Hybrid Deep Learning Model for Real-Time Fire Development Prediction
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Solution Overview
Problem
Current fire analysis software lacks real-time adjustability of countermeasures based on actual data from a fire site, limiting its effectiveness in responding to fire situations.
Innovation Solution
A fire development situation prediction device that utilizes a hybrid deep learning model, combining engineering simulation data with actual fire data to predict fire development and provide decision schemes for rescue operations, incorporating a data collecting unit, storage unit, processor, and display unit to optimize the model and provide real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current fire analysis software is used to analyze fire situations and propose countermeasures, then fire analysis can be performed, but the countermeasures cannot be adjusted in real time according to actual data at the fire site
Solution Approach 1:
The system continuously collects actual fire data from sensors at the fire site and feeds this information back to the deep learning model for real-time prediction updates. This feedback mechanism enables the countermeasures to be dynamically adjusted based on actual fire development, resolving the contradiction between providing analysis and enabling real-time adaptation.
Solution Approach 2:
The system transitions from static fire analysis to dynamic real-time prediction by continuously updating the deep learning model with new actual fire data. This dynamic approach allows the countermeasures to adapt automatically to changing fire conditions, eliminating the time delay in adjustment.
2Measurement precision
If a deep learning model is trained using only simulation data, then the model can be initially built, but the model lacks accuracy when dealing with actual fire conditions
Solution Approach 1:
The system merges simulation data and actual fire data into a unified deep learning model training framework. By combining both data sources, the model achieves both the structural foundation from simulation and the real-world accuracy from actual fire data, resolving the contradiction between initial model building and real-world performance.
Solution Approach 2:
The system performs preliminary training of the deep learning model using simulation data to establish the basic model structure and logic. This preliminary action enables the model to be subsequently optimized with actual fire data, achieving high accuracy while managing system complexity through a staged approach.
3Reliability
If actual fire data is collected and used to optimize the deep learning model, then prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The system establishes a feedback loop where actual fire data is continuously collected, processed, and used to optimize the deep learning model. This feedback mechanism improves prediction reliability by constantly aligning the model with real fire conditions while managing complexity through automated data processing pipelines.
Solution Approach 2:
The system implements self-service optimization where the deep learning model automatically learns from actual fire data without requiring manual retraining for each new fire incident. The model continuously updates itself using new data, improving reliability while reducing the operational complexity of system maintenance.
Data Source
AI summary
A fire development situation prediction method includes collecting simulation data of a fire, establishing a neural network of an engineered deep learning model, training the neural network with the simulation data, determining whether an output value of the neural network is less than or equal to a preset error threshold value, stopping training of the neural network when the output value of the neural network is less than or equal to a preset error threshold value, recollecting the simulation data of the fire when the output value of the neural network is not less than or equal to a preset error threshold value, and evaluating the development situation of the fire according to the engineered deep learning model. The fire development situation prediction method is for predicting a development situation of a fire.