What the data for this category looks like
Data sources for refinery enterprise financial report analysis include internal ERP production modules, manufacturing execution systems (MES), periodic report documents disclosed by stock exchanges, and third-party bulk commodity price databases. Update rhythms fall into three categories: periodic reports are updated quarterly and annually, internal operational data is updated daily, and third-party bulk commodity data is updated hourly.
Document structures include structured production operation logs, cost accounting tables, unstructured production analysis documents, and standardized financial report disclosure templates. Fields include refinery-specific metrics such as crude oil processing volume, refined oil output, and processing energy consumption, with clear physical units attached.
What constraints these characteristics impose on workflow orchestration
Differing update frequencies across multiple data sources require workflows to support mixed triggering modes, combining scheduled pulls of financial report data and real-time pulls of operational data.
Refinery-specific metric fields require preset custom mapping rules in the workflow's data cleaning stage, to prevent generic templates from failing to recognize specialized metrics like processing energy consumption.
The coexistence of structured and unstructured document structures requires workflows to integrate both structured database query nodes and unstructured document parsing nodes.
Cross-data-source field association requirements require workflows to include data matching steps to handle unit conversion and field alignment across different data sources.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
database_connection_timeout | 300 seconds | Refinery financial report-related data has a large storage scale. The default timeout duration is insufficient to complete full data pulls. Extending the connection time reduces the probability of connection failures |
workflow_trigger_mode | Scheduled trigger + Manual trigger | Adapts to the different update rhythms of quarterly financial report updates and daily updated operational data. Manual triggers are used for temporary emergency financial report analysis needs |
variable_format_rule | {{dataset_id}}.{{field_name}} | Follows the platform's built-in knowledge base variable reference specifications to avoid node execution failures caused by format errors |
data_cleaning_schema | Preset refinery financial report template | Matches refinery-specific metric fields such as processing volume and energy consumption, reducing manual mapping workload and error probability |
parallel_task_limit | 2 parallel tasks | Balances resource usage and execution efficiency for multi-data source connections, avoiding system overload caused by pulling too much data simultaneously |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Using the default value of
60 secondsfordatabase_connection_timeouttriggers connection timeouts when pulling full refinery financial report data, with the interface prompting "Workflow verification failed. Please check for missing or null values, and whether connections are normal". The cause is failing to adjust the timeout parameter based on the storage scale of refinery data, as the default duration is insufficient to complete full data pulls. - Using the
[{datasetId: xxx}]format for knowledge base variable references fails to correctly match refinery-specific fields within the dataset. The cause is confusing the reference specifications for plugin parameters and knowledge base variables, and not using double curly braces to wrap variable names as required by the platform. - Directly reusing generic financial report data cleaning templates without adding refinery-specific field mapping rules, resulting in failure to correctly recognize metrics such as processing energy consumption and device operating hours, leading to null values in subsequent analysis node outputs. The cause is failing to adjust the cleaning template for the specialized data characteristics of the refinery industry.
How to Confirm Correct Configuration
- Enter workflow debug mode, manually trigger one execution, and check the execution logs of each node to confirm that the database connection node has no errors and data pulls successfully.
- Enter a test
{{dataset_id}}in the variable reference node, and check whether the returned field list includes refinery-specific metrics such as processing volume and energy consumption. - Check the workflow's trigger configuration page to confirm that the scheduled task cycle matches the data update rhythm, and that the switch function between manual trigger and scheduled trigger works normally.
- Export the workflow execution results, verify that the units of structured data fields match the preset units of refinery metrics, and confirm that the data cleaning step has taken effect.
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.