What this category's data looks like
This category covers the full production process of liquid milk, yogurt, milk powder, and dairy ingredients. Daily documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, sample retention and microbial test reports. Data sources include raw milk testing equipment, production MES systems, homogenization and heat treatment equipment, online temperature and flow collection devices, CIP systems, and LIMS laboratory management systems. Data updates are performed per individual milk tank and production batch. Key links such as heat treatment and filling use real-time synchronization. Fields include test values, equipment parameters, batch numbers, and timestamps. Units include ℃, L/h, CFU/mL, and others.
What constraints these characteristics impose on workflow orchestration
The feature of real-time data updates per milk tank and batch requires workflow nodes to bind exclusive variables for the corresponding batch, to avoid cross-batch data confusion. Multi-source heterogeneous data from different production systems and equipment requires format unification within the workflow, otherwise subsequent audit nodes cannot be triggered. High-frequency real-time data such as online temperature and flow requires workflow node response delay to match the device collection rhythm. Data lag will lead to audit failure. Fixed-format production documents need field extraction rules configured to accurately match key audit items including raw milk antibiotic indicators, heat treatment time and temperature. Fast judgment cannot be completed otherwise. Errors in key audit items can trigger batch-level risks, so the workflow must include a pre-check node to ensure extracted fields are complete and valid.
Configuration settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables binding | Bind exclusive variables for raw milk testing, heat treatment, and CIP links of the current production batch | Matches the data update feature per milk tank and batch, avoids cross-batch data mixing |
nodeId binding | Bind the exclusive node ID of the current audit process | Adapts to scenarios where multiple batches run in parallel, avoids node confusion |
| AI chat node temperature setting | Disable or set to 0 | Ensures consistency of audit judgments, avoids subjective bias |
| Specified reply node line breaks | Use \n or system-built-in line break markers | Meets the multi-line display requirement for multi-field audit results |
| Node timeout threshold | 600 seconds | Matches the conventional return duration of LIMS laboratory reports |
| Workflow trigger timing | Trigger upon completion of production batch nodes | Matches the data update rhythm of this category per batch |
Three common configuration mistakes
- Phenomenon: When configuring an AI chat node, enabling the temperature switch and setting it to 0 results in slight differences in audit results compared to directly disabling the temperature switch. Cause: The underlying logic of the two configurations differs. Disabling the temperature switch skips the model's temperature parameter call logic, while setting it to 0 explicitly passes a temperature parameter of 0. Some models still have minor randomness in handling explicit 0 values.
- Phenomenon: The specified reply node cannot achieve continuous multi-line line breaks, and audit results are displayed as single-line concatenated text. Cause: The
\nescape character or system-built-in line break markers are not used. Only manually entered line breaks cannot be parsed by the workflow. - Phenomenon: The problem classification node processing speed lags behind the online device's data collection rhythm, causing delayed triggering of the audit node. Cause: The processing field range of the classification node is not limited, and the node's priority is not configured, causing the node to process all production data.
How to confirm correct configuration
- Run a single-batch simulated workflow, check whether the bound
variablesfields match the production data of the corresponding batch. - Test the output of the AI chat node, verify result consistency across different temperature configurations, or confirm the display effect of line break markers.
- Trigger the problem classification node, check processing duration through node logs to confirm the duration meets the device collection rhythm requirements.
- Check the workflow's system prompt configuration to confirm that default additional output content is not retained.
The values listed here are common starting points, and should be evaluated against the reader's own samples.