Deployment and Upgrade for Infection Control Registration

Data Characteristics

Infection control registration documents primarily originate from hospital internal infection surveillance data, control plans, training records, equipment maintenance logs, and relevant regulatory policy documents. This data updates frequently. For example, infection surveillance data may update daily or weekly, while regulatory policies update as needed. Document structures are mostly unstructured text. They contain extensive specialized terminology, such as pathogen names, antibiotic types, infection sites, disinfectant components, and medical device models. They also include various statistical reports and charts. Fields and units are specific. For instance, bacterial culture results often report as CFU/mL, antibiotic sensitivity as mm inhibition zone diameter, and disinfectant concentration as % or ppm.

Constraints on Deployment and Upgrade from These Characteristics

High-frequency updates of infection surveillance data require efficient data ingestion and indexing capabilities to ensure information timeliness. Unstructured text document structures and extensive specialized terminology demand high accuracy in text parsing and entity recognition. This requires configuring specialized pre-processing modules. Sensitive medical data, such as patient information, involved in registration documents requires strict adherence to data security and privacy protection protocols during deployment. This ensures data anonymization or encryption. The presence of various statistical reports and charts means the parser must handle multiple file formats and extract key values and trends. This determines the stability and compatibility of the file parsing service.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Registration documents often include large PDFs or scanned images. This ensures successful upload of large files.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient parsing time for large, complex documents, preventing interruptions.
`Chunk size`	`800–1200 characters`	Balances the integrity of professional terms with retrieval efficiency, avoiding semantic breakage during segmentation.
`Similarity threshold`	`0.75`	Ensures recalled document segments are highly relevant to the query, improving accuracy for document preparation.
`Rerank result count`	`Top 5 entries`	Focuses on the most relevant key information, reducing the engineer's screening workload.
`LOG_LEVEL`	`DEBUG`	Detailed logs are needed during initial deployment and upgrades for easier problem identification and troubleshooting.

Common Pitfalls

When uploading large documents, the system displays "file parsing failed" or "connection timeout." This usually indicates UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS are set too low. They cannot handle the large documents common in infection control data.
After document upload, key specialized terms are missing or semantically inaccurate in search results. This may happen if the text segmentation strategy does not adequately preserve the integrity of infection control terminology, splitting important terms during segmentation.
After a system upgrade, existing data import tasks fail to start automatically or stop midway. This often occurs because scheduled task configurations are not adapted to the new version or Docker container startup policies are incorrectly configured. This prevents services from running as expected.

Verification Steps

Upload an infection control PDF document containing various tables and charts. Confirm the parsing status shows "completed" and the preview content is complete.
Search using specific infection control terminology (e.g., "carbapenem-resistant Enterobacteriaceae"). Verify the recall results include relevant documents and check if the recalled entries are accurate.
Set up a scheduled data synchronization task to simulate daily updates of infection control data. Check task logs to ensure data is ingested and indexed automatically at the preset frequency and time.
During high system load, check CPU and Memory usage. Ensure system resource consumption remains within controllable limits and no performance bottlenecks occur.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.