What data for this category looks like
Data for this category mainly comes from the Chinese Center for Disease Control and Prevention Information System, hospital HIS systems, communicable disease reporting systems, and laboratory testing systems. The update frequency is daily. Daily documents include structured communicable disease reporting cards, case correction records, as well as unstructured epidemic analysis reports, cluster epidemic reports, and epidemiological investigation reports. Data fields include case unique identifiers, diagnosis dates, reporting units, correction status, laboratory test results, and more. Primary data units are standard dates, case counts, and test results. No custom units are used.
What constraints do these characteristics impose on database and operations workflows
For daily batch data updates, databases must support efficient incremental synchronization to avoid performance loss from full data pulls. Multiple sources and types of data require databases to support both structured table storage and unstructured document indexing, to adapt to case data and report files in different formats. Databases must include built-in field validation and data consistency constraints to meet statutory reporting rigor requirements, preventing underreporting, delayed reporting, or erroneous data from entering the system. Data anomalies may cause delays in epidemic warnings, so real-time monitoring and alert mechanisms must be configured to allow operations teams to quickly locate data issues.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securepass@db-host:27017/communicable_disease?authSource=admin | Adapts to storage of unstructured epidemiological investigation reports and cluster epidemic reports, supports full-text indexing and flexible document queries |
PG_URL | postgresql://db-user:db-pass@pg-host:5432/monitor_db?sslmode=require | Stores structured communicable disease reporting cards and case correction records, supports complex transactions and field validation to ensure data compliance |
DB_MAX_LINK | 10–15 | Handles daily batch data synchronization tasks, prevents database connection pool exhaustion, and ensures stability for multi-node concurrent access |
REDIS_URL | redis://redis-pass@redis-host:6379/0 | Caches frequently queried case correction status and epidemic alert signals, reduces access pressure on core databases |
Three Common Errors
- Phenomenon: After a workflow calls a database query, the returned results do not include the queried case data, only generic response text. Reason: The output of the database query node was not bound to the context splicing step, so the AI cannot access the query results to generate reports.
- Phenomenon: When parsing a PDF of an epidemic analysis report larger than 10 MB, a
timeout of 360000ms exceedederror is returned. Reason: The database connection timeout parameter was not adjusted, or the cache threshold for chunked processing was not configured, causing the connection to be released early during large file parsing. - Phenomenon: Duplicate data is written when batch synchronizing communicable disease reporting cards. Reason: Unique constraint validation was not configured for the database, or the incremental synchronization logic did not use case correction identifiers for deduplication.
How to Confirm Configuration is Complete
- Log in to the FastGPT v4.17.0 system monitoring panel, check the database connection pool status, and confirm that the number of connections configured via
DB_MAX_LINKis not continuously exhausted. - Manually trigger a case data query task, and verify that the returned results include required fields such as correct case numbers, diagnosis dates, and reporting units.
- Upload a test epidemiological investigation report PDF, and confirm that the parsed text and structured data are correctly stored in the specified MongoDB collection and PostgreSQL table.
- Simulate a daily batch synchronization task, and confirm that data completes incremental synchronization on time, with no abnormal errors or duplicate data writes.
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.