Data Profile for This Category
This category's data comes from EAM/CMMS systems, equipment sensing terminals, operator inspection records, maintenance history ledgers, spare parts inventory lists, and statutory inspection files for special equipment. Data update cycles cover per-shift inspection submissions, daily/weekly/monthly maintenance plan updates, and real-time reporting when faults occur. Daily documents include equipment ledgers, inspection checklists, graded maintenance plans, work orders, fault analysis reports, spare parts requisition forms, and similar records. Fields include equipment numbers, inspection item codes, exception codes, spare part models, inspection validity periods, and other relevant fields. Units include standard asset and operations-related units including units, counts, days, pieces, and similar units.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data sources are scattered and update cycles vary significantly. Multi-turn dialogue must bind a unique session identifier to avoid mixing operation and maintenance records across different equipment. Document fields are numerous and closely interconnected. Prompts must accurately specify the range of callable fields to prevent the AI from generating irrelevant content. Real-time fault data requires conversation contexts to retain the latest maintenance records, rather than relying solely on static historical data. Compliance requirements for equipment ledgers and statutory inspection files mean prompts must embed dedicated verification logic to ensure drafted reports meet operation and maintenance specifications. Additionally, character lengths of category documents vary widely. Context scale must be limited to avoid exceeding input limits.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind equipment number + session creation timestamp | Distinguish operation and maintenance sessions for different equipment to avoid cross-context confusion |
variables | Preset equipment ID, inspection cycle, exception code template | Accurately deliver category-specific fields and reduce redundant AI output |
messages | Concatenate the most recent 5 operation and maintenance records in session order | Ensure the context called by the AI conforms to the logical order of multi-turn dialogue |
| Maximum context length | 800–1200 characters | Adapt to the single-batch document length of inspection checklists and work orders to avoid input exceeding limits |
| Input over-limit interception | Calibrated based on actual measurements | Match the average character volume of category documents and intercept requests exceeding model input limits in advance |
Common Configuration Mistakes
- Issue: Conversation logs cannot be associated with specific equipment or maintenance teams. Cause:
chatIdis not bound to equipment numbers and team identifiers, leading to ambiguous session ownership. - Issue: The AI cannot parse uploaded JPG-format inspection photos. Cause: No tool node for image parsing is configured, and only text-based operation and maintenance data is called in the AI dialogue module.
- Issue: When inputting an overly long equipment maintenance plan, the AI returns empty results or triggers a system error. Cause: No input over-limit interception logic is set, and processing is not terminated early after the request exceeds the model input limit.
How to Verify Proper Configuration
- Launch an inspection session for a specific device, check whether the conversation log includes the bound device number and team information to confirm that the
chatIdconfiguration is effective. - Upload a JPG-format inspection record sheet, verify whether the AI can recognize and extract inspection items and exception descriptions from the table.
- Input maintenance plan text that exceeds the preset length, confirm whether the system pops up an input over-limit prompt and intercepts the request in advance.
- Launch a session deletion operation, verify whether the corresponding conversation record is correctly cleaned to comply with operation and maintenance data privacy requirements.
The values provided are common starting points and should be measured against the reader's own samples.