What the data for this category looks like
Data for textile manufacturing intelligent due diligence primarily comes from customs declarations for upstream raw material purchases, production order ledgers from factory ERP systems, workshop operation data from MES systems, and weekly production capacity and raw material price reports released by industry associations. Update frequencies vary across data sources: raw material purchase data is updated weekly, workshop production data is synced daily, and industry statistical data is updated monthly. Document formats include Excel, PDF, and CSV. Core fields include yarn count (unit: count), loom speed (unit: revolutions per minute), grey fabric width (unit: centimeters), and order delivery deadline (unit: days). Some documents contain multi-page quality inspection reports and production logs.
What constraints these characteristics impose on workflow orchestration
Multi-source heterogeneous data sources require configuring multiple independent pull nodes in the workflow, each adapted to the API interface formats of different systems, while unifying the output data structure. Different update frequencies require configuring differentiated trigger cycles for each data source to avoid repeated pulls or missed latest data. Specific fields and units require adding unit validation nodes in the workflow to prevent incorrect unit usage such as count and meter, or revolutions per minute and revolutions per hour. Longer quality inspection reports and production log documents require configuring reasonable text segmentation parameters in the workflow to avoid parsing failures caused by overly long single text segments. Additionally, due diligence reports need to integrate multi-source data, so context transfer between workflow nodes must support concatenation and formatting of large text blocks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
workflow_trigger_cron | 0 0 2 * * * | Textile manufacturing production data is typically updated before 1 AM daily. Triggering at 2 AM daily allows pulling complete daily production data |
multi_source_sync_timeout | 1200 seconds | Multi-source pulls include customs declarations, ERP orders, and MES data, which involve large data volumes. Set a sufficient timeout period to avoid mid-run interruptions |
text_chunk_size | 800–1200 characters | Quality inspection reports and production logs in textile manufacturing have long single text segments. This segmentation length supports parsing and context transfer for large text blocks |
field_unit_validate_enabled | Enabled | Core fields in textile manufacturing use specific units. Enabling validation prevents due diligence data errors caused by incorrect unit usage |
plugin_call_retry_times | 2 times | Third-party customs data interfaces may experience temporary fluctuations. A retry mechanism reduces workflow failures caused by interface instability |
workflow_node_parallel_limit | 3 | Control the number of parallel multi-source pull nodes to avoid workflow lag caused by excessive system resource consumption |
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 individual analysis. It is recommended to test against local samples before finalizing settings.
Three common mistakes
- Symptom: After referencing a custom plugin in the workflow, the code run node throws the error
Cannot read properties of undefined (reading 'incl'. Cause: Global parameter injection for the plugin was not enabled in the workflow configuration, resulting in missing necessary context fields when the plugin is called. - Symptom: Core fields such as yarn count and loom speed are empty in the generated due diligence report. Cause: The field unit validation node was not configured, or the validation rules did not cover the unique unit types for textile manufacturing, causing abnormal data to be incorrectly filtered.
- Symptom: The scheduled workflow does not execute at the expected time. Cause: The
workflow_trigger_cronexpression format is incorrect, such as adding an extra second-level parameter, causing the scheduled task to fail to parse properly.
How to confirm the configuration is complete
- Manually trigger the workflow, review the running logs of each node to confirm that the fields and units pulled from multiple sources match expected values.
- Input test data containing abnormal units, such as entering "100 meters" as the yarn count, run the field validation node to confirm that the node can correctly identify and handle the abnormal data.
- Enter the scheduled task management page of the workflow, verify that the trigger time configured in
workflow_trigger_cronmatches the expected 2 AM daily. - After calling the custom plugin, check whether the results returned by the plugin can be correctly passed to subsequent code run nodes, with no error prompts for missing parameters.
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.