Patent ID: 11948087
Assignee: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 0:
1. A drop impact prediction method for heavy equipment airdrop based on a neural network, comprising the following steps:
S1: acquiring a plurality of sets of sample data, comprising a landing speed, a transverse wind speed, a position of the center of gravity, a height of the center of mass, an inclination of the ground, a type of the ground, stresses of airbags and a weight of a cargo, by using a finite element model for drop simulation of the heavy equipment airdrop;
S2: determining structural parameters of a BP neural network, taking the landing speed, the transverse wind speed, the position of the center of gravity, the height of the center of mass, the inclination of the ground, the type of the ground, the stresses of the airbags and the weight of the cargo as input parameters, taking situations whether heavy equipment roll over and whether the airbags are ruptured as output parameters, and pre-processing the structural parameters;
S3: constructing a BP neural network model, and dividing the pre-processed structural parameters into a training set and a verification set; training the BP neural network model by using the data in the training set and accumulated test data, and adjusting network parameters of the BP neural network model; and after the training is successful, testing the trained BP neural network model by using the data in the verification set to verify the accuracy of the BP neural network model; and
wherein said training the BP neural network model by using the data in the training set and accumulated test data, and adjusting network parameters of the BP neural network model specifically comprises:
acquiring the sample data by simulation to perform early neural network training, in a later stage, with increase of a number of tests, substituting collected data into a former simulatively-trained model to be retrained, and performing repeated iterative training;
in step S3, the BP neural network model comprises a three-layer structure:
an input layer, an intermediate hidden layer, and an output layer, respectively;
wherein the number of nodes on the input layer is 8, the number of nodes on the intermediate hidden layer is 12, and the number of nodes on the output layer is 2; and
an S-type tangent function tansig is an activation function of neurons on the intermediate hidden layer, and an S-type logarithmic function logsig is an activation function of neurons on the output layer; and
S4: predicting a drop impact situation of the heavy equipment airdrop in an actual application process by using the trained BP neural network model, and determining whether the cargo rolls over and whether the airbags are ruptured after drop impact of the heavy equipment airdrop.