What the data for this category looks like
Business data sources for pulp and paper manufacturing scenarios include wood pulp and waste paper batches, DCS/QCS systems, paper machine online measurement equipment, laboratories, energy and water treatment systems. Data update frequencies fall into three categories: real-time (such as paper machine operating parameters), shift-based (such as beating records), and per-roll (such as sheet break and product change records). Daily documents include pulp preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, and sheet break and product change records. Fields include pulp ratio, beating degree, grammage, moisture content, sheet break duration, and other relevant metrics. Units include tons, seconds, g/㎡, %, and similar standard units.
What constraints these characteristics impose on deployment and upgrade
Multi-source business data with varied update frequencies requires deployment to support mixed workloads of high-frequency real-time queries and batch historical data synchronization. Diverse document formats (text, tables, time-series curves) need storage and indexing strategies compatible with multiple data types. Cross-departmental data integration requires establishing permission links between connected systems during deployment. Upgrades must not interrupt real-time paper machine parameter synchronization, to avoid impacting production processes.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 concurrent connections | Adapts to concurrent query demands from multi-department real-time data, and matches high-frequency requests from DCS/QCS and other systems |
MONGODB_URI | Points to the internal network address of a local sharded cluster | Stores large-volume time-series data such as online quality curves and sheet break records, and supports massive document read and write operations |
PG_URL | Configured as the instance address with read-write separation | Stores structured business data such as pulp preparation sheets and laboratory reports, and ensures transaction consistency |
REDIS_URL | Configured as the address of a master-slave replication instance | Caches real-time beating parameters and paper machine operating status, and reduces database query latency |
ROOT_KEY | A strongly encrypted string determined through actual testing | Ensures the security of system background management permissions, and prevents unauthorized access |
STORAGE_S3_ENDPOINT | Points to the address of a local object storage service | Stores large files such as offline quality curves and batch archives, and reduces local disk usage |
Three common mistakes
- Issue: Database connection failure, with logs showing connection timeout or authentication errors. Cause:
MONGODB_URIorPG_URLis not configured as the internal network address of the corresponding system, or database port permissions are not opened. - Issue: Restarting the service after modifying
ROOT_KEYdoes not take effect. Cause: The associatedFILE_TOKEN_KEYconfiguration is not updated synchronously, or old session data in the Redis cache is not cleared. - Issue: An error occurs during local Windows deployment, indicating missing runtime dependencies. Cause: Docker Desktop is not installed in advance and the WSL2 function is not enabled, resulting in a missing container runtime environment.
How to confirm the configuration is correct
- Run the database connection test script to verify the connectivity of
MONGODB_URIandPG_URL. Pass if no errors are returned. - Submit a pulp preparation sheet query request, and confirm that the returned result contains complete fields such as pulp ratio and batch information, with no missing content.
- Check the Redis monitoring panel, and confirm that the cache hit rate configured via
REDIS_URLis within the normal range. - Log in to the FastGPT v4.17.0 background management interface, complete authentication using
ROOT_KEY, and verify that the permission configuration takes effect.
The values provided here are common starting points, and should be evaluated against the reader's own samples.