Data characteristics for this category
This category of data comes primarily from cell banks and seed lots, process analysis systems, cleanroom monitoring equipment, MES, LIMS, and equipment integrity testing tools. Daily documentation includes cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring data, media fill simulation reports, sterility test results, and batch release documents. Data updates sync with each production batch, and update in real time per monitoring plans. Fields include batch identifiers, process parameter values, monitoring times, test conclusions, and more. Some parameters require industry-standard physical or biological units.
How these data characteristics constrain deployment and upgrades
Multi-source, heterogeneous data sources require configuring interface-specific docking parameters during deployment. This avoids data synchronization interruptions. Differentiated update rhythms — real-time monitoring data and batch-level archived data — need separate low-latency caching and bulk import storage configurations. Diverse document formats and specialized fields require compatibility with new aseptic production record templates during upgrades. This ensures complete ledger parsing. Data compliance requirements also mandate configuring audit log and permission control storage rules during deployment. This aligns with industry data security standards.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
STORAGE_S3_ENDPOINT | Fill in the intranet access address of the local MinIO, such as http://192.168.1.100:9000 | Aligns with common intranet deployment scenarios for biologic manufacturing enterprises, eliminating the mandatory requirement for public network access |
REDIS_URL | Configure as the intranet Redis instance address, set the connection timeout to 30 seconds | Ensures cache reading efficiency for real-time cleanroom monitoring data, adapting to high-frequency update synchronization requirements |
DB_MAX_LINK | Set to 20–30 connections | Matches the concurrent connection requirements of multi-source data docking, preventing ledger synchronization interruptions due to insufficient connections |
MONGODB_URI | Fill in the access address of the intranet MongoDB, enable replica set mode | Stores batch-level archived production ledger data, ensuring data persistence and disaster recovery capabilities |
FILE_TOKEN_KEY | Configure as a randomly generated 32-bit string | Encrypts access tokens for ledger files, preventing unauthorized access to sensitive production records |
ROOT_KEY | Keep the default setting or configure as a strongly random string | Controls system administrator permissions, ensuring basic security configuration after deployment |
Three common configuration errors
- Ledger synchronization failure appears in the interface, with a connection timeout error in logs. The cause is using a public network address instead of configuring the intranet
STORAGE_S3_ENDPOINT, which prevents connection to the storage service in an intranet environment. - System plugin loading fails, with a prompt that the specified API docking configuration is missing. The cause is failing to add the correct interface parameters in the deployment configuration, which prevents pulling cross-system production ledger data.
- Generated reports omit key sterility test fields. The cause is not setting a reasonable
DB_MAX_LINKvalue, which leads to incomplete field pulling during multi-source data synchronization.
How to confirm correct configuration
- Use the FastGPT v4.17.0 deployment script to run connectivity tests for intranet storage services. Access the address set in
STORAGE_S3_ENDPOINTand verify normal upload and download of ledger files. - Import a batch of simulated cell bank records. Check field integrity and update time accuracy after data synchronization.
- Call the system administrator interface to confirm that permission control rules configured via
ROOT_KEYare active. - Review Redis connection logs to confirm that cache reading delays for real-time monitoring data meet expected standards.
The values provided are common starting points and should be measured against the reader's own samples.