Data Structure for This Category
Production data for furniture and wood products manufacturing comes from multiple sources. These include order design systems, sheet and wood batch ledgers, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Daily production documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating process documents, hardware accessory lists, and assembly and packaging records. Data updates happen per order, daily, or per material batch. Fields include panel dimensions (unit: millimeters), sheet moisture content test data, hardware model numbers, order numbers, and other details that change with custom order requirements.
Constraints Imposed on Deployment and Upgrade Processes
Multi-source production data, high-frequency update cadence, and non-standardized document structures create multiple constraints for deployment and upgrade work. Configure connection parameters compatible with multiple interfaces during deployment. This adapts to synchronization requests from different formatted multi-system data sources. Verify parsing logic for non-standardized documents during upgrades. Custom orders drive large panel volumes and inconsistent document structures. Ensure stable database connections to avoid data synchronization interruptions. Test data such as sheet batches and moisture content has high real-time requirements. Validate field mapping rules after upgrades to prevent parameter mismatches that disrupt production. Strict precision is required for fields such as panel dimensions and hole positions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to concurrent data requests from multiple devices and order systems, prevents connection pool exhaustion |
MONGODB_URI | Includes authentication information and read-write separation addresses | Adapts to batch read-write requirements for production data, ensures stable data synchronization between ERP and CNC equipment |
FE_DOMAIN | Exclusive enterprise intranet domain name | Restricts front-end access scope, fits the intranet deployment scenario of custom furniture enterprises |
STORAGE_S3_ENDPOINT | Enterprise-built object storage address | Stores large-volume production documents such as order breakdown drawings and cutting lists, meets high-frequency storage requirements |
FILE_TOKEN_KEY | Set based on actual measurements | Used for security verification of production documents, fits internal enterprise permission control rules |
Three Common Misconfigurations
- Front-end version display error after upgrade: After upgrading to FastGPT v4.17.0, the front-end console still shows the old version. This occurs when only the back-end service code is updated, and the front-end static resource build and packaging step is not executed.
- Intermittent MongoDB connection interruptions: Database connections work normally in the initial deployment phase, but connection timeout errors appear after several hours. This happens when a reasonable threshold for
DB_MAX_LINKis not configured, causing the connection pool to be exhausted by concurrent requests. - Cross-version upgrade failure when skipping versions without upgrade scripts: The system fails to start after directly upgrading past versions with no upgrade scripts. This is caused by not executing database structure change scripts in version order, leading to mismatched data structures.
How to Verify Proper Configuration
- Run the database connection test script to verify that the address configured in
MONGODB_URIcan read and write production data normally, with no connection errors. - Upload an order breakdown drawing document to confirm that the storage service configured in
STORAGE_S3_ENDPOINTcan store and retrieve the document normally. - Access the address configured in
FE_DOMAINto confirm that the front-end page loads normally and the version displayed is FastGPT v4.17.0. - Simulate multiple order data synchronization requests to confirm that the connection count configured in
DB_MAX_LINKsupports concurrent requests, with no connection pool exhaustion.
The values provided are common starting points and should be measured against the reader's own samples.