What data looks like for this use case
Data sources for stamping and sheet forming scenarios include steel coil labels and quality certificates, press and feeder operation data, mold management system records, MES system work orders, and dimensional inspection data from coordinate measuring machines or inspection tools. Daily associated documents include stamping work instructions, material utilization rate sheets, mold inspection sheets, first and last piece records, and mold change logs.
Data updates are triggered per raw material coil, per production work order batch, and each mold change. Fields include raw material grade and thickness, equipment operating pressure and cycle time, mold usage count, inspection deviation values, and more. Units include millimeters, newtons, counts, and others.
What constraints these characteristics impose on database and operations workflows
Multi-source heterogeneous data sources require the database to support storage and associated queries for time-series structured data, unstructured documents, and semi-structured quality inspection reports. The high-frequency update rhythm creates continuous write and query pressure, requiring operations workflows to adapt to dynamic adjustment of connection pools.
Data association requirements from different production batches and mold change operations require the database to support cross-table association to enable rapid traceability of defective products back to raw materials, molds, and production parameters. Additionally, quality traceability requires retaining long-term historical data, so a reasonable storage expansion and cold data archiving strategy must be planned.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:pass@stamping-db:27017/fastgpt?authSource=admin | Stores time-series structured data such as press operation data and mold usage logs, and is compatible with FastGPT v4.17.0's multi-source data storage framework |
PG_URL | postgresql://fastgpt:pass@stamping-pg:5432/stamping_qc?schema=public | Stores structured data with associated relationships such as dimensional inspection reports and mold change records, and supports cross-table queries to trace defect causes |
DB_MAX_LINK | 150–250 connections | Adapts to high-frequency data write and query requests per coil of material and each mold change, avoids connection exhaustion, adjust the value based on actual measurement |
REDIS_URL | redis://:stamping-redis@stamping-redis:6379/1 | Caches frequently accessed parameters corresponding to material grades and first and last piece inspection benchmark data, reducing database load |
Three common configuration mistakes
- A missing dependency package error occurs when building the image with
docker build -f ./projects/app/Dockerfile. This is because the base image version of the local development environment does not match the FastGPT v4.17.0 build environment specified in the official documentation, and industrial data parsing related dependencies are not pre-installed. - A MongoDB query or connection error appears after startup. The FastGPT v4.17.2 deployment config lists
mongo:4.4.29as a fallback for CPUs without AVX, not as the general default; the default is 5.0.32. For an aggregation error, inspect the complete MongoDB error and query instead of attributing it to the version alone (see the official deployment configuration). - Response delay is high when repeatedly initiating the same defect analysis query. This is because Redis caching is not configured, and each request directly reads full historical data without reusing previously queried result sets.
How to confirm the configuration is complete
- Log in to the database management tool, use the connection string corresponding to
MONGODB_URIto verify that the instance can connect normally and write test press operation data. - Execute the
\dtcommand for the PostgreSQL database to confirm that the database pointed to byPG_URLcontains corresponding data tables for mold change records and dimensional inspection reports. - View the Redis monitoring panel to confirm that the instance corresponding to
REDIS_URLhas cache hit request records. - Simulate 10 minutes of high-frequency data writing after a single mold change, and check that the service does not report connection exhaustion-related errors.
The values provided are common starting points and should be measured against the reader's own samples.