What the data for this category looks like
Data sources for thermal coal primarily include public quotation systems of mining enterprises in domestic major coal-producing areas, transaction and delivery ledgers of coastal ports, real-time railway freight shipping data, and public information from third-party coal monitoring platforms. Update rhythms vary: spot quotations from producing areas update daily, port arrival volume data updates every other day, and industry supply and demand briefings update weekly.
A single due diligence data document includes fields such as producing area identifier, calorific value parameter, transportation cost parameter, current transaction price range, and inventory turnover days. Calorific value is measured in kilocalories per kilogram, transportation cost is measured in yuan per ton, and all fields correspond to clear physical measurement standards.
What constraints do these characteristics impose on workflow orchestration
First, multiple data sources with inconsistent update rhythms require workflows to support scheduled triggering per node, pull data at different cycles, and set dependency relationships to avoid invalid pulls and task conflicts. Second, heterogeneous field units and naming conventions require workflow configuration of standardized mapping nodes to unify heterogeneous parameters from different sources into the preset format of due diligence reports. Third, core quotation data has the highest update frequency, requiring workflows to set conditional branches to start the full process generation only after core quotation data is updated, reducing unnecessary computing resource consumption. Fourth, a single document has a large number of fields, requiring workflow configuration of batch verification nodes to check whether required fields exist one by one, avoiding errors in subsequent links due to missing parameters.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Scheduled Trigger Cycle | Configure per node: set quotation pull to Daily 09:00, freight data pull to Every other day 10:00, industry briefing pull to Every Monday 11:00 | Matches the actual update rhythm of each data source to avoid invalid pull tasks |
Field Mapping Rules | Configure a unified mapping table, map "calorific value" from mining enterprise quotations to "calorific value" in the report, map "transport miscellaneous fees" from port ledgers to "unit transportation cost" | Unify field names and meanings of heterogeneous data sources to ensure report format consistency |
Batch Verification Node | Verify all required fields, trigger after all data sources have completed pulling | Thermal coal due diligence has many required fields; pre-verification avoids errors in subsequent generation links |
Number of Retrieved Entries | Set to Top 6 | The effective reference range for thermal coal quotation data is usually the latest 6 trading days' quotations; too many entries increase context load |
PARSE_FILE_TIMEOUT_SECONDS | Set to 900 seconds | A single thermal coal due diligence document contains multi-dimensional data, which requires extended parsing time; extending the timeout prevents interruptions |
maxContext | Set to 8000–12000 characters | Needs to accommodate structured data and analysis logic from multiple data sources, avoiding context overflow |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: The overall execution time of the workflow exceeds expectations, and single-round call duration exceeds the preset threshold. Cause: Scheduled trigger nodes are not split according to data source update rhythms, and multiple cross-cycle data pulls are executed simultaneously, leading to parallel task overload.
- Phenomenon: Workflow runtime error logs show
gpt-4o-mini call failed, but no call node for this model is configured. Cause: The default automatically enabled context completion function in the workflow is not disabled, causing the system to attempt to call an unconfigured model. - Phenomenon: Historical records of global variables in the workflow cannot be modified, and old data remains in generated reports. Cause: The
global variable reset nodein the workflow is not configured to reset historical records before execution, leading to cumulative context data.
How to confirm the configuration is complete
- Verify that the scheduled trigger time of each data source matches the public update rhythm of the corresponding data source.
- Run a test workflow to check whether field-mapped results fully match preset report fields.
- Trigger the batch verification node to confirm that verification logic for all required fields activates normally.
- Check the workflow log panel to confirm no unconfigured model call errors appear.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.