CN112491982A - Refined sensing method based on cloud edge cooperative power transmission line - Google Patents
Refined sensing method based on cloud edge cooperative power transmission line Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02G—INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES
- H02G1/00—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines
- H02G1/02—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines for overhead lines or cables
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/46—Interconnection of networks
- H04L12/4633—Interconnection of networks using encapsulation techniques, e.g. tunneling
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/46—Interconnection of networks
- H04L12/4641—Virtual LANs, VLANs, e.g. virtual private networks [VPN]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/183—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source
- H04N7/185—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source from a mobile camera, e.g. for remote control
Abstract
The invention discloses a refined sensing method based on a cloud-edge cooperative power transmission line, which mainly comprises a data acquisition stage and a cloud-edge cooperative judgment stage; in the data acquisition stage, the acquisition of the structural data of the power transmission line and the tower is realized by using the Internet of things sensing equipment with part of fixed point positions, and the acquisition of the image data of the power transmission line and the tower is realized by using a camera and a multi-rotor unmanned aerial vehicle; in the cloud edge cooperation judging stage, the structured data is compared with a standard threshold value, the data exceeding the standard threshold value range is output to a back-end server platform and is alarmed, the picture data is compared with the standard image for analysis, the characteristic image is judged and is transmitted to the back-end server platform for further analysis and calculation. According to the invention, functions of data fusion, cloud-edge cooperation, local decision, real-time reporting, front-end intelligent identification and the like are realized by utilizing the edge computing gateway, so that the fault hidden danger can be found by the operation and inspection personnel in time, and the refinement and standardization degree of the inspection work can be improved.
Description
Technical Field
The invention relates to the technical field of power equipment detection, in particular to a cloud edge based refined sensing method for a cooperative power transmission line.
Background
Under the background of energy internet, the connection between a regional power grid and a grid line is more and more tight, and trans-regional power transmission is more and more important, so that the urban power supply is influenced by unsafe conditions at any moment in a large area, and the requirement on the safe operation of a power transmission line is greatly improved. At present, the inspection of the power transmission line mainly depends on three independent operations of manual inspection, unmanned aerial vehicle inspection and traditional video monitoring.
1. The manual inspection consumes huge resources and is limited in that the operation and maintenance targets are often not reached at night during working time; the observation visual angle is an elevation angle, so that the inspection of the side surface and the bottom surface of the tower is comprehensive, but the omnibearing inspection and temperature measurement cannot be carried out on the three-dimensional equipment, and the false inspection and even the missing inspection can be caused; the safety operation risk is high, and the working efficiency is low; the lack of the patrol personnel is serious, the power grid construction speed is not matched with the speed increase of the operation and maintenance personnel, and the lean requirement of the power grid patrol cannot be met.
2. The unmanned aerial vehicle has high control difficulty and small number of professional flyers, the unmanned aerial vehicle has low operation intellectualization degree, the routing inspection still mainly depends on manual operation of the flyers, the influence of external environment is large, and the routing inspection quality cannot be guaranteed; the unmanned aerial vehicle patrols massive data, is difficult to process and analyze, and low in effective utilization rate, the existing patrolling image intelligent algorithm is low in recognition rate, and the patrolling data processing intellectualization needs to be further improved; the inspection is difficult at night and in severe weather environment, and the condition of a line channel cannot be mastered in real time for 24 hours; the operation control degree is weak, the phenomenon of black flying and random flying exists, and the data quality cannot be guaranteed.
3. Traditional video monitoring device needs the fortune to examine personnel and carries out naked eye identification to the picture of taking a picture of prison at the surveillance center, and the information volume is big, and the very difficult very first time of breaking down discovers recognition efficiency lowly. Meanwhile, all the monitored pictures need to be uploaded to a data center for processing by the field monitoring equipment, and most of the uploaded pictures are non-abnormal pictures without defects or alarms, so that a lot of unnecessary storage space is occupied, and huge burden is brought to network bandwidth resources. In addition, most of the traditional video monitoring is dispersive monitoring and fixed in angle, the content and the object which need to be monitored cannot be covered comprehensively, and the fault type which cannot be distinguished by video images cannot be monitored to form a monitoring blank.
Disclosure of Invention
The invention aims to make up for the defects of the prior art and provides a refined sensing method based on cloud edge cooperative power transmission line.
In order to solve the technical problems, the invention adopts the following technical scheme:
a refined sensing method based on cloud edge cooperative power transmission line is realized based on a monitoring system, wherein the monitoring system comprises an Internet of things sensing device, a multi-rotor unmanned aerial vehicle, an unmanned aerial vehicle autonomous charging flight control machine nest and an edge computing gateway with front end identification capability; the system comprises an Internet of things sensing device and an edge computing gateway, wherein the Internet of things sensing device comprises a camera, a meteorological sensor, a temperature sensor, a tension sensor and an inclination sensor, is arranged on a pole tower and is used for acquiring state data of a power transmission line and the pole tower and surrounding environment data of the power transmission line and the pole tower in a timing or real-time manner and transmitting the state data and the surrounding environment data to the edge computing gateway; the multi-rotor unmanned aerial vehicle is used for collecting state data of a tower, the unmanned aerial vehicle autonomous charging flight control machine nest is installed on the tower and used for charging and maintaining the multi-rotor unmanned aerial vehicle, and the data collected by the multi-rotor unmanned aerial vehicle are transmitted to the edge computing gateway; the edge computing gateway carries out intelligent discrimination analysis on the acquired data at the front end based on cloud computing, edge computing and big data technologies, filters a large amount of invalid data, and then transmits the invalid data to a back-end server platform.
The sensing method comprises the following steps:
installing an Internet of things sensing device and an unmanned aerial vehicle autonomous charging flight control machine nest on a tower;
setting a standard threshold value for other things-associated sensing equipment except the camera, recording a standard image of the power transmission line acquired by the camera, and collecting the standard threshold value and the standard image of the power transmission line to an edge computing gateway;
starting the Internet of things sensing equipment at fixed time, collecting state data of the power transmission line and the tower and surrounding environment data of the power transmission line and the tower, and collecting the collected data to an edge computing gateway;
the edge computing gateway compares the data acquired by other Internet of things sensing equipment except the camera with a set standard threshold value to obtain a comparison result;
the edge computing gateway judges the states of the power transmission line and the tower and whether the surrounding environment of the power transmission line and the tower has fault hidden danger according to the comparison result, if the fault hidden danger is judged, the edge computing gateway outputs an alarm to a rear-end server platform in time and gives an alarm prompt to a transportation and inspection person;
hovering shooting each preset target position on the tower through the multi-rotor unmanned aerial vehicle, and acquiring a tower standard image;
when in normal inspection, the multi-rotor unmanned aerial vehicle automatically performs repeated shooting on each preset target position on the tower, and obtains a tower repeated shooting image;
when the multi-rotor unmanned aerial vehicle enters the unmanned aerial vehicle autonomous charging flight control machine nest, the unmanned aerial vehicle autonomous charging flight control machine nest collects the tower standard image and the tower repeated image to the edge computing gateway;
respectively comparing and analyzing image data acquired by the camera and the multi-rotor unmanned aerial vehicle through the edge computing gateway, namely comparing and analyzing a power transmission line image shot by the camera and a power transmission line standard image, comparing and analyzing a tower repeated shot image shot by the multi-rotor unmanned aerial vehicle and a tower standard image, and judging a characteristic image which is different from the power transmission line standard image or the tower standard image;
and carrying out information encryption processing on the characteristic image through the edge computing gateway, transmitting the characteristic image to a back-end server platform for further analysis and calculation, judging the type of the hidden fault trouble and carrying out alarm prompt on the operation and inspection personnel.
Further, the camera includes high definition digtal camera and infrared camera, the shooting direction of camera sets up in order to monitor the hidden danger on the transmission line along transmission line's trend, the hidden danger includes that the foreign matter on the transmission line hangs and construction vehicle, trees and the condition of a fire in the transmission line corridor.
Furthermore, the meteorological sensor is a six-element microclimate instrument for monitoring the temperature, humidity, air pressure, wind speed, wind direction and rainfall data of the power transmission line corridor, and the six-element microclimate instrument is respectively arranged on the towers at intervals of five kilometers.
Further, the temperature sensor is installed on a hardware fitting of the tower and used for monitoring the temperature of the hardware fitting.
Furthermore, the tension sensor is installed between the insulator string and the cross beam of the tower and used for monitoring the comprehensive load force of the power transmission line.
Further, the inclination sensor is installed on the tower and used for monitoring the longitudinal and transverse inclination angles of the tower.
And furthermore, the unmanned aerial vehicle autonomous charging flight control machine nests are respectively installed on towers every five kilometers.
Furthermore, in the internet of things sensing equipment, when the data collected by any sensor exceeds the limit, the other sensors can be automatically triggered to monitor in real time, so that comprehensive data confirmation and judgment can be carried out.
Furthermore, the edge computing gateway has a protocol conversion function, uniformly encapsulates front-end data, embeds a security chip in the gateway, establishes a private VPN network on a public network, and encapsulates a data communication tunnel by using an encryption technology to perform encryption communication, thereby ensuring the safety of data transmission.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a refined autonomous sensing method of a power transmission line based on cloud edge coordination, which can improve the state sensing capability of the power transmission line, a tower body and the surrounding environment through all-weather real-time monitoring of an online monitoring device of the power transmission line and timed autonomous inspection of an unmanned aerial vehicle, can discover hidden dangers of the power transmission line and the tower in time and automatically alarm to a background; the standardization degree and the automation degree of the inspection data acquisition are improved, and the working efficiency is improved.
2. The intelligent comprehensive protection system for the power transmission line inspection hidden danger information realizes intelligent study and judgment of inspection hidden danger information, establishes an active comprehensive protection mode, adopts an intelligent image analysis technology, takes a computer vision technology based on an image processing and mode recognition technology as a core, combines a multimedia technology and a computer network technology, actively recognizes tower defects or detects dangerous targets, timely sends alarm information and alarm pictures to a remote management platform (a rear-end server platform), realizes timely and active alarm of information, fundamentally replaces a mode of manually checking the defects and foreign matters of the power transmission line, and improves the external force damage prevention level of the power transmission line.
3. According to the invention, based on cloud-edge cooperative linkage, before routing inspection data of the unmanned aerial vehicle and monitoring data of the internet of things sensing equipment are transmitted back to the data center, storage, fusion, sharing and real-time calculation of the routing inspection data are completed at edge nodes, data processing is closer to a data source, massive data do not need to be uploaded to a cloud for processing any more, the transmission pressure of a core network is reduced, the data transmission delay time is reduced, network blockage is avoided, the data storage space is saved, the traditional equipment has self-sensing capability and self-consciousness, the characteristics of comprehensive sensing of the ubiquitous power internet of things state, efficient information processing and convenient and flexible application are met, and the transition of routing inspection of the power transmission line to a highly intelligent and lean management mode is effectively promoted.
Drawings
In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
FIG. 1 is a flow chart of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that embodiments of the invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in detail so as not to obscure the embodiments of the invention.
In the following description, a detailed structure will be presented for a thorough understanding of embodiments of the invention. It is apparent that the implementation of the embodiments of the present invention is not limited to the specific details familiar to those skilled in the art. The following detailed description of preferred embodiments of the invention, however, the invention is capable of other embodiments in addition to those detailed.
In the description of the present invention, the terms "inside", "outside", "longitudinal", "transverse", "upper", "lower", "top", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are for convenience only to describe the present invention without requiring the present invention to be necessarily constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
Embodiments of the invention are described in further detail below with reference to the accompanying drawings:
the embodiment provides a refined sensing method based on cloud edge cooperative power transmission line, which is realized based on a monitoring system, wherein the monitoring system comprises an internet of things sensing device, a multi-rotor unmanned aerial vehicle, an unmanned aerial vehicle autonomous charging flight control machine nest and an edge computing gateway with front end identification capability; the system comprises an Internet of things sensing device, an edge computing gateway and a monitoring system, wherein the Internet of things sensing device comprises a camera, a meteorological sensor, a temperature sensor, a tension sensor and an inclination sensor, is arranged on a tower and is used for acquiring state data of a power transmission line and the tower and surrounding environment data of the power transmission line and the tower in a timing or real-time manner and transmitting the state data and the surrounding environment data to the edge computing gateway; the multi-rotor unmanned aerial vehicle is used for acquiring state data of a tower, the unmanned aerial vehicle autonomous charging flight control machine nest is installed on the tower and used for charging and maintaining the multi-rotor unmanned aerial vehicle, and the data acquired by the multi-rotor unmanned aerial vehicle are transmitted to the edge computing gateway; the edge computing gateway carries out intelligent discrimination analysis on the acquired data at the front end based on cloud computing, edge computing and big data technology, filters a large amount of invalid data, and then transmits the invalid data to a back-end server platform.
Referring to fig. 1, the refined sensing method based on the cloud-edge collaborative power transmission line provided by the embodiment mainly includes a data acquisition stage and a cloud-edge collaborative discrimination stage; in the data acquisition stage, the acquisition of the structural data of the power transmission line and the tower is realized by using the Internet of things sensing equipment with part of fixed point positions, and the acquisition of the image data of the power transmission line and the tower is realized by using a camera and a multi-rotor unmanned aerial vehicle; in the cloud edge cooperation judging stage, the structured data is compared with a standard threshold value, the data exceeding the standard threshold value range is output to a back-end server platform and is alarmed, the picture data is compared with the standard image for analysis, the characteristic image is judged and is transmitted to the back-end server platform for further analysis and calculation.
The sensing method comprises the following specific steps:
(1) and an Internet of things sensing device and an unmanned aerial vehicle autonomous charging flight control machine nest are arranged on the tower.
The camera comprises a high-definition camera and an infrared camera, the high-definition camera and the infrared camera are installed in a key area of a tower, the direction of the camera faces the trend of the power transmission line, and hidden dangers such as foreign matter suspension on the power transmission line and construction vehicles, trees, fire situations and the like in a power transmission line corridor are monitored in a key mode; the meteorological sensor is a six-element microclimate instrument, and as meteorological conditions almost do not change in a small range, the six-element microclimate instrument is arranged on a tower every five kilometers to monitor information such as temperature, humidity, air pressure, wind speed, wind direction and rainfall of a power transmission line corridor without field calibration; the temperature sensor (thermometer) is arranged on the hardware of the tower and used for monitoring the temperature of the hardware; the tension sensor (tension meter) is arranged between the insulator string of the tower and the cross beam and used for monitoring the comprehensive load force of the power transmission line; the inclination sensor (inclinometer) is arranged on the tower and used for monitoring the longitudinal and transverse inclination angles of the tower; and arranging an unmanned aerial vehicle autonomous charging flight control machine nest on each tower which is five kilometers away, and transmitting the shot images to the edge computing gateway while charging the multi-rotor unmanned aerial vehicle.
(2) And setting a standard threshold value for other things-associated sensing equipment except the camera, recording a standard image of the power transmission line acquired by the camera, and collecting the standard threshold value and the standard image of the power transmission line to the edge computing gateway.
The standard threshold value of each sensor is set in a targeted manner according to the measurement precision of different sensors and the environment of field operation; the standard image of the power transmission line generally refers to an image of the power transmission line in a normal state, which is shot by a camera for the first time.
(3) And setting fixed time to start the Internet of things sensing equipment, collecting state data of the power transmission line and the tower and surrounding environment data of the power transmission line and the tower, and collecting the collected data to the edge computing gateway.
Wherein, for monitoring objects (such as humidity, air pressure, tower inclination and the like) with unobvious change frequency, relatively long acquisition intervals can be set; setting relatively short acquisition intervals for monitoring objects with obvious change frequency (such as hardware temperature, windage yaw and the like); and transmitting the collected data to the edge computing gateway.
(4) The edge computing gateway compares the data acquired by other Internet of things sensing equipment except the camera with a set standard threshold value to obtain a comparison result; and the edge computing gateway judges the states of the power transmission line and the tower and whether the surrounding environment of the power transmission line and the tower has fault hidden danger according to the comparison result, and if the fault hidden danger is judged, the edge computing gateway outputs an alarm to the rear-end server platform in time and gives an alarm prompt to the operation and inspection personnel.
In the internet of things sensing equipment, data acquired by any sensor can automatically trigger other sensors to monitor in real time if the data exceeds the limit so as to confirm and judge comprehensive data, and the data is reported to a back-end server platform at the first time after alarm is triggered.
(5) Hovering shooting each preset target position on the tower through the multi-rotor unmanned aerial vehicle, determining a photo number and a standard image, and acquiring the standard image to be used as a reference image for automatic inspection work; when normally patrolling and examining, each predetermines the target location and carries out the retake by many rotor unmanned aerial vehicle automation on the shaft tower to obtain the shaft tower retake image.
The method comprises the steps that control points are calibrated in stable earth surface areas near towers and power transmission line corridors, coordinates of the control points are obtained through level joint measurement, and accurate coordinates of the control points are determined, so that the position of the tower is kept consistent with the position of an unmanned aerial vehicle when repeated shooting is conducted; the hovering coordinate and the cloud platform posture of the multi-rotor unmanned aerial vehicle are set according to a tower line to be patrolled and examined, the camera orientation of the fixed point installation is specifically combined, the hovering coordinate and the cloud platform shooting angle are set, the blank area which cannot be detected by the Internet of things sensing equipment of the fixed point installation is made up, and all-round three-dimensional monitoring of the tower and the power transmission line is achieved. Regarding the scheme for identifying tower defects by using a multi-rotor unmanned aerial vehicle, reference may be made to a method for identifying tower defects of a multi-rotor unmanned aerial vehicle transmission tower based on high-precision positioning disclosed in the chinese patent application (application No. 201811318451.4, application No. 2018.11.07, application No. CN 109459437 a, application No. 2019.03.12), and details of the related scheme are not repeated herein.
(6) When the multi-rotor unmanned aerial vehicle enters the unmanned aerial vehicle autonomous charging flight control machine nest, the unmanned aerial vehicle autonomous charging flight control machine nest collects the tower standard image and the tower repeated shooting image to the edge computing gateway.
(7) Through the edge calculation gateway of the built-in training sample library, image data collected by the camera and the multi-rotor unmanned aerial vehicle are respectively compared and analyzed, namely, an image of the power transmission line shot by the camera is compared and analyzed with a standard image of the power transmission line, a tower repeated shot image shot by the multi-rotor unmanned aerial vehicle is compared and analyzed with the standard image of the tower, and a characteristic image different from the standard image of the power transmission line or the standard image of the tower is distinguished.
The edge computing gateway provides a strong cloud edge coordination function, takes image identification as a core, and performs identification, comparison and filtering judgment on monitoring data; intelligent image recognition: performing front-end local analysis on a field shot picture, performing data structuring processing, extracting a picture characteristic value, removing redundant content, identifying an alarm, and uploading to a back-end server platform; iteration of the algorithm: and establishing a primary identification model, continuously accumulating the characteristic values in the identification process, providing a deep learning algorithm for the rear-end server platform, continuously improving the characteristics of the monitored object, uniformly issuing the updated model to the edge computing gateway by the rear-end server platform, and improving the identification accuracy.
(8) And carrying out information encryption processing on the characteristic image through the edge computing gateway, transmitting the characteristic image to a back-end server platform for further various types of analysis and calculation, judging the type of the hidden fault trouble and carrying out alarm prompt on the operation and inspection personnel.
The edge computing gateway has a protocol conversion function, front-end data are uniformly packaged, meanwhile, a security chip is embedded in the gateway, a special VPN network is established on a public network, a data communication tunnel is packaged by using an encryption technology for encryption communication, and the safety of transmitted data is guaranteed.
The invention utilizes various sensors such as a high-definition camera, an infrared camera, a weather instrument, a thermometer, a tension meter, an inclinometer and the like to regularly and real-timely acquire the state and operation information of the transmission line and the tower body and upload the state and operation information to an edge computing gateway with front-end identification capability. Adopt many rotor unmanned aerial vehicle to carry out the shooting of refining to demarcating the position to fly the accuse machine nest through setting up unmanned aerial vehicle and independently charging, with the image data transmission to the edge calculation gateway that many rotor unmanned aerial vehicle acquireed when charging. The edge computing gateway performs front-end recognition analysis and judgment on the shot monitoring pictures and data based on cloud computing, edge computing and big data technologies, and gives an alarm for abnormal events. The edge computing gateway can filter a large number of invalid pictures, so that the work of manually identifying massive pictures is omitted, the alarm identification efficiency is improved, the uploaded data volume is reduced, the problems of network transmission bandwidth and flow are solved, and the problem of server storage is solved. In addition, many rotor unmanned aerial vehicle carry on visible light and infrared camera and on-line monitoring complements each other, can effectively compensate the monitoring blank region that two kinds of modes of patrolling and examining produced when using alone, realize multi-angle, all-round, three-dimensional inspection, the relevance ratio of improve equipment hidden danger improves and patrols and examines meticulous level, and fundamentally has realized the 24 hours incessant comprehensive perception to pole tower and transmission line body state and environment, and the system is patrolled and examined to the three-dimensional of construction transmission line. The invention is applied to improving the detection efficiency and quality and reducing the working pressure of operation and maintenance personnel.
In summary, the present invention is not limited to the above-mentioned embodiments, and those skilled in the art can propose other embodiments within the technical teaching of the present invention, but these embodiments are included in the scope of the present invention.
Claims (9)
1. A refined sensing method based on cloud edge cooperative power transmission line is characterized in that the sensing method is realized based on a monitoring system, and the monitoring system comprises an Internet of things sensing device, a multi-rotor unmanned aerial vehicle, an unmanned aerial vehicle autonomous charging flight control machine nest and an edge computing gateway with front end identification capability; the system comprises an Internet of things sensing device and an edge computing gateway, wherein the Internet of things sensing device comprises a camera, a meteorological sensor, a temperature sensor, a tension sensor and an inclination sensor, is arranged on a pole tower and is used for acquiring state data of a power transmission line and the pole tower and surrounding environment data of the power transmission line and the pole tower in a timing or real-time manner and transmitting the state data and the surrounding environment data to the edge computing gateway; the multi-rotor unmanned aerial vehicle is used for collecting state data of a tower, the unmanned aerial vehicle autonomous charging flight control machine nest is installed on the tower and used for charging and maintaining the multi-rotor unmanned aerial vehicle, and the data collected by the multi-rotor unmanned aerial vehicle are transmitted to the edge computing gateway; the edge computing gateway carries out intelligent discrimination analysis on the acquired data at the front end based on cloud computing, edge computing and big data technology, filters a large amount of invalid data and then transmits the invalid data to a back-end server platform;
the sensing method comprises the following steps:
installing an Internet of things sensing device and an unmanned aerial vehicle autonomous charging flight control machine nest on a tower;
setting a standard threshold value for other things-associated sensing equipment except the camera, recording a standard image of the power transmission line acquired by the camera, and collecting the standard threshold value and the standard image of the power transmission line to an edge computing gateway;
starting the Internet of things sensing equipment at fixed time, collecting state data of the power transmission line and the tower and surrounding environment data of the power transmission line and the tower, and collecting the collected data to an edge computing gateway;
the edge computing gateway compares the data acquired by other Internet of things sensing equipment except the camera with a set standard threshold value to obtain a comparison result;
the edge computing gateway judges the states of the power transmission line and the tower and whether the surrounding environment of the power transmission line and the tower has fault hidden danger according to the comparison result, if the fault hidden danger is judged, the edge computing gateway outputs an alarm to a rear-end server platform in time and gives an alarm prompt to a transportation and inspection person;
hovering shooting each preset target position on the tower through the multi-rotor unmanned aerial vehicle, and acquiring a tower standard image;
when in normal inspection, the multi-rotor unmanned aerial vehicle automatically performs repeated shooting on each preset target position on the tower, and obtains a tower repeated shooting image;
when the multi-rotor unmanned aerial vehicle enters the unmanned aerial vehicle autonomous charging flight control machine nest, the unmanned aerial vehicle autonomous charging flight control machine nest collects the tower standard image and the tower repeated image to the edge computing gateway;
respectively comparing and analyzing image data acquired by the camera and the multi-rotor unmanned aerial vehicle through the edge computing gateway, namely comparing and analyzing a power transmission line image shot by the camera and a power transmission line standard image, comparing and analyzing a tower repeated shot image shot by the multi-rotor unmanned aerial vehicle and a tower standard image, and judging a characteristic image which is different from the power transmission line standard image or the tower standard image;
and carrying out information encryption processing on the characteristic image through the edge computing gateway, transmitting the characteristic image to a back-end server platform for further analysis and calculation, judging the type of the hidden fault trouble and carrying out alarm prompt on the operation and inspection personnel.
2. The cloud-edge-based cooperative power transmission line refinement sensing method according to claim 1, wherein the camera comprises a high-definition camera and an infrared camera, a shooting direction of the camera is arranged along a trend of the power transmission line to monitor hidden dangers on the power transmission line, and the hidden dangers include hanging of foreign matters on the power transmission line and construction vehicles, trees and fires in a corridor of the power transmission line.
3. The method for refined sensing of the cloud-edge-based cooperative transmission line according to claim 1, wherein the meteorological sensor is a six-element microclimate instrument for monitoring temperature, humidity, air pressure, wind speed, wind direction and rainfall data of a corridor of the transmission line, and a six-element microclimate instrument is respectively installed on towers every five kilometers.
4. The cloud-edge-based cooperative power transmission line refinement sensing method of claim 1, wherein the temperature sensor is mounted on a hardware of a tower so as to monitor the temperature of the hardware.
5. The cloud-edge-based cooperative power transmission line refinement sensing method as claimed in claim 1, wherein the tension sensor is installed between an insulator string and a cross beam of a tower so as to monitor a comprehensive load force of the power transmission line.
6. The cloud-edge-based cooperative power transmission line refinement sensing method as claimed in claim 1, wherein the tilt sensor is mounted on a tower to monitor longitudinal and transverse tilt angles of the tower.
7. The method for refining sensing of the cloud-edge-based cooperative transmission line according to claim 1, wherein the unmanned aerial vehicle autonomous charging flight control machine nest is respectively installed on towers every five kilometers.
8. The method for refining sensing of the cloud-edge-based cooperative power transmission line according to claim 1, wherein in the internet of things sensing equipment, if data collected by any one sensor exceeds a limit, the other sensors are automatically triggered to monitor in real time, so that comprehensive data confirmation and judgment are performed.
9. The method for refining sensing of the cloud-edge-based cooperative power transmission line according to claim 1, wherein the edge computing gateway has a protocol conversion function, front-end data is uniformly encapsulated, a security chip is embedded in the gateway, a private VPN (virtual private network) network is established on a public network, a data communication tunnel is encapsulated by using an encryption technology, encryption communication is performed, and data transmission security is guaranteed.
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| CN112947585A (en) * | 2021-04-09 | 2021-06-11 | 广东电网有限责任公司电力调度控制中心 | Power grid unmanned aerial vehicle inspection method, device and system based on 5G cloud edge-end cooperation |
| CN113252701A (en) * | 2021-07-02 | 2021-08-13 | 湖南大学 | Cloud edge cooperation-based power transmission line insulator self-explosion defect detection system and method |
| CN113313703A (en) * | 2021-06-17 | 2021-08-27 | 上海红檀智能科技有限公司 | Unmanned aerial vehicle power transmission line inspection method based on deep learning image recognition |
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| CN113537011A (en) * | 2021-07-06 | 2021-10-22 | 广西电网有限责任公司 | Cloud edge fusion-based method for monitoring state of power transmission line of power tower |
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| CN115225851A (en) * | 2021-04-20 | 2022-10-21 | 成都鼎桥通信技术有限公司 | Method, device, equipment and medium for processing peak shifting of ECG (electrocardiogram) video data |
| CN113313703A (en) * | 2021-06-17 | 2021-08-27 | 上海红檀智能科技有限公司 | Unmanned aerial vehicle power transmission line inspection method based on deep learning image recognition |
| CN113569914A (en) * | 2021-06-29 | 2021-10-29 | 山东信通电子股份有限公司 | Power transmission line inspection method and system fusing point cloud data |
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| CN113252701A (en) * | 2021-07-02 | 2021-08-13 | 湖南大学 | Cloud edge cooperation-based power transmission line insulator self-explosion defect detection system and method |
| CN113252701B (en) * | 2021-07-02 | 2021-10-26 | 湖南大学 | Cloud edge cooperation-based power transmission line insulator self-explosion defect detection system and method |
| CN113537011A (en) * | 2021-07-06 | 2021-10-22 | 广西电网有限责任公司 | Cloud edge fusion-based method for monitoring state of power transmission line of power tower |
| CN114184232A (en) * | 2021-12-10 | 2022-03-15 | 重庆大学 | Multi-parameter integrated monitoring system for power transmission line |
| CN114360197A (en) * | 2021-12-16 | 2022-04-15 | 广东电网有限责任公司 | Intelligent cooperative alarm system for power transmission line |
| CN114627388B (en) * | 2022-03-23 | 2024-03-29 | 南方电网数字电网研究院有限公司 | Foreign matter detection equipment and foreign matter detection method for power transmission line |
| CN114627388A (en) * | 2022-03-23 | 2022-06-14 | 南方电网数字电网研究院有限公司 | Power transmission line foreign matter detection equipment and foreign matter detection method thereof |
| CN114973132A (en) * | 2022-05-18 | 2022-08-30 | 慧之安信息技术股份有限公司 | Power transmission line foreign matter and engineering vehicle monitoring system based on edge cloud cooperation |
| CN114979214A (en) * | 2022-05-25 | 2022-08-30 | 广东电网有限责任公司 | Intelligent cooperative alarm system, method and device for power transmission line |
| CN114979214B (en) * | 2022-05-25 | 2024-03-29 | 广东电网有限责任公司 | Intelligent collaborative alarm system, method and device for power transmission line |
| CN114740901A (en) * | 2022-06-13 | 2022-07-12 | 深圳联和智慧科技有限公司 | Unmanned aerial vehicle cluster flight method and system and cloud platform |
| CN114740901B (en) * | 2022-06-13 | 2022-08-19 | 深圳联和智慧科技有限公司 | Unmanned aerial vehicle cluster flight method and system and cloud platform |
| CN115220479A (en) * | 2022-09-20 | 2022-10-21 | 山东大学 | Dynamic and static cooperative power transmission line refined inspection method and system |
| CN117291554A (en) * | 2023-09-27 | 2023-12-26 | 国网江苏省电力有限公司信息通信分公司 | Cloud network collaborative operation method and system in power industry |
| CN117291554B (en) * | 2023-09-27 | 2024-05-03 | 国网江苏省电力有限公司信息通信分公司 | Cloud network collaborative operation method and system in power industry |
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