Firefighting Data Estimation Using Neural Network Simulation
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Solution Overview
Problem
Conventional firefighting data analysis is inefficient and lacks accuracy in determining the best plan to minimize cost and damage while ensuring firefighting safety, relying on statistical charts that fail to provide reliable insights.
Innovation Solution
A method and device for estimating firefighting data using a terminal device to obtain and analyze firefighting condition data, which is then input into a simulation analysis model to estimate firefighting input and damage data, leveraging neural network-based models for improved prediction accuracy and suggesting improvements based on safety standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical charts are used for firefighting data analysis, then the analysis process is simple to implement, but the accuracy and reliability of the analysis results are low
Solution Approach 1:
The patent replaces conventional statistical chart methods with a simulation analysis model that uses neural networks and image recognition technology. This substitution transforms the analysis system from simple statistical processing to an intelligent simulation system, significantly improving measurement precision while accepting increased system complexity.
Solution Approach 2:
The patent introduces a simulation analysis model as an intermediary between raw firefighting data and analysis results. This model acts as a mediator that processes data through multiple layers (data processing layer, simulation analysis layer, and application layer) to produce accurate predictions of firefighting inputs and damages.
2Measurement precision
If simulation analysis model with neural network is used, then the prediction accuracy of firefighting input and damage data is improved, but the computational complexity and time consumption increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing firefighting condition data (image data, text data, structured data) into standardized formats before simulation analysis. The data processing layer prepares training sets and verification sets in advance, which reduces computational time during actual prediction while maintaining high accuracy.
Solution Approach 2:
The patent segments the analysis system into three distinct layers: data processing layer, simulation analysis layer, and application layer. This segmentation allows each layer to specialize in specific tasks, optimizing computational efficiency while maintaining overall prediction accuracy.
3Loss of information
If comprehensive firefighting condition data is collected using image recognition, then the completeness of input data is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a universal data processing framework that handles multiple data types (image data, text data, structured data) through a unified approach. The simulation analysis model can process various firefighting condition parameters (building structure, fire source, weather conditions, etc.) using the same neural network architecture, reducing overall system complexity despite comprehensive data collection.
Data Source
AI summary
A method for estimating firefighting data includes: obtaining firefighting condition data of a site, wherein the firefighting condition data comprises information on firefighting equipment, information on flammable articles; and estimating firefighting input data and firefighting damage data based on the firefighting condition data using a simulation analysis model, wherein the simulation analysis model is created based on firefighting condition data, firefighting input data and firefighting damage data of different sites.