What the Data for This Category Looks Like
Data sources for pulp and paper scenarios include wood pulp and waste paper batches, DCS/QCS systems, on-machine measurement equipment, laboratory testing systems, and energy and water treatment systems. Three update cadences are used: real-time, per shift, and per roll. Daily documents include pulp preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, sheet break and product change records. Data fields include beating degree, pulp ratio, grammage, thickness, operation parameters, and more. Corresponding units are °SR, g/㎡, μm, standard units; percentage units are not used for statistics. This scenario involves high-speed continuous web production, so data correlation must account for both transverse and longitudinal quality distribution characteristics, which differs from the discrete processing models of other consumer goods processing categories.
Constraints Imposed on Deployment and Upgrade by These Characteristics
When deploying using FastGPT v4.17.0, scattered data sources and varying update cadences require adaptive configuration for multiple data sources during deployment, to avoid backlogs caused by mismatched data synchronization frequencies. High-speed continuous production generates large file volumes, so storage must support read and write performance requirements for large files, to ensure fast access to documents such as online quality curves. Audit workflows depend on multi-dimensional data correlation, so a unified data indexing rule must be configured during deployment to ensure correct correlation and matching of fields from different sources. Additionally, since errors directly affect production continuity, a rollback mechanism must be reserved during the upgrade phase, to prevent production data interruptions from failed updates.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapt to concurrent reading requirements for pulp preparation sheets and operation logs across multiple data sources, preventing database connection exhaustion |
REDIS_URL | 127.0.0.1:6379 (local), Redis hosting address provided by the corresponding cloud service provider (cloud) | Implement session caching and vector retrieval acceleration, adapting to low-latency access for real-time data |
STORAGE_S3_ENDPOINT | Regional endpoint provided by the corresponding cloud storage service provider | Store large-volume files such as online quality curves and laboratory reports, ensuring high-speed reading |
FILE_TOKEN_KEY | 16-character random string | Ensure token security for file upload and download, preventing unauthorized access |
ROOT_KEY | Random key generated in accordance with enterprise security policies | Manage FastGPT backend management permissions, preventing unauthorized operations |
Three Common Configuration Mistakes
- Phenomenon: Model tests return API connection errors, with prompts indicating invalid API keys or connection timeouts. Cause:
REDIS_URLorSTORAGE_S3_ENDPOINTparameters are not configured correctly, leading to abnormal caching and storage links for model calls. - Phenomenon: Local deployment versions fail to parse complex quality curve data, while online versions return results normally. Cause: The deployed version does not match the online version. The unstructured data parsing module of the local version has not been updated, so it cannot adapt to the curve data format used in pulp and paper scenarios.
- Phenomenon: Marker tool run logs show OCR errors, and paper-based test reports cannot be processed. Cause: A correct file storage path is not configured, causing the OCR tool to fail to read uploaded report files, or adaptive parameters for the OCR model are not adjusted.
How to Verify Successful Configuration
- Log in to the FastGPT backend management interface, verify that the
ROOT_KEYconfiguration item matches the key in the local environment, run a permission test, and confirm normal access to the management page. - Initiate a retrieval test for a pulp preparation sheet, check if the returned data sources include configured sources such as DCS/QCS systems and laboratory reports, confirming that the data link is operational.
- Upload an online quality curve file, check if the file can be stored and parsed normally, confirming that
STORAGE_S3_ENDPOINTand file upload configurations are active. - Call the model test interface, initiate a request using the configured key, confirm no connection errors, and verify that returned results meet expected standards.
The values provided here are common starting points, and should be evaluated against the reader's own samples.