What Data Looks Like for This Category
Data for this category comes from cement plant mining, raw material, calcination, grinding, quality control, and energy departments. It covers daily documents including raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, energy reports, and more. Data sources include online composition analysis tools, DCS systems, laboratory equipment, kiln and grinding operation equipment, raw and fuel ledgers, and energy metering devices. Update frequencies include real-time (online composition analysis, feed data), per shift (equipment inspection records), and daily (quality laboratory reports, energy reports). Fields include raw and fuel storage volume (tons), kiln cylinder temperature (degrees Celsius), batching deviation value (kilograms), power consumption (kilowatt-hours), and others. Field structures vary across different document types.
Constraints Imposed on Database and Operations Workflows
Multi-source heterogeneous data sources and widely differing field structures require the database to support both structured and unstructured data storage, and avoid hard-coding fixed table schemas. Data with different update frequencies needs tiered storage: high-frequency real-time collected data must be assigned to high-performance storage nodes, and cold data must be archived regularly to free up storage space. Data consistency requirements are high: any loss or deviation of ledger data may lead to clinker quality fluctuations, kiln system scaling and shutdowns, and other issues, so the reliability of data writing and synchronization must be maintained. Concurrent write peaks are high, so database connection counts must be properly configured to prevent service interruptions.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:xxxx@db-host:27017/fastgpt_clinker?authSource=admin | Stores unstructured documents such as equipment inspection records and laboratory reports, and supports storage of multiple types of unstructured data |
PG_URL | postgresql://fastgpt:xxxx@db-host:5432/fastgpt_clinker | Stores structured data such as batching plans, energy reports, and ledger data, and supports complex associated queries |
DB_MAX_LINK | 25–35 | Matches the concurrent write peak of cement production data, and prevents database connection exhaustion |
REDIS_URL | redis://:xxxx@cache-host:6379/1 | Caches real-time collected DCS and online composition analysis data, and reduces query pressure on the primary database |
| Data Archival Cycle | Executed at 2:00 AM daily | Archives historical ledger data older than 7 days to cold storage to free up primary database storage space |
Three Common Misconfigurations
- Symptom: After starting FastGPT v4.17.0, the MongoDB data directory is not automatically generated, and no mongo/data folder exists in the same-level directory. Cause: The mount path for
MONGODB_URIis not specified in the configuration file, or the data volume is not mapped during container deployment. - Symptom: After importing production ledger data using mongorestore, query results are empty. Cause: The correct database authentication source was not specified during import, or indexes were not rebuilt after data import.
- Symptom: The FastGPT v4.17.0 plugin load error mentions a MongoServerError involving a
$-prefixed field. Check the field name generated by the plugin, the database request, and the complete log; this error alone does not establish a version conflict between MongoDB 4.4.29 and FastGPT. The v4.17.2 deployment config documents 4.4.29 as a fallback for CPUs without AVX (see the official deployment configuration).
How to Confirm Successful Configuration
- Access the database management tool, verify that the databases corresponding to
MONGODB_URIandPG_URLcan connect normally and support writing test data. - The Redis monitoring dashboard may be checked to confirm that the cache node configured with
REDIS_URLnormally receives real-time data requests. - The database connection count may be checked to confirm that the current connection count is lower than the value set for
DB_MAX_LINK. - A manual data archival task may be run once to confirm that the archival process triggers normally and historical data is correctly transferred.
The values provided here are common starting points, and should be evaluated against local samples.