What Data for This Category Looks Like
Business data for petroleum refining comes from DCS/APC control systems, LIMS laboratory information management systems, tank farm metering systems, crude oil evaluation databases, equipment management systems, and energy management systems. Update frequencies include real-time, shift-based, and daily. Associated daily documents include operating procedures, production plans, material balance sheets, distillate export quality reports, interlock logs, shift handover records, and change management documents. Data fields include unit load, distillation range, sulfur content, tank farm inventory, equipment operating parameters, and no fictional statistical values are included.
Constraints Imposed on Workflow Orchestration by These Characteristics
Differences in update frequencies across multiple data sources require workflows to support pulling data at different time cycles, to avoid traceability deviations caused by data lag. The business form where multiple continuous production units are coupled through material and hydrogen energy networks requires workflows to use variables to transfer cross-unit material balance parameters, ensuring consistent data linkage between nodes. Document types cover multiple formats such as procedures, reports, and logs, requiring workflow configuration nodes to adapt to field parsing rules for different documents. The serious consequences of business errors require workflows to set exception branches and retry mechanisms to block risk spread.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Timing | Real-time data push + daily scheduled trigger | Matches the update frequencies of DCS real-time data and daily material balance reports |
| Variable Binding | Enable variables to transfer cross-unit material parameters | Adapts to business linkage requirements where multiple units are coupled through material and hydrogen energy networks |
| Exception Retry Configuration | 3 retries, 600-second timeout threshold | Addresses risks of data pull delays or interface exceptions |
| Document Parsing Rules | Match corresponding parsing templates by document type | Adapts to multi-format documents such as operating procedures and distillate export quality reports |
| Node Flow Condition | Bind exception handling branches by nodeId | Accurately triggers subsequent disposal processes after defect analysis |
Three Common Misconfigurations
- Workflow retains AI node thinking content after running, with no abnormalities during debugging. This occurs because the code node does not correctly access the full output stream of the AI node, or does not configure output filtering rules to remove thinking tag sections.
- Workflows cannot pull complete historical traceability data, and can only obtain real-time data. This occurs because the trigger timing is only configured for real-time push triggers, and no shift-based periodic trigger rules are added, missing business data updated per shift such as LIMS laboratory records.
- Workflows cannot complete file upload and download operations via HTTP interfaces. This occurs because the storage path and access permissions of the file node are not configured, or the corresponding data storage node is not bound.
How to Confirm Proper Configuration
- After triggering the workflow, verify that the pulled data covers three types of update frequency content: real-time parameters, shift-based laboratory reports, and daily material balance sheets.
- View workflow flow logs to confirm that cross-unit material parameters bound via
variablesare transferred correctly between nodes. - Manually trigger a simulated scenario of data pull failure, to confirm that the exception branch executes retries and subsequent processing as configured.
- Call the bound HTTP interface to test whether the file upload and download process triggers normally, and matches the configured storage rules.
The values provided are common starting points and should be measured against the reader's own samples.