Data Profile for This Category
This category’s data originates from online composition analysis systems, DCS control systems, laboratory reports, kiln and grinding equipment ledgers, raw fuel inventory ledgers, and energy metering devices. Data updates occur in three frequencies: real-time, shift-based, and daily. Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data fields cover material batching parameters, equipment operation parameters, laboratory test results, and energy consumption values. Corresponding units include quality parameters, temperature, operation duration, energy consumption quotas, and other relevant metrics.
Constraints During Deployment and Upgrade
This category’s data is multi-source and heterogeneous, with significant differences in update frequencies. Deployments must support two data access methods: online real-time interfaces and batch scheduled synchronization. Daily documents have diverse formats, so parsing rules covering multiple formats must be configured to support field extraction for different document types such as batching plans and inspection records. Data fields vary across departments, so flexible field mapping configuration items must be reserved to prevent misalignment during data integration. During upgrades, collection and synchronization of core production data must not be interrupted. Migration formats for historical ledger data must be compatible to avoid loss of access to historical data after the upgrade.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:port/cement_plant?authSource=admin | This category’s ledgers include unstructured inspection records and laboratory reports. MongoDB supports storage requirements for multiple document types |
REDIS_URL | redis://:your_password@host:port/0 | Real-time kiln temperature and online composition data require low-latency caching to ensure fast response for real-time data queries |
STORAGE_S3_ENDPOINT | Fill in according to the access address of the enterprise’s existing object storage service | This category’s batch reports and historical ledgers require large-capacity, traceable storage media to support daily and shift-based batch data archiving |
DB_MAX_LINK | 20–30 | This category’s concurrent data requests include both real-time collection and batch synchronization. A reasonable connection count prevents database resource exhaustion |
FE_DOMAIN | http://internal-fastgpt.cement-plant.com | Ensures users from internal departments including mining, raw material preparation, and calcination can access the front-end interface for ledger aggregation and report generation normally |
ROOT_KEY | Randomly generated 32-character string | Ensures access permission security for system backend configurations, preventing unauthorized modification of ledger configuration logic |
Three Common Configuration Errors
- Symptom: After configuring a custom PDF parsing service, kiln operation record PDFs cannot be parsed normally, and the interface displays a parsing failure prompt. Cause: The port of the parsing service was not opened to the FastGPT deployment node, and the parsing service address was not correctly filled in the configuration.
- Symptom: Database connection overflow errors occur during batch synchronization of daily ledger data. Cause: The value of
DB_MAX_LINKwas not adjusted. Excessive concurrent connections exceed the database’s carrying limit, causing data synchronization to interrupt. - Symptom: After upgrading to FastGPT v4.17.0, new users cannot complete the registration process, and the interface prompts insufficient permissions. Cause:
ROOT_KEYwas not correctly configured, causing the system to fail to generate valid new user registration permission tokens.
How to Confirm Successful Configuration
- Run a real-time data synchronization test to verify that online composition analysis data can be normally imported into the ledger system, and confirm that data fields match the original reports.
- Upload a raw material batching plan PDF to check that key batching parameters are fully extracted in the parsed text content, and verify that the parsing service is operating normally.
- Initiate a monthly ledger aggregation report generation task. After the task completes, check that the generated report format meets preset requirements, and confirm that data sources match actual ledgers.
- Check the database connection count monitor in the system backend to confirm that the current connection count does not exceed the configured
DB_MAX_LINKvalue, with no connection overflow issues.
The values provided are common starting points and should be measured against the reader's own samples.