Data Characteristics
Cold chain logistics pharmacovigilance data originates from temperature and humidity sensors, GPS tracking devices, transport vehicle management systems, warehousing management systems, and drug batch information. This data is primarily structured and semi-structured, such as JSON, XML, or CSV. Data update frequency is high; temperature, humidity, and location information may update every few minutes or even every few tens of seconds. Batch and status information may update at critical points (e.g., inbound, outbound, transit). Documents typically include fields such as waybill number, drug batch number, production date, expiration date, storage conditions, real-time temperature and humidity readings, geographic coordinates, transport route, and anomaly event records. Temperature and humidity data often include timestamps and units (Celsius, Fahrenheit), while location data includes latitude and longitude.
Constraints Imposed by These Characteristics on HTTP Interfaces and External Systems
High-frequency updates of temperature, humidity, and location data require HTTP interfaces with high throughput and low latency. This prevents data accumulation or delays. Real-time requirements make batch imports or long polling unsuitable for core monitoring data. A more stream-like data ingestion approach is necessary. Critical information such as drug batch numbers and expiration dates requires strict data validation to prevent drug expiry or recall risks due to input errors. Multi-source heterogeneous data structures (sensor data, system logs, manual entries) demand interface designs with good compatibility and extensibility, capable of flexibly handling different data field formats. A rapid reporting mechanism for anomaly events (e.g., temperature excursions, transport route deviations) requires interfaces to support real-time alert triggering and timely responses via external systems.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
batchSize | 100–500 records | Balances interface call frequency with single-transfer efficiency, avoiding large payloads that cause network latency or timeouts. |
requestTimeout | 3000–5000 ms | Accommodates network fluctuations and external system processing time, ensuring timely upload of high-frequency data. |
maxConnections | 10–20 | Supports concurrent data upload requirements, preventing connection pool exhaustion from impacting real-time performance. |
contentType | application/json | Widely supported and easy to parse structured data, ensuring consistent data transfer format. |
retryAttempts | 3 | Addresses transient network failures or temporary unavailability of external systems, improving data upload success rate. |
heartbeatInterval | 60 seconds | Maintains active connection with external systems, promptly detecting connection interruptions. |
Common Configuration Mistakes
- HTTP request returns a 500 status code with the message "Data validation failed, batch number format mismatch": The drug batch number field was not formatted as required by the external system or contained invalid characters.
- After uploading temperature and humidity data, the external system displays empty values or default values: The temperature and humidity field names in the data payload might not match the external system's API definition, or unit conversion errors occurred.
- Alert events are not triggered promptly after a temperature excursion: The interface polling interval is too long, failing to acquire the latest temperature and humidity data in real-time, leading to alert delays.
Verification Steps
- Perform simulated data uploads. Check if the corresponding fields in the external system are accurately populated. Verify the units and precision of critical data like temperature, humidity, batch numbers, and geographical locations.
- Through FastGPT's internal logs or external system monitoring, confirm that the HTTP interface call success rate meets expectations. Check for any 4xx or 5xx error codes; if present, investigate further.
- Simulate anomaly conditions, such as temperature excursions. Observe if the external system receives alert messages within the specified time frame. Validate the completeness and accuracy of the alert 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 2026-09-21.