CN113589837A - Electric power real-time inspection method based on edge cloud - Google Patents
Electric power real-time inspection method based on edge cloud Download PDFInfo
- Publication number
- CN113589837A CN113589837A CN202110550735.1A CN202110550735A CN113589837A CN 113589837 A CN113589837 A CN 113589837A CN 202110550735 A CN202110550735 A CN 202110550735A CN 113589837 A CN113589837 A CN 113589837A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- data
- real
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 17
- 230000005540 biological transmission Effects 0.000 claims abstract description 27
- 238000005516 engineering process Methods 0.000 claims abstract description 24
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 6
- 238000007405 data analysis Methods 0.000 claims abstract description 3
- 230000004297 night vision Effects 0.000 claims abstract description 3
- 238000001514 detection method Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 6
- 230000007547 defect Effects 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 4
- 230000001537 neural effect Effects 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 2
- 238000013135 deep learning Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 claims description 2
- 238000005259 measurement Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 238000011160 research Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000012549 training Methods 0.000 description 5
- 238000000605 extraction Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000002567 autonomic effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000005770 birds nest Nutrition 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000012212 insulator Substances 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 235000005765 wild carrot Nutrition 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Signal Processing (AREA)
- Biomedical Technology (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Public Health (AREA)
- General Business, Economics & Management (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention provides a real-time power inspection method based on edge cloud, wherein a Beidou high-precision positioning miniaturized 5G data transmission module is installed in a nacelle; carrying sensors such as a high-definition zoom camera, an infrared camera, a night vision camera, a laser radar and the like by an unmanned aerial vehicle, and collecting related data of power equipment; the obtained data is subjected to high-speed return and feedback of the instruction after data processing through a 5G technology; the data processing module which is formed by taking the MEC server as a core detects and identifies target objects in returned videos and pictures of the power equipment in real time; forming an instruction for the part which is subjected to data analysis and processing, and directly feeding back the instruction to the monitoring equipment; finally, the residual unprocessed data is directly reported to a control management center, namely a ground edge cloud, so that the problems of low transmission speed, high time delay, low positioning precision and the like of videos and image data of the unmanned aerial vehicle are solved; the problem of unmanned aerial vehicle machine carries the real-time identification of front end is solved simultaneously, the efficiency that the unmanned aerial vehicle circuit was patrolled and examined and the rate of accuracy of discernment have been improved.
Description
Technical Field
The invention relates to the technical field of photoelectric real-time inspection, in particular to a real-time electric inspection method based on edge cloud.
Background
The power industry plays a role as a central drive in national economic development, and particularly, with rapid development of society and rapid progress of science and technology, the demand for power becomes greater and greater. In the early stage, the electric power inspection generally mainly adopts a manpower inspection mode, so that the working environment is severe and certain risks are faced. But the development of unmanned aerial vehicle technique provides new platform for transmission line's the work of patrolling and examining, patrols and examines the electric power circuit with unmanned aerial vehicle technique and is applied to, not only can effectively reduce the personnel working strength that patrol and examine, improves the security, can also implement to the circuit fast, safely and patrol and examine, ensures that electric power operation is stable.
But the current unmanned aerial vehicle inspection technology still has many defects and is difficult to popularize and apply on a large scale. The routing inspection line is often remote in position, and in a mountainous area with severe environment, the navigation and positioning precision of the unmanned aerial vehicle during circuit routing inspection is seriously influenced, and the stability of the system is limited by errors generated in an unstable signal area. Meanwhile, the data transmission of the unmanned aerial vehicle is generally realized by adopting a low-power-consumption Bluetooth or WiFi technology and is limited by the transmitting power, the unmanned aerial vehicle can only carry out data transmission within the sight distance range of not more than 500 meters, and the data transmission speed is low and the maximum resolution of the transmitted image cannot exceed 1080p due to the restriction of the communication bandwidth, so that the wide application of the unmanned aerial vehicle in the industry field is greatly limited.
With the multiplied increase of the scale of the power grid equipment, the pressure and the challenge for the operation and maintenance of the power transmission line are also larger and larger. However, at present, unmanned aerial vehicles in 4G networks and Wi-Fi networks have too many application scenes and too small audience size, so that the unmanned aerial vehicles are difficult to popularize in the consumer market and restrict the long-term development and value exertion of the unmanned aerial vehicles.
Fortunately, the successful networking of the Beidou navigation system, the 5G technology, the edge computing technology and the development maturity of the AI technology inject powerful power into the development of the unmanned aerial vehicle power inspection technology. In 13 days 06 and 13 months 2020, with the smooth lift-off of the last Beidou third satellite, the deployment of the Beidou global system constellation is completed, and positioning, navigation and time service are provided for global users. With the development of communication technology, the 5G technology in China gradually develops and matures, the communication capacity, the data transmission speed and the scale of the unmanned aerial vehicle are greatly improved by utilizing the large-bandwidth, high-reliability and low-delay communication provided by the 5G, and the application requirement of the unmanned aerial vehicle industry can be well met. In recent years, attention has been paid to power routing inspection technology, and it is necessary to greatly improve the efficiency of power routing inspection by applying AI technology and image recognition technology to equipment management and fault diagnosis and by using AI technology and edge calculation technology.
Disclosure of Invention
In order to solve the technical problems provided by the background technology, the invention provides an electric power real-time inspection method based on an edge cloud, which belongs to the field of intersection of electric power inspection and artificial intelligence.
In order to achieve the purpose, the invention adopts the following technical scheme:
a real-time power inspection method based on edge cloud comprises the following steps:
Further, in the step 1, the load requirement of the unmanned aerial vehicle is met by designing a miniaturized 5G data transmission module of a high-precision positioning system, and real-time transmission of 4K high-definition videos and pictures is supported; the large-bandwidth, high-reliability and low-delay communication provided by 5G is utilized to realize the remote real-time monitoring of the unmanned aerial vehicle power inspection front end field; by means of coordination of 5G edge cloud computing and a cloud platform, the flight measurement and control delay of the unmanned aerial vehicle is 1 millisecond magnitude, and the performance requirement of remotely controlling the flight of the unmanned aerial vehicle is met.
Further, in the step 2, based on the edge cloud computing framework, computer resources are reasonably distributed, the hardware requirement of data processing and the design requirement of an autonomic service analysis scene matching algorithm are met, and the data processing time delay of key components of the power transmission line is reduced; an AI intelligent chip and a neural convolution network algorithm are utilized to construct an unmanned aerial vehicle airborne front end real-time identification model with miniaturization, light weight and low power consumption, and preprocessing and real-time identification of unmanned aerial vehicle power inspection front end data are achieved.
Further, in step 3, a YOLOV4-tiny type neural network is trained based on research and design, the dependency on hardware including a GPU and a memory is reduced while the accuracy of the model is guaranteed, defect analysis based on the identification result of the image power equipment and disaster influence situation analysis are performed by utilizing deep learning, a panoramic situation early warning mechanism of the power equipment is realized, the quality of the routing inspection operation of the power equipment is improved, and the automatic comprehensive capability of power production is enhanced.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, by utilizing the Beidou +5G data transmission module, the problems of low transmission speed, high time delay, low positioning precision and the like of videos and image data of the unmanned aerial vehicle are solved, and the remote real-time monitoring of the inspection site is realized; meanwhile, an algorithm based on an image rapid identification technology and an unmanned aerial vehicle operation front end data real-time identification technology based on an edge calculation frame are provided, the problem of real-time identification of the airborne front end of the unmanned aerial vehicle is solved, and the efficiency of unmanned aerial vehicle line inspection and the accuracy of identification are improved.
Drawings
FIG. 1 is a flow chart of unmanned aerial vehicle power inspection based on Beidou +5G edge clouds in the embodiment of the invention;
FIG. 2 is a schematic diagram of data labeling of a training picture according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the accuracy of target extraction training according to an embodiment of the present invention;
FIG. 4 is a flow chart of data detection according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an image recognition result of the power equipment of embodiment 1;
FIG. 6 is a schematic diagram of an embodiment 2 of the image recognition result of the power equipment of the present invention;
fig. 7 is a schematic diagram of embodiment 3 of the image recognition result of the power equipment according to the present invention.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings.
The invention provides a brand-new electric power real-time inspection technology based on theories and technologies such as Beidou, 5G, edge cloud computing and image recognition. According to the invention, real-time identification of the front-end data of the unmanned aerial vehicle operation is realized by utilizing the Beidou, the 5G data transmission module, the edge cloud technology and the image identification technology, so that the accuracy and the working efficiency of intelligent power failure diagnosis are greatly improved.
The method provided by the invention can realize the process by using computer software technology, and is shown in figure 1. The embodiment specifically explains the flow of the invention by taking power inspection as an example, and specifically comprises the following steps:
The specific embodiment is as follows:
an unmanned aerial vehicle is used for collecting a large number of transmission tower related data pictures, and LabelImg software is used for marking the transmission tower, the shockproof hammer, the tower number plate, the bird nest, the spacing rod, the insulator, the equalizing ring and other data in the pictures in a manual mode to form an XML file, so that the marked data set is as large as possible. As shown in fig. 2.
And (3) putting the marked data pictures and the XML file into an algorithm yolov4-tiny model for training, wherein the quantity of the marked data pictures is about 2700 in order to ensure the accuracy of target extraction. The target extraction training precision is shown in fig. 3.
And 2, realizing the functions of real-time data transmission and real-time data processing by using a small 5G data transmission module of the Beidou high-precision positioning system.
The specific embodiment is as follows:
firstly, a trained image recognition detection model is led into a modified unmanned aerial vehicle pod AI chip, and then data are processed in real time according to requirements.
The method comprises the steps that an unmanned aerial vehicle (unmanned aerial vehicle) collects high-precision data (such as a transmission tower, a shockproof hammer, a tower number plate and the like) by utilizing a camera (infrared ray, LiDAR and the like), then transmits the data to an AI chip (edge cloud), utilizes a real-time object recognition convolution network to perform real-time processing operation on terminal data, and directly transmits a power grid equipment data (picture data of whether power equipment has defects) forming instruction which can be recognized and judged to an internet receiver terminal through a 5G high-performance data transmission terminal, and transmits the residual data which can not be processed in time to the edge cloud (an edge computing server, an application program and the like) through the 5G high-performance data transmission terminal for processing. Fig. 4 is a real-time data processing flow based on the edge cloud framework.
And 3, identifying, detecting and processing images which cannot be processed by the unmanned aerial vehicle AI chip by using the edge cloud server.
Because AI chip performance that unmanned aerial vehicle carried is weaker (marginal cloud), especially GPU throughput is powerful enough, and the detection recognition algorithm number of levels that provides is less, and the target that patrols and examines to unmanned aerial vehicle draws the testing capability not enough, so utilize marginal cloud to provide more powerful server and solve this problem.
The specific embodiment is as follows:
the ground edge cloud server acquires flight data of the unmanned aerial vehicle through the 5G high-performance data transmission end, then judges the running state of the power equipment by utilizing a stronger image recognition algorithm with more layers, and transmits the detected result to the internet receiver terminal through a network. Fig. 5-7 show the image recognition results of the power equipment.
Unmanned aerial vehicle AI chip testing result and marginal cloud server testing result are unified to be transmitted to internet server terminal, can directly long-rangely look over internet receiver terminal relevant detected data.
Based on the invention, the unmanned aerial vehicle can detect and judge the state of the power equipment quickly, efficiently, accurately and in real time.
The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the above embodiments. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (4)
1. The electric power real-time inspection method based on the edge cloud is characterized by comprising the following steps:
step 1, firstly, modifying an unmanned aerial vehicle pod, and installing a Beidou high-precision positioning miniaturized 5G data transmission module in the pod; then, carrying a high-definition zoom camera, an infrared camera, a night vision camera and a laser radar sensor by the unmanned aerial vehicle, and collecting related data of the power equipment; finally, the obtained data is subjected to high-speed return and feedback of the instruction after data processing through a 5G technology;
step 2, firstly, a data processing module which is formed by taking an MEC server as a core detects and identifies target objects in returned power equipment videos and pictures in real time; then, forming an instruction by the part which is subjected to data analysis and processing, and directly feeding back the instruction to the monitoring equipment; finally, directly reporting the residual unprocessed data to a control management center, namely a ground edge cloud;
step 3, checking or reviewing the power inspection video and the picture of the line through the control center to realize detection and identification of the target object in the video and the picture; and finally, setting a polling strategy to realize dynamic intelligent control on the terminal.
2. The real-time power inspection method based on the edge cloud according to claim 1, wherein in the step 1, the load requirement of an unmanned aerial vehicle is met by designing a miniaturized 5G data transmission module of a high-precision positioning system, and real-time transmission of 4K high-definition videos and pictures is supported; the large-bandwidth, high-reliability and low-delay communication provided by 5G is utilized to realize the remote real-time monitoring of the unmanned aerial vehicle power inspection front end field; by means of coordination of 5G edge cloud computing and a cloud platform, the flight measurement and control delay of the unmanned aerial vehicle is 1 millisecond magnitude, and the performance requirement of remotely controlling the flight of the unmanned aerial vehicle is met.
3. The real-time power inspection method based on the edge cloud according to claim 1, wherein in the step 2, computer resources are reasonably distributed based on an edge cloud computing framework, hardware requirements of data processing and design requirements of an autonomous service analysis scene matching algorithm are met, and data processing time delay of key components of the power transmission line is reduced; an AI intelligent chip and a neural convolution network algorithm are utilized to construct an unmanned aerial vehicle airborne front end real-time identification model with miniaturization, light weight and low power consumption, and preprocessing and real-time identification of unmanned aerial vehicle power inspection front end data are achieved.
4. The real-time power inspection method based on the edge cloud as claimed in claim 1, wherein in step 3, a YOLOV4-tiny ultra-lightweight neural network is trained based on research and design, so that the model accuracy is guaranteed, the dependency on hardware including a GPU and a memory is reduced, defect analysis based on the image power equipment recognition result and disaster influence situation analysis are carried out by deep learning, a power equipment panoramic situation early warning mechanism is realized, the quality of power equipment inspection operation is improved, and the power production automation comprehensive capability is enhanced.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110550735.1A CN113589837A (en) | 2021-05-18 | 2021-05-18 | Electric power real-time inspection method based on edge cloud |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110550735.1A CN113589837A (en) | 2021-05-18 | 2021-05-18 | Electric power real-time inspection method based on edge cloud |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113589837A true CN113589837A (en) | 2021-11-02 |
Family
ID=78243161
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110550735.1A Pending CN113589837A (en) | 2021-05-18 | 2021-05-18 | Electric power real-time inspection method based on edge cloud |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113589837A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113985921A (en) * | 2021-11-10 | 2022-01-28 | 国网山东省电力公司青岛供电公司 | Unmanned aerial vehicle intelligent inspection system and inspection method based on 5G + Beidou |
| CN114047783A (en) * | 2021-11-16 | 2022-02-15 | 北京航空航天大学 | Unmanned aerial vehicle system and unmanned aerial vehicle simulation system |
| CN114442658A (en) * | 2021-12-23 | 2022-05-06 | 河南福多电力工程有限公司 | Automatic inspection system for unmanned aerial vehicle of power transmission and distribution line and operation method thereof |
| CN114536356A (en) * | 2022-02-11 | 2022-05-27 | 国网宁夏电力有限公司营销服务中心(国网宁夏电力有限公司计量中心) | Business hall 5G robot, high-definition video electricity stealing prevention system and patrol method thereof |
| CN114913453A (en) * | 2022-04-20 | 2022-08-16 | 国网上海市电力公司 | Method for intelligently monitoring key power equipment of transformer based on algorithm definition camera |
| CN117092631A (en) * | 2023-10-19 | 2023-11-21 | 江苏翰林正川工程技术有限公司 | Target positioning and ranging method and system for power transmission channel construction machinery |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015131462A1 (en) * | 2014-03-07 | 2015-09-11 | 国家电网公司 | Centralized monitoring system and monitoring method for unmanned aerial vehicle to patrol power transmission line |
| CN107992067A (en) * | 2017-11-24 | 2018-05-04 | 贵州电网有限责任公司 | Unmanned plane inspection fault diagnosis system based on integrated gondola and AI technologies |
| CN110308457A (en) * | 2018-03-27 | 2019-10-08 | 深圳天眼激光科技有限公司 | A kind of power transmission line polling system based on unmanned plane |
| CN110730138A (en) * | 2019-10-21 | 2020-01-24 | 中国科学院空间应用工程与技术中心 | Dynamic resource allocation method, system and storage medium for space-based cloud computing architecture |
| CN111460995A (en) * | 2020-03-31 | 2020-07-28 | 普宙飞行器科技（深圳）有限公司 | Unmanned aerial vehicle-based power line inspection method and inspection system |
| CN111898524A (en) * | 2020-07-29 | 2020-11-06 | 江苏艾什顿科技有限公司 | 5G edge computing gateway and application thereof |
| CN112651516A (en) * | 2020-11-27 | 2021-04-13 | 国网河南省电力公司郑州供电公司 | Intelligent inspection system and method for transformer substation machine room based on AR technology |
| CN112731960A (en) * | 2020-12-02 | 2021-04-30 | 国网辽宁省电力有限公司阜新供电公司 | Unmanned aerial vehicle remote power transmission line intelligent inspection system and method |
-
2021
- 2021-05-18 CN CN202110550735.1A patent/CN113589837A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015131462A1 (en) * | 2014-03-07 | 2015-09-11 | 国家电网公司 | Centralized monitoring system and monitoring method for unmanned aerial vehicle to patrol power transmission line |
| CN107992067A (en) * | 2017-11-24 | 2018-05-04 | 贵州电网有限责任公司 | Unmanned plane inspection fault diagnosis system based on integrated gondola and AI technologies |
| CN110308457A (en) * | 2018-03-27 | 2019-10-08 | 深圳天眼激光科技有限公司 | A kind of power transmission line polling system based on unmanned plane |
| CN110730138A (en) * | 2019-10-21 | 2020-01-24 | 中国科学院空间应用工程与技术中心 | Dynamic resource allocation method, system and storage medium for space-based cloud computing architecture |
| CN111460995A (en) * | 2020-03-31 | 2020-07-28 | 普宙飞行器科技（深圳）有限公司 | Unmanned aerial vehicle-based power line inspection method and inspection system |
| CN111898524A (en) * | 2020-07-29 | 2020-11-06 | 江苏艾什顿科技有限公司 | 5G edge computing gateway and application thereof |
| CN112651516A (en) * | 2020-11-27 | 2021-04-13 | 国网河南省电力公司郑州供电公司 | Intelligent inspection system and method for transformer substation machine room based on AR technology |
| CN112731960A (en) * | 2020-12-02 | 2021-04-30 | 国网辽宁省电力有限公司阜新供电公司 | Unmanned aerial vehicle remote power transmission line intelligent inspection system and method |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113985921A (en) * | 2021-11-10 | 2022-01-28 | 国网山东省电力公司青岛供电公司 | Unmanned aerial vehicle intelligent inspection system and inspection method based on 5G + Beidou |
| CN114047783A (en) * | 2021-11-16 | 2022-02-15 | 北京航空航天大学 | Unmanned aerial vehicle system and unmanned aerial vehicle simulation system |
| CN114442658A (en) * | 2021-12-23 | 2022-05-06 | 河南福多电力工程有限公司 | Automatic inspection system for unmanned aerial vehicle of power transmission and distribution line and operation method thereof |
| CN114536356A (en) * | 2022-02-11 | 2022-05-27 | 国网宁夏电力有限公司营销服务中心(国网宁夏电力有限公司计量中心) | Business hall 5G robot, high-definition video electricity stealing prevention system and patrol method thereof |
| CN114913453A (en) * | 2022-04-20 | 2022-08-16 | 国网上海市电力公司 | Method for intelligently monitoring key power equipment of transformer based on algorithm definition camera |
| CN117092631A (en) * | 2023-10-19 | 2023-11-21 | 江苏翰林正川工程技术有限公司 | Target positioning and ranging method and system for power transmission channel construction machinery |
| CN117092631B (en) * | 2023-10-19 | 2024-04-19 | 江苏翰林正川工程技术有限公司 | Target positioning and ranging method and system for power transmission channel construction machinery |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113589837A (en) | Electric power real-time inspection method based on edge cloud | |
| CN111832398B (en) | Unmanned aerial vehicle image distribution line pole tower ground wire broken strand image detection method | |
| CN202094531U (en) | Power transmission line inspection device suitable for unmanned aerial vehicle | |
| CN115220479B (en) | Dynamic and static cooperative power transmission line refined inspection method and system | |
| CN207473032U (en) | More rotor dual systems unmanned plane inspection fault diagnosis systems | |
| CN116310891A (en) | Cloud-edge cooperative transmission line defect intelligent detection system and method | |
| CN115272888A (en) | Digital twin-based 5G + unmanned aerial vehicle power transmission line inspection method and system | |
| CN113485432A (en) | Photovoltaic power station electroluminescence intelligent diagnosis system and method based on unmanned aerial vehicle | |
| CN115082813A (en) | Detection method, unmanned aerial vehicle, detection system and medium | |
| CN112614130A (en) | Unmanned aerial vehicle power transmission line insulator fault detection method based on 5G transmission and YOLOv3 | |
| CN115620239B (en) | Point cloud and video combined power transmission line online monitoring method and system | |
| CN105633853A (en) | Power transmission line inspection system | |
| CN112233270A (en) | Unmanned aerial vehicle is intelligence around tower system of patrolling and examining independently | |
| CN111060079A (en) | River foreign matter identification method and river foreign matter monitoring platform system | |
| CN110334820A (en) | A kind of power distribution network intelligent inspection system | |
| CN112260402B (en) | Monitoring method for state of intelligent substation inspection robot based on video monitoring | |
| CN114445782A (en) | Power transmission line image acquisition system based on edge AI and Beidou short messages | |
| CN115912183B (en) | Ecological measure inspection method and system for high-voltage transmission line and readable storage medium | |
| CN117975305A (en) | Power transmission line body defect detection method based on cloud edge cooperation | |
| CN112422895A (en) | Image analysis tracking and positioning system and method based on unmanned aerial vehicle | |
| CN111553497A (en) | Equipment working state detection method and device of multimedia terminal | |
| CN114882682B (en) | High-voltage cable state monitoring platform and monitoring method | |
| CN114612855A (en) | Power line hazard source detection system and method fusing residual error and multi-scale network | |
| CN212305486U (en) | Power line inspection system | |
| CN116485160B (en) | Power transmission line inspection processing system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |