Workshop Safety Detection Using Predictive Risk Area Expansion
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Solution Overview
Problem
The spinning process in the textile industry poses significant safety hazards due to high temperatures, harmful gases, and volatile chemical agents, constraining the industry's healthy and sustainable development.
Innovation Solution
A method for safety detection using an electronic device that collects and predicts environmental parameters through a trained time sequence model, expanding detection areas to identify risk zones and generate pre-warning information, ensuring high coverage and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional safety detection methods are used in the spinning process, then the system complexity is low, but the detection coverage and accuracy are insufficient to identify risk areas effectively
Solution Approach 1:
The system performs preliminary actions by collecting historical environmental parameter data and training the time sequence prediction model in advance. This allows the system to predict future environmental conditions and identify potential risk areas before actual hazards occur, improving detection accuracy while maintaining manageable system complexity through proactive rather than reactive monitoring
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing predicted environmental parameters with actual measurements, using the time sequence model to adjust predictions based on historical data patterns. This feedback loop enables the system to improve its risk detection accuracy over time while maintaining a relatively simple architecture through iterative optimization rather than complex multi-layer systems
2Area of stationary object
If detection areas are expanded to cover the entire production workshop, then the detection coverage increases, but the data processing time and computational load increase significantly
Solution Approach 1:
The system divides the production workshop into multiple first detection areas, each monitored independently by the time sequence prediction model. This segmentation allows parallel processing of data from different zones, maintaining comprehensive coverage while reducing the computational burden on any single processing unit and enabling faster overall data processing through distributed analysis
Solution Approach 2:
The system uses the time sequence prediction model to predict environmental parameters for future time periods, enabling partial action by focusing computational resources on predicting only the necessary future states rather than continuously processing all current sensor data. This approach maintains full detection coverage while reducing real-time processing requirements through predictive analytics
3Reliability
If real-time environmental parameter monitoring is implemented across multiple detection areas, then the safety warning effectiveness improves, but the energy consumption and computational resources increase
Solution Approach 1:
The system implements periodic action by using the time sequence prediction model to monitor environmental parameters at scheduled time intervals rather than continuously. This allows the system to maintain effective safety warning capabilities by detecting trends and predicting future conditions, while reducing energy consumption through intermittent rather than constant data collection and processing cycles
Data Source
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AI summary
A method and apparatus for safety detection, and a storage medium are provided, relating to the field of computer technologies. The method includes: obtaining (S101) an actual environmental parameter of each of multiple first detection areas in a first time period; obtaining (S102) a first environmental parameter prediction result of each first detection area in a second time period based on the actual environmental parameter; obtaining (S103) multiple second detection areas based on an area position of each first detection area in the production workshop; obtaining (S104) a second environmental parameter prediction result of each second detection area in the second time period based on the first environmental parameter prediction result; and generating (S105) safety pre-warning information corresponding to a risk area when determining that the risk area is present in the multiple second detection areas based on the second environmental parameter prediction result.