What the data for this category looks like
Core data for grain reserve management comes from grain depot inbound and outbound ledgers, warehouse temperature and humidity inspection logs, employee service ticket systems, and unified reserve management reports. Update cadence: inbound and outbound ledgers update in real time with physical operations, temperature and humidity logs update after daily inspections, employee service tickets update immediately when service requests are submitted. Document structure includes structured ledgers (fields include depot number, grain category, inventory weight, inbound time, units are tons and degrees Celsius), unstructured inspection photos (jpg format), and ticket documents (fields include employee ID, service type, processing progress, etc.).
What constraints these characteristics impose on workflow orchestration
Grain reserve data sources are scattered, including structured ledgers, unstructured photos, and ticket data. This requires workflows to support multi-data source integration. Different data have large differences in update frequency and real-time requirements, so trigger conditions must be set separately. For example, inventory data requires real-time triggers, while inspection photos can be processed in batches on a scheduled basis. Grain categories and inventory units have fixed enumeration ranges, so parameter validation must be configured in workflows to avoid data format errors. Employee service tickets are associated with specific grain depots, so workflow nodes must be bound to the unique identifier of the corresponding depot to avoid cross-depot data confusion.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Limit enumeration values to wheat, rice, corn, set unit to tons | Grain reserve categories are fixed, inventory statistics require unified units to avoid data chaos |
| Timeout Period | 600 seconds | Warehouse data pulling and inspection photo processing require long processing cycles |
| File Upload Node | Support jpg, png formats, maximum single file size 1000 MB | Most inspection photos are in jpg format, and the total size of batch inspection photo collections per depot is large |
| Trigger Timing | Trigger based on real-time data updates, set batch inspection tasks to run every 15 minutes | Inventory data needs immediate synchronization, batch inspections reduce system load |
nodeId | Configure unique identifiers separately based on depot number and node type | Parallel workflows across multiple depots need to distinguish node ownership to avoid process conflicts |
Three Common Configuration Errors
- Phenomenon: The AI chat module in the workflow cannot directly read uploaded jpg format inspection photos, and prompts that the file format is not supported. Cause: The allowed upload types for jpg format were not configured in the file upload node, only the default document formats were enabled.
- Phenomenon: Custom knowledge base selection variables return empty values, and the reserve knowledge base of the corresponding depot cannot be associated. Cause: The data type of the variable was not specified as an enumeration option in the
variablesconfiguration, causing the parameter to fail to be correctly passed to subsequent nodes. - Phenomenon: The imported inbound and outbound audit workflow cannot upload documents, and the upload button is unresponsive. Cause: The internal employee access permission of the file upload node was not enabled in the workflow, causing external users to fail to trigger the upload operation.
How to Verify the Configuration is Complete
- Trigger a simulated inventory update data, check whether the workflow starts automatically and pulls the ledger data of the corresponding depot.
- Upload a jpg format inspection photo, check whether the workflow can correctly identify and pass it to the AI chat module.
- View the enumeration variables in the
variablesconfiguration, confirm that the settings for grain categories and units match the actual inventory data. - Check whether the
nodeIdof each workflow node is unique, to avoid process interruptions caused by node conflicts.
The values provided here are common starting points, and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.