What Data for This Category Looks Like
Coal mining operation ledger data comes from sensor systems on fully mechanized mining and tunneling faces, production scheduling platforms, and safety supervision systems. Data updates follow two rhythms: real-time data such as equipment operation and gas concentration updates every second. Summary ledgers for daily or shift-based periods update on fixed schedules. Each ledger entry includes fields such as face number, production duration, coal output, equipment runtime, gas concentration value, and statistical cycle. All fields have clear units: coal output is measured in tons, production duration in hours, and statistical cycle is marked by shift or calendar day. Data structure is relatively fixed, but single-batch import volume increases as the statistical cycle lengthens.
Constraints Imposed on Database and Operations Workflows
Multi-source data access requires databases to support mixed workloads. They must handle second-level real-time data writes and bulk summary data imports. Data sources with different update rhythms need differentiated write strategies to avoid resource contention. Fields with clear units require databases to retain field metadata during storage to prevent data parsing errors. For scenarios with large single-batch data volumes, databases must support efficient bulk writes and associated queries. Targeted indexes must also be built to speed up summary statistics. Additionally, the intranet environment of coal mining systems restricts external access. Database operations must adapt to intranet deployment network rules.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://coal_user:secure_pass@internal-db-host:27017/coal_ledger?authSource=admin | Adapts to the mixed structured and unstructured storage needs of coal mining operation ledgers, supports bulk writes and associated queries |
PG_URL | postgresql://coal_admin:db_pass@internal-pg-host:5432/coal_standard | Stores standardized ledger fields, supports summary queries and statistical analysis with complex conditions |
DB_MAX_LINK | 20–30 connections | Matches single-batch data volume and concurrent request volume of coal mining systems, avoids service interruptions caused by exhausted connection pools |
REDIS_URL | redis://redis_pass@internal-redis-host:6379/0 | Caches ledger summary results from frequent queries, reduces repeated computational load on databases |
| Database Timeout | 600 seconds | Adapts to the time required for bulk data imports in coal mining systems, prevents write process interruptions due to timeout |
Three Common Configuration Errors
- A database connection error indicating an internal server error appears, with logs showing connection timeout. The cause is an incorrectly configured
MONGODB_URIorPG_URL, or unopened access ports for the coal mining intranet database. - A MongoDB connection failure prompt appears after service startup. The cause is an excessively low
DB_MAX_LINKconnection pool configuration value, or the MongoDB service not starting properly. - The number of ledger summary results does not match the actual statistical results. The cause is that no database index was created based on the face number and statistical cycle fields, leading to missing data in queries.
How to Verify Correct Configuration
- Execute the built-in FastGPT database connection test tool. No errors when entering
MONGODB_URIandPG_URLconfirms correct configuration. - Import a single-shift coal mining operation ledger dataset. Check database write logs to confirm all data was successfully written.
- Submit a ledger summary request. Compare manually counted results with system-generated report data to confirm consistent fields and values.
- Check the Redis monitoring panel. An increase in cache hit count for frequent queries as request volume rises proves the cache configuration is effective.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.