What data for this category looks like
Data sources include cell banks and seed lots, process analytics systems, cleanroom monitoring equipment, MES, LIMS, and equipment integrity test records. Data update frequencies fall into three categories: per batch, real-time, and per monitoring plan. Daily documents include cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring reports, media fill simulation results, sterility test reports, and batch release materials. Fields include batch number, culture temperature (℃), cleanroom settleable microbial count (cfu/㎡), sterility test results, and some fields require compliance with specific unit requirements.
What constraints these characteristics impose on workflow orchestration
Dispersed multi-source data requires workflows to configure multiple nodes connected to different data sources, to avoid missing data collection. Data with different update frequencies needs compatible trigger rules. Real-time monitoring data must support polling or event triggering. Batch-updated materials must bind batch identifiers to trigger processes. Complex document structures require workflows to support branch routing based on record types, to adapt to different audit paths. Specific unit requirements for fields mean workflows must configure parameter validation rules. This ensures extracted values meet business specifications and prevents invalid data from entering audit links.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Timing | Per-batch trigger + real-time monitoring polling (polling interval 15 minutes) | Matches per-batch and real-time data update rhythms, covers full-process record collection |
variables validation rules | Bind field unit validation, e.g., temperature validation range 15-45℃, settleable microbial count validation range 0-10 cfu/㎡ | Adapts to unit requirements for critical parameters in biologic manufacturing, prevents invalid data from entering the workflow |
| Branch routing rules | Map to corresponding audit nodes based on record type | Matches audit paths for different documents such as cell bank records and filling records |
nodeId binding rules | Associate corresponding data pools by data source node ID, support cross-branch reuse | Ensures data from different sources such as MES and LIMS is accurately matched to corresponding workflow branches |
| Single-node timeout setting | 600 seconds | Adapts to calculation and audit wait times for complex inspection data, prevents workflow interruptions |
Three common errors
- Phenomenon: Form nodes cannot be accessed by other chain branches. The interface shows the node is not bound to a valid data source. Dragging other branches produces no response. Cause: No cross-branch association rule for
nodeIdis configured in the workflow, so the node is only bound to the initial chain’s data pool. - Phenomenon: The extracted user name field is empty, only the user ID can be obtained. Cause: No mapping parameters for user information are configured in
variables, only the data source field for user ID is bound. - Phenomenon: The media fill simulation record audit process does not respond after triggering. Logs show no matching data. Cause: Trigger timing is not configured according to data update frequency. Real-time monitoring trigger rules are used for batch-updated filling records, so the process cannot match corresponding batch data.
How to verify a correct configuration
- Trigger simulated test data for a corresponding batch. Check if the workflow automatically routes to the corresponding audit node based on record type.
- Configure test fields in
variables. Input parameters with units. Check if the workflow triggers validation interception or proceeds normally. - Call cross-branch chains. Confirm that form nodes can be accessed by other branches and execute normally.
- View workflow logs in FastGPT v4.17.0. Confirm that the user name field is correctly extracted and displayed in workflow nodes.
The values provided are common starting points and should be measured against the reader's own samples.