What Data for This Category Looks Like
Technical document sources for pulp and paper scenarios include wood pulp and waste paper batches, DCS/QCS systems, on-paper machine measurement equipment, laboratory testing systems, energy and water treatment systems. Daily documents include stock preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, sheet break and grade change records. Fields cover pulp ratio, beating degree, grammage, moisture content, thickness, and similar metrics. Units include tons, seconds, g/㎡, %, and others. Data update frequencies fall into three categories: real-time (such as sheet break records), per shift (such as energy consumption statistics), and per roll (such as single roll paper quality reports).
What Constraints These Characteristics Impose on the "Workflow Orchestration" Link
Multiple data sources with widely varying update rhythms require workflows to support three trigger modes: real-time, scheduled, and by production node. This adapts to the processing timeliness needs of different documents. Document types include structured text, curve charts, and other formats. This requires workflow configurations to include multi-format parsing nodes that align with validation rules for different fields. Pulp and paper production uses high-speed continuous web production. Errors directly lead to roll downgrades, shutdowns, and similar issues. This requires workflows to include failure retry and exception alert links. Specific field units are tied to business logic. This requires workflow parameter validation links to match dedicated units for pulp and paper products, to avoid review failures caused by parameter mismatches.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Cycle trigger timing | Trigger upon completion of each roll production, or scheduled trigger every 8 hours per shift | Matches the business rhythm of roll-based production and shift handovers in pulp and paper operations |
| Single session context length | 8000–12000 token | Adapts to cross-review requirements for multiple documents such as stock preparation sheets and beating records |
| Failure retry count | 2–3 times | Addresses node call failures caused by temporary fluctuations in DCS systems |
variables | Bind [batchNumber, pulpRatio, moistureContent, grammage] | Links core business fields for pulp and paper scenarios |
nodeId | Bind the IDs of the beating record node, quality inspection node, and sheet break record node | Ensures workflow nodes execute in sequence according to business logic |
| Search trigger condition | Trigger when the stock preparation sheet ratio deviation exceeds a normal range | Triggers cross-review directionally to reduce invalid calls |
Three Common Mistakes
- Empty array output during cyclic operation: The cyclic node does not perform expected batch processing, and logs show empty input parameters. The cause is failure to correctly pass the batch number array bound to
variablesto the input end of the cyclic node. - Workflow export failure: The export button is grayed out, or an export failure prompt pops up. The cause is failure to enable the shared export permission switch for workflows in FastGPT v4.17.0.
<think>tags still appear after the model thought output switch is turned off: The question and answer results include<think>xxxxx</think>formatted content. The cause is failure to add a preprocessing node in the workflow to filter the model's native thought output content.
How to Confirm Correct Configuration
- Trigger test single roll production data, and check whether the workflow executes the bound nodes according to the configured number of cycles.
- View workflow node logs, and confirm that the
variablesparameter correctly binds core business fields such as batch number and pulp ratio. - Trigger a test scenario with abnormal stock preparation ratio, and confirm that the search node automatically calls and returns relevant quality inspection records.
- Turn off the model thought output switch, and verify that the generated review results do not contain
<think>formatted tag content.
The values listed above are common starting points, and should be evaluated against the reader's own specific samples.