What the data for this category looks like
In general, inspection and process record data for pulp and paper scenarios comes from pulp and waste paper batch ledgers, DCS/QCS control systems, on-paper machine measuring equipment, laboratory testing systems, energy and water treatment systems. Data update rhythms fall into three categories: online quality curves and paper machine operating parameters are updated in real time; pulp mixing sheets and beating logs are updated per shift; sheet break and product change records are updated after each roll of production completes. Common documents include pulp mixing sheets marked with pulp ratio values and beating degree parameters, beating logs recording beating current and pulp consistency, paper machine operation records containing running speed and web width parameters, online quality curves displayed as line charts, laboratory reports marked with fiber content and ash content indicators, and production ledgers noting sheet break locations and product change times. Data fields are attached with physical units, such as beating degree °SR, grammage g/㎡, and speed m/min.
What constraints do these characteristics impose on deployment and upgrade
Because data sources are scattered and update rhythms vary widely, deployment must support multi-protocol data source access and adapt to different document parsing capabilities. Real-time data requires low-latency storage and synchronization to avoid production monitoring disruptions from data backlogs. Batch shift-based and per-roll data requires scheduled synchronization tasks to avoid overusing real-time service resources. During upgrades, production data continuity must be maintained—service outages cannot cause production record loss. Old version data source configurations must be compatible to avoid reconfiguring all production systems post-upgrade. Additionally, paper machine failures cause significant waste paper losses, so high-availability deployment configurations must meet production stability requirements.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
STORAGE_S3_ENDPOINT | http://minio:9000 | Adapts to storage of large-volume online quality curves and operation logs for pulp and paper scenarios, compatible with MinIO object storage protocol |
REDIS_URL | redis://:custom password@redis:6379/0 | Caches real-time on-paper machine measuring data to reduce repeated request pressure on DCS/QCS control systems |
DB_MAX_LINK | 200–300 connections | Adapts to concurrent access needs of multiple data sources, avoids real-time data synchronization failures due to insufficient connections |
MONGODB_URI | mongodb://mongo:27017/fastgpt?replicaSet=rs0 | Stores unstructured documents such as multi-batch pulp mixing sheets and laboratory reports, supports fast queries by batch and time range |
| Data source synchronization frequency configuration | Real-time synchronization (≤1 second), shift synchronization (every 8 hours), per-roll synchronization (after each roll completion) | Matches update rhythms of different documents, corresponding to synchronization needs of online quality curves, pulp mixing sheets, and product change records respectively |
FILE_TOKEN_KEY | Random 32-bit secure string | Protects access permissions for sensitive production documents such as uploaded quality curves and sheet break records, prevents unauthorized access |
Three Common Misconfigurations
- Issue: Database connection timeout errors (status code 504) occur after upgrading to FastGPT v4.17.0. Cause: The
DB_MAX_LINKparameter was not adjusted, and the multi-data source concurrent connection requirements for pulp and paper scenarios were not met, leading to connection exhaustion during real-time data synchronization. - Issue: MinIO image pull speed is too slow to complete storage configuration. Cause: No domestic image source or private image repository was configured, and no image packages were pulled in advance, causing deployment delays.
- Issue: Task timeouts occur when processing product change records in batches. Cause: Real-time data synchronization and batch product change record synchronization were assigned to the same task queue, and synchronization tasks were not split by data update rhythm, leading to resource preemption.
How to Confirm Proper Configuration
- The
docker pscommand may be executed to confirm that FastGPT, MongoDB, Redis, and MinIO containers are all running. - A pulp mixing sheet document containing beating degree parameters may be uploaded, the keyword "beating degree" may be searched in the FastGPT knowledge base, and matching document results may be confirmed to be returned normally.
- A real-time synchronization task for online quality curves may be configured, backend logs may be checked, and data synchronization every 1 second may be confirmed with no connection errors or data loss prompts.
- The frontend address corresponding to
FE_DOMAINmay be accessed, the backend may be logged into to view the data source configuration list, and all production system data sources related to pulp and paper scenarios may be confirmed to have been added and be in normal status.
The values provided here are common starting points. They should be evaluated against the reader's own specific samples.