A comprehensive anti-theft management method and system based on artificial intelligence
A technology of artificial intelligence and management methods, applied in neural learning methods, electrical alarms, biological neural network models, etc., can solve problems affecting the normal operation of equipment and threats to power grid operation security, and achieve poor anti-theft effects, Realize the effect of artificial intelligence
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Embodiment 1
[0032] This embodiment provides a comprehensive anti-theft management method based on artificial intelligence. This method is applied to the cable that forms the antenna after power-on. Since the signals generated by the antenna formed by cables of different lengths and different diameters are different, it can be used. By detecting the size of the signal and obtaining the length data according to the diameter data, the cable anti-theft is realized, and the problem of poor anti-theft effect in the prior art is solved.
[0033] Specifically, as figure 1 Shown, a kind of comprehensive anti-theft management method based on artificial intelligence, is used for realizing anti-theft management to cable, and described cable is the cable that forms antenna after being powered on; Method comprises the following steps:
[0034] S1: The control center obtains the generated signal data received by the receiver and generated after the cable is powered on, and packs it with the power-on sig...
Embodiment 2
[0054] Based on the same inventive concept as in Embodiment 1, this embodiment provides a comprehensive anti-theft management system based on artificial intelligence, which is used to implement anti-theft management for cables, and the cables are cables that form antennas after being powered on; the system includes:
[0055] The control center is used to obtain the generated signal data received by the receiver after the cable is powered on, and pack it with the power-on signal data, cable diameter and cable length sent by the transmitter to form cable characteristic data; The cable feature data is divided into training samples and test samples; it is also used to input the training samples to the convolutional neural network model for training, obtain the trained convolutional neural network model, and use the test samples to train After the convolutional neural network model is tested, after the test is completed, the trained convolutional neural network model is sent to the ...
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