What this category of data looks like
Data sources for this category include DCS/APC systems, LIMS laboratory management system, tank farm metering system, crude oil evaluation database, equipment management system, and energy management system. Daily documents cover operating procedures, production plans, material balance sheets, distillate export quality reports, interlock ledgers, shift handover records, and change management documents. Data update rhythms cover real-time, per-shift, and daily. Fields include material components, operating parameters, equipment status, quality indicators, and more. Formats vary by document type, with no unified fixed structure.
What constraints these characteristics impose on workflow orchestration
Multiple continuous production units couple via material and hydrogen energy networks. Raw material properties fluctuate widely. Workflows must support real-time access and dynamic adjustment of multi-source heterogeneous data. Multiple document formats are inconsistent. General text extraction rules must be configured in workflows to adapt to structural differences across documents. Data update frequencies have wide spans. Workflows must support triggering different execution nodes at real-time, per-shift, or daily intervals. When handling process fluctuations, documents from multiple departments must be linked quickly. Workflows must support fast aggregation of cross-department data and abnormal trigger logic.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow trigger timing | Trigger based on data update nodes, supports real-time, per-shift, and daily modes | Matches the multi-update-frequency data characteristics of this category |
variables initialization | Bind fixed data sources such as crude oil evaluation and DCS real-time data | Adapts to the preloading requirements of multi-source heterogeneous data |
| Text extraction node configuration | Split into chunks of 800–1200 characters based on actual calibration | Adapts to format differences across different process documents |
| Loop call configuration | Set the number of loops based on the actual number of units, with a 60-second interval | Adapts to the collaborative orchestration requirements of multiple continuous production units |
| Exception branch configuration | Bind interlock ledger and quality report data as trigger conditions | Matches the rapid disposal logic for process abnormalities |
nodeId association configuration | Bind unique identifiers of cross-department nodes | Achieves fast aggregation of cross-department data |
Three Common Mistakes
- Workflow text extraction nodes return empty JSON results. The root cause is that no applicable chunking rules are configured for the multi-format documents in this category, which prevents target fields from being identified.
- The workflow global variable history does not show the output of a specified component. The root cause is that the output of the target component is not bound to the global
variablesstorage node, and variable registration is not completed. - Timeouts occur when calling HTTP interfaces in workflow loops. The root cause is that a reasonable loop call interval is not set based on the real-time update interval of DCS data, which causes the request frequency to exceed the system's carrying limit.
How to Confirm Configuration Completion
- Use the node log viewing function of FastGPT v4.17.0. Trigger a workflow corresponding to the target update frequency, and confirm that all bound data sources are successfully accessed.
- Manually input a typical refinery process document, and verify whether the text extraction node can return structured data containing target fields.
- Configure an exception trigger test, simulate a process fluctuation scenario, and confirm whether the workflow can jump to the preset exception handling branch.
- View the global variable list, and confirm that all component outputs that need to be reused have been registered.
The values provided are common starting points and should be measured against the reader's own samples.