Artificial intelligence-based medical consumable inventory anomaly early warning method

By using edge hardware collaboration and lock-free state merging calculation, the network congestion and warning delay issues of medical consumables inventory warning methods in high-concurrency scenarios are solved, achieving efficient and real-time inventory anomaly warning.

CN121920933BActive Publication Date: 2026-06-19MEDICAL TECH SERVICE (BEIJING) CO LTD +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
MEDICAL TECH SERVICE (BEIJING) CO LTD
Filing Date
2026-03-24
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing medical consumables inventory early warning methods suffer from problems such as network congestion, high early warning delay, and data inconsistency in high-concurrency scenarios, and cannot meet the needs of real-time early warning.

Method used

An AI-based early warning method for abnormal medical consumable inventory is adopted. By utilizing edge hardware collaboration, lock-free state merging calculation, and a simplified binary protocol, and through a circular buffer, radix tree, and CRDT algorithm, lock-free enqueueing and lock-free state merging are achieved, reducing server processing pressure and ensuring the continuity and efficiency of inventory data.

Benefits of technology

It reduces network load and parsing overhead in high-concurrency scenarios, increases throughput by several orders of magnitude, avoids network congestion and I/O paralysis, ensures the continuity and efficiency of inventory data collection, and achieves millisecond-level early warning response.

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Abstract

This invention specifically relates to an AI-based method for early warning of abnormal inventory in medical consumables, encompassing the fields of medical consumables management and artificial intelligence. The method includes: extracting data packets from a circular buffer and routing them; and performing lock-free state merging calculations on the corresponding positive or negative accumulation vectors. In this invention, edge hardware collaboratively deduplicates data, utilizing a DFA micro-state machine and a time-sliding window to filter hardware-interference dirty data, reducing the processing pressure on the core server; employing a simplified binary protocol to encapsulate data, reducing network load and parsing overhead; and achieving lock-free enqueueing through an off-heap memory lock-free circular buffer, CPU CAS atomic instructions, and a spin retry mechanism, avoiding thread blocking and context switching.
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