A batch size determination method for distributed heterogeneous data in the medical industry

By collecting source system status information in medical data processing, constructing prompt words, and combining large models and reinforcement learning to optimize batch size, the problem of balancing data comparison efficiency and stability in distributed heterogeneous data systems is solved, realizing intelligent dynamic adjustment of the data extraction process and system stability.

CN121501615BActive Publication Date: 2026-07-24PEKING UNIVERSITY THIRD HOSPITAL (THE THIRD CLINICAL MEDICAL SCHOOL OF PEKING UNIVERSITY) +1
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Patent Information

Application Number
CN202511647615.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-11
Publication Date
2026-07-24
Estimated Expiration
2045-11-11

AI Technical Summary

Technical Problem

Existing medical data processing methods cannot effectively balance data comparison efficiency and source database system stability when dealing with distributed heterogeneous data systems. Traditional fixed time window partitioning strategies result in a negative correlation between processing efficiency and system stability, and static batch partitioning is difficult to adapt to the dynamic growth characteristics of data, which can easily lead to cascading risks.

Method used

By collecting the current state information of the source system, a large model of prompt word input is constructed to obtain the suggested batch size, risk level and confidence level. The batch size is optimized by combining a reinforcement learning model. A dynamic batch size determination method is adopted, including decisions such as decay, exploration and adoption of suggestions, to ensure the stability and efficiency of the data comparison process.

Benefits of technology

It enables intelligent and dynamic adjustment of batch data extraction during the comparison of heterogeneous medical data, eliminating the risk of database system crashes or severe performance fluctuations, and ensuring the high availability and stability of medical business systems.

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Abstract

The present application relates to a kind of medical industry distributed heterogeneous data batch size determination method, belong to medical information processing technical field, solve the problem that current technology cannot effectively balance data contrast efficiency and source database system stability.It is method that includes: the current state information of source system is collected, based on current state information, the suggested batch size, risk level and confidence are obtained by constructing prompt word input large model;Current decision is determined based on the risk level and confidence;If attenuation decision is contained in current decision, the suggested batch size is attenuated to obtain final batch size;If exploration is contained in current decision, the suggested batch size is optimized to obtain final batch size based on reinforcement learning model;If the suggestion is contained in current decision, the suggested batch size is used as final batch size.Effectively balance data contrast efficiency and the stability of source database system.
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