What Data for This Category Looks Like
Data sources for PCB manufacturing production ledgers include Gerber files, drill files, and BOM lists provided by clients, engineering drafts generated by CAM, real-time parameters from drilling, lamination, and other equipment, tank solution test results, AOI and electrical test reports, and laboratory slice reports. Data is updated by production batch, per shift, or continuously. A single batch ledger includes fields such as drill hole diameter, lamination pressure, chemical solution concentration, and defect type, with units including millimeters, megapascals, ppm, and pieces.
What Constraints These Characteristics Impose on Workflow Orchestration
Differences in format across multiple heterogeneous data sources require configuring multi-format parsing nodes in workflows to adapt to different inputs such as Gerber binary files, CSV-formatted device parameters, and PDF-formatted AOI reports. High-frequency data updates require workflows to support scheduled or event-triggered modes to avoid delays from manual triggering. Large per-batch data volumes require workflows to split large tasks into sub-nodes to prevent single-node timeouts. Ledger data must be associated with multi-process parameters and reports, so variables must be used to pass batch identifiers to ensure accurate data association.
How to Configure the Workflow
This configuration is adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow trigger mode | Trigger on production batch completion event, bind the production batch number in variables | Matches the business rhythm of PCB manufacturing updating ledgers by production batch, uses variables to pass batch IDs to associate multi-process data |
| Single processing data limit | 100,000–200,000 entries | Matches the typical data scale of a single PCB production batch, prevents node memory overflow |
| Node timeout period | 300–600 seconds | Covers the processing duration of single-batch data summary and multi-format document parsing |
| Code run node log output | Output to workflow node console | Facilitates troubleshooting of exceptions in data conversion and parameter calculation, adapts to the complex calculation scenarios of production ledger summary |
| Failure branch configuration | Trigger alert node and record nodeId | Locate abnormal nodes and quickly identify fault points in PCB production ledger generation |
| Model selection configuration | Set based on actual testing, prioritize models that support long-text parsing | Handles the summary requirements of multiple documents such as Gerber parsing records and AOI reports |
| Variable transfer scope | Restrict to variables data for the current production batch | Avoid cross-batch data confusion and ensure the accuracy of ledger data |
Three Common Configuration Mistakes
- No log output from code run nodes after workflow starts, and the console cannot display logs. The cause is that log collection for the corresponding node is not enabled, so print information during the calculation process is not captured.
- The AI model selection dropdown list in the workflow is empty. The cause is that the access key for the corresponding text generation model is not configured on the platform, or the model's available status is not enabled.
- A parameter format error occurs during workflow execution. The cause is that the production batch's data source fields are not correctly bound through
variables, causing the node to fail to read the corresponding PCB production data.
How to Verify a Proper Configuration
- Trigger a simulated production batch workflow and check if corresponding log output appears in the code run node console.
- Check the model selection dropdown list in the workflow to confirm that available text generation model options have loaded.
- Manually input a set of test PCB production data to verify if the workflow can correctly generate preliminary ledger summary content.
- Trigger an exception test to confirm if the alert node in the failure branch triggers normally.
The values provided are common starting points and should be measured against the reader's own samples.