API (Application Program Interface) construction method for industrial big data micro service

By employing heterogeneous protocol parsing, QUIC protocol transmission, Maxwell demon gate mechanism, and time-series dependency graph analysis model, the problem of distinguishing different value requests in industrial microservice API systems was solved, enabling priority processing of high-value API requests and avoiding blocking and delays in critical business processes.

CN122457555APending Publication Date: 2026-07-24BEIJING NANCAL RUIYUAN DIGITAL TECH CO LTD
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Patent Information

Application Number
CN202610519077.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-20
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Industrial microservice API systems cannot effectively distinguish API requests of different values, causing critical control operations to be blocked by ordinary monitoring requests, resulting in unacceptable response delays.

Method used

By deploying a heterogeneous protocol parsing proxy module for preprocessing, a multiplexed transmission channel based on the QUIC protocol is constructed. The information value score is calculated using a dynamic bit rate adaptive algorithm and the Maxwell demon gate mechanism. The token bucket algorithm is used for hierarchical queue allocation, and the time-series dependency graph analysis model is used to predict the service node response latency, thereby realizing differentiated processing of control and monitoring API requests.

Benefits of technology

Effectively differentiate the value of API requests, ensuring that high-value control requests are given priority access to system resources, avoiding blocking of critical business processes, reducing system latency, and improving resource utilization efficiency.

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Abstract

The invention provides an API (Application Program Interface) construction method for industrial big data micro-service, which belongs to the technical field of industrial big data, and comprises the following steps of: establishing a distributed API gateway cluster, calculating an information value score of an API request by adopting a Maxwell gating mechanism, and realizing hierarchical queue scheduling in combination with a token bucket algorithm; a service grid side vehicle agent and an eBPF-based kernel bypass module are deployed, a time sequence dependence graph analysis model is used for predicting service node response time delay to generate an optimal calling path, and a hybrid logic clock protocol is used for carrying out differential consistency processing on a control type API request and a monitoring type API request; an LSM tree storage engine is constructed to cooperate with a time partitioning strategy and a Z-Order curve to index persistent data in a multi-dimensional mode, and the problem that key control services are blocked by common monitoring requests due to the fact that API requests of different values cannot be effectively distinguished is solved.
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