What the Data for This Category Looks Like
Daily yield rate and market report data for the thermal energy category comes primarily from three sources: internal ERP systems of thermal energy enterprises, energy operation monitoring platforms, and publicly submitted data from local public utility regulatory agencies. Data updates run daily at midnight, syncing full statistical results from the previous day. Each data file is 10 to 30 KB in size. Data is provided in structured table format, with core fields including statistical cycle, total thermal energy supply, total operating costs, total revenue, and revenue-to-cost ratio. Total thermal energy supply is measured in gigajoules. Total operating costs and total revenue are measured in yuan. The revenue-to-cost ratio is a unitless value. Data fields strictly correspond to all stages of thermal energy supply chain operations, with no redundant statistical items.
How These Characteristics Impact the "Form and Interaction" Workflow
Since data comes from multiple heterogeneous sources, the form component must support configuring access paths for multiple data sources. It must also include built-in field mapping rules to align raw fields from different sources into unified business fields. The fixed daily update rhythm requires the interaction component to support scheduled pull task configuration. This avoids delays or omissions from manual triggers. The structured table format requires the form to support field mapping by column, and allow standardized conversion of field units. The small per-file data size means the form does not need redundant features like large file chunked upload or resumable upload. It can set a maximum single import data limit to avoid wasted resource usage. Additionally, field binding must strictly match physical units to prevent calculation errors from inconsistent units later on.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
datasource_sync_interval | 86400 seconds | Matches the fixed daily update rhythm of thermal energy daily reports, ensuring data sync timing aligns with source updates |
field_mapping_mode | Match by column | Adapts to structured table format data sources, enabling quick alignment of raw fields and business fields |
unit_conversion_enabled | Enabled | Multiple source data has unit differences; standardized conversion avoids unit bias in subsequent calculations |
import_data_max_size | 50 KB | Individual thermal energy daily report files are 10-30 KB; this value leaves reasonable headroom while avoiding wasted resource usage |
trigger_type | Scheduled trigger | Data update rhythm is fixed; scheduled triggers replace manual triggers to reduce operational overhead |
api_request_timeout | 30 seconds | Multi-source data pulls require stable response waiting; this duration covers most normal pull scenarios and prevents timeout interruptions |
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. Each scenario requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: After deployment, the version number displayed in the interface is
4.8.17, which does not match the4.8.20tag selected on GitHub. Cause: The Docker image was pulled without specifying an exact tag value, and the default branch code was pulled instead, resulting in version mismatch. - Issue: After importing structured data, some business fields show as empty. Cause: Correct field mapping rules were not configured, and raw table columns were not bound one-to-one to business fields, preventing normal data loading.
- Issue: A
504 Gateway Timeouterror appears after manually triggering a data pull. Cause: Theapi_request_timeoutconfiguration value is set to10 seconds, which is shorter than the average response time for multi-source data pulls, causing request timeout.
How to Confirm Configurations Are Correct
- View the sync interval parameter on the data source configuration page, confirm the value matches the business update rhythm, and adjust the corresponding configuration value based on the actual update cycle.
- Upload a test thermal energy daily report file, check that the field mapping results correspond one-to-one with the original table columns, and confirm that the unit conversion function works properly.
- Manually trigger a data pull task, check the response duration and status code in the task log, and confirm no timeouts or errors occur.
- Wait for one full sync cycle, check if the system automatically pulled the latest daily report data, and confirm the scheduled trigger logic runs normally.
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.