What this type of data looks like
Core data for refining and chemical production comes from distributed control systems (DCS), equipment ledger management systems, and operation work order platforms. Data update cycles are split into two categories: real-time second-level operating parameters, and equipment inspection and work order records updated per production shift. Each data entry uses structured format, and includes these fields: equipment unique identifier, operating temperature (unit: ℃), system pressure (unit: MPa), material flow (unit: m³/h), work order number, processing status, and timestamp. There are no nested complex layers.
What constraints these characteristics impose on workflow orchestration
The real-time second-level update of operating parameters requires that workflow trigger nodes match the data collection interval. Overly short polling cycles should be avoided. The structured fixed-field feature requires that workflow variable extraction must accurately bind specified fields such as equipment ID and pressure values. Fuzzy matching cannot be used. Operation data updated per shift requires that the scheduled trigger node of the workflow adapts to the production shift cycle to ensure complete data synchronization. Fixed physical quantity units require built-in parameter verification rules in the workflow to avoid process interruptions caused by unit mismatches.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Triggered by data update events | Matches the real-time/shift-based update rhythm of refining and chemical production data |
variables | Bind equipment ID and operating pressure fields | Corresponds to the fixed field structure of structured data |
| Loop Body Execution Interval | 600 seconds | Adapts to the data collection cycle of distributed control systems |
| Timeout Threshold | 1200 seconds | Covers the processing cycle of routine operation work orders |
| Node Retry Count | 3 times | Addresses temporary system and network fluctuations |
nodeId | Specify the unique identifier of work order transfer nodes | Ensures the workflow accurately jumps to the corresponding link |
| Single Query Limit | Top 5 entries | Adapts to the single return volume limit of refining and chemical databases |
Three Common Misconfigurations
- Workflow calls to environment variables return empty values. The interface shows empty variable values, and logs indicate the specified environment variable was not found. This occurs when the corresponding environment variable is not correctly declared in the Docker deployment configuration, or when the
variablesparameter is not correctly bound within the workflow. - The result of index increment by 1 in the loop body is null. The index field output by the loop node is empty, causing the workflow to interrupt. This occurs when the starting index is not correctly configured during loop initialization, or when the index variable is not bound to the
variablesparameter. - Database connection workflows fail to query refining and chemical production data. The node returns empty results, and the error prompt indicates no matching data was found. This occurs when the workflow
variablesare not bound to the database query condition fields, or when the unit of the query condition does not match the production data.
How to Verify Successful Configuration
- Manually trigger the workflow once, check if the
variablesparameter in the output log includes expected fields such as equipment ID and operating pressure. - Simulate a data update event to confirm whether the workflow automatically triggers at the preset interval.
- Check the condition parameters of the database query node to confirm that they fully match the fields and units of production data.
- View the index output of the loop body node to confirm that the index value increases as expected after each loop.
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.