A method for STK account material self-checking and abnormal self-healing based on device state comparison

By collecting equipment status data to calculate health status and state entropy values, constructing a task dependency chain graph, optimizing storage location selection and anomaly handling, the problem of identifying sub-health and unstable states of STK equipment is solved, and production stability and energy efficiency are improved.

CN122432944APending Publication Date: 2026-07-21JIANGSU DAODA INTELLIGENT TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JIANGSU DAODA INTELLIGENT TECH CO LTD
Filing Date
2026-06-23
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing STK self-verification and anomaly self-healing methods cannot identify sub-health and unstable states of equipment in advance, resulting in frequent abnormal interruptions during task execution, poor accuracy and traceability of material management, and problems such as out-of-order task execution, dependency conflicts, and high energy consumption.

Method used

By collecting equipment status data, calculating health scores and status entropy values, constructing task dependency chain graphs, dynamically adjusting task paths, optimizing storage location selection, and handling anomalies, a self-healing process and task avoidance are achieved, reducing the impact of equipment anomalies on production.

Benefits of technology

Effectively identify sub-health and unstable equipment conditions, reduce abnormal interruptions, improve the accuracy and traceability of material management, avoid task disorder and resource contention, optimize Crane paths, and reduce energy consumption.

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Abstract

The application discloses a kind of STK account material self-checking and abnormal self-healing method based on equipment state comparison, belongs to industrial automation control and intelligent warehousing technical field, and the specific steps of the self-healing method are as follows: I: the real-time state data of each device is collected and preprocessed, and according to each state data after processing, the health degree score and state entropy value of each device are calculated;The present application can identify the sub-health and unstable state of equipment in advance, which not only ensures the production safety in case of serious failure, but also avoids unnecessary downtime caused by slight abnormalities, significantly reduces the abnormal interruption in the task execution process, and improves the accuracy and traceability of material management;At the same time, the present application can find potential problems in advance, avoid task out-of-order execution, dependency conflict and deadlock problem, effectively prevent the preemption of concurrent tasks on process resources, and significantly optimize Crane walking path, reduce the overall energy consumption of warehouse, and greatly shorten the abnormal processing cycle.
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