DSPCNN Doffing Path Planning for Timely Winder Servicing
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Solution Overview
Problem
In winding workshops, the timely doffing of yarn spindles from full tubes is crucial to prevent tube bursts and subsequent winder stoppages, which can lead to damage and reduced production capacity.
Innovation Solution
A method and apparatus utilizing a Dual Source Pulse Coupled Neural Network (DSPCNN) to plan efficient doffing paths by constructing a road network topology structure, determining neurons based on winder positions, selecting source and target neurons, and performing ignition calculations to obtain doffing paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional path planning methods are used, then the system complexity is low, but the doffing path generation efficiency is insufficient leading to delayed doffing tasks
Solution Approach 1:
The patent replaces traditional mechanical/path-based planning systems with a neural network-based computational system. The DSPCNN algorithm uses neural network neurons to model the road network topology and perform ignition calculations, substituting conventional mechanical pathfinding mechanisms with a biological-inspired neural computing approach that achieves superior path generation efficiency.
Solution Approach 2:
The patent transforms the path planning problem by changing the fundamental parameters from geometric coordinate-based approaches to neural network state-based approaches. By representing winders as neurons and roads as connection weights in a neural network, the system changes the computational parameters to achieve more efficient path planning through ignition propagation dynamics.
2Loss of time
If doffing paths are planned efficiently, then the doffing task completion time is reduced, but the path planning algorithm complexity increases
Solution Approach 1:
The patent segments the path planning problem into discrete neural network neurons, each representing a winder position. The road network is segmented into connection channels between neurons with associated weights. This segmentation allows the complex continuous path planning problem to be solved through discrete neural network propagation, reducing computational time while managing algorithm complexity through modular neuron interactions.
Solution Approach 2:
The patent employs periodic ignition propagation through the neural network, where ignition spreads in discrete time steps from source neurons to target neurons through connected channels. This periodic action mechanism allows the system to systematically explore possible paths through iterative ignition propagation, achieving efficient path planning through time-step based computation rather than continuous complex calculations.
Data Source
AI summary
A method for planning a doffing path, an electronic device and a storage medium are provided, relating to the field of computer technology and the field of path planning technology. The method includes: constructing a road network topology structure according to positions of winders to be doffed, the road network topology structure including winder nodes, and one winder node corresponding to one winder; determining neurons in a DSPCNN according to the winder nodes, one winder node corresponding to one neuron; selecting a source neuron and a target neuron from the neurons; performing ignition calculation according to the source neuron and the target neuron to obtain a first path corresponding to the source neuron and a second path corresponding to the target neuron; and determining a doffing path of a winder node in the road network topology structure according to the first and second paths.