This document applies to FastGPT v4.17.0.
What This Type of Data Looks Like
Core data sources for continuous basic chemical production include DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring devices, energy metering devices, and raw material storage tank sensors. Data update frequencies fall into three categories: real-time, shift-based, and daily. Daily associated documents include operating procedures, process index cards, DCS trend charts, laboratory test reports, interlock bypass records, special work permits, and shift logs. Data fields include process parameters (pressure, temperature, flow, with units corresponding to MPa, ℃, m³/h, etc.), batch numbers, detection times, interlock tag numbers, bypass duration, and other fields. Field structures vary across different documents.
Constraints Imposed by These Characteristics on Workflow Orchestration
Differences in update frequencies across multiple data sources require workflows to support mixed trigger modes. Workflows can respond to real-time data triggers, and also execute batch tasks on a shift or daily cycle. Diverse document structures and field differences require workflow components to adapt to inputs of different formats, to avoid parsing failures caused by field mismatches. The high-priority nature of strong interlocks and real-time process data requires that anomaly analysis nodes in workflows can be scheduled preferentially, and data pulling must meet real-time requirements. The closed-loop process characteristics of continuous production require workflows to support cyclic calling of related nodes to complete multiple rounds of data verification and analysis.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Map to core fields such as batch numbers, process parameter values, and detection times according to data source type | Matches core business fields for quality traceability in continuous basic chemical production, to facilitate subsequent associated analysis |
nodeId | Assign unique identifiers to DCS data nodes, laboratory test report nodes, and anomaly analysis nodes | Facilitates precise targeting of target nodes during workflow cyclic calls, avoiding trigger confusion |
| Cyclic trigger interval | 10–30 seconds (real-time data), 8 hours (shift log data) | Matches the update frequency of corresponding data sources, avoiding repeated data pulling or delayed processing |
| Workflow timeout period | 600 seconds | Adapts to the time required for multi-source data splicing and professional large model analysis |
| Large model adaptation configuration | Calibrate based on actual measurements | Select appropriate large model parameters based on the semantic characteristics of chemical professional text |
Three Common Misconfigurations
- Duplicate triggers occur when a workflow calls an HTTP interface cyclically. The cause is that no unique
nodeIdis configured for the cyclic node, causing the workflow to fail to accurately terminate the cyclic process. - The text content extraction component returns empty values. Reconfiguring the component allows normal content extraction. The cause is that chemical professional fields in
variableswere not correctly bound during initial configuration, and the field mapping relationship was corrected during subsequent reconfiguration. - Some large models cannot normally extract content from process index cards. The cause is that the input context was not optimized for chemical professional terms, and some large models have limitations in semantic understanding of long-process chemical documents.
How to Confirm Correct Configuration
- Trigger the workflow, then view the field list of
variables. Confirm that core business fields such as batch numbers and process parameter values are included, and check whether the field mapping matches the configured settings. - Test cyclic calling of the HTTP interface, then view the node execution logs. Confirm that the trigger interval matches the set value, and there are no duplicate trigger records.
- Configure different large models to run the text extraction component, verify that professional content in laboratory test reports and process index cards can be extracted normally.
- View the workflow operation logs, confirm that there are no abnormal interruptions exceeding the set timeout period.
The values provided are common starting points and should be measured against the reader's own samples.