About This Page
- Client industry: Equipment inspection and preventive maintenance
- Business direction: Supplier and process quality audit
- Capability area: Workflow orchestration
What the Data for This Category Looks Like
Core data for this category comes from daily work outputs of manufacturing enterprise equipment departments, workshop maintenance teams, and public utility departments. It includes equipment ledgers, inspection checklists, maintenance plans, work orders, failure analysis reports, spare parts lists, and special equipment inspection records. Data sources include EAM/CMMS systems, equipment sensor data, operator inspection records, maintenance history, spare parts inventory, and statutory inspection information. Update schedules align with business needs: shift-based checklists update per shift, maintenance plans update weekly or monthly, failure analysis and work orders generate immediately when a fault occurs, and statutory inspection records update annually. Document fields include unique equipment identifiers, inspection execution times, fault codes, spare part models, inventory levels, and similar details. No additional statistical percentage data is included.
Constraints Imposed by These Characteristics on Workflow Orchestration
Multi-source heterogeneous data sources require workflows to support cross-system data pulling and association. This avoids duplicate data entry and data inconsistency. Differentiated update frequencies require workflow nodes to have flexible trigger rule configurations. This adaptation supports scenarios such as shift inspections, regular maintenance, and emergency fault handling. Most category documents are structured equipment-related records. Workflows must support structured data extraction and field binding. This ensures tasks accurately match corresponding equipment information during execution. Maintenance windows must comply with production schedules. Workflows must connect to production schedule nodes to avoid conflicts. Verification links must be added to prevent unplanned downtime or batch quality issues caused by parameter errors.
How to Set Configurations
All configurations below are based on the workflow functionality of FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Variable call rules | Prioritize retrieving specified variables from variables, use plugin default values if not retrieved | Adapt to dynamically passed equipment numbers and shift information in inspection processes, while maintaining stability for fixed parameters |
| Batch execution node trigger condition | Trigger batch inspection tasks according to inspection cycles (daily/weekly) | Match the update frequency of daily inspections, cover collection of inspection data for all workshop equipment |
| Knowledge base recall limit | Top 5 entries | Match the volume of equipment-related documents, avoid redundant information interfering with fault location |
| Timeout threshold | 600 seconds | Adapt to average time consumption for equipment sensor data pulling and spare parts inventory queries |
| Long text generation configuration | Calibrate based on actual measurements | Adapt to content length requirements for work orders and failure analysis reports |
| Node association configuration | Bind nodeId to the corresponding equipment ledger node | Ensure accuracy of data association in workflows, avoid confusion of cross-equipment data |
Three Common Misconfigurations
- Phenomenon: The plugin returns a parameter missing error during execution, and does not automatically call preset default values. Cause: Variable call rules are not configured correctly, and the setting to use default values when variables are not retrieved is not enabled.
- Phenomenon: After the batch execution node runs, the number of generated tasks does not match expectations, and some equipment does not have corresponding inspection records generated. Cause:
nodeIdis not correctly bound to the equipment ledger node, causing some equipment to be excluded from the batch task scope. - Phenomenon: When calling the knowledge base to search for equipment fault solutions, returned content does not match the current equipment model. Cause: Corresponding equipment-specific documents are not associated in the knowledge base retrieval link, and irrelevant content for general equipment is recalled.
How to Verify Successful Configuration
- Manually trigger a single workflow, pass in the specified equipment number variable, check if the plugin prioritizes the passed variable value, and calls preset default values if the variable is not retrieved.
- Run the batch execution node, verify that the number of generated tasks matches the number of associated equipment ledgers.
- Call the knowledge base to search for fault records of the specified equipment, check that recalled results include exclusive document content for that equipment.
- Trigger a long text generation task, check that the generated maintenance analysis report covers category-specific data such as fault history and spare parts lists.
The values provided here are common starting points, and should be evaluated against the reader's own samples.