What this category of data looks like
Data for this category typically comes from wood pulp and waste paper batches, DCS/QCS systems, on-paper machine measurement equipment, laboratory tests, energy and water treatment systems. Daily documents include pulp preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, and sheet break and grade change records. Data update frequencies include real-time, per shift, and per roll. Fields include pulp ratio, operation parameters, grammage, thickness, paper defect type, and other items, with no additional statistical value labels.
What constraints these characteristics impose on deployment and upgrade
Data sources for this category are scattered, and update frequencies vary significantly. Deployment must support multi-source data access and synchronization logic for different cycles. Daily documents have large differences in format and fields, so support for custom parsing rules is required to match different types of files such as pulp preparation sheets and operation records. High-speed continuous web production generates large data volumes, so deployment must reserve sufficient storage and concurrent processing resources. Key identifiers such as batches and roll numbers must also be associated to ensure that quality issues can be traced back to corresponding production links. During upgrades, original associated configurations must be compatible to avoid data gaps.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Connection string pointing to a MongoDB instance deployed locally or on a cloud service, in the format mongodb://username:password@host:port/database?authSource=admin | Pulp and paper manufacturing businesses need to store large volumes of time-series production data and documents. MongoDB is suitable for unstructured and semi-structured data storage |
REDIS_URL | Configured as redis://:password@host:port/0, with a connection timeout set to 30 seconds | Real-time online quality curves and batch data need to be cached. A 30-second timeout fits real-time data synchronization requirements |
DB_MAX_LINK | Set to 200–300 | Meets the demand for concurrent queries of production records and quality reports across multiple departments, and prevents connection exhaustion |
STORAGE_S3_ENDPOINT | Configured as the access endpoint for the corresponding cloud storage or local MinIO | Stores large-volume offline files such as laboratory reports and sheet break records |
ROOT_KEY | Generate a random key string based on the deployment environment | Ensures secure verification for internal system interface calls |
FE_DOMAIN | Configured as the front-end access domain name for local or online deployment | Ensures that front-end pages can properly connect to back-end service interfaces |
Three common mistakes
- Phenomenon: The
docker buildcommand fails to pull dependencies, and logs indicate that the specified dependency package cannot be found. Cause: The local image source is not correctly configured to adapt to the domestic network environment, or the correct project build context path is not specified. - Phenomenon: API requests return
404 - Resource Not Found. Cause: The access domain name mapping betweenFE_DOMAINand the backend service is not correctly configured, or the deployment path of front-end static resources is not updated synchronously after an upgrade. - Phenomenon: The third-party API service cannot be connected after starting
pnpm dev. Cause: The corresponding API key and service address are not correctly configured in the.eval.localfile, or the network policy restricts intercommunication between the local service and external services.
How to verify successful configuration
- Run
pnpm devto start the local service, access the configuredFE_DOMAINaddress, and confirm that the page loads normally without errors. - Upload a pulp preparation document, confirm that the system can correctly parse and extract fields such as pulp ratio, with no parsing failure prompts.
- Call the backend interface to test the connectivity of
MONGODB_URI, and confirm that test data can be written and read. - Check the system version identifier, and confirm that FastGPT v4.17.0 is displayed.
The values provided here are common starting points, and should be evaluated against the reader's own samples.