A cell research-oriented experimental process data management method and system
By acquiring the well edge distance and perturbation events in multi-well plate cell experiments, late-mover drift potential and drift contribution values are generated. A reinforcement learning model is used for process management, which solves the problem of inaccurate well data reliability determination in multi-well plate experiments and improves the reliability and traceability of experimental results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-04-21
- Publication Date
- 2026-07-17
AI Technical Summary
In existing cell research and development experiments, it is difficult to uniformly characterize the process disturbance history of different well positions within a multi-well plate, making it difficult to identify the effects of delayed drift, resulting in inaccurate judgment of the reliability of well position data, and affecting the reliability and traceability of experimental results.
By acquiring the edge distances and disturbance events of each hole in the experimental board, a disturbance event chain is generated, the late drift potential and drift contribution value are calculated, and a reinforcement learning model is used for process governance to generate a management ledger to improve the targeting and temporal consistency of data management.
This method enables accurate interpretation of well-level states and identification of drift effects in multi-well plate cell experiments, improving the reliability of experimental data and the traceability of results, and reducing the interference of delayed drift on conclusions.
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