Intelligent Sensing System for Power Operation and Maintenance

Abstract

Embodiments of the present disclosure disclose an intelligent perception system for power operation and maintenance. A specific implementation of the intelligent perception system for power operation and maintenance includes: a power data perception terminal is communicatively connected to a power data storage terminal and a power calculation terminal. Among them, the power data perception terminal includes: a direct reading instrument device, a power inspection drone device, a power inspection robot device, and an image acquisition device; the power data storage terminal is communicatively connected to the power calculation terminal. Among them, the power calculation terminal includes: an algorithm server and an edge computing terminal; the algorithm server is communicatively connected to the edge server. The algorithm server is used to identify abnormal situations in the power operation and maintenance image set, and the edge server is used to perform preprocessing of power operation and maintenance images and analysis of abnormal power operation and maintenance images in the power operation and maintenance image set. This implementation can improve the automation level and accuracy of power equipment inspection.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of intelligent power operation and maintenance, and more particularly to an intelligent perception system for power operation and maintenance. Background Art

[0002] With the continuous expansion of the power grid area, the requirements for operation safety, efficiency and reliability are constantly increasing. At present, when inspecting the power grid area, the commonly adopted method is to ensure the safe and stable operation of power equipment by combining traditional manual inspection with automated equipment monitoring.

[0003] However, it has been found in practice that when inspecting the power grid area by the above method, the following technical problems often exist:

[0004] Manual inspection requires a large amount of labor input, and the professional ability, experience and working status of inspectors will directly affect the inspection quality, resulting in missed faults or misjudgments.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not form the prior art known to ordinary technicians in the art of this country. Summary of the Invention

[0006] The content part of the present disclosure is used to briefly introduce the concepts, which will be described in detail in the specific implementation part later. The content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose an intelligent perception system for power operation and maintenance to solve one or more of the technical problems mentioned in the above background art section.

[0008] In a first aspect, some embodiments of the present disclosure provide an intelligent perception system for power operation and maintenance. The intelligent perception system for power operation and maintenance includes: The above intelligent perception system for power operation and maintenance includes: a power data perception terminal, a power data storage terminal, and a power calculation terminal. It is characterized in that the power data perception terminal is communicatively connected to the power data storage terminal and the power calculation terminal. Among them, the power data perception terminal includes: a direct reading instrument device, a power inspection drone device, a power inspection robot device, and an image acquisition device; the power data storage terminal is communicatively connected to the power calculation terminal. Among them, the power calculation terminal includes: an algorithm server and an edge computing terminal; the above direct reading instrument device is used to collect a set of meter reading images corresponding to each power equipment meter. Among them, the above direct reading instrument device includes: a direct reading instrument and a direct reading instrument gateway; the above power inspection drone device is used to conduct aerial inspections on the targets to be detected and collect a set of detection target images of each target to be detected. Among them, the above power inspection drone device includes: a drone and a drone parking station; the above power inspection robot device is used to conduct inspections on the targets to be inspected and collect a set of inspection target images of each target to be inspected on the ground. Among them, the above power inspection robot device includes: a robot, a base station, and a robot workstation; the above image acquisition device is used to collect a set of area images of each area to be detected. Among them, the above image acquisition device includes: a fixed visible light gun camera, a pan-tilt omnidirectional visible light dome camera, a fixed infrared gun camera, and a pan-tilt omnidirectional infrared dome camera; the above algorithm server is communicatively connected to the above edge server. The above algorithm server is used to identify abnormal situations in the power operation and maintenance image set. The above edge server is used to perform power operation and maintenance image preprocessing and power operation and maintenance image anomaly analysis on the power operation and maintenance image set. Among them, the above power operation and maintenance image set includes: a set of meter reading images, a set of detection target images, a set of inspection target images, and a set of area images.

[0009] Optionally, the above direct reading instrument is configured to: collect meter images corresponding to each power equipment meter to obtain a meter image set; perform grayscale processing on each meter image in the above meter image set to generate grayscale meter images, thereby obtaining a grayscale meter image set; perform image noise reduction processing on each grayscale meter image in the above grayscale meter image set to generate denoised meter images, thereby obtaining a denoised meter image set; perform contrast enhancement processing on each denoised meter image in the above denoised meter image set to generate enhanced meter images, thereby obtaining an enhanced meter image set; perform image binarization processing on each enhanced meter image in the above enhanced meter image set to generate binarized meter images, thereby obtaining a binarized meter image set; perform edge detection processing on each binarized meter image in the above binarized meter image set to generate edge detection binarized meter images, thereby obtaining an edge detection binarized meter image set; perform contour extraction processing on each edge detection binarized meter image in the above edge detection binarized meter image set to generate contour meter images, thereby obtaining a contour meter image set, wherein there are image bounding boxes in the contour meter images; determine the largest contour meter image in the above contour meter image set as the regional meter image; perform contour extraction processing on the above regional meter image to generate a contour regional meter image; in response to determining that the contour area of the contour regional meter image is smaller than the contour area threshold of the preset contour regional meter image, generate a contour regional meter bounding box group; based on the above contour regional meter bounding box group, perform character sorting processing on the above regional meter image to generate a character sequence; perform traversal processing on each character in the above character sequence to generate a traversed character set; perform character recognition processing on each traversed character in the above traversed character set to generate recognized characters, thereby obtaining a recognized character set; based on the above recognized character set, output a meter reading image set and send the above meter reading image set to the direct reading instrument gateway; and the above direct reading instrument gateway is configured to: in response to receiving the above meter reading image set, send the above meter reading image set to the power data storage terminal and the power calculation terminal.

[0010] Optionally, the above power inspection drone device is configured to: in response to receiving an aerial inspection instruction, determine the current position of the inspection drone and the positions of each target to be detected, and plan an inspection path between the current position and the positions of each target to be detected; control the drone to move along the inspection path and control the drone to perform image acquisition processing on each target to be detected to generate detection target images, thereby obtaining a detection target image set, and send the acquired detection target image set to the power data storage terminal and the power calculation terminal.

[0011] Optionally, the above power inspection robot device is configured to: in response to receiving a ground inspection instruction, determine the current position of the inspection robot and the positions of each inspection target to be inspected, plan an inspection path from the current position to the positions of each inspection target to be inspected; control the robot to move along the inspection path, and control the robot to perform image acquisition and processing on each inspection target to be inspected to generate an inspection target image, obtain an inspection target image set, and upload the inspection target image set to the above-mentioned power data storage terminal and power calculation terminal through the above-mentioned base station.

[0012] Optionally, the above fixed infrared camera is configured to: in response to receiving a face detection instruction, collect a first detection area image corresponding to each first detection area to be detected, and obtain a first detection area image set; perform image preprocessing on each first detection area image in the first detection area image set to generate a preprocessed detection area image, and obtain a preprocessed detection area image set; perform face positioning processing on each preprocessed detection area image in the preprocessed detection area image set to generate a face positioning image, and obtain a face positioning image set; perform multi-scale detection processing on each face positioning image in the face positioning image set to generate a multi-scale detected face image, and obtain a multi-scale detected face image set, where the face in the multi-scale detected face image has a bounding box; perform face area cropping processing on each multi-scale detected face image in the multi-scale detected face image set to generate a region-cropped face image, and obtain a region-cropped face image set; perform face frame drawing processing on each region-cropped face image in the region-cropped face image set to generate a frame-drawn face image, and obtain a frame-drawn face image set; perform face image alignment processing on each frame-drawn face image in the frame-drawn face image set to generate an aligned face image, and obtain an aligned face image set; perform image noise reduction processing on each aligned face image in the aligned face image set to generate a noise-reduced face image, and obtain a noise-reduced face image set; perform image sharpening processing on each noise-reduced face image in the noise-reduced face image set to generate a sharpened face image, and obtain a sharpened face image set.

[0013] Optionally, the above-mentioned panoramic infrared PTZ camera is configured to, in response to receiving a target trajectory prediction instruction, collect second detection area images corresponding to each second area to be detected, and obtain a second detection area image set; perform target detection and target bounding box marking processing on each second detection area image in the second detection area image set to generate a target-marked detection area image, and obtain a target-marked detection area image set, where there are bounding boxes of marked targets in the target-marked detection area image; perform target tracking processing on each target-marked detection area image in the target-marked detection area image set to generate the target position of the marked detection area image, and obtain a target position set of the marked detection area image; perform trajectory prediction processing on the target position set of the marked detection area image to generate a target prediction trajectory of the marked detection area image; perform target point feature extraction processing on each second detection area image in the second detection area image set to generate feature extraction target points, and obtain a feature extraction target point set; perform target point feature matching processing on each feature extraction target point in the feature extraction target point set to generate feature matching target points, and obtain a feature matching target point set; based on the feature matching target point set, perform target image registration processing on each second detection area image in the second detection area image set to generate a registered target detection area image, and obtain a registered target detection area image set; perform target image fusion processing on the registered target detection area image set to generate a fused target detection area image; based on the target prediction trajectory of the marked detection area image, perform target locking processing on the fused target detection area image to generate the future position of the target of the marked detection area image.

[0014] Optionally, the above-mentioned power data storage terminal is configured to: perform compression processing on the above-mentioned power operation and maintenance image set to generate a compressed power operation and maintenance image set, and store the compressed power operation and maintenance image set in a hard disk video recorder.

[0015] Optionally, the above-mentioned algorithm server is configured to: perform image recognition processing on each power operation and maintenance image in the power operation and maintenance image set to generate a recognized power operation and maintenance image, and obtain a recognized power operation and maintenance image set; perform anomaly detection processing on the recognized power operation and maintenance image set to generate an anomaly detection result of the power operation and maintenance image.

[0016] Optionally, the above-mentioned edge computing terminal is configured to: perform preprocessing on the power operation and maintenance image set to generate a preprocessed power operation and maintenance image set; based on the anomaly detection result of the power operation and maintenance image, perform anomaly analysis processing on the preprocessed power operation and maintenance image set to generate an anomaly analysis result of the power operation and maintenance image; in response to determining that the anomaly result of the power operation and maintenance image meets the preset power operation and maintenance anomaly warning condition, control the associated alarm device to give an anomaly alarm.

[0017] The above - mentioned various embodiments of the present disclosure have the following beneficial effects: Through the power operation and maintenance intelligent perception system of some embodiments of the present disclosure, the automation level and accuracy of power equipment inspection can be improved. Specifically, the reasons for missing faults or misjudgment are as follows: Manual inspection requires a large amount of manual input, and the professional ability, experience, and working status of inspection personnel will directly affect the inspection quality. Based on this, in the power operation and maintenance intelligent perception system of some embodiments of the present disclosure, first, the power data perception terminal is communicatively connected to the power data storage terminal and the power calculation terminal. Among them, the power data perception terminal includes: a direct - reading instrument device, a power inspection drone device, a power inspection robot device, and an image acquisition device. Thus, real - time monitoring of the power equipment status and collection of the power operation and maintenance image set can be realized, providing a basis for subsequent analysis and decision - making of the power operation and maintenance image set. Secondly, the power data storage terminal is communicatively connected to the power calculation terminal. Among them, the power calculation terminal includes: an algorithm server and an edge - computing terminal. Thus, efficient processing and analysis of the collected power operation and maintenance image set can be realized, improving the processing speed of the power operation and maintenance image set. Thirdly, the above - mentioned direct - reading instrument device is used to collect the meter - reading accuracy image set corresponding to each power equipment meter. Among them, the above - mentioned direct - reading instrument device includes: a direct - reading instrument and a direct - reading instrument gateway. Thus, the automation and accuracy of meter reading can be improved, and the errors of manual meter reading can be reduced. Then, the above - mentioned power inspection drone device is used to conduct aerial inspections on the targets to be detected and collect the detection - target image set of each target to be detected. Among them, the above - mentioned power inspection drone device includes: a drone and a drone parking station. Thus, the inspection efficiency and coverage of remote or inaccessible areas can be improved. Then again, the above - mentioned power inspection robot device is used to inspect the targets to be inspected and collect the inspection - target image set of each target to be inspected on the ground. Among them, the above - mentioned power inspection robot device includes: a robot, a base station, and a robot workstation. Thus, the inspection efficiency and accuracy of ground facilities can be improved, especially in complex terrains or harsh environments. Then, the above - mentioned image acquisition device is used to collect the area image set of each area to be detected. Among them, the above - mentioned image acquisition device includes: a fixed visible - light gun camera, a pan - tilt omnidirectional visible - light dome camera, a fixed infrared gun camera, and a pan - tilt omnidirectional infrared dome camera. Thus, a more comprehensive monitoring perspective and data can be provided, enhancing the monitoring ability of the equipment status. Finally, the above - mentioned algorithm server is communicatively connected to the above - mentioned edge server. The algorithm server is used to identify abnormal situations in the power operation and maintenance image set, and the edge server is used to perform power operation and maintenance image pre - processing and power operation and maintenance image anomaly analysis of the power operation and maintenance image set. Among them, the above - mentioned power operation and maintenance image set includes: the meter - reading image set, the detection - target image set, the inspection - target image set, and the area image set. Thus, using advanced computer vision technology, intelligent monitoring and fault prediction of power equipment can be realized. Brief Description of the Drawings

[0018] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements and components are not necessarily drawn to scale.

[0019] Figure 1 FIG. is a schematic system architecture diagram of a power operation and maintenance intelligent perception system suitable for implementing some embodiments of the present disclosure;

[0020] Figure 2 FIG. is a schematic system architecture diagram of a power data perception terminal suitable for implementing some embodiments of the present disclosure. Specific Embodiments

[0021] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0022] In addition, it should be noted that for the sake of convenience of description, only the parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0023] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules, or units, and are not used to limit the order of functions performed by these devices, modules, or units or their interdependent relationships.

[0024] It should be noted that the modifications of "one" and "plural" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0025] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0026] The present disclosure will be described in detail below with reference to the drawings and in combination with the embodiments.

[0027] Figure 1It is a schematic system architecture diagram of the intelligent power operation and maintenance perception system according to the present disclosure. The system architecture of the intelligent power operation and maintenance perception system 100 according to the present disclosure is shown. The intelligent power operation and maintenance perception system 100 includes: a power data perception terminal 101, a power data storage terminal 102, and a power calculation terminal 103.

[0028] In some embodiments, the power data perception terminal is communicatively connected to the power data storage terminal and the power calculation terminal. Among them, the power data perception terminal includes: a direct reading instrument device, a power inspection unmanned aerial vehicle device, a power inspection robot device, and an image acquisition device.

[0029] Here, the power data perception terminal can refer to Figure 2 It is a schematic structural diagram of the power data perception terminal suitable for implementing some embodiments of the present application, as Figure 2 shown, the above-mentioned power data perception terminal includes: a direct reading instrument device 1, a power inspection unmanned aerial vehicle device 2, a power inspection robot device 3, and an image acquisition device 4. The above-mentioned direct reading instrument device 1 includes: a direct reading instrument 4 and a direct reading instrument gateway 5. The above-mentioned power inspection unmanned aerial vehicle device 2 includes: an unmanned aerial vehicle 6 and an unmanned aerial vehicle parking station 7. The above-mentioned power inspection robot device 3 includes: a robot 8, a base station 9, and a robot workstation 10. The above-mentioned image acquisition device 4 includes: a fixed visible light gun camera 11, a pan-tilt omnidirectional visible light dome camera 12, a fixed infrared gun camera 13, and a pan-tilt omnidirectional infrared dome camera 14. The direct reading instrument 4 is connected to the direct reading instrument gateway 5; the unmanned aerial vehicle 6 is connected to the unmanned aerial vehicle parking station 7; the robot 8 is respectively connected to the base station 9 and the robot workstation 10; the base station 9 is connected to the robot workstation 10; the fixed visible light gun camera 11 is respectively connected to the pan-tilt omnidirectional visible light dome camera 12, the fixed infrared gun camera 13, and the pan-tilt omnidirectional infrared dome camera 14; the pan-tilt omnidirectional visible light dome camera 12 is respectively connected to the fixed infrared gun camera 13 and the pan-tilt omnidirectional infrared dome camera 14; the fixed infrared gun camera 13 is connected to the pan-tilt omnidirectional infrared dome camera 14.

[0030] The above-mentioned direct reading instrument 4 uses a pixel image sensor, has an automatic / manual shutter function, and supports automatic fill light; the above-mentioned direct reading instrument 4 supports a shooting distance within a certain range, has a specific frame rate and field of view angle; the above-mentioned direct reading instrument 4 uses a wireless transmission protocol, supports real-time wake-up and timed image transmission, is powered by a lithium battery, and has low power consumption and a high number of image acquisitions. The above-mentioned direct reading instrument gateway 5 is used to wake up the above-mentioned direct reading instrument, set the image acquisition frequency of the above-mentioned direct reading instrument 4, receive the picture data collected by the above-mentioned direct reading instrument 4, and perform image reading processing.

[0031] The above-mentioned drone 6 has a long flight time and a suitable working environment temperature; the above-mentioned drone 6 has a specific working frequency and transmission power; the above-mentioned drone 6 supports fast charging and has a large charging time and charging power; the gimbal camera carried by the above-mentioned drone 6 uses a high-pixel image sensor, has a variety of shooting parameters, and can collect high-definition images. The above-mentioned drone docking station 7 realizes automatic power on / off, automatic inspection control, image reception and storage, and precise landing; the above-mentioned drone docking station 7 has multiple protection measures to ensure the safe and stable charging of the drone 6; the above-mentioned drone docking station 7 realizes the automatic power on / off and automatic charging functions of the drone 6 through an automatic centering mechanism.

[0032] The above-mentioned robot 8 realizes autonomous navigation through a simultaneous localization and mapping navigation system, and this simultaneous localization and mapping navigation system performs localization and mapping by using a lidar and an inertial measurement unit; the above-mentioned robot 8 collects images and videos and transmits them to the above-mentioned robot workstation 10 in real time through the above-mentioned base station 9; the above-mentioned robot 8 generates an alarm message when detecting an abnormality in the power equipment and records and stores the inspection information.

[0033] The above-mentioned fixed visible light PTZ camera 11 uses a high-performance image sensor, has a high pixel resolution, can work in a low-light environment, and outputs high-definition images; the above-mentioned fixed visible light PTZ camera 11 supports high-frame-rate video output to ensure smooth pictures, supports encoding technology, and realizes high compression ratio and low bitstream transmission; the above-mentioned fixed visible light PTZ camera supports multiple monitoring modes, the above-mentioned fixed visible light PTZ camera 11 supports encoding technology, and adapts to different bandwidths and storage environments; the above-mentioned fixed visible light PTZ camera 11 supports running without a Secure Digital card and has alarm input and output functions; the above-mentioned fixed visible light PTZ camera 11 supports large-capacity Secure Digital card storage; the above-mentioned fixed visible light PTZ camera 11 supports multiple power supply methods; the above-mentioned fixed visible light PTZ camera 11 supports 12-volt power return and has a maximum current requirement; the above-mentioned fixed visible light PTZ camera 11 supports voltage anomaly alarm function, which is convenient for engineering installation.

[0034] The above-mentioned pan-tilt omnidirectional visible light dome camera 12 supports a variety of intelligent monitoring functions; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 uses a high-performance image sensor, has a high pixel resolution, can work in low-illumination environments, and outputs high-definition images; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 supports high-frame-rate video output to ensure smooth pictures; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 supports encoding technology to achieve high compression ratios and low-bitstream transmission; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 is equipped with infrared fill lights and can perform monitoring in environments without visible light; the above-mentioned pan-tilt omnidirectional visible light dome camera supports a variety of monitoring modes and adapts to different monitoring environments; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 supports encoding technology and adapts to different bandwidths and storage environments; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 supports large-capacity memory cards to meet the needs of long-time video storage; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 supports multiple power supply methods and adapts to different power supply environments; the above-mentioned pan-tilt omnidirectional visible light dome camera 12 has a wide-voltage design and can work stably at different voltages.

[0035] The above-mentioned fixed infrared bullet camera 13 is built with a graphics processing unit chip, supports deep learning algorithms, and improves detection accuracy; the above-mentioned fixed infrared bullet camera 13 supports intelligent resource switching for general behavior analysis, face detection, and people counting; the above-mentioned fixed infrared bullet camera 13 supports face detection functions; the above-mentioned fixed infrared bullet camera 13 supports a variety of face attributes; the above-mentioned fixed infrared bullet camera 13 supports people counting; the above-mentioned fixed infrared bullet camera 13 supports a variety of behavior detections; the above-mentioned fixed infrared bullet camera 13 supports multi-bitstream functions to meet the needs of multi-channel high-definition video display; the above-mentioned fixed infrared bullet camera 13 uses a high-performance image sensor to output high-definition images; the above-mentioned fixed infrared bullet camera 13 supports encoding technology to achieve high compression ratios and low-bitstream transmission; the above-mentioned fixed infrared bullet camera 13 supports a variety of monitoring modes; the above-mentioned fixed infrared bullet camera 13 supports encoding and adapts to different bandwidths and storage environments; the above-mentioned fixed infrared bullet camera 13 supports a variety of alarm inputs and outputs and audio inputs and outputs; the above-mentioned fixed infrared bullet camera 13 supports storage with large-capacity secure digital cards; the above-mentioned fixed infrared bullet camera 13 supports multiple power supply methods.

[0036] The above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports vehicle feature recognition and can upload information to the power data storage terminal and the power calculation terminal; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 can cover multiple lanes and can perform video analysis on the proximal lane; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports face detection and people counting; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports heat maps and has high-power optical zoom and digital zoom capabilities; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports super starlight ultra-low illumination and can work in extremely dark environments; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 has a high signal-to-noise ratio and supports privacy masking; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 can process multiple regions simultaneously and can display multiple regions on the same screen; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports wide dynamic range effects and image noise reduction functions and can display images during the day and at night; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports a wiper function and has built-in infrared light for supplementary lighting; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports wide voltage input, reaches a high protection level, and has lightning protection, surge protection, and anti-surge protection; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports an open application programming interface for application integration; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports the three-stream technology and can perform single-scene tracking, multi-scene tracking, and panoramic tracking; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports two tracking methods: manual tracking and alarm tracking; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports multiple behavior detections; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 supports target filtering, tracking algorithms, automatically locks the target, and automatically adjusts the focal length of the panoramic pan-tilt to obtain the best surveillance image; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 has a panoramic pan-tilt and a panoramic algorithm and can perform seamless stitching of panoramic images; the above-mentioned pan-tilt omnidirectional infrared dome camera 14 can continuously rotate horizontally, automatically flip vertically and then continuously monitor, without surveillance blind spots; the horizontal key-controlled speed and vertical key-controlled speed of the above-mentioned pan-tilt omnidirectional infrared dome camera 014 can be adjusted for precise pan-tilt positioning; the above-mentioned pan-tilt omnidirectional infrared dome camera 014 supports multiple preset positions, can complete multiple cruise paths, and can set multiple patrol paths.

[0037] For example, the pixel image sensor can be: a Complementary Metal Oxide Semiconductor (CMOS) sensor. The auto / manual shutter function can be: an exposure control function. The shooting distance can be: 500 meters. The specific frame rate and field of view can be: 30 frames per second (fps) and 60 degrees. The wireless transmission protocol can be: Wi-Fi, Bluetooth. The real-time wake-up and timed image transmission can be: timer trigger and active wake-up mode. The lithium battery can be: a 3.6V lithium battery. The low power consumption and high number of image captures can be: 6000 times. The flight time can be: 21 minutes. The operating ambient temperature can be: -10°C to 40°C. The specific operating frequency can be: 2.400 - 2.4835 GHz, 5.725 - 5.850 GHz. The transmit power can be: 2.400 - 2.4835 GHz, FCC ≤ 26 dBm, CE ≤ 20 dBm, SRRC ≤ 20 dBm, MIC ≤ 20 dBm / 5.725 - 5.850 GHz. The charging time can be: 95 minutes. The charging power can be: 80W. The pixel image sensor can be: a 1 / 2.3 inch CMOS. The multiple shooting parameters can be: exposure time, white balance. The multiple protection measures can be: overvoltage protection, overtemperature protection. The auto-centering mechanism can be: a motor-driven mechanical structure. The lidar can be: Hesai lidar. The high-performance image sensor can be: a Complementary Metal Oxide Semiconductor (CMOS) sensor. The encoding technology can be: H.264, H.265. The non-Secure Digital card can be: an SD card. The infrared fill light can be: a Vstarcam infrared fill light. The large-capacity memory card can be: a 128G Micro SD card. The multiple power supply methods can be: DC12V method, POE method. The graphics processing unit chip can be: a GPU chip. The deep learning algorithm can be: a Convolutional Neural Network (CNN) algorithm. The intelligent resources can be: general behavior analysis, face detection, people counting. The general behavior analysis can be: health behavior analysis. The face detection function can be: tracking, prioritization, capture, face enhancement, face exposure, and face attribute extraction. The multiple behavior detections can be: tripwire intrusion, area intrusion, fence crossing, loitering detection, object left behind, object moved, fast movement. The multi-stream function can be: main stream, sub-stream. The multiple monitoring modes can be: corridor mode, wide dynamic range, strong light suppression, backlight compensation, and digital watermark. The multiple lanes can be: 2 - 3 lanes. The multiple behavior detections can be: tripwire intrusion, area intrusion, fence crossing, loitering detection, object left behind, object moved, fast movement. The face detection function can be: tracking, prioritization, capture, face enhancement, face exposure, and face attribute extraction. The panoramic pan-tilt head can be: an omnidirectional rotating pan-tilt head. The panoramic algorithm can be: a panoramic image stitching algorithm. The multiple preset positions can be: 300. The multiple patrol paths can be: 5. The multiple intelligent monitoring functions can be: tripwire intrusion, area intrusion, fast movement, object left behind, object moved, and face detection.The personnel statistics can be: queuing management and personnel statistics within the area. The vehicle feature recognition can be: license plate recognition, body color recognition, vehicle type recognition, and logo recognition.

[0038] In some embodiments, the power data storage terminal is communicatively connected to the power calculation terminal. The power calculation terminal includes: an algorithm server and an edge computing terminal. For example, the algorithm server can be: a graphics processing server. The edge computing terminal can be: an Internet of Things edge computing terminal.

[0039] In some embodiments, the above-mentioned direct reading device is used to collect the meter reading image sets corresponding to each power equipment meter. Among them, the above-mentioned direct reading device includes: a direct reading instrument and a direct reading instrument gateway. The direct reading instrument is used to directly read the meter reading of the power equipment meter and convert the meter reading into a digital signal entity. The direct reading instrument gateway is used to receive the digital signal transmitted by the direct reading instrument and forward the signal to the power data storage terminal and the power calculation terminal. For example, the direct reading instrument can be: a direct reading spectrometer. The direct reading instrument gateway can be: a spectral data gateway. The power equipment meter can be: a metering meter.

[0040] In some embodiments, the above-mentioned power inspection drone device is used to conduct aerial inspections on the inspection targets to be inspected and collect the inspection target image sets of each inspection target to be detected. Among them, the above-mentioned power inspection drone device includes: a drone and a drone parking station. The drone is used to conduct aerial inspections on the inspection targets to be detected, and the inspection target sets of each inspection target to be detected. The drone parking station is used to store and charge the drone to ensure that the drone can automatically return and prepare for the next task after completing the mission. For example, the drone can be: a multi-rotor drone. The drone parking station can be: an intelligent charging parking station.

[0041] In some embodiments, the above-mentioned power inspection robot device is used to inspect the inspection targets to be inspected and collect the inspection target image sets of each inspection target to be inspected on the ground. Among them, the above-mentioned power inspection robot device includes: a robot, a base station, and a robot workstation. The robot is used to inspect the inspection targets to be inspected and collect the inspection target image sets of each inspection target to be inspected on the ground. The base station is used to communicate with the robot, send control instructions, and receive the inspection target image sets collected by the robot. The robot workstation is used to monitor the running state of the robot and perform analysis on the inspection target image sets. For example, the robot can be: an industrial collaborative robot. The base station can be: a wireless communication base station. The robot workstation can be: an automatic maintenance workstation.

[0042] In some embodiments, the above image acquisition device is used to acquire a set of regional images of each area to be detected. Among them, the above image acquisition device includes: a fixed visible light gun camera, a pan-tilt omnidirectional visible light dome camera, a fixed infrared gun camera, and a pan-tilt omnidirectional infrared dome camera. The fixed visible light gun camera is used to monitor the power equipment area and provide high-definition visible light images. The pan-tilt omnidirectional visible light dome camera is used to monitor the power equipment area omnidirectionally, and can automatically track moving targets and provide continuous image data. The fixed infrared gun camera is used to monitor the power equipment area under bad weather or night conditions and provide infrared images to detect thermal anomalies. The fixed visible light gun camera is used to monitor the power equipment area omnidirectionally, provide infrared images to detect thermal anomalies, and can automatically track heat sources. For example, the fixed visible light gun camera can be: a high-definition surveillance camera. The pan-tilt omnidirectional visible light dome camera can be: an intelligent tracking dome camera. The fixed infrared gun camera can be: a thermal imaging surveillance camera. The pan-tilt omnidirectional infrared dome camera can be: a panoramic thermal imaging surveillance dome camera. The power equipment can include: transformers, circuit breakers, and meters.

[0043] In some embodiments, the above algorithm server is communicatively connected to the above edge server. The above algorithm server is used to identify abnormal situations in the power operation and maintenance image set, and the above edge server is used to perform preprocessing of power operation and maintenance images and analysis of power operation and maintenance image anomalies in the power operation and maintenance image set. Among them, the above power operation and maintenance image set includes: a set of meter reading images, a set of detection target images, a set of inspection target images, and a set of regional images.

[0044] Optionally, the above direct reader is configured to:

[0045] First step, acquire meter images corresponding to each power equipment meter to obtain a set of meter images. For example, the above direct reader can use its built-in imaging device to acquire meter images corresponding to each power equipment meter to obtain a set of meter images. The built-in imaging device can be: a camera.

[0046] Second step, perform grayscale processing on each meter image in the above set of meter images to generate grayscale meter images, and obtain a set of grayscale meter images. For example, the above direct reader can convert each meter image in the above set of meter images into a grayscale meter image through a preset grayscale algorithm to obtain a set of grayscale meter images. The grayscale algorithm can be: the average value method algorithm (using the average value of the three RGB components as the grayscale value).

[0047] Step 3: Perform image noise reduction processing on each grayscale meter image set in the above grayscale meter image sets to generate noise-reduced meter images, and obtain a noise-reduced meter image set. For example, the above direct-reading instrument can use a filter to perform image noise reduction processing on each grayscale meter image set in the above grayscale meter image sets to generate noise-reduced meter images, and obtain a noise-reduced meter image set. The filter can be: a Gaussian filter.

[0048] Step 4: Perform contrast enhancement processing on each noise-reduced meter image in the above noise-reduced meter image set to generate enhanced meter images, and obtain an enhanced meter image set. For example, the above direct-reading instrument can perform contrast enhancement processing on each noise-reduced meter image in the above noise-reduced meter image set through a preset histogram equalization algorithm to generate enhanced meter images, and obtain an enhanced meter image set. The preset histogram equalization algorithm can be: a contrast-limited adaptive histogram equalization algorithm.

[0049] Step 5: Perform image binarization processing on each enhanced meter image in the above enhanced meter image set to generate binarized meter images, and obtain a binarized meter image set. For example, first, the above direct-reading instrument can set a preset pixel threshold. In response to determining that the pixel value in the enhanced meter image is greater than or equal to the preset pixel threshold, the above enhanced meter image is determined as a white area meter image. Second, in response to determining that the pixel value in the above enhanced meter image is less than the preset pixel threshold, the above enhanced meter image is determined as a black area meter image. Finally, use a preset binarization algorithm to perform pixel value classification processing on the above white area meter image and black area meter image to generate a binarized meter image set. The preset binarization algorithm can be: the Otsu algorithm. The preset pixel threshold can be: 0 - 255.

[0050] Step 6: Perform edge detection processing on each binarized meter image in the above binarized meter image set to generate edge-detected binarized meter images, and obtain an edge-detected binarized meter image set. For example, the above direct-reading instrument can perform meter image boundary extraction processing on each binarized meter image in the above binarized meter image set through a preset edge detection algorithm to generate edge-detected binarized meter images, and obtain an edge-detected binarized meter image set. The preset edge detection algorithm can be: the Canny edge detection algorithm.

[0051] Step 7: Perform contour extraction on each edge-detected binary meter image in the above-mentioned edge-detected binary meter image set to generate a contour meter image, obtaining a contour meter image set. Among them, there is an image bounding box in the contour meter image. For example, the above-mentioned direct-reading instrument can, through a preset function, perform contour extraction on each edge-detected binary meter image in the above-mentioned edge-detected binary meter image set to generate a contour meter image, obtaining a contour meter image set. The preset function can be: find contours (cv2.findContours function).

[0052] Step 8: Determine the largest contour meter image in the above-mentioned contour meter image set as the region meter image.

[0053] Step 9: Perform contour extraction on the above-mentioned region meter image to generate a contour region meter image. For example, the above-mentioned direct-reading instrument can perform contour extraction on the above-mentioned region meter image through the preset function to generate a contour region meter image.

[0054] Step 10: In response to determining that the contour region area of the contour region meter image is less than the contour region area threshold of the preset contour region meter image, generate a contour region meter bounding box group. For example, the above-mentioned direct-reading instrument can perform contour filtering on the above-mentioned contour region meter image to generate a filtered contour region. Secondly, use the above-mentioned preset function to perform screening on the above-mentioned filtered contour region to generate a screened filtered contour region. Finally, extract the contour region meter bounding box group from the above-mentioned screened filtered contour region. The contour region area threshold of the contour region meter image can be: 100.

[0055] Step 11: Based on the above-mentioned contour region meter bounding box group, perform character sorting on the above-mentioned region meter image to generate a character sequence. For example, first, the above-mentioned direct-reading instrument can perform horizontal sorting on the above-mentioned contour region meter bounding box group to generate a horizontally sorted meter bounding box group. According to the above-mentioned horizontally sorted meter bounding box group, perform cropping on the above-mentioned region meter image to generate a character sequence.

[0056] Step 12: Traverse each character in the above-mentioned character sequence to generate a traversed character set. For example, the above-mentioned direct-reading instrument can perform feature extraction on each character in the above-mentioned character sequence to generate a feature-extracted character, obtaining a feature-extracted character sequence as the traversed character set.

[0057] The thirteenth step is to perform character recognition processing on each traversed character in the above traversed character set to generate recognized characters, thereby obtaining a recognized character set. For example, the above direct reading instrument can perform character recognition processing on each traversed character in the above traversed character set by using a preset character recognition algorithm to generate recognized characters, thereby obtaining a recognized character set. The preset character recognition algorithm can be: an optical character recognition algorithm (OCR).

[0058] The fourteenth step is to output a set of meter reading images based on the above recognized character set and send the set of meter reading images to the direct reading instrument gateway. For example, the above direct reading instrument can perform image format conversion processing on each recognized character in the above recognized character set to generate a set of meter reading images and send the set of meter reading images to the direct reading instrument gateway.

[0059] Optionally, the above direct reading instrument gateway is configured to:

[0060] In some embodiments, in response to receiving the set of meter reading images, the set of meter reading images is sent to the power data storage terminal and the power calculation terminal.

[0061] Optionally, the above power inspection UAV device is configured to:

[0062] The first step is to, in response to receiving an aerial inspection instruction, determine the current position of the inspection UAV and the positions of each target to be detected, and plan an inspection path between the current position and the positions of each target to be detected. For example, first, the above power inspection UAV device can, in response to determining that the battery power and signal are normal, unlock the motor and control the UAV to take off to a preset height. Next, in response to an error occurring during takeoff, control the UAV to execute an emergency landing procedure. Then, obtain the current position of the UAV and plan an inspection path to reach the next inspection point. Then, control the UAV to move along the planned inspection path and monitor obstacles in real time. Finally, in response to detecting an obstacle, re-plan the inspection path and adjust the flight route of the UAV. The aerial inspection instruction can be: to check the operating status of the targets to be detected along the inspection path. The targets to be detected can be: transmission lines, wind turbines, solar panels.

[0063] Second step, control the drone to move along the inspection path, and control the drone to collect and process images of each target to be detected to generate detected target images, obtain a set of detected target images, and send the collected set of detected target images to the power data storage terminal and the power calculation terminal. For example, first, the above power inspection drone device can control the drone to move along the inspection path until it reaches the position of the target to be detected. Secondly, according to the position of the target to be detected, control the drone to fly to each position of the target to be detected in sequence. Thirdly, at each position of the target to be detected, use the on-board camera to collect images to obtain a set of detected target images. Then, save and send the above set of detected target images to the power data storage terminal and the power calculation terminal. Then, perform anomaly detection processing on the above set of detected target images to generate an anomaly detection result for identifying the detected target. Finally, in response to receiving the set of detected target images of the target to be detected, control the drone to return to the drone parking station. The camera can be: an infrared camera. The anomaly detection result for identifying the detected target can be: the operating state of the target to be detected is abnormal, the operating state of the target to be detected is normal.

[0064] Optionally, the above power inspection robot device is configured to:

[0065] First step, in response to receiving a ground inspection instruction, determine the current position of the inspection robot and the positions of each target to be inspected, and plan an inspection path between the current position and the positions of each target to be inspected. For example, the ground inspection instruction can include: setting an inspection path, target devices, and detection parameters. For example, an inspection path between the current position and the positions of each target to be inspected can be planned through a preset path planning algorithm. The preset path planning algorithm can be: Dijkstra algorithm, RRT (Rapidly-exploring Random Trees) algorithm, MILP (Mixed Integer Linear Programming) algorithm.

[0066] In the second step, control the robot to move along the inspection path, and control the robot to perform image acquisition and processing on each target to be inspected to generate inspection target images, obtain an inspection target image set, and upload the inspection target image set to the power data storage terminal and the power calculation terminal through the above-mentioned base station. For example, first, the above-mentioned power inspection robot device can control the robot to autonomously move along the inspection path according to the planned inspection path, using the front-wheel drive and differential control system, and relying on the lidar to avoid obstacles at the same time. Secondly, control the robot to perform image acquisition and processing on each target to be inspected to generate inspection target images, obtain an inspection target image set. Thirdly, the acquired inspection target image set is transmitted to the robot workstation in real time through the above-mentioned base station. Then, in response to determining that the robot workstation receives the inspection target image set, perform anomaly detection processing on the inspection target image set to generate an anomaly detection inspection target recognition result. Then, in response to determining that the anomaly detection inspection target recognition result meets the preset anomaly warning condition, control the associated alarm device to give an anomaly alarm. Finally, in response to determining that the battery power is insufficient after the inspection instruction is completed, use the automatic docking system to control the robot to autonomously navigate back to the charging pile for charging. The targets to be inspected can be: power distribution equipment, substation equipment. The anomaly detection inspection target recognition result can be: the operating state of the target to be inspected is abnormal, the operating state of the target to be inspected is normal.

[0067] Optionally, the above fixed infrared camera is configured to:

[0068] In the first step, in response to receiving a face detection instruction, collect first detection area images corresponding to each first area to be detected, and obtain a first detection area image set. For example, the face detection instruction can be: voice instruction, programming instruction.

[0069] In the second step, perform image preprocessing on each first detection area image in the first detection area image set to generate preprocessed detection area images, and obtain a preprocessed detection area image set. For example, first, the above fixed infrared camera can perform image cleaning processing on each first detection area image in the first detection area image set to generate cleaned detection area images, and obtain a cleaned detection area image set. Secondly, perform image feature selection processing on each cleaned detection area image in the cleaned detection area image set to generate feature selection detection area images, and obtain a feature selection detection area image set. Then, perform image normalization processing on each feature selection detection area image in the feature selection detection area image set to generate preprocessed detection area images, and obtain a preprocessed detection area image set.

[0070] Step 3: Perform face localization processing on each preprocessed detection area image in the above preprocessed detection area image set to generate face localization images, and obtain a face localization image set. For example, the above fixed infrared camera can perform face localization processing on each preprocessed detection area image in the above preprocessed detection area image set by using a deep learning model and a classifier to generate face localization images, and obtain a face localization image set. The deep learning model can be: Residual Network Model (ResNet), Multi-task Cascaded Convolutional Networks (MTCNN). The classifier can be: Feature Cascaded Classifier (Haar).

[0071] Step 4: Perform multi-scale detection processing on each face localization image in the above face localization image set to generate multi-scale detected face images, and obtain a multi-scale detected face image set. Among them, there are bounding boxes for the faces in the multi-scale detected face images. For example, the above fixed infrared camera can perform multi-scale detection processing on each face localization image in the above face localization image set by using multiple detection scales to generate multi-scale detected face images, and obtain a multi-scale detected face image set. The multiple detection scales can be: 1:1, 1:1.25, 1:1.5, 1:1.75, 1:2.

[0072] Step 5: Perform face region cropping processing on each multi-scale detected face image in the above multi-scale detected face image set to generate region-cropped face images, and obtain a region-cropped face image set. For example, first, the above fixed infrared camera can obtain the bounding box coordinates of each face from the above multi-scale detected face image set. Secondly, based on the bounding box coordinates of each face, crop the face region of the corresponding multi-scale detected face image from the above multi-scale detected face image set to obtain a region-cropped face image set. The bounding box coordinates can be: x, y, z.

[0073] Step 6: Perform face frame drawing processing on each region-cropped face image in the above region-cropped face image set to generate frame-drawn face images, and obtain a frame-drawn face image set. For example, the above fixed infrared camera can perform face frame drawing processing on each region-cropped face image in the above region-cropped face image set through a preset face frame drawing function to generate frame-drawn face images, and obtain a frame-drawn face image set. The preset face frame drawing function can be: detect_faces.

[0074] Step 7: Perform face image alignment on each face image drawn in the above-mentioned boxed face image set to generate aligned face images, thereby obtaining an aligned face image set. For example, the above-mentioned fixed infrared camera can draw face images for each box in the above-mentioned boxed face image set, and perform face image alignment on the face images drawn in the above-mentioned boxes through a preset alignment algorithm to generate aligned face images, thereby obtaining an aligned face image set. The preset alignment algorithm can be: a key point detection algorithm, a geometric transformation algorithm.

[0075] Step 8: Perform image noise reduction processing on each aligned face image in the above-mentioned aligned face image set to generate noise-reduced face images, thereby obtaining a noise-reduced face image set. For example, the above-mentioned fixed infrared camera can perform image noise reduction processing on each aligned face image in the above-mentioned aligned face image set through a preset image noise reduction algorithm to generate noise-reduced face images, thereby obtaining a noise-reduced face image set. The preset image noise reduction algorithm can be: a fast mean denoising algorithm.

[0076] Step 9: Perform image sharpening processing on each noise-reduced face image in the above-mentioned noise-reduced face image set to generate sharpened face images, thereby obtaining a sharpened face image set. For each noise-reduced face image in the above-mentioned noise-reduced face image set, use a filter to perform image sharpening processing on the above-mentioned noise-reduced face images to generate sharpened face images, thereby obtaining a sharpened face image set. The filter can be: a convolutional filter.

[0077] Optionally, the above-mentioned pan-tilt omnidirectional infrared dome camera is configured as follows:

[0078] Step 1: In response to receiving a target trajectory prediction instruction, collect second detection area images corresponding to each second detection area, thereby obtaining a second detection area image set.

[0079] Step 2: Perform target detection and target box marking processing on each second detection area image in the above-mentioned second detection area image set to generate target-marked detection area images, thereby obtaining a target-marked detection area image set. Among them, the target-marked detection area image has a bounding box for the marked target. For example, the above-mentioned pan-tilt omnidirectional infrared dome camera can perform target detection and target box marking processing on each second detection area image in the above-mentioned second detection area image set using an initialized tracker to generate target-marked detection area images, thereby obtaining a target-marked detection area image set. Among them, the target-marked detection area image has a bounding box for the marked target. The initialized tracker can be: a multi-instance learning (MIL) tracker.

[0080] In the third step, perform object tracking processing on each object marker detection area image in the above object marker detection area image set to generate the object position of the marker detection area image, and obtain the object position set of the marker detection area image. For example, the above pan-tilt omnidirectional infrared dome camera can use a tracker to perform object tracking processing on each object marker detection area image in the above object marker detection area image set to generate the object position of the marker detection area image, and obtain the object position set of the marker detection area image. The tracker can be: a Kernelized Correlation Filters (KCF) tracker.

[0081] In the fourth step, perform trajectory prediction processing on the above object position set of the marker detection area image to generate the predicted trajectory of the object in the marker detection area image. For example, the above pan-tilt omnidirectional infrared dome camera can perform trajectory prediction processing on the above object position set of the marker detection area image through a filter to generate the predicted trajectory of the object in the marker detection area image. The filter can be: a Kalman filter.

[0082] In the fifth step, perform object point feature extraction processing on each second detection area image in the above second detection area image set to generate feature extraction object points, and obtain the feature extraction object point set. For example, the above pan-tilt omnidirectional infrared dome camera can perform object point feature extraction processing on each second detection area image in the above second detection area image set through a preset object point feature extraction algorithm to generate feature extraction object points, and obtain the feature extraction object point set. The preset object point feature extraction algorithm can be: the Oriented FAST and Rotated BRIEF (ORB) algorithm.

[0083] In the sixth step, perform object point feature matching processing on each feature extraction object point in the above feature extraction object point set to generate feature matching object points, and obtain the feature matching object point set. For example, the above pan-tilt omnidirectional infrared dome camera can perform object point feature matching processing on each feature extraction object point in the above feature extraction object point set through a preset matcher to generate feature matching object points, and obtain the feature matching object point set. The preset matcher can be: a Brute-Force Matcher (BFMatcher).

[0084] Step 7: Based on the above feature matching of the target point set, perform target image registration processing on each second detection area image in the above second detection area image set to generate a registered target detection area image, and obtain a registered target detection area image set. For example, the above pan-tilt omnidirectional infrared dome camera can perform target image registration processing on each second detection area image in the above second detection area image set through a preset target image registration matrix to generate a registered target detection area image, and obtain a registered target detection area image set. The preset target image registration matrix can be: calculating the homography matrix (Homography).

[0085] Step 8: Perform target image fusion processing on the above registered target detection area image set to generate a fused target detection area image. For example, the above pan-tilt omnidirectional infrared dome camera can use a preset target image fusion technology to perform target image fusion processing on the above registered target detection area image set to generate a fused target detection area image. The preset target image fusion technology can be: the perspective transformation (warpPerspective) technology.

[0086] Step 9: Based on the above target prediction trajectory of the marked detection area image, perform target locking processing on the above fused target detection area image to generate the future position of the target in the marked detection area image. For example, the above pan-tilt omnidirectional infrared dome camera can perform target locking processing on the above fused target detection area image through a preset target locking algorithm to generate the future position of the target in the marked detection area image. The preset target locking algorithm can be: the tracking algorithm (KCF).

[0087] Optionally, the above power data storage terminal is configured to:

[0088] In some embodiments, perform compression processing on the above power operation and maintenance image set to generate a compressed power operation and maintenance image set, and store the above compressed power operation and maintenance image set in a hard disk recorder. For example, the above power data storage terminal can perform compression processing on the above power operation and maintenance image set through a preset compression algorithm to generate a compressed power operation and maintenance image set, and store the above compressed power operation and maintenance image set in a hard disk recorder. The hard disk recorder can be: a personal video recorder (PVR), a network video recorder (NVR). The local hard disk can be: a serial (SATA) hard disk. The preset compression algorithm can be: the Joint Photographic Experts Group (JPEG) algorithm.

[0089] Optionally, the above algorithm server is configured to:

[0090] First step: Perform image recognition processing on each power operation and maintenance image in the above-mentioned power operation and maintenance image set to generate recognized power operation and maintenance images, and obtain a set of recognized power operation and maintenance images. For example, the above algorithm server can perform image recognition processing on each power operation and maintenance image in the above-mentioned power operation and maintenance image set through a preset image recognition algorithm to generate recognized power operation and maintenance images, and obtain a set of recognized power operation and maintenance images. The preset image recognition algorithm can be: Support Vector Machine (SVM) algorithm.

[0091] Second step: Perform anomaly detection processing on the above-mentioned set of recognized power operation and maintenance images to generate the results of anomaly detection of power operation and maintenance images. For example, the above algorithm server can perform anomaly detection processing on the above-mentioned set of recognized power operation and maintenance images through a preset anomaly detection algorithm to generate the results of anomaly detection of power operation and maintenance images. The results of anomaly detection of power operation and maintenance images can be: The operating state of the power equipment is abnormal, or the operating state of the power equipment is normal. The preset anomaly detection algorithm can be: Outlier detection algorithm.

[0092] Optionally, the above edge computing terminal is configured as:

[0093] First step: Perform preprocessing on the above-mentioned power operation and maintenance image set to generate a set of preprocessed power operation and maintenance images. For example, first, the above edge computing terminal can perform cleaning processing on the above-mentioned power operation and maintenance image set to generate a set of cleaned power operation and maintenance images. Secondly, perform feature selection processing on the above-mentioned set of cleaned power operation and maintenance images to generate a set of power operation and maintenance images with feature selection. Then, perform table standardization processing on the above-mentioned set of power operation and maintenance images with feature selection to generate a set of preprocessed power operation and maintenance images.

[0094] Second step: Based on the above-mentioned results of anomaly detection of power operation and maintenance images, perform anomaly analysis processing on the above-mentioned set of preprocessed power operation and maintenance images to generate the results of anomaly analysis of power operation and maintenance images. For example, the above edge computing terminal can perform anomaly analysis processing on the above-mentioned set of preprocessed power operation and maintenance images through a preset anomaly analysis algorithm to generate the results of anomaly analysis of power operation and maintenance images. The preset anomaly analysis algorithm can be: Isolation Forest algorithm.

[0095] Third step: In response to determining that the anomaly result of the power operation and maintenance image meets the preset power operation and maintenance anomaly warning condition, control the associated alarm device to issue an anomaly alarm. Among them, the preset power operation and maintenance anomaly warning condition can be: The operating state of the power equipment is abnormal. For example, the alarm device can be a speaker that emits an alarm prompt sound.

[0096] The above description is only some preferred embodiments of the present disclosure and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the embodiments of the present disclosure that have similar functions.

Claims

1. An intelligent perception system for power operation and maintenance, the intelligent perception system for power operation and maintenance comprising: a power data perception terminal, a power data storage terminal and a power calculation terminal, characterized in that the power data perception terminal is communicatively connected to the power data storage terminal and the power calculation terminal. Among them, the power data perception terminal includes: a direct reading instrument device, a power inspection unmanned aerial vehicle (UAV) device, a power inspection robot device, and an image acquisition device; the power data storage terminal is communicatively connected to the power calculation terminal. Among them, the power calculation terminal includes: an algorithm server and an edge computing terminal; the direct reading instrument device is used to collect a set of meter reading images corresponding to each power equipment meter. Among them, the direct reading instrument device includes: a direct reading instrument and a direct reading instrument gateway; the power inspection UAV device is used to conduct aerial inspections on the targets to be inspected and collect a set of inspection target images of each target to be inspected. Among them, the power inspection UAV device includes: a UAV and a UAV parking station; the power inspection robot device is used to conduct inspections on the targets to be inspected and collect a set of inspection target images of each ground target to be inspected. Among them, the power inspection robot device includes: a robot, a base station, and a robot workstation; the image acquisition device is used to collect a set of area images of each area to be detected. Among them, the image acquisition device includes: a fixed visible light camera, a pan-tilt-zoom (PTZ) all-round visible light dome camera, a fixed infrared camera, and a PTZ all-round infrared dome camera; the algorithm server is communicatively connected to the edge computing terminal. The algorithm server is used to identify abnormal situations in the power operation and maintenance image set, and the edge computing terminal is used to perform preprocessing of power operation and maintenance images and analysis of power operation and maintenance image anomalies in the power operation and maintenance image set. Among them, the power operation and maintenance image set includes: a set of meter reading images, a set of detection target images, a set of inspection target images, and a set of area images; the direct reading instrument is configured to: collect meter images corresponding to each power equipment meter to obtain a set of meter images; perform grayscale processing on each meter image in the set of meter images to generate a grayscale meter image and obtain a set of grayscale meter images; perform image noise reduction processing on each grayscale meter image in the set of grayscale meter images to generate a denoised meter image and obtain a set of denoised meter images; perform contrast enhancement processing on each denoised meter image in the set of denoised meter images to generate an enhanced meter image and obtain a set of enhanced meter images; Perform image binarization processing on each enhanced meter image in the enhanced meter image set to generate a binarized meter image, and obtain a binarized meter image set; wherein, first, the direct-reading instrument sets a preset pixel threshold, and in response to determining that the pixel value in the enhanced meter image is greater than or equal to the preset pixel threshold, determines the enhanced meter image as a white area meter image; second, in response to determining that the pixel value in the enhanced meter image is less than the preset pixel threshold, determines the enhanced meter image as a black area meter image; finally, use a preset binarization algorithm to perform pixel value classification processing on the white area meter image and the black area meter image to generate a binarized meter image set, and the preset pixel threshold is: 0 - 255; Perform edge detection processing on each binarized meter image in the binarized meter image set to generate an edge-detected binarized meter image, and obtain an edge-detected binarized meter image set; Perform contour extraction processing on each edge-detected binarized meter image in the edge-detected binarized meter image set to generate a contour meter image, and obtain a contour meter image set, wherein there is an image bounding box in the contour meter image; Determine the largest contour meter image in the contour meter image set as the area meter image; Perform contour extraction processing on the area meter image to generate a contour area meter image; In response to determining that the contour area of the contour area meter image is less than the contour area threshold of the preset contour area meter image, generate a contour area meter bounding box group; wherein, first, the direct-reading instrument performs contour filtering processing on the contour area meter image to generate a filtered contour area; second, use a preset function to perform screening processing on the filtered contour area to generate a screened filtered contour area; finally, extract the contour area meter bounding box group from the screened filtered contour area, and the contour area threshold of the contour area meter image is: 100; Based on the contour area meter bounding box group, perform character sorting processing on the area meter image to generate a character sequence, wherein the direct-reading instrument performs horizontal sorting processing on the contour area meter bounding box group to generate a horizontally sorted meter bounding box group; according to the horizontally sorted meter bounding box group, perform cropping processing on the area meter image to generate a character sequence; Perform traversal processing on each character in the character sequence to generate a traversed character set; Perform character recognition processing on each traversed character in the traversed character set to generate a recognized character, and obtain a recognized character set; Based on the recognized character set, output a meter reading image set and send the meter reading image set to the direct-reading instrument gateway; The direct-reading instrument gateway is configured to: In response to receiving the meter reading image set, send the meter reading image set to the power data storage terminal and the power calculation terminal.

2. The intelligent perception system for power operation and maintenance according to claim 1, wherein The power inspection UAV device is configured to: In response to receiving an aerial inspection instruction, determine the current position of the inspection UAV and the positions of each target to be detected, and plan an inspection path from the current position to the positions of each target to be detected; Control the UAV to move along the inspection path, and control the UAV to perform image acquisition and processing on each target to be detected to generate a detected target image, obtain a detected target image set, and send the acquired detected target image set to the power data storage terminal and the power calculation terminal.

3. The intelligent perception system for power operation and maintenance according to claim 1, wherein The power inspection robot device is configured to: In response to receiving a ground inspection instruction, determine the current position of the inspection robot and the positions of each inspection target to be inspected, and plan an inspection path from the current position to the positions of each inspection target to be inspected; Control the robot to move along the inspection path, and control the robot to perform image acquisition and processing on each inspection target to be inspected to generate an inspection target image, obtain an inspection target image set, and upload the inspection target image set to the power data storage terminal and the power calculation terminal through the base station.

4. The intelligent perception system for power operation and maintenance according to claim 1, characterized in that, The fixed infrared camera is configured to: In response to receiving a face detection instruction, collect a first detection area image corresponding to each first area to be detected to obtain a first detection area image set; Perform image preprocessing on each first detection area image in the first detection area image set to generate a preprocessed detection area image, and obtain a preprocessed detection area image set; Perform face localization processing on each preprocessed detection area image in the preprocessed detection area image set to generate a face localization image, and obtain a face localization image set; Perform multi-scale detection processing on each face localization image in the face localization image set to generate a multi-scale detected face image, and obtain a multi-scale detected face image set, where there is a bounding box for the face in the multi-scale detected face image; Perform face area cropping processing on each multi-scale detected face image in the multi-scale detected face image set to generate a region-cropped face image, and obtain a region-cropped face image set; Perform face box drawing processing on each region-cropped face image in the region-cropped face image set to generate a box-drawn face image, and obtain a box-drawn face image set; Perform face image alignment processing on each box-drawn face image in the box-drawn face image set to generate an aligned face image, and obtain an aligned face image set; Perform image noise reduction processing on each aligned face image in the aligned face image set to generate a noise-reduced face image, and obtain a noise-reduced face image set; Perform image sharpening processing on each noise-reduced face image in the noise-reduced face image set to generate a sharpened face image, and obtain a sharpened face image set.

5. The intelligent perception system for power operation and maintenance according to claim 1, characterized in that The power data storage terminal is configured to: Perform compression processing on the power operation and maintenance image set to generate a compressed power operation and maintenance image set, and store the compressed power operation and maintenance image set in a hard disk video recorder.

6. The intelligent perception system for power operation and maintenance according to claim 5, wherein The algorithm server is configured to: Perform image recognition processing on each power operation and maintenance image in the power operation and maintenance image set to generate a recognized power operation and maintenance image, and obtain a recognized power operation and maintenance image set; Perform anomaly detection processing on the recognized power operation and maintenance image set to generate an anomaly detection result of the power operation and maintenance image.

7. The intelligent perception system for power operation and maintenance according to claim 6, wherein The edge computing terminal is configured to: Perform preprocessing on the power operation and maintenance image set to generate a preprocessed power operation and maintenance image set; Based on the abnormal detection result of the power operation and maintenance image, perform abnormal analysis processing on the preprocessed power operation and maintenance image set to generate an abnormal analysis result of the power operation and maintenance image; In response to determining that the abnormal result of the power operation and maintenance image meets the preset power operation and maintenance abnormal alarm condition, control the associated alarm device to give an abnormal alarm.

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