Aiming at the problem of false alarm and miss alarm caused by errors of alarm threshold choice under different operating conditions when warning devices approaching the same charged body

a model based on collaborative filtering and BP neural network is proposed to identify three working conditions such as towers climbing

slope climbing

and horizontal walking. Collecting the acceleration and air pressure values of the operator’s head by acceleration and air pressure sensors. Then the effective relative height value is extracted by collaborative filtering and fitting parameters are obtained by first-order fitting. Finally

according to the fitting parameters

BP neural network model is established to identify three working conditions. Outdoor towers

slopes and horizontal road surfaces are selected as experimental platforms and 400 groups of data are collected for each working condition. Then randomly select 350 groups of data for training

50 groups of data for verification. The results show that the accuracy of training samples and test samples is 94. 95 % and 94. 67 %

which meets the requirements of outdoor working conditions identification.