Intelligent optical fiber sensing system based on deep learning
A technology of intelligent optical fiber and sensor system, which is applied in the direction of program control, clothing, and comprehensive factory control in sequence/logic controllers, which can solve the problem of victim injury, helmet quality and performance that cannot meet the requirements, and can not play a substantial role The role of the problem, to achieve the effect of reducing damage
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Embodiment 1
[0075] refer to Figure 1-10 shown.
[0076] Specifically, this embodiment provides an intelligent optical fiber sensing system based on deep learning, including a smart helmet 1, an identification device 2, and a processing chip 3. The smart helmet 1 includes a helmet shell, a windshield, and a wearing unit, and is characterized in that The smart helmet 1 also includes a display screen 10, a ventilation unit 11, an inner filling layer 12, a D3O material 13, an electric drive material nozzle 14, and a protective switch port 15, and the helmet shell is sprayed with LINE-X protective paint; The display screen 10 is arranged on the inner surface of the windshield for displaying information; the ventilation unit 11 includes a ventilation hole and an electric ventilation fan, and the ventilation hole is arranged at the top of the helmet shell for providing opening for circulation; The electric-driven ventilating fan is arranged inside the vent hole for opening the ventilating func...
Embodiment 2
[0109] refer to Figure 1-10 shown.
[0110] Specifically, this embodiment is basically the same as Embodiment 1, except that in this embodiment, the processing chip further includes:
[0111] The identity recognition module 42 is configured to obtain user information fed back by the user's external device.
[0112] The takeaway acquisition module 43 is configured to acquire takeaway information fed back by the user's external device.
[0113] The address extraction module 44 is configured to extract the delivery address included in the takeaway order in the takeaway information.
[0114] The route planning module 45 is used to plan a single sub-movement route according to the delivery address and positioning data, and plan a total movement route including corresponding stopping points according to the delivery time and address according to the planned sub-movement route.
[0115] As a preferred mode of the present invention, the processing chip 3 also includes:
[0116] T...
Embodiment 3
[0132] refer to Figure 1-10 shown.
[0133] Specifically, this embodiment is basically consistent with Embodiments 1 and 2, the difference is that in this embodiment, the processing chip 3 further includes:
[0134] The order extraction module 47 is used to extract the order monitoring images from when the user receives the takeaway order through the external device to the set time.
[0135] The second violation module 48 is used to generate a delivery violation record for the user corresponding to the takeaway exceeding the delivery time.
[0136] As a preferred mode of the present invention, the processing chip 3 also includes:
[0137] The image extraction module 49 is used for extracting the real-time monitoring images captured by the monitoring camera 22 in real time.
[0138] As a preferred mode of the present invention, in S30, the method further includes the following steps:
[0139] S301. Obtain the food delivery information fed back by the external device mainta...
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