What Data for This Category Looks Like
This category of data is sourced from EAM/CMMS systems, equipment sensor collection terminals, operator inspection records, maintenance work order history, spare parts inventory ledgers, and statutory inspection documents for special equipment. Data update cycles cover shift-based inspection submissions, daily/weekly/monthly maintenance plan distribution, and real-time entry when a fault occurs. Daily documents include fields such as unique equipment ID, inspection items, maintenance cycle, fault codes, spare parts usage quantity, inspection validity period, etc. Units include units, times, pieces, days, etc. Some documents must comply with fixed format requirements for special equipment inspections.
Constraints Imposed by These Characteristics During Deployment and Upgrade
Multi-source data access requirements require configuring synchronization rules for multiple types of data sources during deployment to avoid data silos. Diverse update frequencies require flexible scheduled task scheduling configurations to adapt to the inspection reporting rhythms of different work shifts. Fixed format requirements for documents related to special equipment inspections require retaining specific field verification logic during storage and report generation. Maintenance windows must comply with production scheduling, and report generation must be associated with production scheduling data. The upgrade phase must not destroy the original data association links, otherwise maintenance plans may conflict with production. Additionally, scenarios where multiple work shifts access the ledger simultaneously require limiting database connection counts to avoid service overload.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:27017/fastgpt_device_maintain?authSource=admin | Stores core business data such as equipment ledgers and maintenance work orders, matches the default Docker container port and authentication rules for open-source deployments |
REDIS_URL | redis://:password@host:6379/0 | Caches real-time inspection records and sensor data snapshots to reduce latency from repeated queries of multi-source data |
DB_MAX_LINK | 100–200 connections | Supports simultaneous access to the ledger system by multiple workshop shifts, avoids service interruptions caused by exceeding database connection limits |
ROOT_KEY | Randomly generated string of 32 or more characters | Serves as the permission verification key for management-side interfaces, prevents unauthorized access to configuration pages |
FILE_TOKEN_KEY | String consistent with the system encryption key | Encrypts and stores sensitive files such as inspection reports and spare parts lists, ensures data transmission and storage security |
Three Common Configuration Mistakes
- Symptom: A
connection refusederror appears after starting the container, and the MongoDB instance cannot be connected. Cause: TheMONGODB_URIparameter is not filled out correctly, or the network between the Docker container and the MongoDB instance is not configured properly, preventing the service from establishing a connection. - Symptom: The management-side page fails to load normally after upgrading the version, or configuration items do not take effect. Cause: The management-side image is not correctly referenced in
docker-compose.yml, or environment variable parameters in the corresponding configuration file are not updated synchronously. - Symptom: The knowledge base search node in a workflow returns empty results, and cannot match equipment-related documents. Cause: Data source field mapping rules are not configured, or multi-source data synchronization tasks are not enabled, causing equipment inspection records, maintenance work orders and other documents to not be indexed correctly.
How to Verify a Successful Configuration
- Log in to the FastGPT management interface, view the data source management page, and confirm that the connection status of services such as MongoDB and Redis shows normal.
- Manually trigger an inspection ledger summarization task, check whether the generated report includes required fields such as equipment ID, inspection time, and fault type.
- Call the knowledge base search via a workflow node, enter a known fault code, and confirm that the number of returned results matches business expectations.
- View system operation logs, and confirm that there are no error messages such as database connection timeouts or file storage failures.
The values provided are common starting points and should be measured against the reader's own samples.