基于历史先验约束的进程间通信控制方法

By using a historical prior inter-process communication control method to dynamically adjust scheduling priorities and timeout thresholds, and combining dead letter analysis to optimize risk prediction, the stability and resource utilization problems of existing IPC systems under dynamic load environments are solved, and the system's self-correction and optimization are realized.

CN122132200BActive Publication Date: 2026-07-17PACIFIC BUSINESS SOLUTIONS (CHINA) CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
PACIFIC BUSINESS SOLUTIONS (CHINA) CO LTD
Filing Date
2026-04-30
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

In dynamic load fluctuation environments, existing IPC systems lack the use of historical behavior data for scheduling decisions, the fixed timeout mechanism cannot adapt to handling time fluctuations, and dead letter analysis lacks structured modeling, leading to queue backlog, intensified resource competition, and a vicious cycle.

Method used

The inter-process communication control method based on historical prior constraints dynamically adjusts scheduling priority and timeout threshold through behavior profiling, risk prediction, starvation avoidance and load truncation mechanisms, and optimizes the risk prediction function through dead-letter fingerprinting and cluster attribution analysis to achieve unified feedback control of scheduling and anomaly handling.

Benefits of technology

It improves the operational stability and resource utilization efficiency of the IPC system under dynamic load environments, reduces the probability of misjudgment and dead letter, breaks the vicious cycle, and realizes the system's self-correction and optimization.

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Abstract

本发明涉及通信技术领域,提供一种基于历史先验约束的进程间通信控制方法,能够基于历史进程间通信消息及当前进程间通信消息进行行为画像,并根据每类消息的行为特征向量计算每类消息的风险预测值,实现对消息运行风险的量化建模;采用饥饿规避机制,根据每类消息的风险预测值自适应调整每类消息的调度优先级,采用负载截断机制,根据每类消息的行为特征向量动态调整每类消息的超时阈值,以将风险预测结果引入调度与超时控制决策;按照预设周期扫描死信记录表以进行聚类归因分析,并采用反馈机制对风险预测函数进行优化,能够通过对死信异常的结构化分析反向修正预测模型,将调度机制与异常处理机制整合为统一的反馈控制系统。
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