Power station identification system and method based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle cruise, in particular to a power station identification system and method based on an unmanned aerial vehicle, and an unmanned aerial vehicle inspection platform, comprising an industrial grade unmanned aerial vehicle carrying a visible light camera and an infrared thermal imaging camera; the cloud server is used for receiving the inspection picture fed back by the industrial-grade unmanned aerial vehicle in real time and generating a corresponding inspection analysis report based on the inspection picture; and the ground control center is connected with the unmanned aerial vehicle inspection platform and the cloud server, so that operation and maintenance personnel can plan inspection tasks at the ground control center. According to the system, the preset inspection plan is autonomously executed through the unmanned aerial vehicle, traditional manual on-site inspection is replaced, operation and maintenance personnel are liberated from labor in heavy and severe environments, and personal safety risks are reduced. Meanwhile, the unmanned aerial vehicle is high in inspection speed and complete in coverage, multiple times of special inspection and fine inspection every day can be easily achieved, the inspection efficiency is improved in order of magnitude, and the inspection requirements in abnormal weather such as high temperature and high load can be responded in time.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) patrol technology, and more specifically, to a power station identification system and method based on UAVs. Background Technology

[0002] With the transformation of the energy structure, electrochemical energy storage power stations, especially large-scale grid-side independent energy storage power stations, have become important facilities for building new power systems and ensuring stable power supply during peak electricity demand periods such as summer. These power stations are typically large in scale and densely packed with equipment, such as energy storage units composed of dozens of battery compartments and integrated step-up converters. During operation, especially under high-load charging and discharging conditions, these devices generate a large amount of heat, placing extremely high demands on the cooling system. Potential problems such as localized overheating, cooling system malfunctions, or structural damage, if not detected in time, can easily lead to serious failures and even threaten the stable operation of the entire power grid.

[0003] Currently, the inspection of energy storage power stations mainly relies on regular on-site manual inspections by maintenance personnel. This method has significant limitations: First, manual inspections are inefficient and difficult to cover all equipment points, especially in large power stations with vast areas, where inspection cycles are long. Second, the quality of inspections is greatly affected by personnel experience, physical strength, and environmental conditions. For example, in sustained high temperatures, maintenance personnel have to work under the scorching sun, which is not only physically demanding and in a harsh environment, but also poses safety risks such as heatstroke, and makes it difficult to guarantee the thoroughness and accuracy of the inspections. Summary of the Invention

[0004] The purpose of this invention is to provide a power station identification system and method based on unmanned aerial vehicles (UAVs) to improve the technical problems of the prior art, such as reliance on manual labor, low efficiency, inaccurate identification, and insufficient diagnostic capabilities.

[0005] To achieve the above objectives, the embodiments of this application provide the following technical solutions: On one hand, this application provides a power plant identification system based on unmanned aerial vehicles (UAVs). The system includes: a UAV inspection platform, comprising an industrial-grade UAV equipped with a visible light camera and an infrared thermal imaging camera; a cloud server, used to receive inspection images fed back by the industrial-grade UAV in real time and generate corresponding inspection analysis reports based on the inspection images; and a ground control center, connected to the UAV inspection platform and the cloud server, so that maintenance personnel can plan inspection tasks here and receive inspection analysis reports fed back by the cloud server in real time during the inspection by the industrial-grade UAV.

[0006] Optionally, the industrial-grade drone is also equipped with an RTK high-precision positioning module to achieve centimeter-level precise hovering and flight in the complex environment of a power plant.

[0007] Optionally, the cloud server includes a digital map of power plant equipment, which is a pre-imported GIS map of the target power plant, and the GIS map accurately marks the location and identification ID of each battery compartment, step-up converter, transformer, and cooling system outdoor unit in the target power plant.

[0008] Optionally, the cloud server further includes a visual recognition module, which comprises a recognition and positioning unit, an infrared analysis unit, and a visible light analysis unit; The identification and positioning unit is used to automatically identify and select various types of equipment in the inspection screen, and match them with the equipment in the digital map. The infrared analysis unit is used to analyze the infrared images in the inspection screen, automatically identify hot spots on the equipment surface, mark the temperature value, and trigger an alarm when the marked temperature value exceeds the corresponding threshold. The visible light analysis unit is used to identify abnormalities in the appearance of the equipment and to trigger an early warning when such abnormalities occur.

[0009] Secondly, this embodiment provides a power station identification method based on unmanned aerial vehicles (UAVs), the method comprising: Maintenance personnel set up an inspection plan at the ground control center. The inspection plan includes the inspection route, hovering nodes, the shooting angle of each hovering node, and the inspection frequency. During the inspection, the industrial-grade drone synchronously collects corresponding inspection images through a visible light camera and an infrared thermal imaging camera according to the preset inspection route, hovering nodes, and the shooting angle of each hovering node. The inspection images include cruise position parameters, visible light image data, and infrared thermal imaging data. The inspection images are uploaded to the cloud server in real time through the 5G communication mode on the industrial-grade drone. The position parameters include hovering nodes and the shooting angle of the current hovering node. The identification and positioning unit in the cloud server identifies the identity IDs of multiple devices in the visible light image data based on the current cruise position parameters and the device shape in the visible light image data through a shape-based image feature extraction algorithm. The visible light analysis unit in the cloud server identifies the air inlets of the cooling system based on the identity IDs of multiple devices in the visible light image data, and sequentially checks whether there are any foreign objects blocking each air inlet. If so, it generates a corresponding abnormal blockage inspection analysis report based on the corresponding cooling system air inlet ID and sends it to the ground control center. The infrared analysis unit in the cloud server identifies the battery compartments based on the identity IDs of multiple devices in the visible light image data, maps the identified battery compartments onto the infrared thermal imaging data, and then selects temperature distribution cloud maps corresponding to multiple battery compartments. If the local temperature of any battery compartment is greater than 160%-180% of the average temperature of the temperature distribution cloud map, it is determined that the corresponding battery compartment has an overheating risk. Based on the corresponding battery compartment ID, a temperature anomaly inspection analysis report is generated and sent to the ground control center.

[0010] Thirdly, embodiments of this application provide a power station identification device based on a drone, the device including a memory and a processor.

[0011] The memory is used to store computer programs; the processor is used to execute the computer programs to implement the steps of the above-described UAV-based power station identification method.

[0012] Fourthly, embodiments of this application provide a medium on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described UAV-based power station identification method.

[0013] The beneficial effects of this invention are as follows: The power plant identification system based on drones described in this invention replaces traditional manual on-site inspections by autonomously executing preset inspection plans using drones. This frees maintenance personnel from arduous and harsh labor, reducing personal safety risks. Simultaneously, drone inspections are fast and comprehensive, easily enabling multiple daily "special inspections + detailed checks," resulting in an order-of-magnitude increase in inspection efficiency and timely response to inspection needs under abnormal weather conditions such as high temperatures and high loads.

[0014] Secondly, the UAV-based power plant identification system described in this invention, by fusing visible light and infrared thermal imaging data and based on preset specialized analysis models (such as foreign object obstruction detection and overheating determination based on relative temperature difference), can automatically and accurately identify early potential hazards that are difficult for humans to detect, such as minor blockages in cooling air inlets and localized overheating of the battery compartment. This precise diagnostic capability enables maintenance personnel to "prevent problems before they occur" and intervene before faults occur, effectively ensuring the stable operation of the power plant during peak electricity consumption periods.

[0015] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a power station identification system based on a drone, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of a power station identification method based on unmanned aerial vehicles (UAVs) as described in an embodiment of the present invention. Figure 3 This is a schematic diagram of a power station identification device based on a drone, as described in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] It should be noted that similar reference numerals or letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0020] Example 1:

[0021] like Figure 1 As shown, this application provides a power station identification system based on unmanned aerial vehicles (UAVs), the system comprising: The drone inspection platform includes industrial-grade drones equipped with visible light and infrared thermal imaging cameras. These drones also feature an RTK high-precision positioning module, enabling centimeter-level precise hovering and flight in the complex environment of a power plant. By incorporating RTK high-precision positioning and a digital map of the power plant equipment, the system can automatically bind any identified anomalies (such as temperature or appearance abnormalities) to specific equipment IDs and generate structured inspection analysis reports. This allows maintenance personnel to quickly locate fault points, intuitively grasp the health status of all equipment in the plant, and thus make precise maintenance decisions and resource scheduling, significantly improving the level of precision in operation and maintenance management. A cloud server is used to receive inspection images from industrial drones in real time and generate corresponding inspection analysis reports based on the inspection images. The ground control center is connected to the drone inspection platform and cloud server, enabling maintenance personnel to plan inspection tasks and receive real-time inspection analysis reports from the cloud server during industrial-grade drone inspections.

[0022] In this embodiment, the cloud server includes a digital map of power plant equipment. The digital map of power plant equipment is a pre-imported GIS map of the target power plant, and the GIS map accurately marks the location and identification ID of each battery compartment, step-up converter, transformer, and cooling system outdoor unit in the target power plant. The cloud server also includes a visual recognition module, which consists of a recognition and positioning unit, an infrared analysis unit, and a visible light analysis unit. The identification and positioning unit is used to automatically identify and select various types of equipment in the inspection screen, and match them with the equipment in the digital map. The infrared analysis unit is used to analyze the infrared images in the inspection screen, automatically identify hot spots on the equipment surface, mark the temperature value, and trigger an alarm when the marked temperature value exceeds the corresponding threshold. The visible light analysis unit is used to identify abnormalities in the appearance of the equipment and to trigger an early warning when such abnormalities occur.

[0023] In this embodiment, drones autonomously execute pre-set inspection plans, replacing traditional manual on-site inspections. This frees maintenance personnel from arduous and harsh labor, reducing personal safety risks. Simultaneously, drone inspections are fast and comprehensive, easily enabling multiple daily "special inspections + detailed checks," resulting in an order-of-magnitude increase in inspection efficiency. They can also respond promptly to inspection needs under abnormal weather conditions such as high temperatures and high workloads.

[0024] Example 2:

[0025] like Figure 2As shown, this embodiment is based on Embodiment 1 and provides a method for identifying power stations based on unmanned aerial vehicles (UAVs). The method includes: Step S100: The maintenance personnel set up an inspection plan in the ground control center. The inspection plan includes the inspection route, hovering nodes, the shooting angle of each hovering node, and the inspection frequency. Step S200: During the inspection, the industrial-grade drone synchronously collects corresponding inspection images through a visible light camera and an infrared thermal imaging camera according to the preset inspection route, hovering nodes, and the shooting angle of each hovering node. The inspection images include cruise position parameters, visible light image data, and infrared thermal imaging data. The inspection images are uploaded to the cloud server in real time through the 5G communication mode on the industrial-grade drone. The position parameters include hovering nodes and the shooting angle of the current hovering node. In step S300, the identification and positioning unit in the cloud server identifies the identity IDs of multiple devices in the visible light image data based on the current cruise position parameters and the device shape in the visible light image data through a shape-based image feature extraction algorithm. Specifically, the neural network model can be trained in the early stage by taking a large number of device images taken at this shooting angle, thereby improving the accuracy of identification. Step S400: The visible light analysis unit in the cloud server screens out the cooling system air inlets based on the identity IDs of multiple devices in the visible light image data, and sequentially checks whether there are foreign objects blocking each air inlet. If there are, it generates a corresponding abnormal blockage inspection analysis report based on the corresponding cooling system air inlet ID and sends it to the ground control center. The determination of foreign object blockage is obtained by training a neural network through a large number of blockage photos of air inlets of different specifications. In step S500, the infrared analysis unit in the cloud server filters out the battery compartments based on the identity IDs of multiple devices in the visible light image data, maps the filtered battery compartments onto the infrared thermal imaging data, and then selects the temperature distribution cloud maps corresponding to multiple battery compartments. If the local temperature of any battery compartment is greater than 160%-180% of the average temperature of the temperature distribution cloud map, it is determined that the corresponding battery compartment has an overheating risk. Based on the corresponding battery compartment ID, a temperature anomaly inspection analysis report is generated and sent to the ground control center.

[0026] The power plant identification method described in this embodiment integrates visible light and infrared thermal imaging data and is based on a pre-set dedicated analysis model (such as foreign object obstruction detection and overheating determination based on relative temperature difference). This allows for the automatic and accurate identification of early-stage potential problems that are difficult for humans to detect, such as minor blockages in cooling air inlets and localized overheating of the battery compartment. This precise diagnostic capability enables maintenance personnel to "prevent problems before they occur" and intervene before faults happen, effectively ensuring the stable operation of the power plant during peak electricity consumption periods.

[0027] Example 3:

[0028] Corresponding to the above method embodiments, this disclosure also provides a power plant identification device based on unmanned aerial vehicles (UAVs). The UAV-based power plant identification device described below and the UAV-based power plant identification method described above can be referred to and corresponded to each other.

[0029] Figure 3 This is a block diagram illustrating a drone-based power station identification electronic device according to an exemplary embodiment. Figure 3 As shown, the electronic device 800 may include a processor 801 and a memory 802. The electronic device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0030] The processor 801 controls the overall operation of the electronic device 800 to complete all or part of the steps in the aforementioned UAV-based power station identification method. The memory 802 stores various types of data to support the operation of the electronic device 800. This data may include, for example, instructions for any application or method operating on the electronic device 800, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 802 or transmitted via communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication may include Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of these. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0031] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described UAV-based power station identification method.

[0032] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described drone-based power station identification method. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of the electronic device 800 to complete the above-described drone-based power station identification method.

[0033] Example 4:

[0034] Corresponding to the above method embodiments, this disclosure also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the above-described UAV-based power station identification method.

[0035] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the UAV-based power station identification method described in the above method embodiments.

[0036] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A power station identification system based on unmanned aerial vehicles (UAVs), characterized in that, The system includes: Unmanned aerial vehicle (UAV) inspection platforms, including industrial-grade UAVs equipped with visible light cameras and infrared thermal imaging cameras; A cloud server is used to receive inspection images from industrial drones in real time and generate corresponding inspection analysis reports based on the inspection images. The ground control center is connected to the drone inspection platform and cloud server, enabling maintenance personnel to plan inspection tasks and receive real-time inspection analysis reports from the cloud server during industrial-grade drone inspections.

2. The power station identification system based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The industrial-grade drone is also equipped with an RTK high-precision positioning module, which enables centimeter-level precise hovering and flight in the complex environment of a power plant.

3. The power station identification system based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, The cloud server includes a digital map of power plant equipment. The digital map of power plant equipment is a pre-imported GIS map of the target power plant, and the GIS map accurately marks the location and identification ID of each battery compartment, step-up converter, transformer, and cooling system outdoor unit in the target power plant.

4. The power station identification system based on unmanned aerial vehicles (UAVs) according to claim 3, characterized in that, The cloud server also includes a visual recognition module, which consists of a recognition and positioning unit, an infrared analysis unit, and a visible light analysis unit. The identification and positioning unit is used to automatically identify and select various types of equipment in the inspection screen, and match them with the equipment in the digital map. The infrared analysis unit is used to analyze the infrared images in the inspection screen, automatically identify hot spots on the equipment surface, mark the temperature value, and trigger an alarm when the marked temperature value exceeds the corresponding threshold. The visible light analysis unit is used to identify abnormalities in the appearance of the equipment and to trigger an early warning when such abnormalities occur.

5. A method for identifying power plants based on unmanned aerial vehicles (UAVs), applicable to the UAV-based power plant identification system according to any one of claims 1-4, characterized in that, The method includes: Maintenance personnel set up an inspection plan at the ground control center. The inspection plan includes the inspection route, hovering nodes, the shooting angle of each hovering node, and the inspection frequency. During the inspection, the industrial-grade drone synchronously collects corresponding inspection images through a visible light camera and an infrared thermal imaging camera according to the preset inspection route, hovering nodes, and the shooting angle of each hovering node. The inspection images include cruise position parameters, visible light image data, and infrared thermal imaging data. The inspection images are uploaded to the cloud server in real time through the 5G communication mode on the industrial-grade drone. The position parameters include hovering nodes and the shooting angle of the current hovering node. The identification and positioning unit in the cloud server identifies the identity IDs of multiple devices in the visible light image data based on the current cruise position parameters and the device shape in the visible light image data through a shape-based image feature extraction algorithm. The visible light analysis unit in the cloud server identifies the air inlets of the cooling system based on the identity IDs of multiple devices in the visible light image data, and sequentially checks whether there are any foreign objects blocking each air inlet. If so, it generates a corresponding abnormal blockage inspection analysis report based on the corresponding cooling system air inlet ID and sends it to the ground control center. The infrared analysis unit in the cloud server identifies the battery compartments based on the identity IDs of multiple devices in the visible light image data, maps the identified battery compartments onto the infrared thermal imaging data, and then selects temperature distribution cloud maps corresponding to multiple battery compartments. If the local temperature of any battery compartment is greater than 160%-180% of the average temperature of the temperature distribution cloud map, it is determined that the corresponding battery compartment has an overheating risk. Based on the corresponding battery compartment ID, a temperature anomaly inspection analysis report is generated and sent to the ground control center.

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