What the data for this category looks like
Data related to iron ore comes from public APIs of commodity trading platforms, structured data from port management systems, raw material management reports from internal steel mills, and unstructured reports issued by quality inspection institutions. Update rhythms follow multiple tiers: spot quote data updates daily, port inventory data updates weekly, steel mill procurement data updates every ten days, and shipping schedule data updates every half day.
Document structures fall into two main categories: structured and unstructured. Structured data mostly uses CSV or JSON format, and includes fields such as product name, arrival date, storage location, dry weight, particle size range, and origin. Dry weight is measured in tons, and particle size range is measured in millimeters. Unstructured data includes on-site loading and unloading photos and quality inspection report PDFs, with visual quality inspection markers and parameters.
Constraints on workflow orchestration from these characteristics
The differing update frequencies of multi-source data require workflows to support multiple scheduled trigger nodes, each adapted to the update rhythm of different data sources. The coexistence of structured and unstructured data requires workflows to include format conversion and field extraction nodes, to process structured data in CSV or JSON format and unstructured data in PDF or image format.
The need to standardize core iron ore marketing parameters requires workflows to include built-in field mapping rules, to unify parameter field names from different sources and avoid content confusion. Unstructured quality inspection photos must be transcoded to base64 before being sent to visual model analysis, requiring workflows to configure automatic transcoding nodes to adapt to file formats uploaded by the front end.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | The typical file size of iron ore quality inspection report images and PDFs does not exceed this value |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing time for large-volume PDF quality inspection reports is usually within 5 minutes |
base64_convert_trigger | Automatically trigger after file upload completes | Adapts to the need to directly import and transcode images without manual operation |
data_field_mapping | Map by product name, dry weight, particle size range | Unifies the core display fields of iron ore marketing content to avoid parameter confusion |
api_call_batch_size | 10 items per call | Complies with the single-call limit of most commodity data APIs |
knowledge_base_sync_trigger | Trigger after data pull completes | Ensures the knowledge base synchronizes the latest iron ore marketing data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: The base64 encoded field is empty after workflow execution. Cause: No base64 transcoding node is configured after the file import node, or the trigger condition of the transcoding node is not bound to the file upload completion event.
- Issue: A 400 Bad Request error is returned when calling the API to trigger the workflow. Cause: The uploaded image or report file is not encapsulated in multipart/form-data format, or the global variable does not correctly associate the knowledge base access permissions.
- Issue: Inconsistent iron ore parameters are displayed in marketing content. Cause: No data field mapping rules are configured, resulting in parameter fields from different sources not being unified to standard names.
How to Verify Proper Configuration
- Upload a test iron ore quality inspection image, and check whether a non-empty base64 encoded field is generated in the workflow log.
- Call the API to trigger the workflow, check whether the request body conforms to the multipart/form-data format, and whether the response contains the expected field mapping results.
- Wait for the preset scheduled update cycle, and check whether the corresponding data entries are added or updated in the knowledge base.
- View the node execution records of the workflow, confirm that the execution status of each node is successful, with no timeout or error messages.
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.