What the Data for This Category Looks Like
Data related to agrochemical product yield rates comes primarily from public reports released by the China Pesticide Industry Association and the National Agricultural Means of Production Circulation Association, listed quotes from domestic commodity exchanges, and real-time transaction data from leading agricultural means of production e-commerce platforms. Regular bulk agrochemicals such as base fertilizers and insecticides have daily updated quotes. Specialized segmented formulations have weekly updated quotes. Most data documents are structured tables, with fields including product name, specification model, transaction price, corresponding transaction channel type, and statistical cycle. The price unit is uniformly yuan per ton. Some segmented product categories include active ingredient content parameters.
Constraints Imposed on Workflow Orchestration by These Characteristics
Data update frequencies vary between daily and weekly cycles. Workflows must support scheduled trigger nodes configured for different cycles. This prevents repeated pulling of outdated data or missed updated content. Products must be matched to their specification models and active ingredient content. The data extraction link must preset field association rules to avoid mixing data for agrochemical products with the same name but different specifications. The structured table format returned by data sources is fixed. File parsing nodes must specify the start row and column range for table extraction, to reduce interference from irrelevant content. Some data sources require pulling data via API. Workflows must configure request headers and authentication parameters to ensure lawful data retrieval.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
PARSE_FILE_TABLE_START_ROW | 2 | Public quoted tables for agrochemical products usually have a header title in the first row, with valid data starting from the second row |
SCHEDULE_CRON_EXPR | Daily task: 0 8 * * *, Weekly task: 0 10 * * 1 | Matches the update completion time of most data sources, which is before 8 AM daily or before 10 AM every Monday |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Structured quoted tables for agrochemical products usually contain data for multiple product categories, requiring longer parsing time |
DATA_FIELD_MATCH_RULE | Product name + active ingredient content | Prevents incorrect merging of data for agrochemical products with the same name but different specifications |
WORKFLOW_VISIBILITY | Hide reference sources and details | Only retains yield rate calculation results, which meets the information display needs of most business scenarios |
CODE_RUNNER_ALLOWED_LANGUAGES | python | Data cleaning and year-over-year/quarter-over-year calculation for agrochemical data are usually implemented using Python scripts |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: The file parsing node has no execution logs, and returns empty extraction results. Cause: The
PARSE_FILE_TABLE_START_ROWparameter is not configured correctly, so the parsing node cannot identify the valid data area. - Issue: The code running node fails to execute, with an error prompt "Language not authorized". Cause: Python running permissions are not enabled in the
CODE_RUNNER_ALLOWED_LANGUAGESconfiguration. - Issue: The workflow output results include unconfigured reference source content. Cause: The
WORKFLOW_VISIBILITYconfiguration is not used to turn off reference display, or visibility rules are not set separately for the output node.
How to Verify Proper Configuration
- Upload a standard agrochemical product quote table, run the workflow, and check whether the file parsing node extracts valid data rows that meet expectations.
- Manually trigger the configured scheduled task, and verify whether the pulled data update time matches the preset scheduling cycle.
- Run the code running node, and check whether the preset Python script can execute normally to complete data cleaning and yield rate calculation.
- View the final output results of the workflow, and confirm whether reference source content is displayed according to the configured visibility rules.
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.