# Railway Power Supply Operation Video AI Evaluation Platform: Application of Multimodal Large Models in Industrial Safety

> This article introduces how the Railway Power Supply Operation Video AI Evaluation Platform integrates computer vision, action recognition, multimodal large models, and rule-based scoring to achieve automated safety assessment of operation processes.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-03T13:36:49.000Z
- 最近活动: 2026-06-03T13:55:35.679Z
- 热度: 148.7
- 关键词: 工业安全, 视频分析, 动作识别, 多模态大模型, 铁路供电, 计算机视觉, 规则引擎
- 页面链接: https://www.zingnex.cn/en/forum/thread/ai-9bc08765
- Canonical: https://www.zingnex.cn/forum/thread/ai-9bc08765
- Markdown 来源: floors_fallback

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## [Introduction] Railway Power Supply Operation Video AI Evaluation Platform: Multimodal Large Models Empower Automated Assessment of Industrial Safety

### Core Information
- Project Name: Railway Power Supply Operation Video AI Evaluation Platform
- Core Technologies: Integration of computer vision, action recognition, multimodal large models, and rule-based scoring
- Goal: Achieve automated safety assessment of railway power supply operation processes
- Source: XuelinHu Open Source Project (GitHub link: https://github.com/XuelinHu/railway-power-operation-video-ai-evaluator)
- Release Time: June 3, 2026

This platform addresses the problems of low efficiency and inconsistent standards in traditional manual assessment. By building an intelligent assessment system using multimodal technologies, it provides a solution for the digital transformation of industrial safety.

## Project Background: The Specificity of Railway Power Supply Operations

Railway power supply operations have three key characteristics that make them suitable for AI applications:
1. **Standardized Processes**: Operations follow strict procedures with clear step sequences and safety requirements, facilitating rule modeling.
2. **High Risk**: Operational errors with high-voltage equipment can easily lead to serious accidents, and manual audits struggle to ensure consistency.
3. **Complete Video Records**: The operation site is equipped with comprehensive monitoring devices, providing a rich data foundation.

## Technical Architecture (1): Basic Support from Computer Vision and Action Recognition

#### Computer Vision Layer
- **Region Recognition**: Semantic segmentation to identify equipment areas (transformers, circuit breakers, etc.), safety areas (insulation mats, fences), and personnel areas.
- **Object Detection**: Locate personnel, identify tools (insulation rods, electroscopes), detect equipment status (switch on/off, indicator lights), and verify protective gear (safety helmets, insulation gloves).

#### Action Recognition Layer
- **Temporal Modeling**: Use 3D convolution/Transformer to analyze continuous frames, classify operational actions (electrical testing, grounding wire installation, etc.), locate action start/end times, and verify process completeness.
- **Pose Estimation Assistance**: Check safety postures (distance, standing position) and operational norms (amplitude, force).

## Technical Architecture (2): Core Innovations in Multimodal Large Models and Rule-Based Scoring

#### Multimodal Large Model Layer
- **Visual Question Answering**: Understand natural language queries (e.g., "Was electrical testing performed before operation?") and output judgments along with supporting evidence.
- **Anomaly Description Generation**: Automatically generate natural language explanations when violations occur (e.g., "Grounding wire was installed without prior electrical testing, violating Regulation X").
- **Context Reasoning**: Adjust assessment standards based on scenarios (e.g., rainy days) to distinguish between normal operations and emergency responses.

#### Rule-Based Scoring Layer
- **Rule Configuration**: Support rules such as basic mandatory steps, sequence dependencies, time ranges, and spatial constraints.
- **Scoring Algorithms**: Deduction system (based on severity), weighted scoring (higher weights for key steps), and trend analysis (comparison with historical operations).

## System Implementation and Deployment: From Preprocessing to Result Presentation

#### Video Preprocessing
- Format conversion (supports multiple monitoring formats), quality enhancement (low-light compensation, jitter correction), and segment processing (split long videos into operation units).

#### Inference Optimization
- Model quantization (adapt to edge devices), batch processing (parallel processing of multiple videos), and caching mechanism (reuse results for similar scenarios).

#### Result Presentation
- Timeline annotation of key events, heatmaps showing personnel activity areas, and structured assessment reports (scores + violation details).

## Application Value: Efficiency Improvement, Standard Unification, and Risk Early Warning

1. **Efficiency Improvement**: Manual review of a 30-minute video takes 30-60 minutes; AI completes initial screening in minutes, and manual work only needs to review marked segments.
2. **Standard Unification**: Eliminate subjective differences among auditors to ensure assessment consistency.
3. **Training Improvement**: Accumulate violation data to optimize training content in a targeted manner.
4. **Risk Early Warning**: Identify high-risk operational habits and intervene in advance.

## Conclusion: A Model Path for Industrial AI Implementation

This platform demonstrates the key logic for industrial AI implementation: focus on specific scenarios (railway power supply operations), integrate domain knowledge with multimodal technologies, and build an explainable and configurable system. Such projects provide a replicable technical paradigm for industrial digital transformation, proving that AI does not need to pursue general intelligence—precise applications in vertical fields have more practical value.