⋅  应用规程约束风险感知偏好强化学习的⋅  电力设备运维方法

罗海宸; 李明轩; 荣命哲; 杨爱军; 王小华

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⋅ 应用规程约束风险感知偏好强化学习的⋅ 电力设备运维方法

更新时间：2026-08-14

- ⋅ 应用规程约束风险感知偏好强化学习的⋅ 电力设备运维方法
- A Power Equipment Maintenance Method Using Rule-Constrained Risk-Aware Preference Reinforcement Learning
- 西安交通大学学报 2026年
- 作者机构：
  
  西安交通大学电工材料电气绝缘全国重点实验室，710049，西安
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TM 507
- 收稿：2026-06-24，
  
  修回：2026-08-13，
  
  录用：2026-08-14，
- 稿件说明：
移动端阅览
罗海宸, 李明轩, 荣命哲, 等. ⋅ 应用规程约束风险感知偏好强化学习的⋅ 电力设备运维方法[J]. 西安交通大学学报,2026.

LUO Haichen, LI Mingxuan, RONG Mingzhe, et al. A Power Equipment Maintenance Method Using Rule-Constrained Risk-Aware Preference Reinforcement Learning[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026.
罗海宸, 李明轩, 荣命哲, 等. ⋅ 应用规程约束风险感知偏好强化学习的⋅ 电力设备运维方法[J]. 西安交通大学学报,2026. DOI：

LUO Haichen, LI Mingxuan, RONG Mingzhe, et al. A Power Equipment Maintenance Method Using Rule-Constrained Risk-Aware Preference Reinforcement Learning[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026. DOI：

摘要

针对电力设备智能运维中多模态证据融合困难、通用大模型运维规程约束不足，以及高风险任务输出偏好难以控制等问题，提出一种面向电力设备运维的规程约束风险感知偏好强化学习方法。该方法将任务问题、现场多模态证据、规程约束及设备与场景元信息组成结构化运维输入，建立状态判断正确性、证据一致性、规程一致性、风险保守性和建议可执行性五维偏好裁决标准，并将综合偏好得分、安全评分差距、任务复杂度和证据充分性纳入动态间隔偏好优化，使模型输出更保守、可复核且符合运维流程。所构建的数据集覆盖6种任务类别，包括输电线路感知、隔离开关状态监测、电力现场安全监测、变电站监测、电力器件识别和发电厂综合监测。研究结果表明：所提方法在电力设备运维验证集上的选择题准确率达到87.89%，相较初始策略模型准确率提升了13.56个百分点，相较近端策略优化模型提升了5.54个百分点，从而验证了所提方法在离线电力设备运维场景验证中的有效性。该研究为提升多模态大模型在高风险电力设备运维场景中的规程遵循能力与安全输出能力提供了一种可行方案。

Abstract

To address difficulties in multimodal evidence fusion

inadequate enforcement of maintenance rules in general-purpose large models

and hard-to-control output preferences in high-risk power equipment maintenance

this paper proposes a rule-constrained risk-aware preference reinforcement learning method. The method organizes task queries

multimodal field evidence

maintenance rules

and equipment and scenario metadata into structured inputs. It establishes a five-dimensional preference criterion covering state-assessment correctness

evidence consistency

rule compliance

risk conservativeness

and recommendation actionability. Comprehensive preference scores

safety-score gaps

task complexity

and evidence sufficiency are incorporated into dynamic-margin preference optimization to encourage conservative

verifiable

and procedure-compliant outputs. The constructed dataset covers six task categories: transmission-line perception

disconnector-state monitoring

on-site safety monitoring at power facilities

substation monitoring

power-component recognition

and comprehensive power-plant monitoring. Experimental results show that the proposed method achieves a multiple-choice accuracy of 87.89% on the power equipment maintenance validation set

exceeding the initial policy model and the proximal policy optimization model by 13.56 and 5.54 percentage points

respectively. These results validate the method for offline power equipment maintenance scenarios. This study provides a feasible approach to improving rule compliance and safe outputs of multimodal large models in high-risk maintenance.

关键词

Keywords

references

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