Stacker State Prediction Using Failure Time and Stop Position
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Solution Overview
Problem
In automated stereoscopic warehouses, accurately predicting the state of stackers is challenging, which affects the normal completion of delivery or warehousing operations.
Innovation Solution
A state prediction method and apparatus for stackers that utilize historical work data and maintenance data to predict the stacker's state during task execution, including the time of failure and stop position, and simulate task execution to generate a prediction result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional state prediction methods are used for stackers, then the system complexity is low, but the prediction accuracy is insufficient affecting delivery and warehousing efficiency
Solution Approach 1:
The system performs preliminary actions by collecting historical work data and maintenance data before prediction is needed, building a comprehensive data foundation that enables accurate state prediction when required
Solution Approach 2:
The prediction system is segmented into distinct functional modules: data collection module, simulation execution module, and state prediction module. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable system complexity through modular architecture
2Measurement precision
If comprehensive historical data and simulation are used to predict stacker state, then prediction accuracy improves, but calculation time and processing resources increase
Solution Approach 1:
The system applies partial action by selectively simulating only the necessary work tasks required for prediction rather than exhaustively modeling all possible scenarios. This approach achieves sufficient prediction accuracy while avoiding unnecessary calculation overhead and time consumption
Data Source
AI summary
Provided is a state prediction method for a stacker, an electronic device and a storage medium, relating to the field of chemical fiber intelligent technology. The method includes: obtaining historical work data and historical maintenance data of the stacker; obtaining a work task of the stacker within a preset time period; predicting a state of the stacker during execution of the work task based on the historical work data and the historical maintenance data; simulating the execution of the work task by the stacker within the preset time period based on the historical work data, to obtain a simulation execution result of the stacker; and generating a state prediction result of the stacker based on the time when the stacker fails and the stop position of the stacker when failing in combination with the simulation execution result.