Intelligent Operation Ticket Analysis System

Large Model-Based Power Operation Intelligent Review

Project Overview

Automatic analysis system for power industry customer service tickets based on large language models, supporting ticket classification, sentiment analysis, problem identification.

Technical Challenges

Traditional power operation ticket review relies on manual experience, with low efficiency and prone to errors. The complexity of operation tickets requires reviewers to have rich professional knowledge, and manual review cannot meet the high requirements of modern power systems for safety and efficiency.

Solution

Combined large language models with power professional knowledge base to develop intelligent operation ticket analysis system. Through transfer learning technology, adapted general large models to the power professional domain, combined with rule engines and deep learning algorithms to achieve high-precision, high-efficiency automatic operation ticket review.

Key Highlights

Operation ticket recognition accuracy reaches 98.5%

Review efficiency improved by 10 times

Supports 100+ operation types

Real-time risk warning

Complies with power industry standards

Technology Stack

AI Models: GPT architecture, Transformer, BERT

Development: PyTorch, TensorFlow, Hugging Face

Data Processing: Pandas, NumPy, Scikit-learn

Deployment: Docker, Kubernetes, FastAPI

Professional: Power industry knowledge base, operation rule engine

Development Timeline

Months 1-2: Requirements analysis and power knowledge base construction

Months 3-4: Large model transfer learning and adaptation

Months 5-6: Rule engine and system integration

Months 7-8: Testing validation and performance optimization

Months 9-10: On-site deployment and user training