Intelligent transportation inspection notification method and device
A kind of operation inspection and intelligent technology, which is applied in the direction of measuring devices, fault locations, instruments, etc., can solve problems such as delaying fault repair time, achieve the effects of reducing fault power outage time, improving power supply reliability, and saving costs and time
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Embodiment 1
[0052] figure 1 It is a flow chart of an intelligent traffic inspection notification method according to an embodiment of the present disclosure.
[0053] likefigure 1 As shown, the intelligent transportation inspection notification method of the present embodiment includes:
[0054] S101: Judging the location of the transmission line fault tower and the cause of the fault, the process is:
[0055] Using the fault distance measurement algorithm, the distance between the current measuring point and the fault point is obtained, and then converted into the fault tower number, and the fault tower position is judged;
[0056] At least two fault features matching the fault cause are selected to train the neural network to determine the fault cause.
[0057] In this embodiment, the fault features include meteorological data features, seasonal features, image recognition features, waveform features, historical fault features and transmission channel conditions;
[0058] The causes ...
Embodiment 2
[0105] figure 2 It is a structural schematic diagram of an intelligent traffic inspection notification device according to an embodiment of the present disclosure.
[0106] like figure 2 As shown, the intelligent inspection notification device of the present embodiment includes:
[0107] (1) Fault location and cause judgment module, which is used to judge the location of the transmission line fault tower and the fault cause.
[0108] Specifically, the fault location and cause judgment module includes:
[0109] (1.1) fault location judgment sub-module, which is used to utilize the fault ranging algorithm to obtain the distance between the current measuring point and the fault point, and then convert it into a faulty tower number to judge the faulty tower position;
[0110] (1.2) fault cause judgment sub-module, select at least two kinds of fault characteristics matched with the fault cause to train the neural network, and judge the fault cause;
Embodiment approach
[0111] As a specific implementation, in the fault cause judging submodule, the fault features include meteorological data features, seasonal features, image recognition features, waveform features, historical fault features and transmission channel conditions; fault causes are divided into four categories , including lightning strike failures, engineering vehicles, wildfires and equipment itself.
[0112] It can be understood that, in other embodiments, fault features include but are not limited to meteorological data features, seasonal features, image recognition features, waveform features, historical fault features and transmission channel conditions, and can also be meteorological data features, seasonal features A combination of any at least two of the features of , image recognition features, waveform features, historical fault features, and power transmission channel conditions can be selected by those skilled in the art according to specific conditions.
[0113] The faul...
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