What data for this category looks like
Core data for continuous basic chemical production comes from DCS/SIS systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring modules, energy metering devices, and raw material storage tank sensors. Data update frequencies cover three tiers: real-time, shift-based, and daily. Routinely associated documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift logs. Data fields include standardized chemical parameters such as reaction temperature (℃), system pressure (MPa), material flow rate (m³/h), and storage tank liquid level (%).
What constraints these characteristics impose on the workflow orchestration link
Decentralized data sources with varying update frequencies require workflows to be configured with multiple data source access nodes, with trigger intervals set according to real-time, shift-based, and daily tiers. Multiple types of business documents need corresponding data validation rules bound by document category to avoid logical confusion from mixed calls. Validation of key interlock parameters must strictly match the safety ranges specified in process index cards, and any abnormal node must trigger a workflow interrupt. Bid document and quotation preparation relies on historical production data, so workflows must link archived data such as shift logs and laboratory reports from the corresponding period to avoid calling expired information.
How to set the configurations
This page applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Global variable binding | Bind the reaction temperature and material flow rate fields in variables | Matches the core business data dimensions of continuous basic chemical production |
| Node ID mapping | Name nodeId according to process links, such as synthesis tower control, distillation tower verification | Facilitates process debugging and error localization |
| Trigger node interval | 10–60 seconds | Adapts to the real-time update rhythm of DCS data |
| Data source association rules | Bind in groups by document type | Corresponds to structural differences across multiple types of business documents |
| Node validation threshold | Key parameter deviation ≤5% | Based on the safety control range specified in process index cards |
| Context cache duration | 300 seconds | Adapts to the information update cycle of shift reports |
Three common mistakes
- Mixed use of node connection points: Configuring the same trigger logic for right-side and bottom-side connection points. The symptom is chaotic execution of process branches. The cause is failing to clarify the rule that the right side is for branch triggers and the bottom side is for serial execution.
- Triggering large model search when the knowledge base is empty. The symptom is an empty response with no matching results returned. The cause is not configuring automatic skip logic for empty knowledge bases.
- No output from node code runs. The symptom is the node status showing success but no return results. The cause is failing to correctly bind input parameters in
variables.
How to confirm proper configuration
- Trigger a test process and check whether the
variablesparameters of each node correctly load the relevant process data. - Simulate a scenario where the knowledge base is empty and confirm that the process automatically skips the large model search step.
- Manually modify key parameters beyond the threshold range and verify whether the node validation rule triggers an intercept.
- Run the process and check the logs to confirm that the execution order of the links matching
nodeIdconforms to the design.
The values provided are common starting points and should be measured against samples relevant to the specific use case.