What this category of data looks like
This category of data comes from material grade libraries, customer technical specifications, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic analyzers, and workshop MES systems. The update frequency is per furnace run or production batch. Daily generated documents include heat treatment process cards, charging charts, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and abnormal disposal forms. Core fields include heating temperature (℃), holding duration (h), furnace temperature deviation value, hardness test results (HV), charging position number, and more. The data structure is dynamically adjusted alongside production batches.
What constraints do these characteristics impose on the deployment and upgrade phase
The high-frequency production data updates of this category require deploying storage components configured to support high-concurrency writes. The need to process multiple types of documents including charts and structured reports requires configuring storage and parsing rules adapted to multimodal data. Dispersed equipment and system data sources require pre-configuring unified docking parameters to ensure data synchronization accuracy. The complex field structure of abnormal reports requires presetting field mapping and association logic during the deployment phase to avoid subsequent data matching errors. The high production risk characteristic requires retaining historical logs and a rollback mechanism during upgrades to enable rapid recovery in abnormal scenarios.
How to set the configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:port/fastgpt?authSource=admin | Structured process data and report data volume for this category grows with production batches, requiring high-throughput MongoDB storage |
REDIS_URL | redis://:password@host:port/0 | High-frequency furnace run data caching and session state storage require low-latency in-memory database support |
DB_MAX_LINK | 50–80 | Multiple documents are generated per furnace run. The concurrent connection count must match the workshop's average daily furnace run scale, calibrated via actual testing |
FE_DOMAIN | https://your-heat-treatment-fastgpt-domain.com | A dedicated domain must be bound to ensure the stability of process document sharing links and prevent failure after restart |
FILE_TOKEN_KEY | 32-character random string | Encryption security of sharing links must be ensured to prevent unauthorized access to production data |
STORAGE_S3_ENDPOINT | http://internal-s3:9000 | Image documents such as charging charts and furnace temperature curves must be hosted via a unified object storage service |
Three common misconfigurations
- Symptom: After restarting a Docker container on a Windows system, process document sharing links become inaccessible, with a 404 error displayed on the page. Cause: The
FE_DOMAINparameter was not configured correctly. The domain binding becomes invalid after the container restarts, causing the base domain relied on by the sharing link generation logic to not take effect. - Symptom: When configuring
MONGODB_URI, theauthSource=adminparameter was not added. Historical process data cannot be read after startup. Cause: The MongoDB authentication database was not specified, causing connection verification to fail. - Symptom: After upgrading the version, field mapping for abnormal reports becomes misaligned, with hardness values displayed as furnace temperature values. Cause: Field configurations were not backed up and verified in advance, and existing field association rules were overwritten during the upgrade process.
How to confirm the configuration is properly set
- Access the domain bound to
FE_DOMAINand confirm that the page loads normally with no redirect errors. - Perform a simulated furnace run data write operation and check whether corresponding process document records are generated in the database specified by
MONGODB_URI. - Generate a test process document sharing link, restart the Docker container, and access the link again to confirm it opens normally.
- View the container logs to confirm there are no timeout errors in the connection pool corresponding to
DB_MAX_LINK, and that the connection count is within a normal range.
The values provided are common starting points and should be measured against the reader's own samples.