What this category of data looks like
This data covers full-process output from professional heat treatment plants and automotive parts heat treatment workshops. Data sources include material grade archives, customer technical specification documents, real-time temperature control instrument data, furnace temperature uniformity test reports, hardness tester and metallographic equipment test reports, and workshop MES system records. Daily generated documents include heat treatment process cards, charging charts, furnace temperature curves with time and temperature parameters, hardness and metallographic analysis reports, equipment inspection forms, and exception handling sheets.
Data updates per furnace charge and per batch. Structured fields include furnace temperature range (unit: ℃), holding time (unit: min), hardness value (unit: HV), and charging batch number, among others. The dataset also includes time-series instrument collection data. The core of this data category revolves around internal material structure and mechanical properties, with clear differences from adjacent categories such as surface coating.
What constraints these characteristics impose on deployment and upgrade
High-frequency updates per furnace charge and per batch require database connection pools to adapt to high-frequency read/write demands, to avoid retrieval interruptions caused by connection exhaustion. Multiple heterogeneous data sources (instruments, equipment reports, MES) require deployment to support configuration of different storage media, and targeted setup of file storage and cache services.
Diverse document types (time-series curves, metallographic images, process cards) require pre-planning file storage paths and access permissions during deployment, to prevent large file upload failures. Professional and diverse fields require index configurations to cover core retrieval dimensions such as material grades and furnace temperature parameters, to avoid retrieval results deviating from business needs.
For local deployment scenarios, common issues with database initialization failures must also be addressed, corresponding to deployment obstacles in actual implementation.
How to set the configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:password@localhost:27017/fastgpt?authSource=admin (local deployment) or the corresponding connection string from the cloud service provider | Adapts to storage needs for multi-source structured and time-series heat treatment data, supports batch furnace charge data writes |
REDIS_URL | redis://:redis_pass@localhost:6379/0 (local deployment) or authenticated cloud cache connection string | Caches furnace charge retrieval sessions and frequently accessed furnace temperature curve data, reduces database read/write pressure |
STORAGE_S3_ENDPOINT | http://192.168.1.100:9000 (local MinIO deployment) or cloud storage service provider endpoint | Stores large files such as heat treatment process cards, metallographic reports, furnace temperature curves, ensures file access permissions are controllable |
DB_MAX_LINK | 80–150 | Adapts to high-frequency data writes and retrieval requests per furnace charge, avoids service interruptions caused by connection pool exhaustion |
FE_DOMAIN | http://192.168.1.100:3000 (local deployment) or bound enterprise domain name | Configures the frontend access path, ensures registration pages and retrieval interfaces load normally |
FILE_TOKEN_KEY | Randomly generated 32-character English string | Encrypts file access tokens, prevents unauthorized personnel from accessing sensitive data such as heat treatment test reports |
Three common mistakes
- Phenomenon: An error occurs when running the database initialization script after local deployment, with a prompt of connection refused or insufficient permissions. Cause: The username and password in
MONGODB_URIare not configured correctly, or the default database port is not open. - Phenomenon: No knowledge base editing entry is found during retrieval debugging, and retrieval results cannot match furnace temperature anomaly data. Cause: Index configurations for data sources such as heat treatment process cards and furnace temperature curves are not properly associated, leading to missing retrieval dimensions.
- Phenomenon: The system prompts a CVE-2024-46982 security vulnerability. Cause: The version of NextJS relied on by the used FastGPT version is lower than the fixed version, and framework dependencies have not been upgraded in a timely manner.
How to confirm the configurations are correct
- Run the built-in database connectivity test script, verify that the connection status of
MONGODB_URIandREDIS_URLis normal, with no timeout or permission errors. - Upload a heat treatment process card and furnace temperature curve file, verify that the file can be uploaded normally to the configured storage path, and can be previewed normally through the frontend interface.
- Access the frontend address corresponding to
FE_DOMAIN, confirm that the registration page loads normally, and new users can complete the registration process. - Initiate an exception retrieval for a specified furnace charge, confirm that the returned results include core business fields such as furnace temperature parameters and hardness test data, and the number of results matches the preset threshold.
The values provided here are common starting points, and should be evaluated against the reader's own samples.