What the data for this category looks like
Data for the pulp and papermaking scenario comes from wood pulp and waste paper batches, DCS/QCS systems, on-paper machine measurement equipment, laboratories, and energy and water treatment systems. Daily documents include pulp preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, sheet break and product change records. Data update frequencies are divided into real-time, per shift, and per roll. Fields include physical parameters such as grammage, moisture, and thickness, with units mostly grams per square meter, percentage, millimeters, and the like. This setup supports full-process data collection for high-speed continuous web production.
What constraints these characteristics impose on deployment and upgrade
The multi-source and scattered data sources in this scenario require completing multi-system interface adaptation configuration during the deployment phase, to support access to devices and systems with different protocols. Differentiated update frequencies require configuring high-concurrency vector storage links for real-time data streams, and scheduled synchronization tasks for batch documents. Diverse document types require preset parsing rules adapted to structured reports and curve charts. The high-speed continuous production feature requires ensuring system latency matches production rhythms during deployment and upgrade, to avoid interrupting online data processing. Extension interfaces must also be reserved to support data from new production links.
How to set the configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 concurrent connections | Adapts to the concurrent read requirements of multi-source real-time data, avoiding database connection exhaustion |
MONGODB_URI | Local deployment dedicated instance connection string | Stores large volumes of unstructured documents such as quality curves and sheet break records, ensuring read and write performance |
REDIS_URL | Cache instance address with 300–600 second persistence | Caches real-time updated pulp preparation parameters and paper machine operating status, reducing repeated query overhead |
STORAGE_S3_ENDPOINT | Object storage endpoint bound to a single bucket with capacity no less than 1000 GB | Stores massive volumes of historical production records and exception reports, reducing local storage pressure |
ROOT_KEY | Randomly generated 32-character secret key | Ensures identity verification security for system interface calls, complying with permission requirements for production environments |
FE_DOMAIN | Enterprise internal production network domain name | Configures the frontend access address, adapting to access rules of the internal production network |
Three common mistakes
- Phenomenon: An error
text index required for $text queryis returned after executing the upgrade script. Cause: The database index initialization script was not run during the upgrade, resulting in missing necessary full-text index configuration for the vector database. - Phenomenon: Configuration cannot be completed when selecting a model provider not in the default list. Cause: The environment variable corresponding to
ROOT_KEYwas not correctly configured during deployment, leading to failed permission verification and inability to load additional model configurations. - Phenomenon: The system returns empty results after batch importing pulp preparation sheets and beating records. Cause: The concurrent connection count for
DB_MAX_LINKwas not adjusted, resulting in exhausted database connections during batch data import, causing request timeouts with no response.
How to verify proper configuration
- Run the database connectivity test script to verify the connection status of configuration items such as
MONGODB_URIandPG_URL, confirming there are no timeouts or permission errors. - Upload a single pulp preparation sheet and online quality curve document to trigger the parsing process, and check that the parsing result includes preset fields such as grammage and moisture.
- Initiate a simulated real-time data push request to confirm that the cache write and read link for
REDIS_URLis normal, and that latency meets production requirements. - Access the frontend address configured via
FE_DOMAIN, and verify that the model provider list includes custom configured options after logging in.
The values provided are common starting points and should be measured against the reader's own samples.