What this category’s data looks like
Non-ferrous metal smelting declaration materials and case review data mainly comes from production ledger systems, quality inspection lab reports, environmental emission monitoring platforms, work safety filing archives, and raw mineral procurement documents. Data update rhythm follows declaration cycles: monthly declaration materials are updated monthly, and annual cases are archived quarterly. Document structures mostly combine structured forms and unstructured reports. Fields include batch number, smelting furnace number, single-furnace operation duration, energy consumption value, and compliance inspection item number. Units include hours, tons of standard coal, batches, and other standard units.
What constraints these characteristics impose on workflow orchestration
Data for this category is multi-source, scattered, and updated on fixed cycles. Configure workflows to match declaration cycle trigger nodes to avoid pulling expired data repeatedly. Many structured fields have significant unit differences, so add field mapping nodes to unify parameter formats before entering the review stage. Unstructured quality inspection reports mixed with ledger data require text parsing nodes to extract structured fields first. Without this step, subsequent review nodes cannot accurately match verification rules. Cross-system data pulls have delays, so set wait nodes to ensure all data sources finish syncing before running verification.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Trigger Mode | Trigger by declaration batch, poll unprocessed queue every 120 seconds | Matches the batch processing rhythm of monthly declarations, avoids repeated triggering of the same batch |
| Variable Mapping Rules | Bind batch number and furnace number fields in variables | Corresponds to core identification fields of declaration materials, ensures accurate data matching |
| Text Parsing Node | Extract report content from 1000–1500 characters | Covers core inspection data of quality inspection reports, avoids parsing failures caused by overly long content |
| Branch Judgment Node | Split execution verification logic by declaration type (monthly/annual) | Adapts to differentiated verification rules for declaration materials of different cycles |
| Timeout Setting | 600 seconds | Adapts to the wait time for pulling multi-source data across systems, avoids task interruptions |
| Node ID Configuration | Bind nodeId of the corresponding approval node | Ensures workflow node execution order matches preset logic |
Three common mistakes
- Workflows return an error after API calls, with a 400 status code and a prompt about missing parameters. The cause is that required fields such as declaration batch number are not configured in
variables, causing the verification node to fail to read valid data. - Cannot select workflow variables in the JSON input box, and input content cannot be auto-filled. The cause is that the variable binding switch is not enabled, so the JSON input box only supports static text input.
- Workflow node execution order is chaotic, leading to incorrect verification logic. The cause is that the
nodeIdof the corresponding approval node is not bound, and the system executes nodes in default order without matching the preset approval process.
How to confirm the configuration is complete
- Trigger a test task, check whether the workflow log correctly pulls declaration data of the corresponding batch, and verify whether the fields in
variablesmatch actual business data. - Simulate a JSON input scenario, verify that workflow variables can be bound normally, and confirm that input content can be dynamically replaced with corresponding field values.
- Execute the complete workflow process, check whether the final output only includes the content of the last review node, with no redundant intermediate results.
- View task timeout logs, confirm that the wait time for pulling cross-system data meets business requirements, and no task interruptions occur.
The values provided here are common starting points and should be measured 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.