What This Category of Data Looks Like
Core data for coal transportation and sales comes from on-site weighing systems, coal quality inspection systems, port or railway dispatching systems, and ERP settlement modules. Data is generated in real time once each individual transportation and sales batch is completed. Daily aggregation generates daily ledgers, and monthly aggregation generates monthly reports. Each individual data entry includes fixed structured fields: coal type identifier, shipped tonnage, calorific value, transportation batch number, shipment time, receiving unit, and settlement amount. Each field has a clear physical unit, with no redundant unstructured content.
Constraints Imposed on Workflow Orchestration by These Characteristics
Coal transportation and sales data is scattered across multiple independent systems. After a single batch of data is generated, it must be integrated into the workflow in real time. The workflow must support triggering nodes based on the batch number defined in variables, to align with the batch-by-batch update rhythm. Fields include numerical values with explicit units, so the workflow must have unified format conversion rules configured to prevent summary errors caused by unit discrepancies across different systems. Multi-batch ledger aggregation requires circular processing of batch records, so loop nodes must be enabled to aggregate data by batch before generating reports. Quality inspection data must also undergo legality verification to prevent abnormal values from entering the aggregation process and compromising report accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
| Trigger Condition | Use the batch number passed in via variables | Align with the batch-by-batch update rhythm of coal transportation and sales data, avoid redundancy or omissions from scheduled triggers |
| Data Source Node | Bind APIs from the weighing, inspection, and dispatching systems | Cover all sources of transportation and sales data, ensure ledger completeness |
| Field Mapping Rules | Use unit conversion parameters calibrated through actual measurements | Unify the tonnage and calorific value formats returned by different systems, eliminate unit discrepancies |
| Loop Configuration | Loop through the first 800 records in a single batch | Adapt to the typical number range of single-batch transportation and sales records |
| Node ID Configuration | Assign a unique nodeId to each node | Avoid workflow node conflicts, facilitate log troubleshooting and debugging |
| Report Generation Node | Bind industry standard ledger templates | Match the fixed field requirements of coal transportation and sales reports |
Three Common Misconfigurations
- Scenario: The workflow returns a "data format not supported" error when calling a transportation and sales system API that returns a file stream. Cause: No file stream parsing node was configured, and the returned content was treated as plain text directly.
- Scenario: The workflow cannot skip quality inspection data retrieval based on user input. Cause: No optional trigger parameters were configured in
variables, and no conditional branch node was bound. - Scenario: The field order of the ledger generated by the workflow does not match the template. Cause: The field mapping rules were not configured according to the template order, leading to incorrect filling positions.
How to Verify Proper Configuration
- Manually pass a test batch number in
variablesto confirm whether the workflow automatically starts the corresponding data pull node. - Review the return logs of the data source node to verify that the field format matches the preset mapping rules.
- Trigger the loop processing node to confirm that batch records are aggregated correctly by batch.
- Generate a test ledger report and check that the field filling order aligns with the industry template.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.