Stacker State Prediction Using Historical Data and Task Simulation
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Solution Overview
Problem
In automated stereoscopic warehouses, accurately predicting the state of stackers is crucial for normal operation, as failures can disrupt yarn spindle delivery and warehousing tasks.
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, combined with simulation results to generate a prediction outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used for stackers, then the system is simple to operate, but the prediction accuracy of stacker state is insufficient
Solution Approach 1:
The system performs simulation execution of work tasks before actual execution to predict potential failures. Historical work data and maintenance data are analyzed in advance to build prediction models, allowing the system to identify potential issues before they occur during actual stacker operation.
Solution Approach 2:
The prediction system is divided into multiple independent modules: data acquisition module for collecting historical data, simulation execution module for virtual task completion, state prediction module for analyzing failure probabilities, and result generation module for outputting predictions. This modular architecture improves accuracy while managing complexity through clear separation of functions.
2Measurement precision
If simulation execution is performed to predict stacker state, then the prediction accuracy is improved, but the computational time and resources increase
Solution Approach 1:
Instead of executing actual physical tasks for testing and prediction, the system creates virtual copies of work tasks in a simulation environment. The simulation execution module replicates stacker operations digitally, allowing comprehensive state prediction without consuming real operational time or physical resources.
Solution Approach 2:
The system performs simulation and prediction calculations before actual stacker operations begin. By completing the computational work in advance during planning phases, the system avoids time-consuming calculations during critical operational periods, reducing real-time computational burden.
3Reliability
If historical work data and maintenance data are analyzed comprehensively, then the prediction reliability is improved, but the data processing complexity increases
Solution Approach 1:
The data processing system is segmented into specialized modules: historical work data acquisition for operational records, historical maintenance data acquisition for service records, and integrated analysis module for combining both data types. Each module handles specific data processing tasks, improving reliability through comprehensive analysis while managing complexity through functional separation.
Data Source
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AI summary
Provided is a state prediction method and apparatus for a stacker, and a storage medium, relating to the field of chemical fiber intelligent technology. The method includes: obtaining (S101) historical work data and historical maintenance data of the stacker; obtaining (S 102) a work task of the stacker within a preset time period; predicting (S103) a state of the stacker during execution of the work task based on the historical work data and the historical maintenance data; simulating (S104) 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 (S 105) 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.