What this category’s data looks like
This category’s data sources include registered process documents, raw material batch ledgers, DCS production control systems, weighing record systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Updates are triggered per production batch and each process step. Daily documents include batch production records, material balance ledgers, reaction and purification process records, cleaning validation records, impurity profile analysis reports, and deviation and change control documents. Data fields include process parameters, material feeding quantities, intermediate transfer information, yield-related data, solvent residue test values, impurity content data, batch identifiers and supplier information.
What constraints these characteristics impose on deployment and upgrade
This category’s data sources are scattered across multiple industrial and quality management systems. During deployment, adapt to the docking protocols of different systems; general static file import configurations cannot be used directly. Data updates occur frequently and are tied to production batches. During deployment, configure a near-real-time data synchronization mechanism to avoid referencing expired process and quality data when preparing bids. Professional documents include compliance-related content such as impurity profiles and change control documents. During deployment, configure fine-grained permission control rules to ensure that sensitive data is only accessible to authorized personnel. Data fields are highly specialized. During upgrades, retain the original field mapping logic to avoid data field misalignment or missing content in bid documents.
Configuration Settings
These configurations are based on the deployment logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 150 connections | This category needs to connect to multiple industrial and quality management systems, so a relatively large number of database connections must be maintained to synchronize batch and quality data |
REDIS_URL | Intranet dedicated Redis instance address, memory no less than 8 GB | Used to cache real-time production batch status and impurity test data, avoiding repeated queries to industrial systems |
STORAGE_S3_ENDPOINT | Intranet S3-compatible storage service address | Used to store large-volume documents such as batch impurity profile reports and batch production records, ensuring access efficiency in intranet environments |
FILE_TOKEN_KEY | Encryption key consistent with the LIMS system | Used to connect to the document interface of the laboratory data system, ensuring that document access permissions are aligned with the original system |
ROOT_KEY | Key generated according to intranet permission control rules | Used to configure administrator-level access permissions, controlling the viewing and editing scope of professional compliance documents |
Three Common Mistakes
- Symptom:
ERROR: failed to solve: archive/tar: unknown file modappears during Docker build. Cause: Local image cache is not properly configured in the intranet environment, resulting in file permission exceptions when extracting dependency packages. - Symptom: No subsequent docking response after submitting the commercial version purchase form. Cause: The deployment environment is not configured with an external network callback address, so notifications from form submissions cannot be delivered to the official service.
- Symptom: Custom industrial system data source configurations are lost after upgrade. Cause: The original docker-compose file was not backed up, and the locally customized deployment configuration was overwritten directly by the official updated default configuration.
How to Confirm Proper Configuration
- Perform a batch synchronization test, check the consistency between production batch data pulled from DCS and LIMS systems and the original ledgers, and confirm that the data synchronization link is working properly.
- Upload an impurity profile report to the storage service, access the document via the front-end domain name, and confirm that access permissions and encryption configurations are effective.
- Trigger a bid document generation test, check that the process parameters and batch information included in the generated document match the input data, and confirm that the field mapping configuration is correct.
- Check the Redis instance connection count monitoring, confirm that the database connection count does not exceed the configured
DB_MAX_LINKthreshold, and confirm that the connection pool configuration is normal.
The values provided are common starting points and should be measured against the reader's own samples.