What the data for this category looks like
Data sources for ready-mixed concrete production include engineering orders, inspection records for sand, gravel, cement and admixtures, aggregate moisture content measurement data, real-time data from mixing plant control systems, and test data from project laboratory testing rooms. Data update cadence follows real-time updates for each truck’s production data, updates after each batch of raw material inspection, and daily summary generation of quality reports. Daily documents include mix proportion notices, opening identifications, production and transport orders, raw material inspection records, test block retention records, and structural strength reports. Fields cover aggregate moisture content, water consumption, batching ratio, slump, strength grade, and more, with corresponding units including kilograms, cubic meters, millimeters, megapascals, and more.
What constraints these characteristics impose during deployment and upgrade
The high-frequency real-time data updates in ready-mixed concrete production require adjusting database connection pool parameters during deployment to avoid connection exhaustion. Multi-source heterogeneous data sources require configuring unified storage and data mapping rules to adapt to access formats of different systems such as mixing plants and laboratories. Fixed-format business documents and exception reports require presetting field parsing templates in advance to reduce subsequent configuration costs. Exception report generation with high real-time requirements requires optimizing service scheduling logic during deployment to adapt to per-truck trigger frequencies while ensuring data storage stability.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 | Adapt to high-frequency per-truck data writes and queries, avoid database connection exhaustion |
MONGODB_URI | Local Docker container MongoDB service address or cloud service address | Store unstructured documents and time-series business data such as test block records and strength reports |
PG_URL | Fill in according to the actual deployed PostgreSQL service path | Store structured business data such as mix proportion notices and production and transport orders |
STORAGE_S3_ENDPOINT | http://minio:9000 or corresponding cloud storage endpoint | Store attached files such as raw material inspection reports and test block images |
FILE_TOKEN_KEY | 32-bit random string generated through actual measurement | Encrypt access tokens for process documents and exception reports, ensure data access security |
Three common mistakes
- The symptom is a connection timeout when calling the model, with logs showing failure to resolve the ollama service address. The cause is that the Docker container is not configured with host network mode, so it cannot access the locally deployed ollama service port.
- The symptom is a database connection failure prompt after service startup, with a MongoDB authentication error returned in the logs. The cause is that
MONGODB_URIdoes not carry correct database authentication parameters, or the database service is not started normally. - The symptom is missing fields after parsing uploaded mix proportion notices. The cause is that no dedicated field mapping rules for ready-mixed concrete were configured in advance, and exclusive business fields such as aggregate moisture content and slump were not matched.
How to confirm the configuration is correct
- View the FastGPT v4.17.0 system status panel, confirm that the database services corresponding to
PG_URLandMONGODB_URIshow normal connection status. - Upload a real ready-mixed concrete mix proportion notice document, trigger automatic parsing, and check whether the parsed fields include exclusive business fields such as aggregate moisture content and water consumption.
- Simulate submitting production data for one truck of concrete, check whether the system automatically generates the corresponding exception report draft, and whether the fields match actual business logic.
- View the storage directory corresponding to
STORAGE_S3_ENDPOINT, confirm that attached files such as uploaded raw material inspection reports have been correctly stored.
The values provided here are common starting points and should be measured against the reader's own samples.