Deployment and Upgrade of Intelligent Due Diligence Reports

What the data for this category looks like

Livestock and poultry farming due diligence data comes from three sources: daily ledgers of breeding entities, monthly monitoring reports from local agricultural and rural authorities, and public data from third-party livestock monitoring institutions. Two update frequency categories apply:

Basic inventory and slaughter data is updated weekly.
Epidemic prevention and cost data is updated monthly.

Most documents use a structure of structured tables paired with text descriptions. Core fields include: breeding entity name, inventory volume (unit: head/feather), slaughter cycle (unit: days), total feed purchase volume (unit: tons), number of epidemic prevention batches, per-head breeding cost (unit: yuan/head). Some historical documents are in scanned format. These require OCR recognition to extract structured fields.

What constraints these characteristics impose on deployment and upgrade

The multi-source nature, mixed update frequencies, differing field units, and mixed format of livestock and poultry farming due diligence data impose multiple constraints on deployment and upgrade. Three types of data sources must be integrated simultaneously: structured ledgers, scanned report files, and third-party APIs. The deployment phase requires configuration of OCR recognition and structured parsing modules. The differing update rhythms of weekly inventory data and monthly cost data require upgrade logic to support scheduled synchronization tasks with multiple frequencies. Local differences in inventory units across breeding entities require pre-configured unit conversion mapping rules. Some data sources are deployed in intranet environments. Intranet access permissions must be configured in advance to avoid synchronization interruptions after upgrade.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Livestock and poultry farming due diligence reports often include scanned document OCR and multi-table parsing, which takes a long time. 600 seconds covers the full parsing process
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Monthly reports from large-scale breeding operations include scanned records of multiple batches of epidemic prevention. Single file size can exceed 1.5 GB
`SYNC_CRON_EXPR`	`0 0 2 * * `, `0 0 4 * *`	Weekly inventory data requires synchronization every Tuesday morning. Monthly cost data requires synchronization on the 4th day of each month. This matches the dual-frequency update rhythm
`FIELD_UNIT_MAPPING`	`{"存栏量": ["头", "羽"], "单头养殖成本": "元/头"}`	Local differences in inventory units across breeding entities require pre-configured conversion rules to unify field formats
`INTRANET_ACCESS_ENABLE`	`true`	Most breeding entities deploy data sources in intranet environments. Enable intranet access permissions to ensure data synchronization

The parameter values provided on this page are common starting points for configuration. Actual values are affected by document format, data volume, and business rules. Analyze specific cases individually, and test on your own samples before finalizing settings.

Three common mistakes

Issue: Service fails to start after running git pull to pull the latest code and upgrade to version 4.9.0. Cause: Default values for new configuration items were not adapted in advance. Existing PARSE_FILE_TIMEOUT_SECONDS parameter configuration conflicts with the new version's validation rules.
Issue: The ERROR: failed to solve: archive/tar: unknown file mod error appears when running the Docker build command. Cause: Imported scanned livestock farming due diligence report files include embedded resources with non-standard file permissions. Exception permission files were not filtered when building the container image.
Issue: API requests return the 404 - Resource Not Found error. Cause: Intranet data source access whitelist is not configured, or the SYNC_CRON_EXPR parameter format for scheduled synchronization tasks is incorrect, preventing the service from triggering data pulling.

How to confirm proper configuration

Run a local file parsing test. Upload a livestock farming due diligence report that includes scanned documents and structured tables. Verify that parsing time falls within the range specified by the PARSE_FILE_TIMEOUT_SECONDS parameter.
Manually trigger a scheduled synchronization task. Check data source pulling logs to confirm that weekly and monthly data were successfully synchronized.
View the field mapping configuration page. Confirm that the FIELD_UNIT_MAPPING parameter covers all unit fields that require conversion.
Test data source access in an intranet environment. Confirm that data can be pulled normally after INTRANET_ACCESS_ENABLE is set to true.

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.