An automatic control method and device for oil and gas pipeline detection, an electronic device, and a storage medium

By constructing a multi-head attention TCN network and a factor graph model, and combining time decay and redundancy value factors, the problem of unreasonable resource allocation in the scheduling of oil and gas pipeline inspection tasks was solved, and the accuracy of inspection tasks and efficient utilization of resources were achieved.

CN120991243BActive Publication Date: 2026-04-17GUANGZHOU YUANJING SECURITY EVALUATION & TESTING CO LTD
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Patent Information

Application Number
CN202511116519.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-11
Publication Date
2026-04-17
Estimated Expiration
2045-08-11

AI Technical Summary

Technical Problem

The current scheduling of oil and gas pipeline inspection tasks does not fully consider the physical properties of the pipeline and the status of the inspection equipment, resulting in unreasonable allocation of inspection resources, equipment running out of power or overloading midway, lack of ability to respond to dynamic changes, and failure to scientifically assess scheduling risks and priorities.

Method used

By collecting physical properties of oil and gas pipelines and status parameters of detection equipment, a multi-head attention TCN network is constructed for status prediction. Probability analysis is performed using a factor graph model to calculate the time decay factor and redundancy value factor. The Hungarian algorithm is then used for task scheduling decisions to achieve real-time feedback and dynamic adjustment.

Benefits of technology

It improves the accuracy of testing tasks and the utilization rate of resources, can cope with dynamic changes, optimize task priority ranking, and ensure that the capabilities of testing equipment are matched with resources.

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Abstract

This application discloses an automatic control method for oil and gas pipeline inspection, specifically comprising the following steps: Step 1 provides basic data; Step 2 predicts future states based on the data; Step 3 transforms the predictions into probability distributions; Step 4 calculates dynamic factors based on probability; Step 5 achieves optimal scheduling using probability and factors; and Step 6 maintains scheduling timeliness through real-time feedback. By collecting the physical properties of oil and gas pipelines and multi-dimensional state parameters of inspection equipment, comprehensive and accurate scheduling decisions are achieved. A multi-head attention-based TCN network effectively captures the temporal dependencies between pipeline and equipment states, improving the accuracy of state prediction and providing a reliable basis for scheduling decisions. A factor graph model enables probabilistic quantitative analysis of pipeline and equipment states, scientifically assessing the execution risks of inspection tasks. The introduction of time decay factors and remaining power redundancy value factors considers the dynamic changes in task urgency and equipment power redundancy, optimizing task priority ranking. Through real-time feedback and dynamic adjustment mechanisms, the scheduling scheme adapts to state changes during the inspection process, improving inspection efficiency and resource utilization.
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