What data for this category looks like
This category covers data across the entire foundry workflow. Sources include ERP material master data, melting furnace thermometers, spectrometers, MES heat records, mold management systems, and customer technical drawings. Daily generated documents include foundry process cards, batching lists, heat records, mold histories, first-piece inspection records, material reports, and nonconforming product review forms.
Data updates follow a core rhythm per heat and per batch, supplemented by daily summaries. Fields include alloy composition, temperature parameters, mold cycle information, defect types, and more. Units include mass percentage, temperature, duration, and others.
What constraints do these characteristics impose on deployment and upgrade
Multi-source heterogeneous data sources require adapting to access specifications of different formats such as industrial sensors, MES systems, and ERP during deployment. Configuration space for custom data mapping must be reserved.
High-frequency heat and batch-level updates require database connection parameter configurations to meet high-concurrency write requirements and avoid data synchronization delays.
Complex document structures and multi-dimensional fields require retaining the editing capability for custom ledger templates during upgrades, to adapt to differentiated ledger formats of different foundry workshops.
The business logic of tracing defects to heat and mold cycles requires configuring indexed strategies for associated data during deployment, to ensure cross-table query efficiency.
How to configure settings
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 connections | Foundry ledger data updates frequently, with new data generated per heat. Meets high-concurrency connection requirements to ensure stable database access |
MONGODB_URI | mongodb://[username]:[password]@[host address]:[port]/fastgpt?authSource=admin | Stores unstructured ledger data such as heat records and mold histories, adapts to multi-document format storage requirements |
REDIS_URL | redis://:[password]@[host address]:[port]/0 | Caches frequently accessed process parameters and mold cycle information to reduce database query pressure |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Ensures access security for attachments such as uploaded material reports and nonconforming product review forms |
STORAGE_S3_ENDPOINT | Configured according to the enterprise's internal object storage address | Stores massive heat temperature measurement data and spectrometer detection files, adapts to the enterprise's existing storage architecture |
Three common misconfigurations
- Issue: Image pull failure during deployment, with
image pull failederror returned. Cause: Domestic mirror acceleration source not configured, or the enterprise intranet cannot access the official image repository. - Issue: Ledger data synchronization delay, with some heat records not appearing in the summary report. Cause:
DB_MAX_LINKconfiguration value is too low, exhausting the database connection pool and failing to write new data in a timely manner. - Issue: Custom ledger fields lost after upgrade. Cause: Configuration of the associated database not backed up during upgrade, or custom field migration steps not executed.
How to confirm the configuration is complete
- Run the
docker pscommand to confirm that FastGPT-related containers are in a running state. - Upload a test heat record document and check whether it can be parsed normally and synchronized to the database.
- Access the address configured in
FE_DOMAIN, log in to the system, and view the editing interface of the custom ledger template to confirm that the configured fields have been saved. - Simulate a heat data write and check whether the associated mold cycle and process parameters can be queried normally.
The values provided here are common starting points and should be measured against the reader's own samples.