What data for this category looks like
Business data sources for pulp and paper production include wood pulp and waste paper batches, DCS/QCS systems, on-paper machine measurement equipment, laboratory testing systems, and energy and water treatment systems. Daily documents include pulp preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, and sheet break and product change records. Fields covered include pulp consistency, beating degree, paper grammage, and moisture, with units of %, °SR, g/㎡, and % respectively. Data update frequencies fall into three categories: real-time, per shift, and per roll. These are adapted to the rhythm of high-speed continuous production.
What constraints these characteristics impose on workflow orchestration
High-speed continuous production requires workflows to support short-interval triggers. This matches real-time on-paper machine data and shift-based team records. Heterogeneous formats from multiple data sources require built-in format conversion nodes in workflows. These nodes align field naming rules across different systems. Each roll’s production record must be bound to a unique batch identifier. This ensures process Q&A references accurately correspond to production links. Complex quality verification logic needs to be split across multiple sub-nodes. This avoids single-node overload that disrupts real-time responses.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables | Business entity variables split by pulp preparation sheets, beating records, and similar items | Matches core business fields of pulp and paper production, covers core query dimensions for process Q&A |
nodeId | Bind node IDs by data source type, such as DCS data node, laboratory report node | Differentiates call priorities for multiple data sources, ensures loading order of different data types aligns with business logic |
| Single recall count | Top 8 entries | Adapts to the real-time nature of paper machine operation records, avoids excessive context causing model response delays |
| Context window | 8000–12000 token | Covers total content volume of multiple segments of online quality curves, laboratory reports and shift team records |
| Debug timeout | 600 seconds | Adapts to complex verification logic for sheet break and product change records, reserves sufficient calculation time |
Three common mistakes
- The variable dropdown menu for workflow nodes only displays a single selectable value, and multiple business fields cannot be selected. The cause is that multi-variable binding configuration is not enabled. The system loads only the single variable pool for the current node by default, and does not associate the
variableslist from other data sources. - After entering node debug mode, manual termination is not possible, and the process continues to occupy running resources. The cause is that debug timeout trigger rules are not configured. The default debug timeout for FastGPT v4.17.0 is not enabled, causing nodes to fail to automatically release resources.
- The process Q&A results returned by the large model do not mark knowledge base reference content that does not meet technical standards. The cause is that no reference verification sub-node is added to the workflow, and matching rules for process standards are not bound. Rationality verification cannot be performed on referenced content such as pulp preparation sheets and beating records.
How to confirm correct configuration
- Enter the workflow editing page, open the variable configuration panel, confirm that multiple pulp and paper production business fields have been loaded into the
variableslist. - Submit simulated sheet break and product change record data, trigger workflow operation, verify that debug mode starts and terminates normally.
- Enter query questions related to process standards, check whether the returned results automatically associate corresponding business document references.
- Check the
nodeIdconfiguration of each data source node, confirm that each node is bound to the correct data source type identifier.
The values provided are common starting points and should be measured against matching production samples.