What This Category of Data Looks Like
Data for petroleum refining production ledgers 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. Data update rhythms fall into three categories: real-time, shift-based, and daily. Daily covered documents include operating procedures, production plans, material balance sheets, distillate export quality reports, interlock ledgers, shift handover records, change management documents, and more. Data fields include unit load, material flow, product fractions, laboratory test indicators, equipment operating status, and others. Corresponding units include cubic meters per hour, degrees Celsius, tons, kilograms per cubic meter, and more.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Differentiated update rhythms across multiple data sources require workflows to support configuring node trigger timing separately for real-time, shift-based, and daily updates, to avoid pulling unupdated data prematurely. Large structural differences between multi-source heterogeneous ledger documents require workflows to use variables configuration to adapt to the field formats of different data sources, ensuring correct data parsing. Multiple continuous units are coupled through material and energy networks, requiring workflows to set cross-unit material balance verification nodes to avoid overall logical errors caused by single-unit data deviations. Severe consequences of data fluctuations and errors require workflows to configure failure retry mechanisms and exception alert nodes to ensure process stability.
How to Set Configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Variable Configuration | Split variables by data source type, corresponding to exclusive fields such as crude oil evaluation and DCS data | Adapt to field differences in multi-source heterogeneous ledgers and avoid data parsing errors |
| Node Trigger Condition | Bind the update node of the corresponding data source by nodeId | Match the differentiated update rhythms of real-time, shift-based, and daily updates |
| Problem Optimization Component Placement | Place after the data pull node | Clean redundant and abnormal data from original ledgers first before performing subsequent summary operations |
| Maximum Context Length | 8000–12000 token | Accommodate long document content such as material balance sheets and shift handover records across multiple units |
| Failure Retry Count | 2–3 times, with a 60-second interval | Address abnormal scenarios such as temporary DCS communication fluctuations and temporary system freezes |
| Parallel Pull Limit | Parallelize the first 6 nodes | Match the common unit count scale of refinery enterprises and avoid resource overload |
Three Common Misconfigurations
- Workflow dialogs show failure, but the model backend has response logs. The cause is failure to use
variablesconfiguration to adapt to the multi-field format of refinery ledgers, resulting in model output that cannot match downstream summary logic. - Agent runtime errors prompt that a field is empty. The cause is failure to bind the data source's update node by
nodeId, resulting in tank farm metering data being pulled before it is updated and entering the summary link. - Prompts do not execute in node order. The cause is failure to correctly set dependency relationships between nodes, resulting in the problem optimization component running before data pull, unable to effectively clean original ledger data.
How to Confirm Proper Configuration
- Trigger the workflow, check the pull logs of each data source node, and confirm that all corresponding fields have return data that meets expectations.
- Run the problem optimization component, and verify that redundant fields and abnormal values in the original ledger data have been cleaned.
- Simulate a data fluctuation scenario, and verify that the workflow can return to normal operation after the failure retry mechanism is triggered.
- Cross-check the final generated report fields to ensure consistency with the field units of the original ledgers.
The values provided here are common starting points and should be measured against the reader's own samples.