Identification method and device for application scenario of mobile terminal
A mobile terminal and application scene technology, applied in the field of data processing, can solve the problems of high recognition cost, low recognition accuracy, complex recognition algorithm, etc., and achieve the effect of simple recognition algorithm, low cost of scene recognition, and high recognition accuracy
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Embodiment 1
[0054] This embodiment provides a method for identifying application scenarios of a mobile terminal, wherein the mobile terminal includes a terminal state detection module, such as figure 1 As shown, the identification methods of mobile terminal application scenarios include:
[0055] Step 101. Obtain the first time-series state data collected by the terminal state detection module in a preset application scenario.
[0056] The terminal state detection module includes at least one of a magnetic sensor, an acceleration sensor, an air pressure sensor, a brightness sensor, a gyroscope sensor, a temperature sensor, a GPS positioning module, a Wi-Fi module, and a base station module.
[0057] The preset application scenarios include indoor and outdoor, and / or, the motion state of the mobile terminal, and the motion state includes at least one of stationary, walking, exercise, and riding a vehicle, and / or, the placement state of the mobile terminal, and the placement state includes ...
Embodiment 2
[0077] This embodiment provides a method for identifying an application scene of a mobile terminal. When the preset application scene includes at least two of indoor and outdoor, the motion state of the mobile terminal, and the placement state of the mobile terminal, the deep neural network model is a multi-task deep neural network model. Network model, the difference between this embodiment and embodiment 1 is that, as Figure 5 As shown, step 103 includes:
[0078] 103'. Set the first time window data with a corresponding scene label according to each corresponding preset application scene.
[0079] Step 104 includes:
[0080] 104'. Taking the first time window data as input and using the corresponding multiple scene labels as output to train a multi-task deep neural network model to obtain a multi-scene scene prediction model output.
[0081]In the training phase, a certain amount of state data needs to be collected through the terminal state detection module. In order to...
Embodiment 3
[0086] This embodiment provides an identification device for an application scene of a mobile terminal. The mobile terminal includes a terminal state detection module, such as Figure 7 As shown, the mobile terminal application scene recognition device includes a sample collection module 201, a first time window module 202, a label setting module 203, a model training module 204, a prediction data collection module 205, a second time window module 206 and a prediction module 207.
[0087] The sample collection module 201 is used to obtain the first time series state data collected by the terminal state detection module in a preset application scenario.
[0088] The terminal state detection module includes at least one of a magnetic sensor, an acceleration sensor, an air pressure sensor, a brightness sensor, a gyroscope sensor, a temperature sensor, a GPS positioning module, a Wi-Fi module, and a base station module.
[0089] The preset application scenarios include indoor and ...
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