What This Category of Data Looks Like
This category of data originates from daily outputs of manufacturing enterprise equipment departments and workshop maintenance teams. It includes documents such as equipment ledgers, point inspection forms, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Data sources cover EAM/CMMS systems, equipment sensor data, operator point inspection records, maintenance history, spare parts inventory, and statutory inspection information. Update frequencies follow shift-based, daily, weekly, monthly cycles, and real-time triggers when faults occur. Fields include equipment number, point inspection time, fault type, spare parts model. Some sensor data includes numerical fields with units such as power and temperature.
Constraints for Deployment and Upgrade
This category of data has diverse sources and varied update rhythms. The deployment phase requires compatibility with multi-system integration and document processing in different formats. Multi-data source synchronization rules must be configured in advance. The upgrade phase must support incremental synchronization to adapt to real-time sensor data and temporary data triggered by faults. Full synchronization must be avoided to prevent excessive resource usage. A large portion of equipment-related documents are unstructured. Appropriate storage paths and permissions must be configured to ensure normal access to point inspection records, inspection reports, and other documents required for supplier audits. The concurrent read and write volume of data fluctuates with workshop shifts. Sufficient database connection resources must be reserved.
Configuration Settings
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
MONGODB_URI | MongoDB connection string with authentication information | Stores structured business data such as equipment ledgers, work orders, and fault analysis |
REDIS_URL | redis://:{custom access password}@{deployment server address}:6379/0 | Caches real-time equipment sensor data and point inspection task queues to reduce database query pressure |
DB_MAX_LINK | 30–50 | Adapts to the concurrent read and write scale of equipment data, prevents database connection pool exhaustion |
STORAGE_S3_ENDPOINT | Access endpoint corresponding to the internal object storage service | Stores unstructured document attachments such as point inspection form scans and maintenance plan PDFs |
FE_DOMAIN | Front-end access domain name deployed within the enterprise | Configures the access address of the front-end page, adapts to internal system integration for supplier and process quality audits |
Three Common Misconfigurations
- Symptom: After upgrading to FastGPT v4.17.0, calling the knowledge base for question answering returns a 404 error. Cause:
STORAGE_S3_ENDPOINTor the access permissions of the corresponding storage bucket are not configured correctly. Unstructured documents such as point inspection forms and inspection reports cannot be loaded. - Symptom: Knowledge base synchronization of equipment point inspection data frequently interrupts. Cause: A reasonable value for
DB_MAX_LINKis not set. Database connections are exhausted, and synchronization tasks cannot execute continuously. - Symptom: The internal audit system cannot call the maintenance suggestions generated by FastGPT. Cause:
FE_DOMAINis configured as a local test address, and it is not replaced with the actual deployed internal access domain name. Cross-domain access fails.
How to Verify Correct Configuration
- Run the built-in database connection test tool to verify that the configurations of
MONGODB_URIandREDIS_URLcan connect normally. - Submit a simulated equipment point inspection data synchronization task to confirm that data can be written normally and indexed to the knowledge base.
- Access the configured
FE_DOMAINaddress to confirm that the front-end page loads normally and has no cross-domain errors. - Check the system operation logs to confirm that the connection count of
DB_MAX_LINKdoes not show exhaustion or over-limit error reports.
The values provided are common starting points and should be measured against the reader's own samples.