CN117789058B - Unmanned aerial vehicle plateau electric wire netting safety inspection system based on machine vision - Google Patents
Unmanned aerial vehicle plateau electric wire netting safety inspection system based on machine vision Download PDFInfo
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Abstract
The invention relates to an unmanned aerial vehicle plateau power grid safety detection system based on machine vision, which belongs to the field of power grid safety monitoring, and comprises: the visual capturing device is used for acquiring a nodding visual picture of a target power grid located in a highland area, which is shot by the unmanned aerial vehicle at the current moment; and the intelligent identification equipment is used for identifying abnormal fault coding values of the target power grid at the current moment by adopting an artificial intelligent model based on pixel values of all pixel points of the nodding visual picture and all monitoring parameters of the target power grid at the current moment when the target power grid is abnormal. According to the invention, aiming at the technical problems of time and labor waste and limited maintenance effect in the safety detection of the plateau power grid, the unmanned aerial vehicle aerial photographing mode can be adopted to acquire the field visual data of the target power grid in the plateau region, and the artificial intelligent model is adopted to judge the current abnormal cause of the target power grid based on the field visual data and other multiple basic data, so that the intelligent level of the safety detection of the plateau power grid is improved.
Description
Technical Field
The invention relates to the field of power grid safety monitoring, in particular to an unmanned aerial vehicle plateau power grid safety detection system based on machine vision.
Background
The power grid safety monitoring industry directly affects the normal operation and maintenance cost of the power grid, and particularly, the power grid with more medium and high repeated ice areas is used for some mountain areas at the land and has low temperature in winter. When the power transmission lines in the plateau areas pass through high altitude and severe weather sections, the lines are lightly covered with ice to cause flashover tripping, and the lines are heavily covered with ice to cause disconnection and tower inversion, so that a power grid system is damaged in a large range, and the consequences are serious. Therefore, some power grid safety monitoring strengthens the icing design of a power transmission line, reasonably arranges a power generation plan, adjusts an operation mode, properly absorbs the internet load, and strengthens the power grid safety monitoring of various modes, and is expected to provide powerful support for the safe and stable operation of the power grid in the plateau area.
For example, a satellite technology-based method and a satellite technology-based device for monitoring fire points near a power grid in a plateau area are provided in chinese patent publication CN113361323a, where the device includes a data acquisition module, an absolute fire point identification module, a potential fire point identification module, a coverage type identification module, a cloud layer identification module, a fixed heat source rejection module, and an output result module. When the satellite remote sensing technology is used for monitoring mountain fire conditions in a target area in a large range, the influence of accumulated snow, water, unused land, cloud cover and fixed heat sources on satellite monitoring results is removed, and meanwhile, a plurality of absolute fire point judging methods are added, so that the detection rate of absolute fire points is improved.
For example, an arc current detection device for a plateau power distribution overhead line proposed by chinese patent publication CN210294444U includes an arc current measurement module and a spring clip mounted inside the arc current measurement module; the arc current measuring module comprises a metal shell with a circular ring structure; a circular current sensor is arranged in the metal shell; the current sensor is provided with a joint which can be opened and closed; the spring clamping piece comprises two clamping assemblies which are symmetrically arranged; the clamping assembly is respectively connected with the first circular ring part and the second circular ring part in a nested manner; the clamping assembly comprises a clamp, a fixing piece and a spring; one end of the spring is connected with the clamp, and the other end of the spring is connected with the outer wall of the inner ring of the metal shell; the clamp is connected with the outer wall of the metal shell in a sliding way and can be fixed on the metal shell through a fixing piece; the metal shell is provided with a signal connector; the signal connector is electrically connected with the current sensor. The device can be fixed on overhead lines with different specifications, is easy to operate and fix and can realize the detection of arc current.
However, the safety detection system of various plateau power grids in the prior art is only limited to the targeted detection of the directional fault type or the abnormal detection of the fixed power grid parameters, and can not accurately analyze the fault cause causing the plateau power grid abnormality based on various field data of the plateau power grid when the plateau power grid abnormality occurs, so that a safety maintenance party of the plateau power grid needs to be additionally provided with field safety inspection personnel for safety maintenance of the plateau power grid, and even so, compared with the characteristics of the number limitation of the field safety inspection personnel and the narrow detection surface of the plateau power grid, the safety detection of the plateau power grid in the prior art is time-consuming and labor-consuming and has limited maintenance effect.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides an unmanned aerial vehicle plateau power grid safety detection system based on machine vision, which can acquire field vision data of a target power grid in a plateau region by adopting an unmanned aerial vehicle aerial photographing mode, and constructs a custom-designed artificial intelligent model to complete automatic and intelligent positioning of fault types of the target power grid in an abnormal state based on the field vision data and other multiple pieces of targeted screening basic data, so that the maintenance performance of the plateau power grid is improved, and the maintenance cost of the plateau power grid is reduced.
According to a first aspect of the present invention, there is provided a machine vision-based unmanned aerial vehicle altitude grid security detection system, the system comprising:
The visual capturing device is used for obtaining a nodding visual picture of a target power grid at the current moment, wherein the nodding visual picture of the target power grid is a nodding picture downloaded from an aerial unmanned aerial vehicle based on positioning data of the target power grid at the current moment, and the target power grid is located in a plateau region;
The branch acquisition equipment is used for acquiring all monitoring parameters of the target power grid at the current moment, wherein the all monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration time, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment;
The code mapping equipment is used for storing various abnormal fault code values corresponding to various power grid abnormal faults respectively by adopting a database, wherein each abnormal fault code value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short-circuit discharge between hardware fittings taking precipitation as a medium;
the abnormality detection device is used for detecting whether the current moment of the target power grid is in an abnormal state or not, and sending out a first detection signal when the current moment of the target power grid is in the abnormal state, and otherwise, sending out a second detection signal;
the intelligent identification equipment is respectively connected with the vision capturing equipment, the analysis acquisition equipment, the coding mapping equipment and the anomaly detection equipment and is used for intelligently identifying the anomaly fault coding value of the target power grid at the current moment by adopting an artificial intelligent model based on the pixel values of all pixel points of the nodding vision picture and all monitoring parameters of the target power grid at the current moment when the first detection signal is received.
According to a second aspect of the present invention there is provided a machine vision based unmanned aerial vehicle altitude grid security detection system, the system comprising a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the steps of:
Acquiring a nodding visual picture of a target power grid at the current moment, wherein the nodding visual picture of the target power grid is a nodding picture downloaded from an aerial unmanned aerial vehicle based on positioning data of the target power grid at the current moment, and the target power grid is positioned in a plateau zone;
Acquiring all monitoring parameters of a target power grid at the current moment, wherein the all monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment;
the method comprises the steps of adopting a database to store various abnormal fault coding values corresponding to various power grid abnormal faults respectively, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short-circuit discharge between the electric pole and a hardware fitting by taking precipitation as a medium;
When detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in the abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal;
When a first detection signal is received, intelligently identifying an abnormal fault coding value of the target power grid at the current moment by adopting an artificial intelligent model based on pixel values of all pixel points of the nodding visual picture and all monitoring parameters of the target power grid at the current moment, otherwise, temporarily executing intelligent identification of the abnormal fault coding value of the target power grid at the current moment;
The artificial intelligent model is a feedforward neural network after each learning of a set number, and the value of the set number is monotonically and positively associated with the resolution of the nodding visual picture;
when the fact that the value of a certain power grid parameter at the current moment of the target power grid is not in the value range corresponding to the certain power grid parameter is detected, a first detection signal is sent out, and the certain power grid parameter is any one parameter of voltage, current, electric control, the number of power grid load bearing devices and real-time transmission power.
Compared with the prior art, the invention has at least the following three important invention concepts:
The invention is characterized in that: and acquiring a nodding visual picture of the target power grid at the plateau position at the current moment by adopting a visual capturing mode, wherein the nodding visual picture of the target power grid is the nodding picture downloaded from the aerial unmanned aerial vehicle based on the positioning data of the target power grid at the current moment, so that reliable data is provided for fault type analysis of the target power grid of a follow-up artificial intelligent model.
The invention conception II is as follows: and when the target power grid abnormality is detected, intelligently judging the fault type of the target power grid abnormality which causes the plateau position by adopting an artificial intelligent model through a nodding picture at the current moment, the current of the target power grid, the current precipitation, the current rainfall duration and the current ground water quantity, wherein the fault type comprises electric pole inclination, electric pole collapse and short circuit discharge between the electric pole and a hardware fitting with precipitation as a medium.
The invention concept is three: to ensure the reliability and stability of the intelligent analysis result of the artificial intelligent model, the following targeted design is performed on the artificial intelligent model: the artificial intelligent model is a feedforward neural network after completing each learning of a set number, the value of the set number is monotonically and positively correlated with the resolution of a nodding visual picture, in each learning executed on the feedforward neural network, an abnormal fault coding value corresponding to an abnormal fault of a target power grid known at a certain historical moment is taken as output content of the feedforward neural network, and pixel values of each pixel point of the nodding visual picture corresponding to the target power grid at the certain historical moment and each monitoring parameter of the target power grid at the certain historical moment are taken as each input content of the feedforward neural network so as to ensure the effect of each learning.
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Embodiments of the present invention will be described below with reference to the accompanying drawings, in which:
fig. 1 is a technical flow chart of a machine vision-based unmanned aerial vehicle altitude power grid safety detection system according to the present invention.
Fig. 2 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 1 of the present invention.
Fig. 3 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 2 of the present invention.
Fig. 4 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 3 of the present invention.
Fig. 5 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 4 of the present invention.
Fig. 6 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude grid security detection system according to embodiment 5 of the present invention.
Fig. 7 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 6 of the present invention.
Fig. 8 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 7 of the present invention.
Fig. 9 is a flowchart of steps of a method for detecting the security of a power grid of an unmanned aerial vehicle on the basis of machine vision according to embodiment 8 of the present invention.
Detailed Description
As shown in fig. 1, a technical flowchart of the unmanned aerial vehicle altitude power grid safety detection system based on machine vision is provided.
As shown in fig. 1, the specific technical process of the present invention is as follows:
First,: establishing wireless communication link connection with an aerial unmanned aerial vehicle of an upper airspace of a target power grid at a plateau, and downloading a nodding picture matched with the positioning data of the target power grid from the aerial unmanned aerial vehicle at the current moment based on the positioning data of the target power grid to serve as visual data for performing intelligent analysis subsequently;
illustratively, the nodding picture comprises the panorama of the target power grid, and has a horizontal resolution and a vertical resolution;
secondly: acquiring various abnormal associated parameters of a target power grid at the current moment, including the current of the target power grid at the current moment, the current precipitation amount, the current rainfall duration and the current ground area water amount;
again: designing an artificial intelligent model of a customized structure for a target power grid, and providing an intelligent analysis mechanism for identifying the abnormal reasons of the target power grid in an abnormal state;
illustratively, in order to ensure the stability and reliability of the analysis results of the designed artificial intelligence model, the following specific measures are taken:
Measure A: the artificial intelligent model is a feedforward neural network after completing the set number of each learning;
measure B: the value of the set number is monotonically and positively associated with the resolution of the nodding visual picture;
Measure C: in each learning executed on the feedforward neural network, taking an abnormal fault coding value corresponding to an abnormal fault of a target power grid known at a certain historical moment as output content of the feedforward neural network, and taking pixel values of all pixel points of a nodding-down vision picture corresponding to the target power grid at the certain historical moment and all monitoring parameters of the target power grid at the certain historical moment as all input contents of the feedforward neural network so as to ensure the effect of each learning;
finally: the method comprises the steps of intelligently analyzing the fault type of an abnormal condition of a target power grid at the current moment by adopting an artificial intelligent model based on visual data of a nodding picture matched with positioning data of the target power grid and various abnormal associated parameters of the target power grid at the current moment;
Specifically, as shown in fig. 1, the visual data of the nodding-picture may be an R-component value, a G-component value, and a B-component value of each pixel point of the nodding-picture in the RGB color space;
Further, each abnormal fault type comprises electric pole inclination, electric pole collapse and short-circuit discharge between the electric pole and the hardware by taking precipitation as a medium;
Meanwhile, in order to reduce unnecessary power consumption expenditure of the safety detection system of the plateau power grid, the intelligent analysis processing is started only when the plateau power grid is abnormal.
The key points of the invention are as follows: including targeted screening of visual data for basic data of anomaly cause analysis, custom structural design of artificial intelligence model, and targeted triggering mechanism for triggering anomaly cause intelligent analysis only when anomalies occur.
The unmanned aerial vehicle altitude power grid safety detection system based on machine vision of the invention is specifically described in the following by way of example.
Example 1
Fig. 2 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 1 of the present invention.
As shown in fig. 2, the unmanned aerial vehicle altitude power grid safety detection system based on machine vision comprises the following components:
The visual capturing device is used for obtaining a nodding visual picture of a target power grid at the current moment, wherein the nodding visual picture of the target power grid is a nodding picture downloaded from an aerial unmanned aerial vehicle based on positioning data of the target power grid at the current moment, and the target power grid is located in a plateau region;
Further, the photoelectric sensor and the pitching type aerial photographing lens can be used for cooperation to complete the photographing of the pitching type image on the aerial photographing unmanned aerial vehicle;
for example, the cooperation of the photoelectric sensor and the pitching type aerial photographing lens to complete the photographing of the aerial photographing picture on the aerial photographing unmanned aerial vehicle comprises: the photoelectric sensor can be selected from a CMOS sensor or a CCD sensor;
The branch acquisition equipment is used for acquiring all monitoring parameters of the target power grid at the current moment, wherein the all monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration time, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment;
Further, the current transmitted by the power grid of the target power grid at the current moment, the current precipitation of the target power grid at the current moment, the rainfall duration of the target power grid at the current moment, the current ground area water quantity of the target power grid at the current moment and the number of power grid load bearing devices of the target power grid at the current moment are respectively subjected to corresponding data acquisition by different data acquisition units;
The code mapping equipment is used for storing various abnormal fault code values corresponding to various power grid abnormal faults respectively by adopting a database, wherein each abnormal fault code value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short-circuit discharge between hardware fittings taking precipitation as a medium;
Specifically, for each abnormal fault including the inclination of the electric pole, the collapse of the electric pole and the short-circuit discharge between the electric pole and the hardware using precipitation as a medium, respectively having different abnormal fault coding values;
the abnormality detection device is used for detecting whether the current moment of the target power grid is in an abnormal state or not, and sending out a first detection signal when the current moment of the target power grid is in the abnormal state, and otherwise, sending out a second detection signal;
The intelligent identification equipment is respectively connected with the vision capturing equipment, the analysis acquisition equipment, the code mapping equipment and the anomaly detection equipment and is used for intelligently identifying the anomaly fault code value of the target power grid at the current moment by adopting an artificial intelligent model based on the pixel values of all pixel points of the nodding vision picture and all monitoring parameters of the target power grid at the current moment when the first detection signal is received;
further, the pixel values of the pixels of the nodding visual picture are an R component value, a G component value and a B component value of each pixel of the nodding visual picture in an RGB color space;
The intelligent identification device is further used for suspending execution of intelligent identification of the abnormal fault coding value of the target power grid at the current moment when the second detection signal is received;
The artificial intelligent model is a feedforward neural network after each learning of a set number, and the value of the set number is monotonically and positively associated with the resolution of the nodding visual picture;
Further, the monotonically positive association of the set number of values with the resolution of the nodding-down visual frame includes: the higher the resolution of the nodding visual picture is, the larger the value of the set quantity is;
Wherein, when detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in an abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal comprises: when the fact that the value of a certain power grid parameter at the current moment of the target power grid is not in the value range corresponding to the certain power grid parameter is detected, a first detection signal is sent out;
When detecting that the value of a certain power grid parameter at the current moment of the target power grid is not in the value range corresponding to the certain power grid parameter, sending a first detection signal comprises: the certain power grid parameter is any parameter of voltage, current, electric control, the number of load bearing devices of the power grid and real-time transmission power.
Example 2
Fig. 3 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 2 of the present invention.
As shown in fig. 3, unlike the embodiment in fig. 2, the machine vision-based unmanned aerial vehicle altitude grid safety detection system further comprises the following components:
The successive processing equipment is connected with the intelligent identification equipment and is used for executing each learning of a set number on the feedforward neural network so as to obtain the feedforward neural network after each learning of the set number is completed and sending the feedforward neural network to the intelligent identification equipment as an artificial intelligent model for use;
Wherein, carry out each study of the presumption quantity to the feedforward neural network, in order to obtain the feedforward neural network after finishing each study of presumption quantity and send to the said intelligent authentication equipment to use as the artificial intelligence model includes: in each learning executed on the feedforward neural network, taking an abnormal fault coding value corresponding to an abnormal fault of a target power grid known at a certain historical moment as output content of the feedforward neural network, taking pixel values of all pixel points of a nodding-down vision picture corresponding to the target power grid at the certain historical moment and all monitoring parameters of the target power grid at the certain historical moment as all input content of the feedforward neural network, and completing the learning process.
Example 3
Fig. 4 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 3 of the present invention.
As shown in fig. 4, unlike the embodiment in fig. 3, the machine vision-based unmanned aerial vehicle altitude grid safety detection system further comprises the following components:
and the parameter storage device is connected with the successive processing device and used for storing various model parameters of the artificial intelligent model.
Example 4
Fig. 5 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 4 of the present invention.
As shown in fig. 5, unlike the embodiment in fig. 2, the machine vision-based unmanned aerial vehicle altitude grid safety detection system further comprises the following components:
And the wireless transmission mechanism is connected with the branch acquisition equipment and is also connected with the nearest weather observation server in a wireless way, and is used for downloading the current precipitation amount, the rainfall duration and the current ground area water amount of the target power grid at the current moment from the nearest weather observation server by adopting a wireless transmission network.
Example 5
Fig. 6 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude grid security detection system according to embodiment 5 of the present invention.
As shown in fig. 6, unlike the embodiment in fig. 2, the machine vision-based unmanned aerial vehicle altitude grid safety detection system further comprises the following components:
The fault display device is respectively connected with the code mapping device and the intelligent identification device and is used for displaying the name character string of the abnormal fault corresponding to the abnormal fault code value of the target power grid at the current moment in real time after receiving the abnormal fault code value of the target power grid at the current moment;
The method for storing the abnormal fault code values of the power grid by adopting the database comprises the steps of storing the corresponding abnormal fault code values of various power grid abnormal faults respectively, wherein each abnormal fault code value is in a binary value representation form, each abnormal fault comprises electric pole inclination, electric pole collapse and short circuit discharge between the electric pole and hardware by taking precipitation as a medium, and the method comprises the following steps: one database of a relational database, a non-relational database, an object-oriented database, a hierarchical database, a net-shaped database and a memory database is used for storing each abnormal fault coding value corresponding to each abnormal fault of the power grid, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short circuit discharge between the electric pole and hardware fittings taking precipitation as a medium;
The method comprises the steps of adopting one database of a relational database, a non-relational database, an object-oriented database, a hierarchical database, a netlike database and a memory database for storing various abnormal fault coding values corresponding to various power grid abnormal faults respectively, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short circuit discharge with hardware fittings taking precipitation as a medium, and the method comprises the following steps: when the relational database is used for storing the abnormal fault coding values corresponding to the abnormal faults of various power grids, the relational database is one of an Oracle database, a MySQL database and a SQL SERVER database.
Example 6
Fig. 7 is a schematic structural diagram of a machine vision-based unmanned aerial vehicle altitude power grid security detection system according to embodiment 6 of the present invention.
As shown in fig. 7, unlike the embodiment in fig. 2, the machine vision-based unmanned aerial vehicle altitude grid safety detection system further comprises the following components:
the data transceiver is connected with the visual capturing device and is wirelessly connected with the aerial unmanned aerial vehicle, and is used for downloading a corresponding aerial picture at each moment from the aerial unmanned aerial vehicle based on the positioning data of the target power grid at each moment;
The data receiving and transmitting equipment comprises a positioning executing unit, a data receiving unit, a data transmitting unit and a main controller;
In the data transceiver, the main controller is connected with the positioning executing unit, the data receiving unit and the data transmitting unit respectively.
Next, detailed descriptions of various embodiments of the present invention will be continued.
In the unmanned aerial vehicle altitude power grid safety detection system based on machine vision according to any embodiment of the invention:
When a first detection signal is received, intelligently identifying abnormal fault coding values of the target power grid at the current moment by adopting an artificial intelligent model based on pixel values of all pixel points of the nodding visual picture and all monitoring parameters of the target power grid at the current moment comprises the following steps: when a first detection signal is received, inputting pixel values of all pixel points of a nodding visual picture and all monitoring parameters of a target power grid at the current moment into an artificial intelligent model in parallel to operate the artificial intelligent model, and obtaining an abnormal fault coding value of the target power grid at the current moment, which is output by the artificial intelligent model;
When a first detection signal is received, inputting pixel values of all pixel points of a nodding visual picture and all monitoring parameters of a target power grid at the current moment into an artificial intelligent model in parallel to run the artificial intelligent model, and obtaining an abnormal fault coding value of the target power grid at the current moment, which is output by the artificial intelligent model, comprises the following steps: before the pixel values of all the pixel points of the nodding visual picture and all the monitoring parameters of the target power grid at the current moment are input into the artificial intelligent model in parallel, binary value conversion processing is respectively carried out on the pixel values of all the pixel points of the nodding visual picture and all the monitoring parameters of the target power grid at the current moment.
And in the unmanned aerial vehicle plateau power grid safety detection system based on machine vision according to any embodiment of the invention:
When detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in the abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal comprises: when the value of each power grid parameter at the current moment of the target power grid is detected to be in the range of the value corresponding to each power grid parameter, a second detection signal is sent out;
The method for acquiring each monitoring parameter of the target power grid at the current moment, wherein each monitoring parameter of the target power grid at the current moment comprises power grid transmission current, current precipitation, rainfall duration, current ground area water quantity and power grid load bearing equipment quantity of the target power grid at the current moment, and comprises the following steps: downloading the current precipitation amount, the rainfall duration and the current ground area water amount of the target power grid at the current moment from the nearest weather observation server based on a wireless transmission network;
The method for acquiring the monitoring parameters of the target power grid at the current moment comprises the steps of acquiring the monitoring parameters of the target power grid at the current moment, wherein the monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment, and further comprises the following steps: the number of the load devices borne by the power grid of the target power grid at the current moment is the total number of the load devices of the power supply of the target power grid at the current moment.
Example 7
Fig. 8 is a block diagram showing a structure of a machine vision-based unmanned aerial vehicle altitude grid security detection system according to embodiment 7 of the present invention.
As shown in fig. 8, the machine vision based unmanned aerial vehicle altitude grid safety detection system comprises a memory storing a computer program configured to be executed by one or more processors to perform the steps of:
Acquiring a nodding visual picture of a target power grid at the current moment, wherein the nodding visual picture of the target power grid is a nodding picture downloaded from an aerial unmanned aerial vehicle based on positioning data of the target power grid at the current moment, and the target power grid is positioned in a plateau zone;
Further, the photoelectric sensor and the pitching type aerial photographing lens can be used for cooperation to complete the photographing of the pitching type image on the aerial photographing unmanned aerial vehicle;
for example, the cooperation of the photoelectric sensor and the pitching type aerial photographing lens to complete the photographing of the aerial photographing picture on the aerial photographing unmanned aerial vehicle comprises: the photoelectric sensor can be selected from a CMOS sensor or a CCD sensor;
Acquiring all monitoring parameters of a target power grid at the current moment, wherein the all monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment;
Further, the current transmitted by the power grid of the target power grid at the current moment, the current precipitation of the target power grid at the current moment, the rainfall duration of the target power grid at the current moment, the current ground area water quantity of the target power grid at the current moment and the number of power grid load bearing devices of the target power grid at the current moment are respectively subjected to corresponding data acquisition by different data acquisition units;
the method comprises the steps of adopting a database to store various abnormal fault coding values corresponding to various power grid abnormal faults respectively, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short-circuit discharge between the electric pole and a hardware fitting by taking precipitation as a medium;
Specifically, for each abnormal fault including the inclination of the electric pole, the collapse of the electric pole and the short-circuit discharge between the electric pole and the hardware using precipitation as a medium, respectively having different abnormal fault coding values;
When detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in the abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal;
When a first detection signal is received, intelligently identifying an abnormal fault coding value of the target power grid at the current moment by adopting an artificial intelligent model based on pixel values of all pixel points of the nodding visual picture and all monitoring parameters of the target power grid at the current moment, otherwise, temporarily executing intelligent identification of the abnormal fault coding value of the target power grid at the current moment;
further, the pixel values of the pixels of the nodding visual picture are an R component value, a G component value and a B component value of each pixel of the nodding visual picture in an RGB color space;
The artificial intelligent model is a feedforward neural network after each learning of a set number, and the value of the set number is monotonically and positively associated with the resolution of the nodding visual picture;
Further, the monotonically positive association of the set number of values with the resolution of the nodding-down visual frame includes: the higher the resolution of the nodding visual picture is, the larger the value of the set quantity is;
when the fact that the value of a certain power grid parameter at the current moment of the target power grid is not in a value range corresponding to the certain power grid parameter is detected, a first detection signal is sent out, and the certain power grid parameter is any one parameter of voltage, current, electric control, the number of power grid load bearing devices and real-time transmission power;
as shown in fig. 8, exemplarily, M processors are given, where M is a natural number of 1 or more.
Example 8
Fig. 9 is a flowchart of steps of a method for detecting the security of a power grid of an unmanned aerial vehicle on the basis of machine vision according to embodiment 8 of the present invention.
As shown in fig. 9, the method for detecting the security of the altitude power grid of the unmanned aerial vehicle based on machine vision, which is shown in embodiment 8 of the invention, specifically comprises the following steps:
S901: acquiring a nodding visual picture of a target power grid at the current moment, wherein the nodding visual picture of the target power grid is a nodding picture downloaded from an aerial unmanned aerial vehicle based on positioning data of the target power grid at the current moment, and the target power grid is positioned in a plateau zone;
Further, the photoelectric sensor and the pitching type aerial photographing lens can be used for cooperation to complete the photographing of the pitching type image on the aerial photographing unmanned aerial vehicle;
for example, the cooperation of the photoelectric sensor and the pitching type aerial photographing lens to complete the photographing of the aerial photographing picture on the aerial photographing unmanned aerial vehicle comprises: the photoelectric sensor can be selected from a CMOS sensor or a CCD sensor;
s902: acquiring all monitoring parameters of a target power grid at the current moment, wherein the all monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment;
Further, the current transmitted by the power grid of the target power grid at the current moment, the current precipitation of the target power grid at the current moment, the rainfall duration of the target power grid at the current moment, the current ground area water quantity of the target power grid at the current moment and the number of power grid load bearing devices of the target power grid at the current moment are respectively subjected to corresponding data acquisition by different data acquisition units;
S903: the method comprises the steps of adopting a database to store various abnormal fault coding values corresponding to various power grid abnormal faults respectively, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short-circuit discharge between the electric pole and a hardware fitting by taking precipitation as a medium;
Specifically, for each abnormal fault including the inclination of the electric pole, the collapse of the electric pole and the short-circuit discharge between the electric pole and the hardware using precipitation as a medium, respectively having different abnormal fault coding values;
s904: when detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in the abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal;
S905: when a first detection signal is received, intelligently identifying an abnormal fault coding value of the target power grid at the current moment by adopting an artificial intelligent model based on pixel values of all pixel points of the nodding visual picture and all monitoring parameters of the target power grid at the current moment, otherwise, temporarily executing intelligent identification of the abnormal fault coding value of the target power grid at the current moment;
further, the pixel values of the pixels of the nodding visual picture are an R component value, a G component value and a B component value of each pixel of the nodding visual picture in an RGB color space;
The artificial intelligent model is a feedforward neural network after each learning of a set number, and the value of the set number is monotonically and positively associated with the resolution of the nodding visual picture;
Further, the monotonically positive association of the set number of values with the resolution of the nodding-down visual frame includes: the higher the resolution of the nodding visual picture is, the larger the value of the set quantity is;
when the fact that the value of a certain power grid parameter at the current moment of the target power grid is not in the value range corresponding to the certain power grid parameter is detected, a first detection signal is sent out, and the certain power grid parameter is any one parameter of voltage, current, electric control, the number of power grid load bearing devices and real-time transmission power.
In addition, the present invention may also refer to the following technical matters to characterize the salient essential features of the present invention:
Based on pixel values of all pixel points of a nodding visual picture, all monitoring parameters of a target power grid at the current moment adopt an artificial intelligent model to intelligently identify abnormal fault coding values of the target power grid at the current moment, and the method comprises the following steps: the pixel values of all pixel points of the nodding visual picture are represented by a numerical conversion function, and all monitoring parameters of the target power grid at the current moment cooperate with the numerical correspondence relation of the abnormal fault coding numerical value of the target power grid at the current moment;
In each learning executed on the feedforward neural network, taking an abnormal fault coding value corresponding to an abnormal fault of a target power grid known at a certain historical moment as output content of the feedforward neural network, taking pixel values of all pixel points of a nodding-down vision picture corresponding to the target power grid at the certain historical moment and all monitoring parameters of the target power grid at the certain historical moment as all input content of the feedforward neural network, and completing the learning process comprises: each learning performed on the feedforward neural network is accomplished using a programmable logic device.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (10)
1. Unmanned aerial vehicle plateau electric wire netting safety inspection system based on machine vision, characterized in that, the system includes:
The visual capturing device is used for obtaining a nodding visual picture of a target power grid at the current moment, wherein the nodding visual picture of the target power grid is a nodding picture downloaded from an aerial unmanned aerial vehicle based on positioning data of the target power grid at the current moment, and the target power grid is located in a plateau region;
The branch acquisition equipment is used for acquiring all monitoring parameters of the target power grid at the current moment, wherein the all monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration time, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment;
The code mapping equipment is used for storing various abnormal fault code values corresponding to various power grid abnormal faults respectively by adopting a database, wherein each abnormal fault code value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short-circuit discharge between hardware fittings taking precipitation as a medium;
the abnormality detection device is used for detecting whether the current moment of the target power grid is in an abnormal state or not, and sending out a first detection signal when the current moment of the target power grid is in the abnormal state, and otherwise, sending out a second detection signal;
The intelligent identification equipment is respectively connected with the vision capturing equipment, the analysis acquisition equipment, the code mapping equipment and the anomaly detection equipment and is used for intelligently identifying the anomaly fault code value of the target power grid at the current moment by adopting an artificial intelligent model based on the pixel values of all pixel points of the nodding vision picture and all monitoring parameters of the target power grid at the current moment when the first detection signal is received;
The artificial intelligent model is a feedforward neural network after each learning of a set number, and the value of the set number is monotonically and positively associated with the resolution of the nodding visual picture;
When a first detection signal is received, pixel values of all pixel points of a nodding visual picture and all monitoring parameters of a target power grid at the current moment are input into an artificial intelligent model in parallel to operate the artificial intelligent model, and an abnormal fault coding value of the target power grid at the current moment, which is output by the artificial intelligent model, is obtained.
2. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of claim 1, wherein:
The intelligent identification equipment is also used for suspending the execution of intelligent identification of the abnormal fault coding value of the target power grid at the current moment when receiving the second detection signal;
Wherein, when detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in an abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal comprises: when the fact that the value of a certain power grid parameter at the current moment of the target power grid is not in the value range corresponding to the certain power grid parameter is detected, a first detection signal is sent out;
When detecting that the value of a certain power grid parameter at the current moment of the target power grid is not in the value range corresponding to the certain power grid parameter, sending a first detection signal comprises: the certain power grid parameter is any parameter of voltage, current, electric control, the number of load bearing devices of the power grid and real-time transmission power.
3. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of claim 2, further comprising:
The successive processing equipment is connected with the intelligent identification equipment and is used for executing each learning of a set number on the feedforward neural network so as to obtain the feedforward neural network after each learning of the set number is completed and sending the feedforward neural network to the intelligent identification equipment as an artificial intelligent model for use;
Wherein, carry out each study of the presumption quantity to the feedforward neural network, in order to obtain the feedforward neural network after finishing each study of presumption quantity and send to the said intelligent authentication equipment to use as the artificial intelligence model includes: in each learning executed on the feedforward neural network, taking an abnormal fault coding value corresponding to an abnormal fault of a target power grid known at a certain historical moment as output content of the feedforward neural network, taking pixel values of all pixel points of a nodding-down vision picture corresponding to the target power grid at the certain historical moment and all monitoring parameters of the target power grid at the certain historical moment as all input content of the feedforward neural network, and completing the learning process.
4. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of claim 3, wherein the system further comprises:
and the parameter storage device is connected with the successive processing device and used for storing various model parameters of the artificial intelligent model.
5. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of claim 2, further comprising:
And the wireless transmission mechanism is connected with the branch acquisition equipment and is also connected with the nearest weather observation server in a wireless way, and is used for downloading the current precipitation amount, the rainfall duration and the current ground area water amount of the target power grid at the current moment from the nearest weather observation server by adopting a wireless transmission network.
6. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of claim 2, further comprising:
The fault display device is respectively connected with the code mapping device and the intelligent identification device and is used for displaying the name character string of the abnormal fault corresponding to the abnormal fault code value of the target power grid at the current moment in real time after receiving the abnormal fault code value of the target power grid at the current moment;
The method for storing the abnormal fault code values of the power grid by adopting the database comprises the steps of storing the corresponding abnormal fault code values of various power grid abnormal faults respectively, wherein each abnormal fault code value is in a binary value representation form, each abnormal fault comprises electric pole inclination, electric pole collapse and short circuit discharge between the electric pole and hardware by taking precipitation as a medium, and the method comprises the following steps: one database of a relational database, a non-relational database, an object-oriented database, a hierarchical database, a net-shaped database and a memory database is used for storing each abnormal fault coding value corresponding to each abnormal fault of the power grid, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short circuit discharge between the electric pole and hardware fittings taking precipitation as a medium;
The method comprises the steps of adopting one database of a relational database, a non-relational database, an object-oriented database, a hierarchical database, a netlike database and a memory database for storing various abnormal fault coding values corresponding to various power grid abnormal faults respectively, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short circuit discharge with hardware fittings taking precipitation as a medium, and the method comprises the following steps: when the relational database is used for storing the abnormal fault coding values corresponding to the abnormal faults of the various power grids, the relational database is one of Oracl e database, mySQL database and SQL SERVER database.
7. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of claim 2, further comprising:
the data transceiver is connected with the visual capturing device and is wirelessly connected with the aerial unmanned aerial vehicle, and is used for downloading a corresponding aerial picture at each moment from the aerial unmanned aerial vehicle based on the positioning data of the target power grid at each moment;
The data receiving and transmitting equipment comprises a positioning executing unit, a data receiving unit, a data transmitting unit and a main controller;
In the data transceiver, the main controller is connected with the positioning executing unit, the data receiving unit and the data transmitting unit respectively.
8. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of any one of claims 2 to 7, wherein:
When a first detection signal is received, inputting pixel values of all pixel points of a nodding visual picture and all monitoring parameters of a target power grid at the current moment in parallel into an artificial intelligent model to operate the artificial intelligent model, and obtaining an abnormal fault coding value of the target power grid at the current moment, which is output by the artificial intelligent model, comprises the following steps: before the pixel values of all the pixel points of the nodding visual picture and all the monitoring parameters of the target power grid at the current moment are input into the artificial intelligent model in parallel, binary value conversion processing is respectively carried out on the pixel values of all the pixel points of the nodding visual picture and all the monitoring parameters of the target power grid at the current moment.
9. The machine vision-based unmanned aerial vehicle altitude grid safety detection system of any one of claims 2 to 7, wherein:
When detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in the abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal comprises: when the value of each power grid parameter at the current moment of the target power grid is detected to be in the range of the value corresponding to each power grid parameter, a second detection signal is sent out;
The method for acquiring each monitoring parameter of the target power grid at the current moment, wherein each monitoring parameter of the target power grid at the current moment comprises power grid transmission current, current precipitation, rainfall duration, current ground area water quantity and power grid load bearing equipment quantity of the target power grid at the current moment, and comprises the following steps: downloading the current precipitation amount, the rainfall duration and the current ground area water amount of the target power grid at the current moment from the nearest weather observation server based on a wireless transmission network;
The method for acquiring the monitoring parameters of the target power grid at the current moment comprises the steps of acquiring the monitoring parameters of the target power grid at the current moment, wherein the monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment, and further comprises the following steps: the number of the load devices borne by the power grid of the target power grid at the current moment is the total number of the load devices of the power supply of the target power grid at the current moment.
10. A machine vision based unmanned aerial vehicle altitude grid safety detection system, the system comprising a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the steps of:
Acquiring a nodding visual picture of a target power grid at the current moment, wherein the nodding visual picture of the target power grid is a nodding picture downloaded from an aerial unmanned aerial vehicle based on positioning data of the target power grid at the current moment, and the target power grid is positioned in a plateau zone;
Acquiring all monitoring parameters of a target power grid at the current moment, wherein the all monitoring parameters of the target power grid at the current moment comprise power grid transmission current, current precipitation amount, rainfall duration, current ground area water amount and power grid load bearing equipment quantity of the target power grid at the current moment;
the method comprises the steps of adopting a database to store various abnormal fault coding values corresponding to various power grid abnormal faults respectively, wherein each abnormal fault coding value is in a binary value representation form, and each abnormal fault comprises electric pole inclination, electric pole collapse and short-circuit discharge between the electric pole and a hardware fitting by taking precipitation as a medium;
When detecting whether the current moment of the target power grid is in an abnormal state or not, and when the current moment of the target power grid is in the abnormal state, sending out a first detection signal, otherwise, sending out a second detection signal;
When a first detection signal is received, intelligently identifying an abnormal fault coding value of the target power grid at the current moment by adopting an artificial intelligent model based on pixel values of all pixel points of the nodding visual picture and all monitoring parameters of the target power grid at the current moment, otherwise, temporarily executing intelligent identification of the abnormal fault coding value of the target power grid at the current moment;
The artificial intelligent model is a feedforward neural network after each learning of a set number, and the value of the set number is monotonically and positively associated with the resolution of the nodding visual picture;
when the fact that the value of a certain power grid parameter at the current moment of the target power grid is not in a value range corresponding to the certain power grid parameter is detected, a first detection signal is sent out, and the certain power grid parameter is any one parameter of voltage, current, electric control, the number of power grid load bearing devices and real-time transmission power;
When a first detection signal is received, pixel values of all pixel points of a nodding visual picture and all monitoring parameters of a target power grid at the current moment are input into an artificial intelligent model in parallel to operate the artificial intelligent model, and an abnormal fault coding value of the target power grid at the current moment, which is output by the artificial intelligent model, is obtained.
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