What Data for This Category Looks Like
This category of data typically comes from senior care service information platforms, civil affairs statistics systems, fire safety inspection records, and facility operation ledgers, with a monthly update frequency. Daily processed documents include facility registration materials, operation subsidy applications, service quality inspection checklists, safety hazard rectification ledgers, and elderly admission files. Data fields cover facility qualifications, bed counts, service records, safety hazard items, subsidy application details, and more. Units involved include bed counts, number of inspection items, rectification time limits, and others. The data structure is heterogeneous, containing both structured statistical fields and unstructured document attachments.
Constraints on Database and Operations Workflows
Multi-source heterogeneous data sources require the database to support importing structured and unstructured data in different formats, to avoid data conversion losses. The monthly update frequency means the database must support scheduled batch synchronization tasks. Synchronization time windows must be properly allocated to avoid peak business hours. Diverse document types and significant field differences require flexible table structure configurations. Fixed table designs with strict constraints should not be used. Additionally, data involves facility qualifications and subsidy disbursements, so data read-write consistency must be ensured. Data deviations during verification steps will increase subsequent audit time.
Configuration Guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Database Connection Address | Configure MongoDB and PostgreSQL addresses separately based on data source type | Store unstructured elderly files and rectification ledgers separately from structured registration and subsidy data |
| Maximum Database Connections | 15–25 | Match the concurrency requirements of monthly batch synchronization, avoid synchronization failures caused by exceeding database capacity limits |
| Cache Service Address | Configure a dedicated Redis instance address | Cache intermediate data verified monthly and task execution status, reduce repeated database query operations |
| Data Synchronization Cycle | Perform full synchronization on the 1st–3rd of each month | Match the monthly business update frequency, avoid peak daily business hours |
| Data Validation Rules | Associate registration and operation data by facility ID | Resolve the core issue of time-consuming multi-source data verification, improve ledger summary efficiency |
Three Common Configuration Mistakes
- Calling a database node returns empty JSON or an empty array, which cannot be used directly for ledger summary. The cause is that multi-source data association mapping rules are not configured, leading to field matching failures during cross-system queries.
- Returning none when calling a workflow with high concurrency. The cause is that the maximum database connections setting is too low, exceeding database capacity limits and causing connection requests to be rejected.
- Field missing or format errors occur after data synchronization. The cause is that automatic conversion rules are not configured for unstructured fields in senior care service documents, leading to some inspection records and rectification ledgers failing to be stored normally.
How to Verify Proper Configuration
- Run a single database synchronization task, check the synchronization logs for prompts about connection failures or data import errors.
- Call the database node to query facility registration data, verify that the returned results include expected fields such as facility qualifications and bed counts.
- Simulate a monthly batch synchronization task, check that database connection counts stay within the preset range, with no blocking errors.
- Trigger the ledger summary workflow, confirm that the generated report includes all required document data with no missing fields.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.