What Data for This Category Looks Like
Power generation equipment manufacturing ledger data is collected from unit operation sensors, monthly maintenance work orders, spare parts inbound and outbound records, and equipment factory archives. Sensor data updates at the minute level. Maintenance work orders update when equipment maintenance events occur. Spare part ledgers and factory archives sync quarterly. Each single ledger document includes fields such as unique device serial number, collection timestamp, operation parameters like turbine vibration values and boiler pressure, maintenance staff ID, and spare parts replacement batches. Parameter units include μm/s, MPa, hours and other dedicated industrial measurement units.
Constraints Imposed on Database and Operations Workflows
Minute-level sensor updates create high-frequency write pressure. Database connection stability and concurrent processing capability must be ensured. Mixed storage of multiple field types requires databases to support efficient queries for both unstructured parameter data and structured work order data. Joint queries using device serial number and timestamp occur frequently. Index structures must be optimized for these scenarios. Differences in data source update rhythms require operations teams to balance low-latency backups for real-time data and regular archiving of static archives.
Configuration Settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securepass@db-host:27017/equipment_logs?replicaSet=rs-gear&authSource=admin | Operation log data for power generation equipment requires high-availability storage. Replica set configuration ensures service continuity when a single node fails |
PG_URL | postgresql://maintain:pass@pg-host:5432/equipment_main?sslmode=require | Structured maintenance work orders and spare part ledgers are suitable for relational databases. SSL encryption ensures secure transmission of sensitive data |
DB_MAX_LINK | 200–300 | Matches the concurrent connection limit of a single database server. Prevents service interruptions caused by exhausted connections. Can be adjusted based on actual measurements |
REDIS_URL | redis://cache-host:6379/0?maxmemory-policy=allkeys-lru&maxmemory=1000 MB | Caches frequently queried device parameter snapshots to reduce direct database access. 1000 MB of memory can cover popular device data from the past 7 days |
Three Common Misconfigurations
- Service startup displays
mongo connect error.MONGODB_URIreplica set parameters are not configured. High concurrent writes for power generation equipment cause unstable single-node connections. - Empty results return during ledger summary queries. Joint indexes for device serial number and timestamp are not created. Time range queries for sensor data time out without returning valid data.
- Database connection exhaustion occurs with too many concurrent requests.
DB_MAX_LINKis set below actual concurrent demand, and cannot support simultaneous queries for multiple units.
How to Verify Proper Configuration
- Start the FastGPT v4.17.0 local service, and confirm the console has no database connection-related error logs.
- Run a device ledger query covering 24 hours, and verify the number of returned results matches collection records for the corresponding time period.
- Simulate 200 concurrent ledger summary requests, and use database monitoring tools to confirm current connection count does not exceed the
DB_MAX_LINKsetting. - Check the cache monitoring panel, and confirm the cache hit rate for frequently accessed device parameter data falls within a reasonable range.
The values provided are common starting points and should be evaluated 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.