What This Category's Data Looks Like
Dairy product profitability-related data originates from three sources: the fresh milk price monitoring system of the Ministry of Agriculture and Rural Affairs’ Animal Husbandry and Veterinary Bureau, public operating disclosure documents from domestic dairy enterprises, and third-party food and beverage industry data platforms. Two update schedules apply:
- Fresh milk purchase prices are updated daily.
- Factory settlement prices and profitability metrics for terminal dairy products are updated weekly.
Most data documents use structured JSON or CSV formats. Included fields are product name, production batch, raw material cost per unit, processing cost, factory settlement price, statistical cycle, production area code, and others. Units are uniformly yuan per kilogram and yuan per piece. No percentage-based statistical indicators are included.
Constraints for HTTP Interface and External System Integration
The multi-source nature of dairy data requires integration with multiple external interfaces. Each interface may use different authentication methods and return formats. Differences in data update frequencies require configuring distinct scheduled trigger rules for HTTP request nodes. This prevents frequent pulls of low-frequency data or delayed pulls of high-frequency data. Fields include breakdown dimensions such as production area and batch. HTTP request parameters must carry corresponding filter conditions to accurately retrieve data for the target category. Minor unit inconsistencies exist across different data sources. An additional unified conversion step must be added after interface calls to ensure consistent data calculations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | https://api.dairy-data-monitor.com/v1/daily-report | Connects to publicly available industry dairy monitoring data sources, matches data update frequency and field requirements |
AIPROXY_API_TOKEN | sk-dairy-xxxxxx (replace with the actual applied platform key) | Used for interface identity verification to prevent unauthorized access to data interfaces |
HTTP_REQUEST_TIMEOUT | 25 seconds | Dairy market data has a high update frequency. An overly long timeout will cause reporting delays and impact the timeliness of daily report generation |
REQUEST_RETRY_TIMES | 2 times | Addresses temporary network fluctuations or interface rate limiting, prevents a single failed request from interrupting the entire workflow |
REQUIRED_FIELDS | ["product_name", "raw_material_price", "settlement_price"] | Ensures pulled data includes core basic fields required for profitability calculations, avoids missing critical data in subsequent steps |
DATA_UNIT_CONVERT | Unify to yuan per kilogram | Raw material cost units vary across different data sources. Unifying units facilitates subsequent profitability calculations and comparisons |
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 on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Workflows skip the HTTP request node and proceed directly to the AI dialogue step. Cause: The HTTP request node is not configured as a pre-execution node of the workflow, or the node's forced trigger switch is not enabled.
- Phenomenon: HTTP request body parameters cannot be correctly mapped to workflow variables. Unreplaced placeholders appear in the request body. Cause: The request body template is not configured using the variable syntax required by the node, or the variable name does not match the variables defined in the workflow.
- Phenomenon: Pulled data contains empty fields or inconsistent units. Cause: Required field validation rules are not configured, or unit automatic conversion configuration is not enabled, resulting in a mismatch with the data source's field definitions.
How to Verify Successful Configuration
- Call the configured HTTP interface and check if the returned JSON data includes the preset required fields, and that units conform to the unified standard.
- Review the workflow node execution logs to confirm the HTTP request node returns a status code of 200, and that variables in the request body have been correctly replaced with actual values.
- Simulate a workflow trigger to verify that the complete link of data pulling, variable mapping, and subsequent processing executes normally.
- Manually trigger an abnormal request to confirm the interface retry mechanism activates normally and records error logs.
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.