Assembly line experiment automatic scheduling method and system and related equipment
By employing random generation of permutation arrays, crossover operations, and mutation operations on the automated detection line, combined with the linkage gene probability in biological genetics, the target population is determined, thus solving the problem of inconsistent detection time and achieving optimal efficiency in the detection experiment.
CN122114429APending Publication Date: 2026-05-29CHENGDU LANTHANUM & BARIUM TECH CO LTD
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Patent Information
- Application Number
- CN202512018189.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2024-12-30
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-29
AI Technical Summary
Technical Problem
The testing times at different stations on the existing automated production line are inconsistent, and improper testing sequences result in low efficiency of the production line testing experiments, failing to achieve optimal efficiency.
Method used
By randomly generating a permutation array as the initial population, performing crossover and mutation operations, introducing the probability of linked genes from biological genetics, determining the target population, and selecting the optimal detection order for detection.
Benefits of technology
The efficiency of the testing experiment has been optimized, enabling the testing experiment to achieve its optimal efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of detection experiments, in particular to an automatic scheduling method and system for assembly line experiments and related equipment, and the method comprises the steps: randomly generating a full-permutation array as an initial population; taking the initial population as a parent, carrying out crossover operation, and determining a first filial generation; pairing the individuals in the initial population in pairs, determining paired individuals, and performing mutation operation by taking the paired individuals as parent generations to determine second filial generations; determining a target population based on the first filial generation, the second filial generation and the parent generation; performing detection according to the detection sequence corresponding to each individual in the target population, and determining a first total detection duration corresponding to each individual; and taking an individual meeting a first preset requirement in each first detection total duration as a target individual, and detecting each detection experiment according to a detection sequence corresponding to the target individual. According to the method, the tasks are scheduled and arranged by adopting the genetic algorithm, and the detection efficiency is optimized.
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