What data for this category looks like
This category covers the full equipment management workflow of manufacturing enterprises, including documents such as equipment ledgers, inspection forms, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Data sources include EAM/CMMS systems, equipment sensor collection data, operator inspection records, maintenance history archives, spare parts inventory ledgers, and statutory inspection information. Data update frequencies cover shift-based inspections, daily/weekly/monthly maintenance plan updates, and real-time reporting when faults occur. Fields include unique equipment identifiers, inspection times, fault codes, spare part codes, inspection validity periods, and more. Units include operating hours, inspection counts, spare part quantities, and others.
What constraints these characteristics impose on the database and operations maintenance link
Multi-source heterogeneous data structures require the database to support storage and associated queries for structured relational data, such as work orders and ledgers, time-series sensor data such as inspection temperature and vibration parameters, and unstructured documents such as fault analysis reports. Uneven update frequencies create peak concurrent pressure: request volumes during shift inspection periods and sudden fault occurrences are significantly higher than regular periods, requiring adaptation to sudden concurrent scenarios. Data such as statutory inspection records must retain complete audit trails and cannot be modified arbitrarily, requiring configuration of read-only data and version rollback mechanisms. Equipment ledgers have strong relational links with work orders and spare parts lists, requiring foreign key constraints to ensure data consistency and avoid missing or incorrect associated fields.
How to configure settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Database connection string | postgresql://db_user:db_pass@eam-db-host:5432/device_manage?charset=utf8mb4 | Connect to the enterprise's existing EAM/CMMS relational database to store structured equipment ledgers, work orders, and spare parts list data. Specify the character set to avoid garbled Chinese characters |
| Non-relational database connection | mongodb://mongo_user:mongo_pass@sensor-db-host:27017/device_sensor?authSource=admin | Store time-series equipment sensor inspection data and unstructured fault analysis documents to adapt to multi-type data storage requirements |
| Maximum database connections | 20–30 connections | Adapt to concurrent connection requests during shift inspection peaks and sudden fault occurrences to avoid service interruptions caused by exhausted database connections |
| Cache service connection | redis://redis_pass@cache-host:6379/0 | Cache frequently queried basic equipment ledger and spare parts inventory data to reduce query load on the primary database |
| Maximum number of records returned per query | Top 1000 records | Adapt to batch equipment ledger summary query requirements, avoiding service lag caused by excessively large single return data volumes |
| Database timeout period | 600 seconds | Adapt to long-duration query scenarios such as batch ledger summary and monthly inspection report generation |
Three common configuration mistakes
- Symptom: Chinese characters display as garbled text after connecting to MySQL. Cause: The character set parameter is not specified in the database connection string, or the database default character set does not match the FastGPT configuration.
- Symptom: Generated SQL statements cannot be parsed. Cause: Syntax adaptation is not performed for associated fields of equipment ledgers, such as equipment ID and work order number, resulting in mismatched field names and database table structures.
- Symptom: Database connections are exhausted, causing request timeouts. Cause: The maximum database connection count configuration value is too low, failing to adapt to concurrent request volumes during shift inspection peaks.
How to confirm successful configuration
A basic equipment ledger query is executed, and returned fields such as equipment names and inspection records are verified to have no garbled characters, with data matching the EAM/CMMS system. A work order summary task is triggered, and the generated SQL statements are checked to correctly associate inspection forms, work orders, and spare parts list data with no syntax errors. 10 concurrent query requests are simulated, and database connection counts are observed to not exceed the configured maximum database connection threshold, with no connection timeout errors occurring. Cache service monitoring data is reviewed, and frequently queried basic equipment data is confirmed to hit the cache, with response times meeting expected standards.
The values given are common starting points and should be measured against the reader's own samples.