What the data for this category looks like
Data for the basic chemical continuous production category comes from DCS/SIS systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring modules, energy metering devices, and raw material tank account books. Daily generated documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift logs. Data update frequencies cover real-time, per-shift, and daily. Fields include process pressure, temperature, material flow, tank volume, laboratory concentration, and other metrics, with corresponding industry standard units attached.
What constraints these characteristics impose on forms and interactions
Data sources for this category are scattered, and update frequencies vary significantly. This requires forms and interactions to support real-time pulling and scheduled synchronization of multi-source data, to avoid delays from manual entry. There are diverse document types, so forms must adapt to structured extraction rules for different documents. For example, interlock bypass records need to match number and bypass time fields. Shift logs need to support statistics fields for shift output. Fields come with industry standard units, so interactions must automatically verify unit consistency to prevent parameter matching errors. Bid quotations must be linked to process steady-state data, so forms must support direct calls to historical records from corresponding data sources to reduce repeated data entry.
How to configure the settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Form field verification rules | Enforce matching of units and data types. Trigger alerts when values exceed reasonable process ranges | Basic chemical parameters have fixed reasonable ranges, to avoid invalid bid data |
| Data source synchronization frequency | Synchronize DCS key parameters in real time. Synchronize shift logs and laboratory reports per shift | Match the category’s data update frequency to ensure timeliness of bid data |
userSelect trigger condition | Trigger when formInput cannot extract process parameters | Adapt to interactive completion requirements when parameters are missing in bid scenarios |
| Reranking model switch | Enable, take the top 3 high-similarity results | Chemical process parameters have strong correlation. Reranking improves matching accuracy |
| Context length limit | 800–1200 characters | Adapt to the standardized document length of bid documents, to avoid context overflow |
| Result extraction threshold | Calibrate based on actual measurements | Match document differences across different bid projects |
Three common mistakes
- Phenomenon:
formInputfails to extract process parameters, and form fields have no filled content. Cause: No automatic mapping rule is configured between data sources and form fields. Corresponding data from DCS and LIMS systems cannot be automatically pulled. - Phenomenon: After enabling the reranking model, only 1 matching result is returned. Cause: No upper limit is set for the number of results taken after reranking. The default setting retains only the highest-similarity result, which cannot meet the needs of multi-parameter combinations in bid documents.
- Phenomenon: Unit verification fails when submitting the form. Cause: No automatic unit matching rule is configured for fields. Manually entered units do not match industry standard units, resulting in invalid data.
How to confirm successful configuration
- Simulate passing text without process parameters via
formInput, confirm that theuserSelectuser selection pop-up is triggered. - Configure a data source synchronization task, check whether corresponding document data is pulled at the set real-time or per-shift frequency.
- Submit parameter tests with different units, confirm whether the verification rule triggers the corresponding alert.
- After enabling the reranking model, check whether the number of returned results matches the set upper limit.
The values provided here are common starting points and should be measured against the reader's own samples.