What Data Looks Like for This Category
Data for pulp and paper manufacturing scenarios comes from wood pulp and waste paper batch ledgers, DCS/QCS systems, on-paper machine measurement devices, laboratory test reports, energy and water treatment systems, as well as batch mixing slips, beating records, paper machine operation logs, and sheet break and product change archives from the stock preparation, pulping, and papermaking stages. Data update frequencies cover real-time, shift-by-shift, and per-roll. Fields include process parameters such as pulp ratio, beating degree, grammage, moisture content, and thickness, plus quality judgment results for corresponding batches. Common units include %, g/m², seconds, meters, and other standard process units.
Constraints Imposed on Model Integration and Configuration
Data in pulp and paper manufacturing scenarios comes from scattered sources with significant differences in update frequencies. Real-time online data requires high-frequency integration. Shift-by-shift and per-roll batch data must be linked along the production chain. This requires model integration to support scheduled pulling and real-time synchronization for multiple data sources. A single batch of data includes multi-dimensional process parameters and quality judgment results. Model input must support long context splicing, while data must be split by production batches to avoid interference from cross-batch information. On-paper machine online quality curves are time-series data. Models must adapt to time-series feature parsing. Abnormal events such as sheet breaks and product changes require fast trigger analysis. Therefore, configuration must balance compatibility between real-time inference and batch backtracking.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the historical data splicing requirements of the full stock preparation, beating, and papermaking chain for a single batch, covering the context length of multiple process records and quality reports |
maxTokens | 2000–3000 token | Controls the output length of a single analysis round, avoids inference timeouts caused by excessive process parameters, and fits the short conclusion output scenario for paper defect traceability |
quoteMaxToken | 1500–2000 characters | Limits the length of recalled source document fragments, matching the typical length of single documents such as online quality curves and beating records |
temperature | 0.2–0.4 | Reduces random output, ensures consistency of quality traceability results, and fits the precise matching requirement between process parameters and quality results |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address according to the deployment environment | Adapts to the private deployment or dedicated line access requirements of the pulp and paper manufacturing scenario, avoids public network latency affecting real-time analysis |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call retrieval tools, adapting to different types of quality issues such as known paper defect matching and unknown anomaly traceability |
Three Common Configuration Mistakes
- Incorrect address entered when configuring
AIPROXY_API_ENDPOINT, which returns 404 page not found during testing. The cause is failure to match the proxy interface path corresponding to the FastGPT deployment environment, resulting in requests failing to route correctly to the target model service. - Failure to split recalled data by production batch, leading to inflexible problem classification recognition by the model. The cause is that recalled context includes irrelevant process parameters from cross-batches, interfering with the model's feature extraction for current anomalies.
- Failure to adjust the
maxTokensparameter, triggering request failure after uploading batch process records. The cause is that the single-round inference output limit does not fit the parsing requirements of multiple documents, leading to request timeout or interception by the server.
How to Confirm Successful Configuration
- Initiate a single-batch quality traceability test, check whether the model return results are linked to source documents such as the current batch's mixing slip and beating records.
- Simulate an abnormal event to trigger analysis, confirm whether the model can correctly recall paper machine operation data and quality curves from the corresponding time period.
- Check the FastGPT log panel, confirm that the response status code of model requests is 200, with no timeout or routing errors.
- Adjust the
temperatureparameter, compare the consistency of two analysis results, and verify whether the parameter configuration takes effect.
Note: The configuration values listed above are common starting points. They should be validated against the reader's own samples.