What data for this use case looks like
Data for this use case primarily comes from meteorological warning systems, hydrological monitoring systems, emergency command platforms, grassroots disaster reporting systems, and video surveillance. Its update frequency is daily. Documents processed daily include duty logs, warning notices, consultation minutes, rapid disaster reports, emergency response plans, and material allocation slips. Core fields include disaster level, rainfall and water level values, affected area scope, material allocation quantities, and personnel transfer data. Some documents include metadata such as resolution and duration of video surveillance footage.
Constraints Imposed on HTTP Interfaces and External Systems by These Characteristics
Multi-source data sources require interfaces to support standardized output formats from different systems. This prevents parsing failures caused by differing field names. The daily update frequency means interface call frequency must align with business rhythms. Frequent repeated full data pulls should be avoided. Differing fields across document types mean request bodies must adapt to multiple data structures, including text parameters, file links, and metadata. Core fields use specific units. Interfaces must verify that parameter units meet business requirements. For example, rainfall data must use millimeters, and water level data must use meters. Non-compliant units will disrupt subsequent review and scheduling.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer ${system docking key}, valid for 12 hours | Complies with identity verification standards of external systems, ensures secure interface access |
variables | Pass regional flood prevention thresholds, disaster level enumeration values | Adapts to flood prevention business rules for different regions, improves review accuracy |
responseData | Only return core fields of rapid disaster reports and material allocation slips | Reduces invalid data transmission, lowers interface response latency |
| Interface timeout period | 600 seconds | Accommodates time requirements for multi-source data pulls, prevents data acquisition interruptions from timeouts |
| Retry count | First 3 retries with 10-second intervals | Addresses temporary network fluctuations, improves interface call success rate |
| Data filtering rules | Filter redundant data not updated on the current day | Aligns with daily update business rhythms, reduces invalid data processing |
Common Configuration Errors
- Symptom: Request returns
400 Bad Requeststatus code, and no valid data is returned by the interface. Cause: Field naming rules of external systems are not aligned. Local custom field names are mistakenly used as request parameter names, which does not meet external system verification requirements. - Symptom: Parameter values in the request body do not match expectations, leading to deviation in disaster data in review results. Cause: The actual data type corresponding to the parameter values in the external system is not clarified. For example, replacing millimeters with centimeters for rainfall data causes incorrect numerical calculations.
- Symptom: Interface calls frequently trigger timeouts, and full data pulls cannot be completed. Cause: Timeout settings adapted for multi-source data pulls are not configured. The default timeout duration is too short to complete cross-system data aggregation.
How to Verify Successful Configuration
- Use the interface debugging tool of FastGPT v4.17.0, enter the configured
Authorizationparameter, send a test request, and check that the returned status code is200 OK. - Compare the returned
responseDatafield with actual data from external systems, confirm that core field names and values meet business requirements. - Simulate temporary network interruption, check if the interface triggers the preset retry mechanism, and pull data normally after network recovery.
- Configure a scheduled call task, verify that the interface automatically pulls data during daily update periods, with no duplicate or missing content.
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.