What the data for this category looks like
Core data sources for livestock and poultry farming financial reports include the public monitoring database of the Ministry of Agriculture and Rural Affairs and regular disclosure reports of listed farming entities. Data updates follow a weekly cadence for segmented category slaughter and inventory monitoring data, and a quarterly cadence for official consolidated financial report documents. Most documents use a structure of structured statistical tables plus annotation notes. Core fields include inventory volume, slaughter volume, average individual body weight, unit farming cost, and total feed consumption. Units are ten thousand head/ten thousand poultry, ten thousand head/ten thousand poultry, kilograms, yuan per kilogram of live weight, and tons respectively. Most fields are numeric, with no redundant unstructured text fields.
What constraints these characteristics impose on HTTP interfaces and external systems
The multi-source and multi-unit nature of livestock and poultry farming financial report data requires HTTP interfaces to support custom field mapping and unit conversion rules. This adapts to format differences across data sources. The layered weekly and quarterly update cadence requires interface configurations to support custom scheduled pull time ranges. This avoids frequent calls that trigger third-party platform rate limits. The large number of structured fields and rich segmented categories requires interfaces to support filtering by farming category and statistical cycle. This reduces invalid data pulls. Some disclosure documents are in unstructured PDF format. This requires the interface’s integrated parsing module to support table area extraction, to ensure accurate acquisition of core fields.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
API_REQUEST_TIMEOUT | 300 seconds | Structured parsing and large document pulls for livestock and poultry farming financial reports require long processing times, to avoid early request termination |
FIELD_MAPPING_RULES | Map inventory volume, slaughter volume, and unit farming cost as core mapping fields | Core analysis dimensions for livestock and poultry farming financial reports are fixed; mapping core fields ensures consistency of analysis results |
UNIT_CONVERSION_CONFIG | Support bidirectional conversion between ten thousand head/ten thousand poultry and head/poultry, and between yuan per kilogram and yuan per ton | Units vary across different data sources, to meet the needs of accessing multi-source data |
SCHEDULED_PULL_INTERVAL | Pull monitoring data weekly, pull financial report documents quarterly | Matches the weekly monitoring and quarterly disclosure update cadence of the livestock and poultry farming industry |
BASIC_AUTH_CREDENTIALS | Configure according to the account and password provided by the third-party platform | Some agricultural monitoring platforms and enterprise disclosure interfaces require basic authentication to ensure access security |
FILTER_PARAMS_ENABLED | Enable the farming category and statistical cycle filtering switches | Reduce invalid data pulls and improve interface call efficiency |
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 Mistakes
- The external financial report data interface returns a 401 Unauthorized status code. Cause: The
BASIC_AUTH_CREDENTIALSparameter is not configured correctly, the basic authentication switch is not enabled, or the provided account and password do not match the third-party platform requirements. - Parsed financial report analysis results do not match user queries, and the AI response does not link to the correct data source fields. Cause: The
FIELD_MAPPING_RULESare not configured correctly, and data source fields are not properly mapped to analysis requirements, leading to extracted data that does not match the query. - The
Get Conversation History Listinterface returns incomplete historical records, with some early records not pulled. Cause: The offset parameter is used incorrectly. When the offset is set to a non-zero value, the number of pulled entries is not adjusted synchronously, resulting in skipped historical data.
How to Confirm Successful Configuration
- Initiate a test call, check that the returned HTTP status code is 200 OK, to confirm normal interface connectivity.
- Review the field list returned by the interface, verify that the preset core fields are included, to confirm the field mapping configuration is active.
- Adjust the offset parameter and initiate multiple calls, verify that the returned historical record range matches expectations, to confirm the offset parameter configuration is correct.
- Trigger a scheduled pull task, verify that the update time of the pulled data aligns with the third-party platform’s update cadence, to confirm the scheduled configuration is active.
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.