What data for this category looks like
Data sources include wood pulp and waste paper batch ledgers, DCS/QCS control systems, paper machine online measurement equipment, laboratory testing systems, and energy and water treatment systems. Update frequencies include real-time (paper machine operating parameters), per-shift (stock preparation and beating records), and per-roll (sheet break and product change ledgers). Documents include stock preparation sheets, beating logs, paper machine operation records, online quality curves, laboratory reports, and sheet break and product change ledgers. Fields cover stock proportion ratios, beating degree, grammage, moisture content, sheet break duration, and more. Significant format differences exist across documents.
What constraints these characteristics impose on model integration and configuration
Multi-source heterogeneous data sources require the integration link to adapt to different systems' field formats and output structures to avoid parsing failures. Real-time paper machine operating parameters require model call response delays to match production cycles, and cannot tolerate excessive waiting times. Documents with different time granularities (real-time parameters, shift records, per-roll ledgers) need separate context grouping to avoid interference across granularity data. Unstructured online quality curves require visual processing capabilities to identify abnormal curve points. High-frequency updated data will quickly fill the context window, so reasonable context length limits are needed to prevent overflow.
How to set configurations
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-source heterogeneous historical data context for pulp and paper production, covering correlation analysis of single-shift production records and real-time parameters |
maxTokens | 4000–6000 token | Matches text and curve analysis requirements for single-roll quality audits, avoiding exceeding model output limits |
vision | Enabled | Used to parse chart content from online quality curves and laboratory reports, identify abnormal fluctuations |
temperature | 0.2–0.4 | Maintains consistency of audit results, avoids quality judgment deviations caused by parameter fluctuations |
AIPROXY_API_ENDPOINT | Points to the enterprise internal large model gateway address | Adapts to the security requirement that production data stays on-site, ensuring sensitive production information is not leaked |
AIPROXY_API_TOKEN | Generated according to enterprise identity authentication rules | Ensures access security for model calls, prevents unauthorized access |
Three common configuration mistakes
- Phenomenon: The model call returns a
context length exceedederror, and the interface displays a context overflow prompt. Cause: ThemaxContextparameter was not adjusted for high-frequency production data in pulp and paper production, causing historical operating records to quickly fill the context window. - Phenomenon: Online quality curves still cannot be parsed after
visionis enabled, and logs show image parsing failure. Cause: Curve images matching the model input format were not uploaded, or the visual inference capability of the corresponding model was not enabled. - Phenomenon: Intermittent response timeout occurs after FastGPT is connected to OneAPI, and the console displays
request timeoutlogs. Cause: The response delay requirement for real-time paper machine data was not met, and themaxTokenssetting was too high, leading to excessive model inference time.
How to confirm correct configuration
- Import a single-roll sheet break and product change ledger and the corresponding time period's paper machine operating data, trigger an audit task, and check whether the output result includes correlation analysis of stock proportion and sheet break duration.
- Upload an online quality curve image, enable visual analysis, and check whether abnormal fluctuation points of the curve can be identified.
- View the model call logs to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no identity authentication failure errors. - Run a 10-minute continuous real-time data audit task, check whether the response duration matches the production cycle, and there is no continuous timeout.
The values provided are common starting points and should be measured against the reader's own samples.