What this category of data looks like
Data sources include material grades, customer technical specifications, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic equipment, and MES systems. Daily documents cover heat treatment process cards, furnace loading diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling sheets. Data is updated per furnace charge and per batch. Core fields include furnace number, heating temperature, holding duration, cooling rate, hardness value, and metallographic structure grade. Corresponding units include ℃, hours, HV, and others.
What Constraints These Characteristics Impose During Deployment and Upgrade
The high-frequency update requirement for each furnace charge and batch means connection pool parameters adapted for high-concurrency, small-batch writes must be configured during deployment. Multi-source heterogeneous data sources require pre-configured data source access logic for different formats. Storage needs including image documents require configuring a storage service that supports large file uploads and previews. Mapping of professional fields and units requires pre-configured metadata rules. Features with severe error consequences require adding data verification and exception alert configuration items during deployment.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 | Adapts to the concurrent demand of high-frequency data writes per furnace charge, avoids database connection pool exhaustion |
MONGODB_URI | Cluster connection string with read-write separation suffix | Stores multi-source heterogeneous heat treatment ledger data, ensures high availability and read-write performance |
REDIS_URL | redis://:custom password@instance address:6379/0 | Caches real-time furnace temperature data and session status, reduces access pressure on the core database |
STORAGE_S3_ENDPOINT | Standard endpoint provided by the object storage service provider | Stores image documents such as furnace loading diagrams and furnace temperature curves, supports resumable uploads and permission control |
FILE_TOKEN_KEY | 32-character random string | Encrypts file access tokens, prevents unauthorized access to attached documents in heat treatment ledgers |
ROOT_KEY | Complex random string of no less than 16 characters | Verifies super administrator privileges of the management platform, ensures system security after deployment |
Three Common Configuration Errors
- Phenomenon: An "invalid token" message appears in the interface during conversation, and large model calls fail. Cause: The access permission for
MONGODB_URIis not configured correctly, or the connected oneapi token is not bound to the call permission for the corresponding model. - Phenomenon: Uploaded furnace loading diagrams and furnace temperature curves cannot be previewed normally. Cause: The bucket read-write permission for
STORAGE_S3_ENDPOINTis not configured correctly, or the encryption logic ofFILE_TOKEN_KEYdoes not match the file access verification. - Phenomenon: No response or delayed timeout occurs after submitting single furnace charge ledger data. Cause:
DB_MAX_LINKis set too low, causing the database connection pool to be exhausted and unable to process data write requests in a timely manner.
How to Confirm Configuration Is Correct
- Log in to the FastGPT v4.17.0 management backend, enter the database connection detection page, confirm that the configurations for
MONGODB_URIandREDIS_URLcan connect normally. - Upload a heat treatment furnace loading diagram file, verify that the file can be previewed normally and the access link has no permission error, confirming that the configurations for
STORAGE_S3_ENDPOINTandFILE_TOKEN_KEYare correct. - Simulate submitting single furnace charge ledger data, check that the data can be written to the database normally and a summary entry is generated, confirming that the
DB_MAX_LINKconfiguration meets the concurrent demand. - Configure oneapi to connect to domestic large models, initiate a test conversation, confirm that there is no invalid token error, verifying the correctness of the permission configuration.
The values provided are common starting points and should be measured against the reader's own samples.