Data Characteristics for This Category
Ledger data for active pharmaceutical ingredient and pharmaceutical intermediate manufacturing originates from registered process documents, raw material batch ledgers, DCS control systems, weighing recording systems, LIMS systems, chromatography data systems, and quality management systems. Data updates trigger once per production batch and per process step. Daily documents include batch production records, material balance forms, reaction and purification process records, cleaning validation records, impurity profile reports, deviation and change control documents. Data fields cover batch identifiers, process parameters, material consumption, test results, deviation records, and more. Units include professional measurement categories such as temperature, mass, and concentration.
Constraints Imposed on Workflow Orchestration
The multi-source heterogeneous data sources, batch-and-step-based update rhythm, diverse document types, and specialized field requirements for this category create multiple constraints for workflow orchestration. Nodes must be configured in the order of process steps to pull corresponding data from different systems sequentially, preventing cross-system data misalignment. A batch trigger mechanism must be bound to ensure each workflow run corresponds to exactly one production batch, avoiding duplicate or missing data. Adapted parsing nodes must be configured for different document types to ensure complete extraction of content from files such as impurity profile reports and deviation records. Mapping rules for specialized fields must be preset to ensure the format and units of core data such as yield and impurity content align with business standards.
Configuration Settings
This configuration guide is written for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Timing Configuration | Bind to each batch production completion event, trigger after a 15-minute delay | Matches system data synchronization rhythm after process step completion |
| Data Source Mapping Rules | Associate corresponding system nodes in sequence according to reaction, purification, and testing steps | Corresponds to the collection order and business logic of multi-source data |
| Field Validation Configuration | Enable required field validation, covering batch number, yield, and impurity content | Ensures ledger data completeness and compliance |
variables Preset | Preset batch identifier, process route ID, and impurity limit parameters | Unifies calls to core business parameters across nodes |
| Node Retry Settings | Retry 2 times after failure, 30-second interval between retries | Addresses abnormal scenarios caused by temporary system fluctuations |
| Exception Alert Configuration | Push alerts when field validation failure or data pull timeout nodes are triggered | Matches risk prevention and control requirements for error consequences |
Three Common Configuration Errors
- A workflow debug session displays a validation failure prompt: "Please check whether nodes are filled correctly and whether connections are normal". The cause is that node connections are not configured in process step order, or core business fields are not preset in
variables. - A parsing error occurs when the workflow processes an impurity profile report containing double quotes. The cause is that no special character escaping node is configured, and special symbols in the document are not processed for compliance.
- The number of report data entries generated after workflow execution does not match the actual ledger. The cause is that no reasonable pull rule is configured for the single-batch data scale, resulting in some data not being collected.
How to Verify Proper Configuration
- Manually trigger the workflow for a test batch, and verify that node connections and field mappings match the preset process route.
- Import simulated ledger data for a single batch, run the workflow, and check that data flow is complete with no missing fields.
- Configure a baseline test node, compare workflow execution results across different parameter configurations, and confirm alignment with business requirements.
- Trigger an abnormal scenario such as missing required fields, and check that the preset validation and alert mechanisms activate.
The values provided are common starting points and should be measured against the reader's own samples.