Prediction model training method, time prediction method, training device and terminal
A time prediction and training method technology, applied in the field of electronic information, can solve problems such as production impact, follow-up link impact, and assembly line failure to operate normally, and achieve the effect of reducing the probability of deviation and improving accuracy
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Embodiment 1
[0059] see figure 1 , the present disclosure provides a training method of a time prediction model that can be applied to terminals such as mobile phones, computers, or tablet computers. When the training method is applied to the terminal, the following steps are performed:
[0060] Step S110: Obtain multiple pieces of sample data, wherein the sample data includes the departure time and arrival time of the logistics vehicle from the departure place to the destination, and the influencing factors affecting the departure time and arrival time, the influencing factors include traffic information factors, weather information factors, route information factors.
[0061] Step S120: Perform correlation analysis on the multiple pieces of sample data to obtain the target correlation relationship between the target time and other data except the target time in each piece of sample data, wherein the target time is the arrival time or departure time time.
[0062] Step S130: Input the d...
Embodiment 2
[0113] see Figure 5 , in the present disclosure provides a time prediction method applicable to terminals such as mobile phones, computers or tablet computers, when the time prediction method is applied to the terminal, step S210 and step S220 are executed:
[0114] Step S210: Obtain the actual influencing factors affecting the predicted target time of the target logistics vehicle and the reference time from the departure point to the destination of the target logistics vehicle.
[0115] In this embodiment, when the reference time is the departure time of the target logistics vehicle, the predicted target time is the predicted arrival time; when the reference time is the arrival time of the target logistics vehicle, the predicted target time is Estimated departure time.
[0116] Step S220: Input the actual influencing factors and the reference time into the time prediction model obtained by the training method of the time prediction model, so as to obtain the predicted targe...
Embodiment 3
[0133] see Figure 8 , the present disclosure provides a training device for a time prediction model applicable to terminals such as mobile phones, computers or tablet computers, the training device includes:
[0134] The obtaining module 301 is used to obtain multiple pieces of sample data, wherein the sample data includes the departure time and arrival time of the logistics vehicle from the departure place to the destination, and the influencing factors affecting the departure time and arrival time, the influencing factors Including traffic information factors, weather information factors, route information factors.
[0135] The implementation principle of the obtaining module 301 is similar to that of step S110, therefore, the specific description of the obtaining module 301 can refer to Embodiment 1, and details are not repeated here.
[0136] A correlation determination module 302, configured to perform correlation analysis on the multiple pieces of sample data to obtain...
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