A method, system, device and medium for coordinating and scheduling resources of a telecommunications service field operation

By splitting work orders into atomic tasks and building a dependency state network, combined with optimization algorithms and sensor data, the problem of low efficiency in cross-professional collaboration in telecommunications network operation and maintenance was solved, and precise on-site operation resource scheduling and real-time optimization were achieved.

CN122334809APending Publication Date: 2026-07-03HUNAN VOCATIONAL & TECH COLLEGE OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUNAN VOCATIONAL & TECH COLLEGE OF POSTS & TELECOMM
Filing Date
2026-04-01
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

In existing telecommunications network operation and maintenance, work orders lack detailed status and logic that machines can understand, resulting in low efficiency of cross-professional collaboration, difficulty in verifying manual progress reports, slow response and easy rework, affecting the reliability and complexity of operations.

Method used

By splitting the original work order into atomic tasks, constructing a dependency state network, querying the task candidate list based on verified state snapshots, and performing collaborative scheduling through optimization algorithms, precise work assignment instructions are generated, and on-site sensor data is used for verification and adjustment.

Benefits of technology

It enables precise perception and real-time optimized scheduling of on-site telecommunications service operations, improving the reliability and relevance of collaborative scheduling, and reducing processing time and resource waste.

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Abstract

This application relates to a method, system, device, and medium for collaborative scheduling of on-site telecommunications service resources. The method includes: acquiring an original work order and, based on a business rule engine, splitting and instantiating each task instruction in the original work order to obtain atomic tasks; constructing a dependency state network using the atomic tasks as nodes and the dependencies between the atomic tasks as edges to obtain an executable task network blueprint; querying all preceding dependent nodes in the executable task network blueprint that have been verified as completed based on verified state snapshots to obtain a task candidate list; and performing collaborative optimization scheduling for each atomic task based on the task candidate list to obtain a precise dispatch instruction set. This method can decompose macro-level work orders into a network of atomic tasks with temporal sequences and dependencies, thereby effectively improving the targeting and reliability of collaborative scheduling and increasing the speed of collaborative scheduling.
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