What the data for this category looks like
Data sources for coal chemical industry market and yield rate data include public statistical data released by industry regulatory authorities, transaction data from commodity spot trading markets, and settlement data for products listed on futures exchanges. Update schedules vary across sources:
- Daily spot data updates after 16:00 each day
- Futures data pushes in real time during trading hours
- Monthly summary data updates on the first workday of the following month
Data is stored in structured format, with support for export in JSON and CSV. Included fields are product classification, origin identifier, trading venue, same-day average transaction price, previous trading day's average transaction price, and statistical cycle. The unit is uniformly yuan/ton.
What constraints these characteristics impose on workflow orchestration
Differing update schedules across multi-source coal chemical data require separate scheduled rules for daily spot data and real-time futures data, to avoid overlapping tasks that consume system resources. Data fields include multi-dimensional identifiers such as origin and trading venue. Precise filter conditions must be configured on data extraction nodes to screen valid data for target categories and regions. Differences in format across data sources require matching parsing rules: JSON data returned by APIs needs field mapping rules, while CSV files need specified correspondence between column names and data types. Monthly summary data uses a different update cycle than daily tasks, so an independent execution cycle must be set separately to avoid resource conflicts. Some fields must be validated for existence, to prevent downstream node failures caused by missing data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
schedule_cron_expression | Configure multiple sets of expressions by data source type: daily spot uses 0 16 * * *, futures real-time uses 0 9-15 * * 1-5, monthly summary uses 0 9 1 * * | Matches update times of different coal chemical data sources, avoids task overlap |
ai_node_output_mode | Return structured results only, hide conversation flow | Avoids redundant conversation content from AI node output, retains only yield and market data required for the task |
data_filter_condition | Configure filter rules for product = coal chemical category and trading venue = target market | Accurately screens valid data for target categories, eliminates interference from unrelated products |
parse_format | Select JSON or CSV based on data source format | Adapts to data formats from different sources, ensures the parsing node correctly reads fields |
retry_max_times | 3 times | Addresses network fluctuations or temporary source unavailability, reduces task failure probability |
field_validation_rules | Validate that same-day average price and previous trading day average price fields exist and are of numeric type | Prevents downstream node failures caused by missing or incorrectly formatted fields |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The AI node returns redundant conversation content with no structured data after running. Cause: The
ai_node_output_modeparameter is not configured, and the conversation flow output mode is enabled by default. - Phenomenon: Context data from previous steps is lost when the workflow runs across nodes. Cause: The global context transfer switch for the workflow is not enabled, and each node defaults to using only its own input parameters.
- Phenomenon: Attempting to copy workflow components results in inability to move or paste, with no response to operations. Cause: The component selection function in workflow edit mode is not enabled, or browser caching causes interactive exceptions.
How to confirm correct configuration
- Manually trigger a test task, check the running logs of each node, and confirm that the trigger time matches the configured
schedule_cron_expression. - Check the output content of the AI node, confirm that it only contains structured yield and market data with no redundant conversation information.
- View the return results of the data parsing node, confirm that valid fields for the target coal chemical category have been screened, with no missing or incorrect data.
- Copy any workflow component and paste it to another location, confirm that the operation completes normally and the component configuration is not lost.
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.