What Data for This Category Looks Like
Data sources for refinery intelligent due diligence reports include internal production ledgers of petroleum and petrochemical enterprises, public regulatory disclosure documents, and third-party supply chain and energy consumption monitoring platforms. There are two data update schedules: monthly production reports and quarterly financial statements are updated on fixed cycles, while real-time operating parameters of equipment are synced via sensors. Document structures combine structured tables and long-text reports. Each individual report includes core fields such as equipment operating rate, crude oil processing volume, energy consumption coefficient, and environmental emission concentration. Corresponding units for fields include %, tons/day, kg standard coal/ton, mg/m³, and others. Some reports also attach PDF-format equipment maintenance records and compliance description documents.
What Constraints These Characteristics Impose on Workflow Orchestration
The need to pull data from multiple sources requires workflows to support both scheduled batch pulls and real-time API calls, to accommodate fixed-cycle financial reports and ad-hoc equipment parameter queries. Data formats vary widely across different sources, so structured parsing and format conversion nodes must be configured to handle Excel-format production reports, PDF-format compliance reports, and JSON-format data returned by third-party platforms. The diverse range of field units requires workflows to include built-in unified unit conversion logic to prevent measurement inconsistencies in subsequent analysis. The mixed document structure of long-text and structured fields requires split nodes to retain field relevance, avoiding information breaks that could lead to deviations in due diligence conclusions.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
workflow_trigger_mode | Scheduled Trigger + Real-Time API Trigger | Refinery due diligence data includes monthly batch production reports and real-time equipment operating parameters. Both trigger modes cover regular due diligence and ad-hoc query scenarios |
structured_parse_chunk_size | 800–1200 characters | Structured fields in refinery due diligence reports are mostly long text. This split length retains field relevance and avoids information breaks |
data_source_timeout | 300–600 seconds | Refinery data requires pulling multi-source content including enterprise financial reports, third-party supply chain data, and environmental regulatory data. Single pull operations take relatively long |
unit_conversion_enabled | Enabled | Refinery data includes multiple measurement units (tons, kg standard coal, mg/m³, etc.). Automatic conversion unifies data formats |
custom_plugin_input_auto_bind | Enabled | Refinery data has many fields with inconsistent naming. Automatic binding reduces errors from manual configuration of input and output parameters |
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 targeted analysis, and testing on internal samples is recommended before finalizing settings.
Three Common Misconfigurations
- An "uncaught exception" popup appears during workflow runtime, with no clear error in logs. Cause: In a private deployment scenario, the configuration file is not properly adapted to the multi-source data pull path for refinery data, resulting in failed node calls.
- After adding a custom plugin to a workflow, no input or output parameter configuration options appear in the node panel. Cause: The
custom_plugin_input_auto_bindconfiguration is not enabled, or the plugin schema does not properly declare units and data types for refinery-related fields. - After an HTTP node calls an external service, only file path parameters can be obtained, and no file binary content can be retrieved. Cause: The corresponding parameter is not configured for binary mode, resulting in abnormal file stream parsing.
How to Verify Proper Configuration
- Manually trigger the workflow, check if each data pull node can normally return refinery-related fields and their corresponding units, and verify that field splitting conforms to the preset
structured_parse_chunk_sizerules. - Enable the workflow's error retry mechanism, simulate temporary network fluctuation scenarios, and confirm that nodes can resume normal operation after retries.
- After adding a custom plugin, check if the input and output parameter configuration options appear in the workflow node panel, and verify that the parameters include options for measurement units unique to refinery data.
- Trigger a scheduled task once, confirm that the workflow can automatically pull the latest refinery production data and financial information according to the set time cycle.
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.