任务重要度感知的结果篡改自适应检测方法研究OA

[Purposes]In distributed collaborative computing environments,to address the chal-lenge that computation results are tampered and detection costs are high,an adaptive detection strate-gy is designed by incorporating task importance.This strategy will improve detection efficiency and re-duce unnecessary resource consumption.[Methods]In this paper,a task-importance-aware adap-tive detection method was proposed for result tampering.A large language model was used to automat-ically decompose the complex tasks submitted by users'and quantify the importance of each sub-task.Then,detection resources were dynamically allocated according to task importance.Critical sub-tasks were prioritized,and detection frequency was adaptively adjusted to balance coverage and resource overhead.The simulation experiments were conducted by using the GSM8K dataset and Worfbench dataset.[Results]The results show that the proposed method significantly reduces detection over-head compared with traditional fixed-frequency strategies.The overhead is reduced by approximately 68%,with no noticeable degradation in detection capability.The results verify the method's effective-ness and feasibility.[Conclusions]This work achieves targeted detection for high-importance tasks,improving detection performance while maintaining good applicability and scalability.It is help-ful for scenarios that require high result integrity,such as autonomous driving,multi-agent collabora-tion,and edge computing.