What the data for this category looks like
Data related to pulp and paper equipment inspection and maintenance comes from wood pulp and waste paper batches, DCS/QCS systems, on-paper machine measurement equipment, laboratories, energy and water treatment systems. Daily generated documents include pulp preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, sheet break and product change records. Data update frequencies fall into three categories: real-time, per shift, and per roll. These correspond to record requirements for different production links. Core business parameters include pulp ratio, beating degree, paper machine speed, grammage, and moisture content.
What constraints these characteristics impose on workflow orchestration
Data for this category comes from multiple sources and has widely varying update frequencies. Workflows must support multiple trigger rules, balancing low-latency processing of real-time DCS data with summary analysis of shift and per-roll production data. Differences in format across multiple document types require workflows to use unified data parsing nodes, to avoid field conflicts from different sources. High-risk error links such as sheet breaks and quality deviations require workflows to include built-in fast exception branches. These branches can immediately trigger alerts or standby maintenance processes, to meet on-site emergency processing needs.
Configuration settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
variables | Include pulp ratio, beating degree, paper machine speed, grammage threshold | Matches core business fields for pulp and paper equipment inspection and maintenance |
| Node trigger rules | Trigger based on DCS real-time data updates, shift production completion, single roll production completion | Corresponds to the real-time, per-shift, per-roll update frequencies of the data |
nodeId | Bind three tool nodes: knowledge base recall, equipment manual query, exception diagnosis | Covers core information sources for equipment maintenance Q&A |
| Timeout threshold | 600 seconds | Covers processing time for multi-data-source calls and complex maintenance logic |
| Result filtering rules | Only retain recall content matching the current equipment model | Avoids irrelevant data interfering with maintenance judgments |
| Exception handling configuration | Trigger two branches: data missing alert, sheet break alert | Matches high-risk error links for this category |
Three common mistakes
- Symptom: Workflow nodes cannot retrieve content from a specified knowledge base, with a parameter missing prompt. Cause: The associated parameters of the target knowledge base are not configured in
variables, or the correctnodeIdis not bound when triggering the node. - Symptom: The retrieval process content of the tool call module is forcibly returned, and redundant retrieval details cannot be hidden. Cause: No result filtering node is configured, or the default output switch for tool calls is not turned off.
- Symptom: After the MongoDB replica set primary node switches, the workflow's data synchronization node disconnects and cannot automatically reconnect. Cause: No reconnection retry rule is configured for the node, or a data source configuration adapted to the replica set is not used.
How to confirm proper configuration
- Manually trigger the workflow for the corresponding production scenario, check whether matching equipment inspection documents and real-time production data can be retrieved.
- Simulate data exceptions such as grammage exceeding the preset threshold, confirm whether the workflow triggers the preset exception branch process.
- View the workflow running logs, confirm that the node call timeout time matches the configured 600-second threshold, and there are no disconnection errors.
- Verify that the business parameters in
variablescan be correctly read and used by subsequent nodes.
The values provided here are common starting points and should be measured against the reader's own samples.