Neural Network Drop Impact Prediction for Heavy Equipment Airdrop
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Solution Overview
Problem
Current methods for predicting heavy equipment airdrop are cumbersome and time-consuming, requiring extensive simulation and testing, which increases costs and risks.
Innovation Solution
A neural network-based drop impact prediction method that uses a finite element model to acquire sample data, constructs a BP neural network model, and predicts whether heavy equipment will roll over or airbags will rupture during airdrop, significantly reducing calculation time and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulation method is used for predicting heavy equipment airdrop, then prediction can be performed without actual testing, but it takes a lot of time to construct different models and perform simulative calculation
Solution Approach 1:
The patent pre-trains the neural network model using finite element simulation data before actual airdrop predictions are needed. This preliminary training phase creates a ready-to-use prediction system that can quickly evaluate new scenarios without requiring time-consuming simulations each time, resolving the contradiction between reliable prediction and calculation time.
Solution Approach 2:
The patent creates a neural network model that learns from simulation data and reproduces the complex physical behaviors of airdrop scenarios. Instead of running full simulations each time, the system uses the trained neural network copy that captures the essential physics, providing fast predictions while maintaining reliability.
2Measurement precision
If multiple models are constructed for different cargoes with different masses, center-of-gravity positions, and falling speeds, then accurate predictions can be made, but the process becomes more troublesome
Solution Approach 1:
The patent designs a universal neural network model that can handle multiple cargo types, masses, center-of-gravity positions, and falling speeds within a single framework. The model takes these varying parameters as inputs and provides predictions without requiring separate models for each scenario, thus maintaining precision while reducing complexity.
Solution Approach 2:
The patent uses parameter changes by incorporating cargo-specific variables (mass, center of gravity position, falling speed) as dynamic inputs to the neural network. This allows the single model to adapt to different scenarios through parameter variation rather than requiring structural model changes, resolving the contradiction between precision and complexity.
Data Source
AI summary
The present disclosure provides a drop impact prediction method and system for heavy equipment airdrop based on a neural network. The drop impact prediction method includes the following steps: S1: acquiring a plurality of sets of sample data by using a finite element model for drop simulation of heavy equipment airdrop; S2: determining structural parameters of a BP neural network, and pre-processing the structural parameters; S3: constructing a BP neural network model; and S4: predicting a drop impact situation of heavy equipment airdrop in an actual application process by using the trained BP neural network model.