What Data for This Category Looks Like
This category of data covers production asset health-related content from manufacturing enterprise equipment departments, maintenance teams, and public utility departments. Daily documents include equipment ledgers, inspection checklists, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Data sources include EAM/CMMS systems, equipment sensor data, operator inspection records, maintenance history archives, spare parts inventory data, and statutory inspection information. Update cycles cover shifts, daily, weekly, monthly intervals, and scenarios triggered in real time when faults occur. Document fields include equipment numbers, inspection parameters, fault codes, spare parts models, and more. Field units change dynamically based on equipment type and inspection items.
What Constraints These Characteristics Impose on Deployment and Upgrade
Data sources for this category are scattered, and update cycles vary widely. During deployment, support must be provided for connecting multiple heterogeneous data sources. Corresponding storage connection parameters must be configured to ensure data synchronization stability. Document fields have no unified units, so dynamic field mapping capabilities must be retained to adapt to the inspection item requirements of different equipment. The statutory attributes of special equipment inspection records require compliance configuration for data retention during deployment. During upgrade, compatibility must be maintained for format migration of historical equipment ledgers and work order data, to avoid interrupting daily inspection and maintenance workflows of maintenance teams. Incremental synchronization logic changes for multiple data sources must also be adapted.
How to Configure
This configuration is compatible with FastGPT v4.17.0. Specific parameters must be adjusted based on the enterprise intranet environment.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 connections | Adapts to concurrent access demands from multiple device sensor data and work order systems, preventing database connection exhaustion |
MONGODB_URI | Local private network address | Stores unstructured documents such as equipment inspection records and fault analysis, ensuring enterprise intranet data security |
REDIS_URL | Intranet address on port 6379 | Caches real-time device sensor data and inspection task queues, reducing overhead from frequent database reads |
STORAGE_S3_ENDPOINT | Enterprise-built object storage endpoint | Stores large files such as equipment maintenance images and inspection reports, adapting to intranet storage and access requirements |
FILE_TOKEN_KEY | Generated per enterprise key management rules | Encrypts access tokens for equipment-related files, preventing unauthorized access to spare parts lists or inspection records |
FE_DOMAIN | Enterprise intranet domain name | Configures the frontend access address used by maintenance teams, ensuring system access is limited to the intranet |
Three Common Mistakes
- Scenario: A curl request error occurs when running the upgrade script, returning connection timeout or verification failure. Cause: The enterprise intranet proxy is not configured, preventing access to the FastGPT official upgrade source, or the
REDIS_URLparameter is configured incorrectly, causing dependent services to be unavailable. - Scenario: A checksum verification failure error occurs when building an image with docker build. Cause: The intranet image source is not specified, leading to incomplete files due to network interruption when pulling base images, or storage connection parameters are not configured correctly, causing file cache verification failure.
- Scenario: Device fault analysis documents cannot be retrieved after connecting to a local Ollama instance. Cause: The
FILE_TOKEN_KEYis not configured correctly, preventing access to the document storage, orDB_MAX_LINKis set too low, causing retrieval requests to be rejected by the database.
How to Verify Successful Configuration
- Run the
docker pscommand to confirm that database, Redis, and FastGPT service containers are all running. - Access the configured
FE_DOMAINaddress, log in to the system, and attempt to import an equipment inspection checklist to verify that the data upload process works correctly. - Trigger a simulated fault reporting process to confirm that the system can associate historical maintenance work orders and spare parts inventory data for the corresponding equipment.
- Check the storage directory corresponding to
STORAGE_S3_ENDPOINTto confirm that files such as equipment inspection reports can be uploaded and downloaded normally.
The values provided are common starting points and should be measured against the reader's own samples.