What Data for This Category Looks Like
Ready-mixed concrete production data originates from engineering orders, sand, gravel, cement and admixture inspections, moisture content tests, mixing station control systems, and project laboratories. Daily business documents include mix proportion notices, opening identifications, production and delivery slips, raw material inspection records, test block records, and strength reports. Data updates follow this cadence: production data is generated in real time per truck, raw material inspection data is updated per batch, and production summaries and quality statistics are generated daily. Data fields include slump, strength grade, aggregate moisture content, departure time, test block age, and others, with corresponding units of millimeters, megapascals, percentages, timestamps, and days.
Constraints Imposed on Database and Operations Workflows
High-frequency real-time write operations require the database to support high-throughput connection processing, to accommodate concurrent production scenarios with multiple trucks operating simultaneously. Multiple heterogeneous data sources require the database to support multi-format data access and field mapping, to adapt to differing data structures from mixing station control systems and laboratory systems. Long-term storage of test block strength and age data requires a stable persistence mechanism to support cross-age quality traceability requirements. The rigor of quality problem traceability also requires the database to support transaction consistency and regular backups, to avoid liability disputes caused by data loss. Service recovery capabilities in abnormal scenarios such as power outages must be factored into operational planning, to prevent service interruptions caused by data corruption.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:pass@concrete-db:27017/concrete_trace?authSource=admin | Supports mixed storage of unstructured test report attachments and structured production data |
PG_URL | postgresql://fastgpt:pass@concrete-pg:5432/concrete_biz?sslmode=require | Supports strongly consistent transactional queries for structured business data such as mix proportions and strength reports |
DB_MAX_LINK | 200–300 connections | Matches the number of equipment operating simultaneously at the mixing station to avoid write failures caused by exhausted connection pools |
REDIS_URL | redis://concrete-redis:6379/0 | Caches high-frequency queried production parameters such as real-time aggregate moisture content and slump to reduce database load |
Three Common Misconfigurations
- Database connection error, with log showing
Failed to connect to jyfkk:1433 - 38BBDDC3AF7F0000, while the MongoDB visualization tool can connect normally. The cause is that the configured database hostname is not correctly mapped to the actual deployed database service address, or the firewall restricts outbound access to the corresponding port. - After a power outage restart following Docker deployment, FastGPT fails to start, and PostgreSQL and MongoDB refuse connections. The cause is that the containers were not shut down properly during the power outage, resulting in incomplete database file flushing, dirty data in the data directory, and the service being unable to load corrupted database files on startup.
- The age field of test block strength reports is empty during quality traceability. The cause is that field mapping rules from the laboratory system were not matched during multi-source data synchronization, resulting in key business fields not being correctly written to the database.
How to Verify Proper Configuration
- Run the built-in database connectivity detection script of FastGPT to verify the connection status of
MONGODB_URIandPG_URL, and confirm that a connection successful prompt is returned. - Import a simulated ready-mixed concrete production data set containing fields such as mix proportion notice number, slump value, and test block strength grade, and confirm that the data can be correctly written and the corresponding records can be retrieved through the business query interface.
- Test container restarts or simulated power outage scenarios, and confirm that the database service can automatically recover and that stored business data has not been lost or corrupted.
- Monitor database connection usage during high-concurrency write scenarios, adjust the value of
DB_MAX_LINKto ensure that the number of connections does not exceed the set upper limit and there is no connection leakage.
The values provided are common starting points and should be measured against the reader's own samples.