What the data for this category looks like
This category covers data from liquid milk, yogurt, milk powder, and dairy ingredient manufacturers. Daily documents include raw milk acceptance records, standardized batching sheets, pasteurization/UHT sterilization records, CIP cleaning records, filling operation records, sample retention and microbial test reports. Data comes from raw milk testing equipment, production MES systems, homogenization and heat treatment equipment, online temperature and flow monitoring devices, CIP systems, and LIMS laboratory management systems. Updates occur in real time per milk tank and per production batch. Fields include test values, operation time, equipment number, and similar details, with no additional custom units.
What constraints these characteristics impose on workflow orchestration
The real-time update requirement per milk tank and per batch means workflows must support short-cycle triggers and precise data pulling. This avoids data lag that would affect inspection judgments. Multiple heterogeneous data sources (equipment, MES, LIMS) require workflows to include built-in format adaptation nodes. These nodes unify cross-system field structures, otherwise data matching errors will occur. High-frequency inspections for raw milk acceptance and sterilization links require workflow node execution delay to be controlled within a reasonable range. Otherwise, real-time exception response cannot be supported. Associated queries for sample retention and microbial test reports require workflows to support precise association of corresponding documents by batch number. This prevents mixing of data from different production batches.
How to set the configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Timing | Trigger by combining batch number and equipment number | Matches the dairy processing production logic based on milk tanks and batches, avoids unnecessary triggers |
| Variable Scope | Limited to the three core data types of raw milk acceptance, sterilization, and CIP within variables | Reduces context scope and lowers redundant model computation |
| Node Timeout Threshold | 60-120 seconds | Adapts to the response requirements of real-time production links, avoids blocking subsequent processes |
| Single Processing Data Volume | 1000-2000 characters | Matches the average length of documents per production batch |
| Knowledge Base Recall Limit | Top 8 entries | Matches the data volume of single-batch production, reduces interference from invalid information |
nodeId Naming Rule | Name by equipment type + process | Facilitates quick location of corresponding links during fault troubleshooting |
Three common mistakes
- Workflow runs timeout and returns status code 504 | The cause is that a reasonable node timeout threshold is not set, which cannot adapt to the response requirements of real-time dairy processing production.
- The output result includes the input and response fields of knowledge base search | The cause is that the result output format is not configured to retain only the answer text, leading to redundant information leakage.
- Problem classification recognition accuracy is low | The cause is that the
variablesscope is not limited to core inspection and maintenance scenarios, leading the model to confuse irrelevant business vocabulary.
How to confirm the configuration is properly set
- Manually trigger the workflow for a known production batch, check whether the node execution delay meets the preset threshold.
- View the workflow output result, confirm that the input and response fields of knowledge base search are not included.
- Submit inspection questions for different equipment types, check whether
nodeIdcorrectly associates the corresponding process node. - Import simulated multi-batch data, check whether the workflow accurately pulls corresponding documents by batch number.
Note: The values provided here are common starting points and should be evaluated against the reader's own specific samples.