What the data for this category looks like
Data related to yield rates in the chemical pharmaceutical field comes primarily from publicly disclosed periodic reports of listed pharmaceutical enterprises, pharmaceutical supply chain monitoring platforms, and drug bidding price databases. Update rhythms vary across sources: periodic reports are released quarterly, supply chain raw material and sales price data is updated daily, and bidding price data is updated monthly.
Each individual data document includes structured fields such as enterprise identifier, reporting period, core product category, raw material cost, sales unit price, total revenue, total profit, with some data accompanied by unstructured business notes. Field units are uniformly specified as: raw material cost yuan/kilogram, sales unit price yuan/pill, total revenue ten thousand yuan, total profit ten thousand yuan.
Constraints on Workflow Orchestration
Multiple update frequencies across data sources require workflows to support multiple scheduled trigger configurations, to match daily, monthly, and quarterly data pull tasks. Cross-data source association requirements demand that workflows use a unique business association key to prevent data matching errors. Multi-category product dimensions require workflows to support dynamic parameter filtering, rather than hardcoding fixed products. Differences in units across data sources require workflows to include built-in unified unit conversion rules, to ensure consistent benchmarks for yield rate calculations. The presence of unstructured notes requires workflows to have configured text extraction rules to pull key business information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
cron_expression | 0 0 2 * * * (for daily updated data), 0 0 1 1,4,7,10 * (for quarterly financial report data) | Matches the multiple update cycles of chemical pharmaceutical data: daily updated supply chain data triggers at midnight daily, and quarterly financial report data follows the financial report release cycle |
postgresql_connection_timeout | 30 seconds | Chemical pharmaceutical database tables have large data volumes; 30 seconds covers conventional data pull durations and avoids timeout interrupts |
join_key | enterprise_code + product_name | Chemical pharmaceutical data requires association across enterprise and product dimensions; this combined key uniquely matches the same business data across data sources |
data_clean_unit_convert | Unify to yuan/kilogram (raw materials), yuan/pill (dosage forms) | Resolves inconsistent units across different data sources and ensures consistent benchmarks for yield rate calculations |
variable_reference_format | {{datasetId}} | Complies with workflow variable reference specifications and avoids format errors |
retry_max_count | 3 times | Addresses network fluctuations or temporary data source unavailability and reduces workflow failure rates |
The parameter values provided on this page are all 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 Configuration Mistakes
- Symptom: When using a database connection plugin to connect to PostgreSQL, the interface prompts "Workflow verification failed, please check for missing or empty values, and whether connections are normal". Cause: The
postgresql_connection_timeoutparameter is not configured, or the configured timeout period is too short, causing verification to fail during data pulling due to timeout; or thejoin_keyfield is not set correctly, leading to null values during data association. - Symptom: After configuring a data analysis node in the workflow, the operation path for a specific ticket cannot be fixed. Cause: The ticket ID is not configured as an input variable for the workflow, making dynamic binding of the target ticket impossible.
- Symptom: Using the
[{datasetId: xxx}]format for knowledge base variable reference fails, and the workflow reports an error during runtime. Cause: The workflow variable reference specification is not followed, and an object array format is incorrectly used. The correct format should be the placeholder form required by the platform.
How to Confirm Configuration Is Complete
- Manually trigger the workflow once, review the running logs for errors such as database connection timeouts or data association failures, and confirm that the trigger cycle matches the data source update rhythm.
- Extract a test data record, verify that the fields and units in the workflow output match those in the original data, and confirm that the data cleaning rules are active.
- Check the workflow's variable reference configuration, replace it with a test business identifier, verify that the corresponding data loads correctly, and confirm that the variable format is correct.
- Simulate a scenario where the data source is temporarily unavailable, check whether the workflow automatically retries according to the configured maximum retry count, and confirm that the failure handling logic is effective.
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.