Cold Chain Logistics Pharmacovigilance Deployment and

Data Characteristics for This Category

Cold chain logistics pharmacovigilance data primarily originates from real-time monitoring devices during transport. This includes sensor data such as temperature, humidity, and location. This data is typically generated as time series, with high update frequencies, potentially reaching seconds or minutes. The data document structure is relatively fixed, usually structured or semi-structured data like CSV, JSON, or database records. Key fields include timestamp, device_id, temperature_celsius, humidity_percent, latitude, longitude, and batch-related fields such as batch_id and product_sku. Anomaly events (e.g., temperature excursions) typically include event_type and event_description fields.

Deployment and Upgrade Constraints Imposed by These Characteristics

High-frequency real-time data streams require FastGPT to have efficient data ingestion capabilities during deployment to prevent data backlog and latency. Structured data simplifies preprocessing and cleaning. However, non-standard event descriptions still require large language models for semantic understanding and normalization. Geographic location and timestamp fields necessitate FastGPT's vector database to support geospatial indexing and time-range queries for quick anomaly localization and historical tracebacks. This large volume of data demands significant storage and computational resources. During upgrades, ensuring smooth data migration and service continuity is critical. Analyzing and tracing historical data requires robust batch processing and offline analytical capabilities to support anomaly pattern recognition and risk trend prediction.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Supports large file sizes for batch uploads of cold chain device logs.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Prevents parsing timeouts when processing large log files and complex structured data.
`maxContext`	`3000 characters`	Ensures complete capture of single anomaly event context, including sensor readings and event descriptions.
`Chunk size`	`500 characters`	Balances semantic completeness with vector retrieval efficiency for structured event descriptions.
`Recall count`	`10 entries`	Multiple relevant contexts aid comprehensive judgment in pharmacovigilance event analysis.
`Similarity threshold`	`0.78`	Identifies similar anomaly patterns and potential risk events, balancing recall and accuracy.

Three Common Mistakes

Reviewing session logs shows blank or incomplete event details. This may be due to maxContext being set too small, truncating critical information.
The frontend interface responds slowly or freezes when concurrently processing real-time data streams from cold chain devices. This could be because PARSE_FILE_TIMEOUT_SECONDS is insufficient, causing file parsing to block the main thread or exhaust resources.
After a system upgrade, historical event query results are inconsistent or erroneous. This typically indicates discrepancies in database field mapping or data format conversion during data migration, preventing the new system from correctly parsing old data.

How to Confirm Correct Configuration

Upload a cold chain log file containing a temperature excursion event. Verify that FastGPT's session logs completely display the timestamp, temperature_celsius, and event_description fields for the event.
Simulate multiple cold chain devices uploading data simultaneously. Observe system resource usage and ensure all uploaded events are ingested and processed promptly, without significant delay or errors.
Select historical cold chain anomaly events from a random month. Use FastGPT's query function to trace them back. Verify that query results match original records and accurately identify similar anomaly patterns.

Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.

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.