What the Data for This Category Looks Like
Data related to residential development yield reporting draws from three core sources: internal real estate enterprise cost accounting systems, sales data ledgers filed with housing and urban-rural development authorities, and bank financing repayment records. Data updates follow project development milestones or monthly cycles, with support for immediate synchronization of temporary adjustments. Each data document corresponds to one residential development project. Structured storage fields include project unique identifier, land acquisition cost, construction and installation expenditure, salable floor area, contracted sales revenue, and recovered fund amount. The corresponding units are yuan per square meter, ten thousand yuan, ten thousand yuan, square meter, and ten thousand yuan respectively. Single data volume varies based on project development scale, and there is no need for real-time high-frequency updates.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The single-project structured format of residential development data requires project-specific data sharding storage rules during deployment. This prevents mixing data across multiple projects, which would degrade query performance. The monthly update rhythm requires the synchronization mechanism to support a hybrid model of scheduled triggering and milestone event triggering. This balances standard updates and temporary adjustment needs. Multi-source data integration scenarios require compatibility with legacy system docking protocols during upgrades. This avoids interrupting data synchronization for historical projects. Long-cycle project historical data retention requirements mandate a full data backup before upgrades. Migration scripts must also support legacy version field naming rules.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGO_VERSION | 6.0–7.0 | Matches the officially supported stable version range, and meets the concurrent storage requirements for multi-project residential development data |
DATA_SYNC_INTERVAL | 7200 seconds | Adapts to the monthly update rhythm of residential development data, and avoids frequent synchronization consuming system resources |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the maximum time required to parse full cost data for a single project, and prevents interruptions when processing large-volume ledgers |
SYNC_TRIGGER_TYPE | Scheduled triggering + milestone event triggering | Adapts to the scenario where residential development data is primarily updated monthly, and requires immediate synchronization when milestones change |
BACKUP_STORAGE_PATH | /var/lib/fastgpt/backup/ | Reserves a dedicated storage path for project data backups before upgrades, to support rollback capabilities during upgrades |
LOCAL_MODEL_PATH | /data/models/vision/ | Specifies the storage directory for local video and image processing large models, to support content generation for data visualization workflows |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volumes, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom:
500 Internal Server Erroroccurs after cross-version upgrade, and historical project data fails to load normally. Cause:MONGO_VERSIONis not configured within the officially supported range, and database index structures between old and new versions do not match, causing data read requests to fail. - Symptom: Third-party cost API synchronization tasks frequently time out and interrupt. Cause:
API_CONFIG_TIMEOUTis not set to a reasonable duration matching the third-party interface. The default timeout value is too short, and does not cover the full interface response cycle. - Symptom: The local video and image processing model cannot be invoked after import. Cause: Model files are not placed in the directory specified by
LOCAL_MODEL_PATH, and the system's local model loading permission is not enabled, so the system cannot recognize the model path.
How to Verify Proper Configuration
- Log in to the system configuration backend, verify that the
MONGO_VERSIONconfiguration value matches the actual running database version, and confirm it falls within the officially supported version range. - Manually trigger a single-project data synchronization task, review field mapping records in the synchronization log, and confirm that imported residential development data fields fully match configured mapping rules.
- Check disk available space in the
BACKUP_STORAGE_PATHdirectory, and confirm that the upgrade backup process can generate backup files normally. - Call the local model test interface, verify that the video and image processing model can load normally and generate data visualization content, and confirm that the model path configuration is correct.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.