A precise prediction and multi-scenario coordination control system for container truck arrival time sequence

By constructing a causal propagation network and handling latency through non-accumulative logical operations, combined with adjustments to individual logical files, the problem of individualized timing in truck arrival forecasting was solved, achieving accurate truck arrival timing forecasting.

CN122264230BActive Publication Date: 2026-08-25ZHEJIANG YIGANGTONG ELECTRONIC COMMERCE CO LTD
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Patent Information

Application Number
CN202610709707.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-22
Publication Date
2026-08-25
Estimated Expiration
2046-05-22

AI Technical Summary

Technical Problem

Existing container truck arrival forecasting solutions fail to achieve accurate individualized time-series forecasting, struggle to handle causal propagation networks and nonlinear overlaps in time delays among multiple events, and lack cross-chain dynamic constraints and individualized adjudication mechanisms.

Method used

A causal propagation network is constructed between events. The event propagation module triggers related events and generates delays. The delay processing module uses non-accumulative logical operations to process multiple delays. The inter-chain constraint module maintains the conditional constraints between event chains. The file self-correction module adjusts individual logical files. The deduction control module generates time series prediction results.

Benefits of technology

It improves the accuracy of container truck arrival time prediction, avoids redundant calculation and improper superposition of delays, and achieves accurate prediction for each truck.

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Abstract

The application discloses a precise prediction and multi-scene cooperative control system for container truck arrival time sequence, and relates to the technical field of port logistics scheduling. The system comprises an event propagation module, a time delay processing module, an inter-chain constraint module, an archive self-correction module and a deduction control module. The event propagation module constructs a causal propagation network and triggers associated event concomitant time delay. The time delay processing module performs non-accumulative logical operation on multiple time delays with causal correlation or time overlap based on a time delay fusion rule set. The inter-chain constraint module maintains a set of conditional constraint relationships between event chains. The archive self-correction module maintains an individual logical archive composed of logical state bits for each transportation unit, dynamically adjusts the logical state bits and ignores misjudgment event chains. The deduction control module obtains the individual logical archive, performs conditional verification with the logical state bits in deduction, executes inter-chain constraint rules and generates a time sequence prediction result.
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