Service fault information processing method and device
An information processing method and a business fault technology, which are applied in the field of business fault information processing methods and devices, can solve the problems of reducing the flexibility of processing fault problems, reducing the processing efficiency of fault problems, and high labor cost, and achieving the improvement of user experience, fast delivery, and high labor cost. The effect of reducing labor costs
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Embodiment 1
[0054] figure 1A schematic flowchart of the method for processing service failure information provided by Embodiment 1 of the present invention is shown. Wherein, the service failure information processing method provided in this embodiment can be applied to various service platforms, such as an after-sales service platform of an operator, a maintenance service platform of an application program, and the like. The service fault information processing method provided in this embodiment may be specifically executed by a computing device with corresponding computing capabilities, and this embodiment does not limit the specific type of computing device.
[0055] Different from the fault problem handling method of manually dispatching orders in the prior art, the business fault information processing method provided in this embodiment is based on a pre-trained machine learning model to predict the business fault information reported by users to identify The intention of the user t...
Embodiment 2
[0069] figure 2 A schematic flowchart of the method for processing service failure information provided by Embodiment 2 of the present invention is shown. Wherein, the service fault information processing method provided in this embodiment is specifically aimed at further optimization of the method in the first embodiment.
[0070] Such as figure 2 As shown, the method includes:
[0071] Step S210: Generate a machine learning model.
[0072] In this embodiment, a machine learning model is pre-generated, and this embodiment does not limit the specific way of constructing the machine learning model. For example, machine learning models can be built on artificial neural networks.
[0073] In a specific implementation process, the artificial neural network-based machine learning model in this embodiment includes at least one sub-network. Wherein, any sub-network contains 4 functions, and the 4 functions may be 3 sigmoid functions and 1 tanh function.
[0074] Step S220: Ob...
Embodiment 3
[0101] image 3 A schematic structural diagram of an apparatus for processing service failure information provided by Embodiment 3 of the present invention is shown. Such as image 3 As shown, the device includes: a fault information acquisition module 31 , a task generation module 32 , a preprocessing module 33 , a machine learning model 34 , an input module 35 , a fault type acquisition module 36 and a scheduling module 37 .
[0102] The fault information acquisition module 31 is adapted to obtain service fault information;
[0103] A task generation module 32, adapted to generate a business failure task corresponding to the business failure information;
[0104] A preprocessing module 33, adapted to preprocess the service failure information to obtain preprocessed service failure information;
[0105] The machine learning model 34 is adapted to output the fault category corresponding to the business fault information; wherein, the machine learning model is obtained accor...
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