time operation;
the real-time display unit is used for displaying the image information in running in real time;
the storage unit is used for storing the mapping chart, the infrared image, the constant light image and the historical temperature curve.
8. The computer vision and machine learning based dual light monitoring and analysis system of a photovoltaic power plant of claim 7,
the alarm unit comprises a first alarm unit, a second alarm unit and a third alarm unit;
the first alarm unit is used for setting an alarm threshold value, and when the image change contrast or the temperature is higher than the alarm threshold value, the alarm unit gives an alarm;
the second alarm unit is used for giving an alarm when the image analysis is abnormal;
the third alarm unit is used for giving an alarm when the video transmission signal is abnormal.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117095010A (en) * | 2023-10-20 | 2023-11-21 | 中诚华隆计算机技术有限公司 | Chiplet data communication transmission authenticity verification method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105160792A (en) * | 2015-07-30 | 2015-12-16 | 国家电网公司 | Monitoring system for power grid substations |
| CN111754714A (en) * | 2020-07-08 | 2020-10-09 | 南阳师范学院 | Security monitoring system and monitoring method thereof |
| CN112309075A (en) * | 2020-10-20 | 2021-02-02 | 东南大学 | Fire early warning system of photovoltaic power station |
| CN112910094A (en) * | 2021-03-05 | 2021-06-04 | 国网河南省电力公司郑州供电公司 | Remote automatic transformer substation inspection system and method based on ubiquitous power Internet of things |
-
2023
- 2023-01-29 CN CN202310043253.6A patent/CN116054405A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105160792A (en) * | 2015-07-30 | 2015-12-16 | 国家电网公司 | Monitoring system for power grid substations |
| CN111754714A (en) * | 2020-07-08 | 2020-10-09 | 南阳师范学院 | Security monitoring system and monitoring method thereof |
| CN112309075A (en) * | 2020-10-20 | 2021-02-02 | 东南大学 | Fire early warning system of photovoltaic power station |
| CN112910094A (en) * | 2021-03-05 | 2021-06-04 | 国网河南省电力公司郑州供电公司 | Remote automatic transformer substation inspection system and method based on ubiquitous power Internet of things |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117095010A (en) * | 2023-10-20 | 2023-11-21 | 中诚华隆计算机技术有限公司 | Chiplet data communication transmission authenticity verification method and system |
| CN117095010B (en) * | 2023-10-20 | 2023-12-19 | 中诚华隆计算机技术有限公司 | Chiplet data communication transmission authenticity verification method and system |
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