---
title: "LLM引导的测量可信度修正：为工业过程推理提供可信输入"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.06111v1"
permalink: "https://feed.astralor.com/items/itm_2423b54fdce18ce92261304f"
published: "2026-07-07T10:21:55.000Z"
score: 79
channel: "tech"
tags: ["LLM","MCC","可信AI","工业过程推理","软测量"]
---

# LLM引导的测量可信度修正：为工业过程推理提供可信输入

## AI 点评

> 这篇论文值得关注，因为它巧妙地用LLM解决了一个工业落地中的棘手问题——测量可信度。对于从事工业AI、预测性维护或软传感器研发的工程师，该方法提供了一种轻量化、可解释的预处理方案，且不依赖昂贵的传感器冗余。更关键的是，其思路可以推广到其他依赖传感器数据流的场景，如智能制造、能源管理等。

## 速览

工业预测和软测量常因输入测量值不可信而失效，现有方法依赖数值相关性、故障标签或过程方程，但未必完备。本文提出LLM引导的测量可信度修正（MCC）框架，将过程文档中的测量语义转化为数值模型可用的语义信息，构建独立的过程参考，在预测前修正局部测量冲突。在多个复杂工业预测和软测量任务上，MCC使真实测试协议的平均相对MAE降低30.7%，受控污染协议降低80.3%，且仅增加0.5~2.0k在线参数，推理最慢步时0.089ms。这表明测量语义可借助LLM将过程文档转化为轻量级预推理可信度修正，显著提升预测精度。

## 关键点

- 提出LLM引导的测量可信度修正（MCC）框架，利用过程文档中的测量语义来提升工业预测输入的可靠性。
- MCC仅增加0.5~2.0k在线参数，推理步时低至0.089ms，在真实测试和污染协议上分别降低MAE 30.7%和80.3%。
- 解决了现有方法依赖于数值相关性、故障标签或过程方程而在复杂场景下失效的问题。

## 全文

Industrial prediction and soft sensing depend on credible input measurements. In field deployment, a predictor may receive biased, delayed, stale, or derived measurements that still look plausible. Prediction can then fail before the forecasting backbone becomes the main limitation, because the input window no longer represents the real process. Sensor reconstruction, data reconciliation, and fault-tolerant soft sensing reduce this risk, but they often rely on numerical correlation, alarms, fault labels, or explicit process equations. These assumptions are not always available. A correlated variable can also be an unsafe reference when variables share instruments, derived formulas, soft-sensing chains, or control actions. The key issue is to decide before prediction which external measurements can credibly support the current measurement. To address this issue, this article proposes LLM-Guided Measurement Credibility Correction (MCC). MCC converts measurement meanings in process documents into measurement semantics usable by numerical models. It builds independent process references from semantically qualified external measurements and corrects local measurement conflicts before prediction. The predictor therefore receives a more credible input window. Across multiple complex industrial forecasting and soft-sensing tasks, +MCC achieves average relative MAE reductions of 30.7% on real-test protocols and 80.3% on controlled-corruption protocols. It adds only 0.5--2.0k online parameters, with the slowest +MCC inference time at 0.089 ms/step. These results show that measurement semantics can turn process documents into lightweight pre-inference credibility correction and improve prediction accuracy.

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