Neural Network Disaster Security Resource Calculation
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Solution Overview
Problem
Existing methods for calculating disaster security resources are inefficient and lack intelligence, making it difficult for enterprises to accurately determine the required resources for disaster scenarios.
Innovation Solution
A disaster security resource calculation method using a user terminal and computing device that obtains disaster prevention and loss assessment data, employing a preset calculation model based on neural networks to determine the required security resources, with data simulation and prediction systems to analyze environmental and item information, and adjust the model for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional calculation methods are used for disaster security resources, then the calculation process is simple, but the efficiency and intelligence of resource determination are insufficient
Solution Approach 1:
The patent replaces traditional mechanical calculation methods with an intelligent system comprising neural networks, data simulation modules, and prediction systems. The calculation module uses trained neural network models to automatically compute disaster security resources, substituting manual or formula-based approaches with automated intelligent processing, thereby dramatically improving calculation efficiency and intelligence.
2Measurement precision
If intelligent calculation systems are introduced, then the accuracy of resource determination is improved, but the system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training neural network models using historical disaster data and loss assessment data before actual disaster scenarios occur. The system performs data simulation and model training in advance, creating ready-to-use prediction models that can quickly and accurately determine security resources when disasters actually happen, thereby improving accuracy without requiring complex real-time processing.
3Measurement precision
If more data and models are used to improve calculation accuracy, then the precision of disaster security resource determination is enhanced, but the computational requirements and system complexity increase
Solution Approach 1:
The system performs computationally intensive tasks in advance by pre-training neural network models with large datasets during peacetime or low-demand periods. Once trained, the models can make rapid predictions with minimal computational energy during actual disaster scenarios, effectively shifting energy consumption from the prediction phase to the training phase.
Solution Approach 2:
The patent creates simplified copies or representations of complex disaster scenarios through data simulation. Instead of processing all possible disaster variations in real-time, the system uses simulated training data that captures essential patterns, allowing the neural network to learn from representative examples without requiring exhaustive computational resources during actual predictions.
Data Source
AI summary
A disaster security resource calculation method includes obtaining disaster prevention data of a place to be evaluated and loss assessment data of the place in a disaster scenario, and determining disaster security resources required by the place to be evaluated in the disaster scenario using a preset calculation model according to the disaster prevention data and the loss assessment data. The disaster prevention data includes environmental information, item information, and personnel information.