Transformer remote monitoring system

Abstract

The invention provides a transformer remote monitoring system. The transformer remote monitoring system comprises a transformer oil leakage indicating device, a personnel identification module and a monitoring platform, and the transformer oil leakage indicating device is used for monitoring the oil level of a transformer oil tank in real time and generating an oil level abnormal signal under the condition that the oil level of the transformer oil tank is not within a preset range; the personnel identification module is used for identifying whether personnel which do not conform to the specification exist in the transformer area, and generating a personnel abnormal signal when the personnel which do not conform to the specification exist in the transformer area; and the monitoring platform is based on a cloud computing technology and is used for receiving, recording and processing the oil level abnormal signal and the personnel abnormal signal, generating an alarm report according to the oil level abnormal signal and the personnel abnormal signal and sending the alarm report to operation and maintenance personnel. According to the scheme, the problem that a comprehensive monitoring system capable of automatically monitoring transformer oil level abnormity and unauthorized personnel entering a transformer area in real time is lacked in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the technical field of remote monitoring of electric power equipment, and in particular to a remote monitoring system for a transformer. Background Art

[0002] In the power system, transformers are key equipment for power transmission and distribution, and the stability and safety of their operating status are of vital importance. During the operation of transformers, the change of oil level is one of the important indicators for monitoring its health status. Abnormal oil level may indicate internal faults or potential risks of the equipment, such as internal overheating, aging of insulation materials, etc., which not only affects the normal operation of the transformer, but also may cause more serious accidents. The traditional oil level monitoring method relies on manual regular inspection of the transformer oil level indicator, which is inefficient and lacks real-time performance. In addition, the safety management of the transformer area (i.e., the working area around the transformer) is equally important. Unauthorized personnel or social personnel entering the area to carry out illegal activities such as theft, destruction or fishing may not only cause equipment damage, but also pose a threat to personnel safety. Traditional security measures, such as setting up fences and warning signs, lack effective real-time monitoring and active early warning capabilities.

[0003] At present, there is a lack of a comprehensive monitoring system that can not only automatically monitor the abnormal oil level of the transformer in real time, but also monitor whether there are people who do not meet the requirements entering the transformer area. Summary of the invention

[0004] The main purpose of the present application is to provide a transformer remote monitoring system to at least solve the problem in the prior art of lacking a comprehensive monitoring system that can monitor transformer oil level abnormalities and unauthorized personnel entering the transformer area in real time and automatically.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a transformer remote monitoring system is provided, including: a transformer oil leakage indication device, used to monitor the oil level of the transformer oil tank in real time, and generate an oil level abnormality signal when the oil level of the transformer oil tank is not within a preset range, wherein the oil level of the transformer oil tank is detected by a pressure sensor installed at a preset position of the transformer oil tank; a personnel identification module, used to identify whether there are non-compliant personnel in the transformer area, and generate a personnel abnormality signal when the non-compliant personnel exist in the transformer area; a monitoring platform, the monitoring platform is based on cloud computing technology, and is used to receive, record and process the oil level abnormality signal and the personnel abnormality signal, and generate an alarm report according to the oil level abnormality signal and the personnel abnormality signal and send the alarm report to the operation and maintenance personnel.

[0006] Optionally, the transformer remote monitoring system also includes: a video surveillance probe, which is installed at multiple locations of the transformer substation and covers all key viewing angles to comprehensively monitor personnel entering the transformer substation, and the video surveillance probe communicates with the personnel identification module to send the captured real-time video images of the transformer substation to the personnel identification module; wherein the focal lengths of the video surveillance probes installed at different locations are different, and / or the resolutions of the video surveillance probes installed at different locations are different.

[0007] Optionally, the transformer remote monitoring system further includes: an audible and visual alarm device, which communicates with the transformer oil leakage indication device and the personnel identification module respectively, and is used to give an alarm when receiving the abnormal oil level signal and / or the abnormal personnel signal.

[0008] Optionally, the sound and light alarm device includes: an alarm light and a buzzer, wherein the alarm light displays a predetermined color or a predetermined pattern when an alarm is sounded, and the buzzer emits a buzzing sound at a preset frequency when an alarm is sounded.

[0009] Optionally, the monitoring platform further includes: a communication module, which is used to receive the oil level abnormality signal and the personnel abnormality signal and transmit them to the monitoring platform, wherein the communication module includes a mobile network communication module, a Wi-Fi module, and a satellite communication module.

[0010] Optionally, the transformer oil leakage indication device further includes an electronic control unit, which is used to receive a pressure signal sent by the pressure sensor and determine whether the oil level of the transformer oil tank is within the preset range according to the pressure signal.

[0011] Optionally, the personnel identification module uses a target detection algorithm to identify whether there are personnel who do not meet the requirements in the transformer area, wherein the personnel who do not meet the requirements include at least personnel who are not wearing work clothes or safety helmets, and the target detection algorithm includes a YOLO algorithm.

[0012] Optionally, the personnel identification module is further used to automatically trigger video recording when identifying that a person has entered the transformer area.

[0013] Optionally, the monitoring platform includes an operation and maintenance personnel dispatching module, which is used to dispatch the nearest operation and maintenance personnel to go to the site to inspect and handle the abnormality after receiving the oil level abnormality signal and the personnel abnormality signal.

[0014] Optionally, the transformer remote monitoring system is solar powered and is equipped with an intelligent battery management system for monitoring the power output status of the solar panels and intelligently scheduling the energy usage of the system.

[0015] Applying the technical solution of the present application, the transformer remote monitoring system includes a transformer oil leakage indication device, a personnel identification module and a monitoring platform, wherein the transformer oil leakage indication device is used to monitor the oil level of the transformer oil tank in real time, and generate an oil level abnormality signal when the oil level of the transformer oil tank is not within a preset range, wherein the oil level of the transformer oil tank is detected by a pressure sensor installed at a preset position of the transformer oil tank; the personnel identification module is used to identify whether there are non-compliant personnel in the transformer area, and generate a personnel abnormality signal when there are non-compliant personnel in the transformer area; the monitoring platform is based on cloud computing technology, and is used to receive, record and process oil level abnormality signals and personnel abnormality signals, and generate an alarm report based on the oil level abnormality signals and personnel abnormality signals and send the alarm report to the operation and maintenance personnel. This solution solves the problem in the prior art of lacking a comprehensive monitoring system that can monitor transformer oil level abnormalities and unauthorized personnel entering the area in real time and automatically. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A schematic diagram of a transformer remote monitoring system provided in an embodiment of the present application is shown;

[0018] Figure 2 The YOLOv8 deep object detection model architecture diagram is shown;

[0019] Figure 3 A flowchart of real-time video target detection based on YOLOv8 is shown;

[0020] Figure 4 A specific implementation flow chart of a transformer remote monitoring system provided according to an embodiment of the present application is shown.

[0021] The above drawings include the following reference numerals:

[0022] 10. Transformer remote monitoring system; 11. Transformer oil leakage indication device; 12. Personnel identification module; 13. Monitoring platform; 14. Video monitoring probe; 15. Sound and light alarm device. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] As introduced in the background technology, the prior art lacks a comprehensive monitoring system that can not only automatically monitor the abnormal oil level of the transformer in real time, but also monitor whether there are unauthorized personnel entering the transformer area. In order to solve the problem of the lack of a comprehensive monitoring system that can automatically monitor the abnormal oil level of the transformer and the entry of unauthorized personnel into the transformer area in real time, the embodiment of the present application provides a transformer remote monitoring system.

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0028] Figure 1 is a schematic diagram of a transformer remote monitoring system according to an embodiment of the present application, such as Figure 1 As shown, the transformer remote monitoring system 10 includes a transformer oil leakage indication device 11, a personnel identification module 12 and a monitoring platform 13. The transformer oil leakage indication device is used to monitor the oil level of the transformer oil tank in real time, and generate an oil level abnormality signal when the oil level of the transformer oil tank is not within a preset range, wherein the oil level of the transformer oil tank is detected by a pressure sensor installed at a preset position of the transformer oil tank; the personnel identification module is used to identify whether there are personnel who do not meet the requirements in the transformer area, and generate a personnel abnormality signal when there are personnel who do not meet the requirements in the transformer area; the monitoring platform is based on cloud computing technology, and is used to receive, record and process oil level abnormality signals and personnel abnormality signals, and generate an alarm report based on the oil level abnormality signal and the personnel abnormality signal and send the alarm report to the operation and maintenance personnel.

[0029] Specifically, the core of the transformer oil leakage indication device is the pressure sensor, which is installed at a preset position in the transformer oil tank and can sense the impact of oil level changes on the internal pressure of the oil tank. When the oil level changes, the pressure sensor will detect the change in pressure and determine whether the oil level is within the normal range. The installation position of the pressure sensor in the transformer oil tank needs to be determined according to the monitoring requirements and the sensor type. Generally speaking, the pressure sensor can be installed in the following key positions:

[0030] Oil level indicator: This is the most direct location to monitor oil level changes. The pressure sensor can be installed at the bottom of the oil level indicator. When the oil level changes, the pressure inside the tank changes accordingly. The sensor can monitor and feedback the pressure changes in real time, indirectly reflecting the oil level changes.

[0031] Top of the tank: Installing a pressure sensor on the top or upper part of the tank can monitor the pressure change caused by air entering the tank when the oil level drops. This method is particularly suitable for monitoring whether the oil level is below the safety threshold.

[0032] Lower part of the fuel tank: The sensor is installed at the bottom of the fuel tank to monitor the pressure changes when the oil level rises. It is mainly used to monitor whether there is oil overflow or other potential leakage to ensure that the oil level does not exceed the safe range.

[0033] Oil pillow or expansion oil reservoir: For transformers equipped with an oil pillow or expansion oil reservoir, pressure sensors can be installed on the oil pillow or oil reservoir to monitor the oil level and pressure changes in these parts, indirectly reflecting the oil level status of the main oil tank.

[0034] Near the pressure relief valve: The sensor installed near the pressure relief valve can monitor the increase of abnormal pressure in the fuel tank and issue a timely warning to prevent safety accidents caused by excessive pressure.

[0035] To ensure the safe and efficient operation of the transformer, a preset range of oil level is set, which generally includes an upper threshold value (such as 80% of the full oil volume) and a lower threshold value (such as 20% of the full oil volume). This range is determined based on the cooling requirements of the transformer, the insulation effect, and the avoidance of oil overflow and failures caused by low oil level. Once the oil level detected by the pressure sensor is not within the preset range, the electronic control unit will determine whether the oil level is abnormal based on the pressure signal from the sensor and compare it with the preset threshold. If the oil level is lower than 20% or higher than 80%, it is judged as an oil level abnormality, and the control unit will immediately generate an oil level abnormality signal.

[0036] Compared with the traditional method of manual periodic oil level inspection, the transformer oil leakage indication device combined with pressure sensor technology provides an automated and continuous oil level monitoring solution, which not only improves the accuracy and timeliness of monitoring, but also significantly reduces the labor intensity of operation and maintenance personnel and reduces potential human inspection errors.

[0037] The personnel identification module is usually based on advanced computer vision and artificial intelligence technology to identify people in the video surveillance images of the transformer area in real time and determine whether there are people who do not meet the requirements. If it is identified that there are people who do not meet the requirements, and the people who do not meet the requirements at least include people who are not wearing work clothes and helmets, the personnel identification module will generate a personnel abnormality signal.

[0038] The implementation of the personnel identification module makes the safety monitoring of the transformer area intelligent and immediate. It can quickly respond when unqualified personnel approach the equipment, effectively prevent illegal intrusion, and reduce potential safety accidents.

[0039] The monitoring platform is built on cloud computing technology, which means it utilizes powerful distributed computing resources and storage capabilities. The cloud computing platform can provide high scalability and high availability, and can handle large amounts of data while ensuring data security and privacy protection. The platform includes the following key parts:

[0040] Data collection layer: responsible for receiving raw data and abnormal signals from the transformer oil leakage indication device and personnel identification module.

[0041] Data processing layer: Clean, format and perform preliminary analysis on the collected data to ensure data quality and consistency.

[0042] Intelligent analysis layer: The data analysis and artificial intelligence algorithms used are used to conduct in-depth analysis of data, identify patterns, trends and anomalies, and generate alarm reports.

[0043] User interface layer: Provides a user interface that allows operation and maintenance personnel to view real-time monitoring status, historical data, and alarm reports, as well as perform remote control and management operations.

[0044] When the oil level abnormality signal or personnel abnormality signal is transmitted from the monitoring equipment to the monitoring platform, the receiving module of the monitoring platform will immediately capture these signals. The signal usually contains detailed information of the event, such as the type of abnormality (oil level abnormality or personnel abnormality), time of occurrence, location, etc. The platform will record these signals in the database to form a comprehensive history of abnormal events, which will help with subsequent fault analysis and operation and maintenance planning. When processing abnormal signals, the monitoring platform will analyze the severity and urgency of the signal according to preset rules and algorithms. For example, for oil level abnormalities, the platform may evaluate the speed and amplitude of the oil level drop to predict the possible risk of equipment failure. For personnel abnormalities, the platform may analyze the behavior patterns of the entering personnel to determine whether there is potential illegal intrusion or security threats. These analysis results will be used to generate more specific and targeted alarm reports. Alarm reports usually include the following information:

[0045] Signal type: clearly indicates whether it is an oil level abnormality or a personnel abnormality.

[0046] Timestamp: records the specific time when the abnormal signal occurs.

[0047] Location information: Provides the precise location where the abnormal event occurred, such as transformer ID or substation coordinates.

[0048] Abnormality details: Describe the specific circumstances of the abnormality, such as how much the oil level dropped, or the specific abnormal clothing and behavior of the personnel.

[0049] Recommended measures: Based on the analysis results, the platform may provide preliminary response measures, such as starting on-site recording, dispatching personnel for inspection, etc.

[0050] After the alarm report is generated, the monitoring platform will immediately send the report to the terminal device of the operation and maintenance personnel, such as a mobile phone or tablet computer, through the mobile network or other communication channels. The report is usually sent in a variety of ways to ensure the immediacy and reliability of information, such as SMS, email, mobile application notification, etc. In addition, the platform also supports sending alarm reports to multiple recipients to ensure that key information can be quickly conveyed to all relevant operation and maintenance and security managers.

[0051] In addition to instant alarms, the monitoring platform also provides remote management and data analysis capabilities. Operation and maintenance personnel can remotely view and control monitoring equipment through the platform's user interface, such as adjusting the viewing angle of video surveillance or changing alarm thresholds. In addition, the platform will continuously record all monitoring data and alarm records, making it easier for operation and maintenance personnel and managers to analyze historical data, identify potential operation and maintenance patterns or security vulnerabilities, optimize operation and maintenance strategies, and improve system security.

[0052] In one embodiment of the present application, Figure 1As shown, the transformer remote monitoring system 10 further includes: a video monitoring probe 14, which is installed at multiple locations of the transformer substation and covers all key viewing angles to comprehensively monitor personnel entering the transformer substation, and the video monitoring probe 14 communicates with the personnel identification module 12 to send the captured real-time video images of the transformer substation to the personnel identification module; wherein the focal lengths of the video monitoring probes installed at different locations are different, and / or the resolutions of the video monitoring probes installed at different locations are different.

[0053] Specifically, the installation location of the video surveillance probe is crucial to the monitoring effect. Different installation locations and settings can meet different monitoring needs. Video surveillance probes are installed at multiple locations in the transformer area to achieve comprehensive coverage of the entire area. Depending on the geographical layout and key viewing angles of the transformer area, the probes may be installed around the transformer, at the entrance, near key operating points, and in any area where blind spots or high risks may exist. Such a multi-location deployment and comprehensive coverage strategy can ensure that any personnel activities approaching or entering the transformer area will be captured and recorded, thereby improving the recognition rate and response speed of abnormal events.

[0054] Technical parameters of video surveillance probes, such as focal length and resolution. The focal length determines the field of view and magnification of the probe. At the entrance or open area of ​​the station area, a probe with a wide-angle focal length may be used to capture a wider field of view and more active personnel. In locations closer to the transformer or where the facial and behavioral characteristics of operators need to be observed in detail, a long-focal-length probe may be used to capture more detailed images and improve the accuracy of personnel identification. The resolution of the monitoring probe determines the clarity of the image. In locations where high-definition images are required, such as for facial recognition of personnel, high-resolution probes are used to ensure that personal features can be clearly identified. In locations where the main focus is on the range of personnel activities and behavioral patterns, standard or lower-resolution probes may be used to balance image quality and data transmission bandwidth.

[0055] The choice of video surveillance probes may vary according to the needs of different locations. Here are some common video surveillance probe settings:

[0056] 1. Indoor monitoring probe:

[0057] Installation location: Indoor environments such as offices, shops, warehouses, schools, etc. In transformer areas, it can be installed at the main entrance and exit or inside the transformer room.

[0058] Resolution: Choose HD or SD according to your needs. For example, in areas where facial features need to be clearly captured or small tags need to be read (such as entrances and exits), 1080p or higher resolution (such as 4K) probes can be used; in areas where large-scale monitoring is the main focus, such as large areas of the station, 720p or 1080p probes are used to balance image quality and storage requirements.

[0059] Lens: Wide-angle lenses are suitable for monitoring larger areas, while telephoto lenses are suitable for long-distance monitoring.

[0060] Infrared night vision: If night monitoring is required, you can choose a probe with infrared night vision function.

[0061] 2. Outdoor monitoring probe:

[0062] Installation location: parking lots, streets, factory perimeter and other outdoor environments. In transformer areas, it can be installed on surrounding walls and fences or around transformers.

[0063] Waterproof and dustproof: Choose a probe with a waterproof and dustproof rating, such as IP66 or higher.

[0064] Weather resistance: Ensures the probe can withstand extreme weather conditions.

[0065] Heaters and fans: Heaters and fans may be needed to protect equipment during extreme temperatures.

[0066] 3. High altitude monitoring probe:

[0067] Installation location: high buildings, towers, bridges and other high places. In the transformer area, it can be installed at the high point of the area, the surrounding high places or on the nearby trees.

[0068] High resolution: For long-distance monitoring, high resolution is required.

[0069] Zoom lens: The focal length can be adjusted to monitor targets at different distances.

[0070] Stability: A stable stand is required to reduce vibrations caused by wind.

[0071] 4. Hidden surveillance probe:

[0072] Installation location: places where covert monitoring is required, such as conference rooms, hotel rooms, etc. In transformer areas, it can be installed inside or around the equipment, near waters or fishing spots.

[0073] Concealment: Choose a probe that is difficult to detect from the outside, such as a probe disguised as a smoke detector or clock.

[0074] Wireless transmission: In order to reduce wiring, you can choose a probe with wireless transmission.

[0075] 5. Mobile monitoring probe:

[0076] Installation location: Mobile carriers such as vehicles and drones. In the transformer area, it can be installed in the transformer maintenance area or areas where personnel frequently enter and exit or operate.

[0077] Shockproof: Need to be able to withstand vibrations during movement.

[0078] Battery life: Long battery life is required to support long-term monitoring.

[0079] 6. Panoramic monitoring probe:

[0080] Installation location: Where 360-degree monitoring without blind spots is required, such as shopping mall entrances, squares, etc. In the transformer area, it can be installed at the central high point of the transformer area or on the top of the transformer equipment.

[0081] Panoramic view: can provide 360-degree monitoring view.

[0082] High resolution: ensures clear panoramic images.

[0083] 7. Intelligent monitoring probe:

[0084] Installation location: Where intelligent analysis is required, such as traffic monitoring, crowd analysis, etc. In the transformer area, it can be installed at the main entrance and exit, around the transformer, near waters, densely treed areas or near overhead lines.

[0085] Intelligent analysis: It has intelligent analysis functions such as face recognition and vehicle recognition.

[0086] Data storage: Sufficient storage space is required to save analytical data.

[0087] Correctly selecting probe settings can improve monitoring efficiency and effectiveness, ensuring safety and compliance.

[0088] The video surveillance probe and the personnel identification module establish communication to achieve real-time data transmission. The real-time video images captured by the probe will be compressed and encoded, and then sent to the personnel identification module through the mobile network, Wi-Fi or other communication technologies. After receiving the video stream, the identification module will immediately analyze it, identify the people and behaviors in the picture, and determine whether there are any people who do not meet the requirements. This instant communication and processing mechanism ensures the real-time and accuracy of personnel identification, and can trigger alarm responses in time to prevent potential security risks.

[0089] In one embodiment of the present application, Figure 1As shown, the transformer remote monitoring system 10 further includes an audible and visual alarm device 15, which communicates with the transformer oil leakage indication device 11 and the personnel identification module 12 respectively, and is used to give an alarm when receiving the abnormal oil level signal and / or the abnormal personnel signal.

[0090] Specifically, the sound and light alarm device is the alarm trigger in the system, which is connected to the oil leakage indication device and the personnel identification module. When the sound and light alarm device receives an abnormal oil level signal or an abnormal personnel signal, it will immediately activate the alarm, send an emergency alarm to the surrounding environment through a strong flash and a high-decibel alarm, and also send the alarm information to the remote monitoring center or the operation and maintenance personnel's mobile phone and other devices through the network.

[0091] In a specific embodiment, the sound and light alarm device includes: an alarm light and a buzzer, wherein the alarm light is displayed in a predetermined color or a predetermined pattern when an alarm is sounded, and the buzzer emits a buzzing sound at a preset frequency when an alarm is sounded.

[0092] Specifically, the warning light is a device used to provide a visual alarm signal. When the system detects safety events such as abnormal oil levels or non-compliant personnel entering, the warning light will be activated according to a preset scheme. The warning light can be designed to display a predetermined color or a predetermined pattern to enhance the recognition and urgency of the alarm. The warning light can be set to display a specific color when the alarm sounds, such as red, which is a universal visual signal for emergencies and warnings. By directly changing to red, it can quickly alert people around. In addition to color, the warning light may also display a predetermined pattern or animation, such as a flashing warning symbol, a rotating warning light, etc. These patterns can further enhance the visual impact of the alarm and ensure that it can be clearly identified even in complex environments.

[0093] A buzzer is a device used to provide an auditory alarm signal. It can emit a buzzer at a preset frequency to ensure that people around can receive the alarm information through sound even in the case of visual interference. When the buzzer triggers the alarm, it will emit a sound at a pre-set frequency. This frequency is usually high-pitched to ensure that the sound can penetrate the ambient noise and can be clearly heard even in a noisy environment. The preset frequency can also be intermittent, such as a fast "beep" sound, which can not only ensure the intensity of the alarm, but also avoid auditory fatigue caused by long-term continuous sound.

[0094] The integrated use of alarm lights and buzzers can ensure that alarm information can be effectively conveyed under various conditions. The combination of visual signals (predetermined colors or patterns of alarm lights) and auditory signals (predetermined frequency beeps of buzzers) can cover a wider range of alarm reception, and can attract attention whether it is day or night, and whether the surrounding environment is quiet or noisy.

[0095] For example, when the transformer oil leakage indicator detects that the oil level is too low, or the personnel identification module identifies that an unqualified person has entered the transformer area, the system will automatically activate the sound and light alarm device, the alarm light will immediately change to red and may display a rotating warning pattern, and the buzzer will sound at a rapid high-frequency beep. This strong combination of sound and light can quickly alert all surrounding personnel and prompt them to take appropriate safety measures immediately.

[0096] In one embodiment of the present application, the above-mentioned monitoring platform also includes: a communication module, the above-mentioned communication module is used to receive the above-mentioned oil level abnormality signal and the above-mentioned personnel abnormality signal and transmit them to the above-mentioned monitoring platform, wherein the above-mentioned communication module includes a mobile network communication module, a Wi-Fi module, and a satellite communication module.

[0097] Specifically, the communication module integrated in the monitoring platform is a crucial component of the entire remote monitoring system. It is responsible for transmitting data information from different sensors and modules (such as abnormal oil level signals and abnormal personnel signals) to the monitoring platform in real time, ensuring that the monitoring center can receive key information on site in a timely manner. The diversified design of the communication module, including mobile network communication module, Wi-Fi module, and satellite communication module, is to adapt to different environments and ensure the reliability and stability of information transmission.

[0098] The communication module is a bridge connecting on-site equipment (such as sound and light alarm devices, personnel identification modules, etc.) and the remote monitoring platform. It can transmit the data collected by the on-site equipment to the monitoring center in a timely and accurate manner, so that the monitoring center can monitor and manage the safety status of the transformer area in real time.

[0099] The mobile network communication module uses existing mobile communication network technologies, such as 4G and 5G networks, to transmit data. The advantage of this module is that it has a wide coverage range and can send information through the mobile network even in remote areas. It is very suitable for application scenarios such as transformer stations that may be located on the edge of the city or in rural areas. When abnormal oil level signals or abnormal personnel signals are triggered, the mobile network communication module will quickly send these signals to the remote monitoring platform through the mobile network to achieve instant alarm.

[0100] The Wi-Fi module is suitable for situations where there is stable Wi-Fi network coverage on site. It can provide high-speed data transmission capabilities and is particularly suitable for transmitting large amounts of information such as video and images. Under the condition of a Wi-Fi network, the oil level abnormality signal and personnel abnormality signal generated by the system can be quickly transmitted to the monitoring platform through the Wi-Fi module. At the same time, the Wi-Fi module can also be used to remotely control and adjust the settings of on-site equipment, such as adjusting the monitoring angle or monitoring range of the camera.

[0101] The satellite communication module is designed to maintain the ability to transmit information when both the mobile network and Wi-Fi network are unavailable. It transmits the on-site signal to the monitoring platform via the satellite communication network. Although it may not be as fast and costly as the mobile network and Wi-Fi, it can provide critical communication guarantees under extreme conditions (such as remote mountainous areas, offshore platforms, etc.). Especially in emergencies, such as when natural disasters cause the ground communication network to be interrupted, the satellite communication module becomes the lifeline of information transmission.

[0102] In actual deployment, the monitoring platform can be equipped with the above three communication modules at the same time to improve the communication capability and adaptability of the system. The system will automatically select the most appropriate communication method according to the on-site network environment and signal strength, for example:

[0103] In areas with stable mobile network coverage, the system will give priority to using the mobile network communication module for data transmission.

[0104] In buildings inside or near transformer stations, Wi-Fi modules may be a better choice, especially when large amounts of video data need to be transmitted.

[0105] When the location is remote or the communication conditions are poor, the satellite communication module will become the last communication guarantee to ensure that the alarm information can be sent to the monitoring center under any circumstances.

[0106] Through the comprehensive use of communication modules, the monitoring platform can ensure the establishment of a stable and efficient communication link with the on-site equipment, realize real-time monitoring and management of the safety status of the transformer substation, and maintain the continuous operation and responsiveness of the system even in extreme environments, greatly improving the safety level and operation and maintenance efficiency of the transformer substation.

[0107] In one embodiment of the present application, the transformer oil leakage indication device further includes an electronic control unit, which is used to receive a pressure signal sent by the pressure sensor and determine whether the oil level of the transformer oil tank is within the preset range based on the pressure signal.

[0108] Specifically, the Electronic Control Unit (ECU) in the transformer oil leakage indicator is the core part, which is responsible for processing the signals from the pressure sensor and judging whether the oil level in the transformer tank is within the safe range based on these signals. The following is its detailed working principle and process:

[0109] The electronic control unit receives the electrical signal from the pressure sensor, and then determines whether the oil level is normal based on the preset algorithm and logic. Specifically, the work of the ECU can be divided into three main links: signal reception, data processing, and judgment feedback.

[0110] Signal reception: The electronic control unit receives the pressure signal inside the fuel tank in real time through the connection with the pressure sensor. These signals reflect the changes in the oil level inside the fuel tank, because the oil level will directly affect the pressure value of the oil on the pressure sensor. When the oil level decreases, the oil pressure applied to the sensor will also decrease accordingly, and vice versa.

[0111] Data processing: After receiving the pressure signal, the electronic control unit will analyze and process the signal. It usually converts the signal into a digital signal for more accurate calculation and logical judgment. The ECU has a set of algorithms preset inside that can calculate the oil level in the tank based on the geometry of the tank, the density of the oil, and the current pressure signal.

[0112] Judgment feedback: After calculating the oil level, the electronic control unit will compare the calculation result with the preset safe oil level range. The preset range usually includes the upper and lower limits of the normal operating oil level of the fuel tank, such as 80% to 20% of the full oil volume. If the calculated oil level is lower than the preset lower limit or higher than the preset upper limit, the ECU will determine that the oil level is abnormal, and immediately activate the alarm mechanism to send an abnormal oil level signal to the monitoring platform.

[0113] At the same time, ECU also has the function of recording and storing data. It can record the results of each oil level test and the details of oil level abnormalities. These data are very important for later troubleshooting, maintenance records and data analysis. In addition, ECU may also integrate other intelligent functions, such as self-test, remote configuration and upgrade, to ensure its long-term stable operation and facilitate remote management by operation and maintenance personnel.

[0114] The application of the electronic control unit in the transformer oil leakage indication system not only realizes the automatic monitoring of the oil level, but also can timely and accurately determine whether the oil level is abnormal, and send the alarm signal to the monitoring platform through the network, so that the automation of the safety monitoring of the entire transformer area is improved, which greatly reduces the workload of the operation and maintenance personnel and improves the response speed and processing efficiency of the system. Through the intelligent control of the electronic control unit, the system can effectively prevent and promptly handle safety issues such as transformer oil leakage, and ensure the normal operation of power equipment and the safety of the surrounding environment.

[0115] In one embodiment of the present application, the above-mentioned personnel identification module uses a target detection algorithm to identify whether there are personnel who do not meet the requirements in the above-mentioned transformer substation, wherein the above-mentioned personnel who do not meet the requirements at least include personnel who are not wearing work clothes and safety helmets, and wherein the above-mentioned target detection algorithm includes a YOLO algorithm.

[0116] Specifically, the personnel identification module is a key component of the intelligent monitoring system. It uses target detection algorithms, especially the YOLO algorithm, to identify and monitor whether there are people who do not comply with safety regulations in the transformer area, such as those who are not wearing work clothes or helmets. The personnel appearing in the designated area in the video are detected, captured and identified, and the facial features of the personnel are obtained through the video acquisition equipment. After preprocessing, they are fed into the back-end recognition algorithm and compared with the personnel database to quickly identify the real identity of the personnel. The information of the personnel is analyzed for each connected video channel, and finally displayed and informed to the user by recording the captured pictures on the platform.

[0117] Working principle of personnel identification module:

[0118] Video acquisition: The personnel identification module first captures real-time video streams using high-definition cameras installed in the transformer area. These cameras cover key areas and channels within the area to ensure that all personnel entering the area can be monitored.

[0119] Object Detection: The captured video stream is then fed into an object detection algorithm, which looks for and identifies a pre-set target, i.e. a person, in each frame of video. Unlike traditional image processing methods, object detection algorithms are able to detect and classify multiple objects in an image simultaneously.

[0120] Feature extraction and analysis: The YOLO algorithm is an advanced target detection technology that performs real-time detection directly on the image without first selecting the region. YOLO divides the input image into multiple grids, each of which is responsible for detecting the object in it. It extracts features from the image through a convolutional neural network (CNN) and determines whether there is a target in each grid and the category of the target based on these features. The YOLOV8 algorithm is used in this embodiment.

[0121] Safety standard comparison: After identifying a person, the algorithm will further analyze their appearance characteristics, such as whether they are wearing work clothes and helmets. This step usually requires a pre-trained model, which can "learn" the characteristics of work clothes and helmets, so as to accurately identify whether these characteristics appear in actual applications.

[0122] Abnormal personnel identification: If the detected personnel are not wearing the prescribed work clothes or helmets, the personnel identification module will mark the personnel as non-compliant personnel and generate personnel abnormality signals. These signals will be immediately sent to the monitoring platform, which will initiate the corresponding early warning and processing mechanism.

[0123] YOLO algorithm application:

[0124] The YOLO algorithm is suitable for the person recognition module mainly because of its real-time performance and high accuracy. Traditional sliding window-based object detection methods (such as the R-CNN series) are accurate, but have slow processing speeds and are not suitable for real-time video stream analysis. In contrast, YOLO can complete object detection with a single prediction, which improves processing speed and can monitor video streams in real time, ensuring that abnormal situations can be identified immediately.

[0125] The YOLO algorithm uses a large number of annotated images to learn the characteristics of different types of targets (such as people, work clothes, and helmets) during training. In actual application scenarios, it can quickly locate and identify targets from complex backgrounds, and maintain a high recognition accuracy even in situations such as lighting changes, occlusion, and angle changes.

[0126] Through the deployment of the above-mentioned personnel identification module, the transformer substation can automatically monitor the safety regulations of people entering and promptly detect behaviors that do not comply with safety dress regulations, which helps to improve workplace safety and prevent potential accident risks, while also improving operation and maintenance efficiency and response speed.

[0127] Figure 2 This is the YOLOv8 deep target detection model architecture diagram, such as Figure 2 As shown in Figure 1, the input of the model is a single image or video frame, usually with a fixed size, such as 640×640 pixels, to ensure the uniformity and efficiency of model processing. The image may need to be preprocessed, such as scaling, cropping, color space conversion, etc., to adapt to the input requirements of the model. The backbone network is the core part of the model architecture and is responsible for extracting multi-level features from the input image. In YOLOv8, the backbone network usually adopts a structure similar to Darknet-53, containing multiple convolutional layers, each of which is able to extract image features of a specific scale. The lower convolutional layers extract more fine-grained features (such as edges and textures), while the higher convolutional layers extract more abstract features (such as object shape and category). After the backbone network, the feature fusion network fuses features at different levels to generate a more comprehensive and rich feature representation. This process usually includes upsampling, downsampling, skip connections, and feature fusion operations (such as weighted summation). The feature fusion network design of YOLOv8 is designed to enhance the model's detection capabilities for small, medium, and large scale objects. The detection layer is the final output part of the model, which performs target detection based on the fused features. The detection layer usually contains multiple convolutional layers to predict the target category, bounding box coordinates, and object existence probability of each grid cell. YOLOv8 adopts a multi-scale detection strategy, that is, target detection is performed on feature maps of different resolutions to improve the detection accuracy and speed of the model.

[0128] Figure 3 This is a real-time video target detection flow chart based on YOLOv8, such as Figure 3 As shown in the figure, the first is the video frame input, which usually comes from a surveillance camera. Each video frame is an image containing scene information captured from the camera. In video surveillance applications, these video frames are continuously input into the model at a certain frame rate for real-time processing. After the video frame is input, Darknet-53 feature extraction is performed to identify and extract basic features in the video frame, such as edges, textures, shapes, and colors. This process generates multi-scale feature maps, which provide a basis for subsequent detection and recognition. Upsampling and feature fusion help the model detect targets on feature maps of different scales. Upsampling recovers spatial details from low-resolution feature maps, while feature fusion combines features from different layers, enabling the model to recognize targets of various sizes, from smaller objects to larger objects. The combined use of upsampling and feature fusion can significantly improve the comprehensive recognition ability of the model. By recovering and fusing multi-scale features, the model can more accurately understand and describe the image content, which is especially important for target detection and recognition in complex scenes. Ultimately, the model can provide more accurate and comprehensive recognition results, including the precise location, category, and possible attributes of the object. Based on the feature map, the detection layer of the model performs regression analysis on each grid cell to predict the position and size of the target's bounding box. Regression analysis locates the object at each position in the feature map based on the parameters learned during model training. This analysis involves the prediction of information such as coordinates, width, and height. The result of the regression analysis is the prediction box information, including the center coordinates (x, y), width (w), height (h) of each prediction box, and the confidence score of the object's existence. The prediction box is drawn on the original video frame to circle the target location that the model believes may exist.

[0129] Target feature modeling refers to the process of extracting features of target objects from images or video streams and constructing a feature vector or feature description for each target. In deep learning, this process is usually completed by a convolutional neural network (CNN), where the front end of the network is responsible for extracting low-level features of the image, such as edges, textures, and color patterns, while the back end is responsible for combining these features into higher-level representations, such as the shape, category, and orientation of the object. The feature vector contains the key characteristics of the target and can be used for subsequent recognition and tracking. Match tracking is the process of continuously identifying and tracking targets in multiple frames of images or video streams. The matching stage usually involves comparing the features of the target detected in the current frame with the features of the target in the previous frame to determine whether they belong to the same object. Tracking is the process of continuously predicting the location of the target in subsequent frames after determining the continuity of the target. This process may use a variety of algorithms, including but not limited to distance metrics (such as cosine similarity), optical flow, or appearance model matching. Kalman filtering is a mathematical tool for data fusion and prediction, especially suitable for state estimation of dynamic systems (such as moving targets). In target tracking, Kalman filtering is used to predict the future position of the target and correct the prediction in combination with current observations (such as the position and velocity of target features) to obtain more accurate tracking results. Kalman filtering can handle noise and uncertainty and is a popular method for state estimation and prediction in matching tracking. According to the newly observed data or the prediction results of Kalman filtering, the estimate of the target state is adjusted and the target is updated, including updating the target's position, velocity, direction or predicted bounding box. The updated results are a key step in the iteration and optimization of the target tracking algorithm to ensure the accuracy and stability of tracking. The final output results, that is, the final output of the target detection and tracking algorithm, usually include information such as the target's category, location, bounding box, tracking ID and predicted trajectory. In video surveillance or automated vision systems, these results can be used for alarm triggering, automated control or user interface display. The output results not only reflect the detection and tracking performance of the algorithm, but may also contain analysis and prediction information of the target behavior, which is crucial for intelligent monitoring and decision support systems.

[0130] In a specific embodiment, the above-mentioned personnel identification module is also used to automatically trigger video recording when it is identified that a person enters the above-mentioned transformer station area.

[0131] Specifically, when the personnel identification module confirms that a person has entered the transformer area, the video recording function will be triggered immediately. This process is usually automatic and does not require human intervention. The module sends a command to the video surveillance system, and the system starts recording the video stream of the current camera after receiving the command until the person leaves or the preset recording time is reached.

[0132] Recorded video can serve as a record of security incidents, especially when unauthorized personnel enter the station area, it can capture the details of the person's activities and provide direct evidence for subsequent accident investigations or safety management.

[0133] In the power industry, personnel entering transformer stations for work must comply with strict safety regulations, such as wearing personal protective equipment (PPE). Video recording helps monitor and verify whether personnel comply with these regulations, such as whether they wear work clothes and helmets.

[0134] The recorded video can be transmitted to the monitoring platform in real time, so that the operation and maintenance personnel or security management personnel can immediately check the on-site situation and take corresponding measures according to actual needs, such as dispatching a security team to the site for inspection or issuing remote warnings.

[0135] Video start and stop: The start and stop of recorded video is automatically controlled by the personnel recognition module. Based on the detection results of the personnel, the module can accurately control the recording time to avoid unnecessary video storage and processing. The recorded video will be saved to local or cloud storage for subsequent viewing and analysis. The storage device needs to have sufficient storage space and security to protect the video data from tampering or loss. All recorded videos will be marked and archived for quick retrieval and playback. The management system may include information such as timestamps, personnel types, event categories, etc. to improve the management and utilization efficiency of videos. When recording videos, personal privacy protection needs to be considered. For example, recording can be started only when a person enters a specific area to avoid excessive monitoring of irrelevant personnel.

[0136] In one embodiment of the present application, the monitoring platform includes an operation and maintenance personnel dispatching module, which is used to dispatch the nearest operation and maintenance personnel to the site to inspect and handle the abnormality after receiving the abnormal oil level signal and the abnormal personnel signal.

[0137] Specifically, the operation and maintenance personnel dispatching module in the monitoring platform is a key component of the intelligent operation and maintenance system. It can efficiently and quickly dispatch the nearest operation and maintenance personnel to the site for inspection and processing based on the real-time monitoring of abnormal oil level signals and abnormal personnel signals. The design and operation mechanism of this module ensures that power facilities can receive timely responses when encountering abnormal situations, thereby reducing potential safety risks and equipment damage.

[0138] The monitoring platform continuously receives data sent from the transformer area, including oil level monitoring data and personnel identification data. When the oil level is lower than or higher than the preset safety range, or the system detects unauthorized personnel entering the area (such as personnel not wearing work clothes or helmets), the monitoring platform will receive abnormal oil level signals and abnormal personnel signals. The electronic control unit (ECU) or similar data processing module in the platform will conduct real-time analysis and judgment on these abnormal signals. Once the abnormal situation is confirmed, the ECU will immediately start the operation and maintenance personnel scheduling process. The operation and maintenance personnel scheduling module can quickly locate the operation and maintenance personnel closest to the transformer area by integrating geographic location information (such as GPS positioning) and the real-time location data of the operation and maintenance personnel. This function usually requires connection with the operation and maintenance personnel's mobile device (such as a smartphone) to obtain their location information in real time. The module will analyze the location of all available operation and maintenance personnel, as well as their current status (such as whether they are performing other tasks), so as to select the most suitable and nearest personnel for scheduling. Task assignment and notification: After the operation and maintenance personnel are selected, the scheduling module will automatically create and assign tasks, including a detailed description of the abnormality, the exact location of the area, and preliminary handling suggestions. Subsequently, the task details are sent to the operator's mobile device via mobile network, Wi-Fi or satellite communication, and may be accompanied by phone calls, text messages or in-app notifications to ensure that the operator can receive the task information in a timely manner. In order to help the operator quickly reach the site, the dispatch module may also provide a path planning service. Based on the current traffic conditions, weather conditions and the location of the operator, the dispatch module calculates the shortest and safest arrival path, and guides the operator to the transformer station through navigation software or embedded map functions. After the operator arrives at the site, the dispatch module will continue to follow up on the progress of the exception handling. The operator can upload on-site photos, videos or text reports in real time through mobile devices to update the status of the exception handling. Based on these feedbacks, the monitoring platform will decide whether further technical support, personnel reinforcement or equipment replacement are needed. All dispatch and processing records will be recorded in detail in the database of the monitoring platform, including dispatch time, response speed of the operator, on-site processing, etc. These data are used for subsequent analysis, improving dispatch strategies, optimizing the distribution and resource allocation of operator personnel, and improving overall operation and maintenance efficiency.

[0139] Advantages of the operation and maintenance personnel scheduling module:

[0140] Improve response speed: By automatically dispatching the nearest operation and maintenance personnel, the response time can be significantly reduced, ensuring that abnormal situations are handled in the shortest possible time.

[0141] Optimize resource allocation: Schedule operations and maintenance personnel based on their real-time location and status to avoid wasting resources and ensure that they can perform their tasks efficiently.

[0142] Improve safety and accuracy: The dispatch module not only dispatches personnel, but also provides a detailed description of the on-site situation and preliminary treatment suggestions to help operation and maintenance personnel prepare before going to the site and improve the safety and accuracy of on-site processing.

[0143] Data-driven decision-making: Collect and analyze operation and maintenance scheduling data to provide data support for operation and maintenance management, which helps to formulate more scientific and reasonable operation and maintenance plans and strategies.

[0144] The efficient operation of the operation and maintenance personnel dispatching module is an important guarantee for ensuring the safety and stability of power facilities and reducing fault response time. It reflects the trend of modern power operation and maintenance towards intelligence and automation.

[0145] In one embodiment of the present application, the above-mentioned transformer remote monitoring system is powered by solar energy and is equipped with an intelligent battery management system for monitoring the power output status of the solar panels and intelligently scheduling the energy usage of the system.

[0146] Specifically, the transformer remote monitoring system is powered by solar energy and is equipped with an intelligent battery management system (BMS). This is an environmentally friendly and economical energy solution, especially suitable for transformer stations in remote areas or those that are difficult to access the power grid. The system not only reduces dependence on traditional electricity, but also ensures long-term stable operation of the system through intelligent management.

[0147] Solar power supply system:

[0148] Solar panels collect energy: Solar panels are installed in appropriate locations in the transformer area to maximize the area of ​​sunlight exposure. They use the photovoltaic effect to convert solar energy into electrical energy. When there is sufficient sunlight, solar panels can provide enough power to meet the energy needs of the monitoring system.

[0149] Energy storage: The converted electrical energy is stored in batteries. Usually, high-energy-density, long-life lithium-ion batteries or lead-acid batteries are used so that the system can still operate normally in low-light conditions such as at night or on rainy days.

[0150] Intelligent battery management system:

[0151] Power status monitoring: The intelligent battery management system continuously monitors the power output status of the solar panels, detects key parameters such as the battery's charge and discharge status, temperature, voltage and current, to ensure that the battery operates in the best condition. The BMS can evaluate the system's power demand and the battery's power supply in real time, prevent overcharging or over-discharging of the battery, and extend the battery life.

[0152] Intelligent energy scheduling: The intelligent battery management system has an energy scheduling function, which can intelligently manage energy usage based on weather forecasts, historical power consumption data, and the current system power demand. For example, when it is predicted that there will be continuous rainy weather, the BMS will automatically adjust the system energy consumption, reduce the operation of non-critical functions, and ensure the continuous operation of the monitoring system; when there is sufficient sunlight, it will guide the system to make full use of renewable energy for efficient operation or charging.

[0153] Fault warning and management: BMS can also monitor any abnormal conditions in the system, such as solar panel damage, battery failure, or abnormal increase in system energy consumption, and immediately send warning signals when problems are detected so that operation and maintenance personnel can take timely measures to prevent energy supply interruptions.

[0154] Remote control and optimization: The intelligent battery management system allows remote operation. Operation and maintenance personnel can adjust the system's energy usage strategy through the monitoring platform, such as limiting the activation of certain functions during specific time periods to optimize energy distribution. In addition, by analyzing the system energy consumption data, the BMS algorithm can be further optimized to improve energy efficiency.

[0155] The combination of intelligent battery management system and solar power supply provides a sustainable, low-cost and reliable power solution for the transformer remote monitoring system. Through intelligent scheduling, the system can automatically adapt and adjust energy use in different time periods and weather conditions to ensure the stable operation of the monitoring system, while reducing operation and maintenance costs and reducing the impact on the environment. This is of great significance for the operation and maintenance of power facilities and environmental protection in remote areas.

[0156] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0157] A specific implementation flow chart of the transformer remote monitoring system in the embodiment of the present application is as follows: Figure 4As shown, the oil level of the transformer oil tank is monitored by the transformer oil leakage indication device, and the personnel entering the transformer area are monitored by the video monitoring probe. In this embodiment, the oil level display can be used to indicate the oil level in the oil tank. The body of the oil level display is made of brass and has a self-floating float. As the oil level goes up and down, the float also floats up and down to display the oil level. When the oil level is not within the preset range, the float falls and triggers the pressure sensor switch. The electronic control unit in the transformer oil leakage indication device receives the pressure signal sent by the pressure sensor, and determines whether the oil level of the transformer oil tank is within the above preset range according to the pressure signal. If it is not within the preset range, an oil level abnormality signal is generated, the oil level abnormality signal is transmitted to the monitoring platform, and an on-site sound and light alarm is triggered. The personnel identification module receives the video data of the video monitoring probe, automatically identifies whether there are personnel who do not meet the requirements, and if there are personnel who do not meet the requirements, a personnel abnormality signal is generated, the generated personnel abnormality signal is transmitted to the monitoring platform, and an on-site sound and light alarm is triggered. When the monitoring platform receives an oil level abnormality signal or a personnel abnormality signal, it responds immediately and dispatches the operation and maintenance personnel to handle the abnormality.

[0158] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0159] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0162] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0163] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0164] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0165] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0166] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A transformer remote monitoring system, characterized in that: include: A transformer oil leakage indicating device is used to monitor the oil level of the transformer oil tank in real time and generate an oil level abnormality signal when the oil level of the transformer oil tank is not within a preset range, wherein the oil level of the transformer oil tank is detected by a pressure sensor installed at a preset position of the transformer oil tank; A personnel identification module, used to identify whether there are personnel who do not meet the requirements in the transformer area, and generate a personnel abnormality signal when the personnel who do not meet the requirements exist in the transformer area; A monitoring platform, which is based on cloud computing technology, is used to receive, record and process the abnormal oil level signal and the abnormal personnel signal, and generate an alarm report according to the abnormal oil level signal and the abnormal personnel signal and send the alarm report to the operation and maintenance personnel.

2. The transformer remote monitoring system according to claim 1, characterized in that: The transformer remote monitoring system also includes: Video surveillance probes, which are installed at multiple locations in the transformer area and cover all key viewing angles to comprehensively monitor personnel entering the transformer area, and the video surveillance probes communicate with the personnel identification module to send captured real-time video images of the transformer area to the personnel identification module; The video surveillance probes installed at different positions have different focal lengths, and / or the video surveillance probes installed at different positions have different resolutions.

3. The transformer remote monitoring system according to claim 1, characterized in that: The transformer remote monitoring system also includes: An audible and visual alarm device, which communicates with the transformer oil leakage indication device and the personnel identification module respectively, and is used to give an alarm when receiving the oil level abnormality signal and / or the personnel abnormality signal.

4. The transformer remote monitoring system according to claim 3, characterized in that: The sound and light alarm device comprises: An alarm light and a buzzer, wherein the alarm light displays a predetermined color or a predetermined pattern when an alarm occurs, and the buzzer emits a buzzing sound at a preset frequency when an alarm occurs.

5. The transformer remote monitoring system according to claim 1, characterized in that: The monitoring platform also includes: A communication module is used to receive the oil level abnormality signal and the personnel abnormality signal and transmit them to the monitoring platform, wherein the communication module includes a mobile network communication module, a Wi-Fi module, and a satellite communication module.

6. The transformer remote monitoring system according to claim 1, characterized in that: The transformer oil leakage indication device further includes an electronic control unit, which is used to receive the pressure signal sent by the pressure sensor and determine whether the oil level of the transformer oil tank is within the preset range according to the pressure signal.

7. The transformer remote monitoring system according to claim 1, characterized in that: The personnel identification module uses a target detection algorithm to identify whether there are personnel who do not meet the requirements in the transformer area, wherein the personnel who do not meet the requirements at least include personnel who do not wear work clothes or safety helmets, and wherein the target detection algorithm includes a YOLO algorithm.

8. The transformer remote monitoring system according to claim 7, characterized in that: The personnel identification module is also used to automatically trigger video recording when identifying that a person has entered the transformer area.

9. The transformer remote monitoring system according to claim 1, characterized in that: The monitoring platform includes an operation and maintenance personnel dispatching module, which is used to dispatch the nearest operation and maintenance personnel to go to the site to check and handle the abnormality after receiving the oil level abnormality signal and the personnel abnormality signal.

10. The transformer remote monitoring system according to claim 1, characterized in that: The transformer remote monitoring system is powered by solar energy and is equipped with an intelligent battery management system for monitoring the power output status of the solar panels and intelligently scheduling the energy usage of the system.

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