Data Characteristics for This Category
Dairy industry financial report data comes from three primary sources: periodic reports of listed companies disclosed by domestic and overseas stock exchanges, production and sales monitoring data released by industry associations, and public quotation information from upstream raw milk suppliers. Quarterly financial reports are updated within 30 days after the end of each quarter. Monthly industry data is updated in the middle of each month. Annual financial reports are disclosed before April of the following year. Document structures include consolidated financial statements, revenue breakdowns by dairy product subcategory, detailed cost composition data, and capacity-related metrics. Available fields include total revenue for each dairy product subcategory, raw milk procurement costs, unit packaging costs, sales channel investment amounts, and the share of raw milk procurement costs in total operating costs. Corresponding units are ten thousand yuan, yuan/ton, yuan/item, ten thousand yuan, and yuan per yuan of total operating costs, respectively.
Constraints Imposed on Workflow Orchestration
Dairy financial report data sources are scattered, including public periodic reports, industry monitoring data, and upstream quotation information. Workflows must include multi-source data access nodes adapted to different data source formats and permission requirements. Update cycles vary across data types. Quarterly financial reports and monthly industry data use different trigger periods. Configure independent scheduled trigger parameters for each data node to avoid delayed or duplicate data pulls. Dairy financial reports include detailed revenue breakdowns across multiple subcategories and granular cost compositions. Documents are long and structurally complex. Workflows must include structured parsing nodes, with separate parsing rules for table content and text descriptions. This ensures accurate extraction of subcategory data. Field types and units have specific requirements. For example, raw milk procurement costs use yuan/ton as their unit. Configure field verification nodes to confirm that extracted field units match preset rules, preventing data confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CRON_EXPRESSION | Monthly data: 0 0 10 1 * *; Quarterly financial reports: 0 0 10 1 1,4,7,10 * | Matches the schedule where monthly dairy industry data is updated on the 1st of each month and quarterly financial reports are disclosed on the 1st of each quarter, to avoid delayed or duplicate data pulls |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Dairy financial reports include multi-category detailed tables and long text descriptions, which require extended parsing time. Reserve sufficient time to avoid parsing interruptions |
PARSE_TABLE_ENABLED | Enabled | Dairy financial reports contain a large number of tables for subcategory revenue and cost composition. Enabling this option enables accurate extraction of structured data |
field_extract_rules | Specify extraction of four core fields: total revenue, raw milk procurement costs, packaging costs, and channel investment amounts | Focus on core analysis dimensions of dairy financial reports, filter irrelevant fields to improve analysis efficiency |
text_split_chunk_size | 800–1200 characters | Adapt to the mixed content length of tables and text in dairy financial reports, avoid chunking that disrupts data integrity and relevance |
UPLOAD_FILE_MAX_SIZE | 500 MB | Annual dairy financial reports include multi-page detailed content, typically with large file sizes, to align with maximum upload limits |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Dairy financial report analysis results generated by the Specified Reply node include an automatic leading line break. Reason: The default output template of the Specified Reply node includes a leading line break configuration, and the starting format parameter of the template is not adjusted.
- Phenomenon: Differences appear in analysis results when setting the AI chat node temperature parameter, between turning off the temperature switch and turning it on with a value of 0. Reason: When the temperature switch is turned off, the model uses fixed deterministic generation logic. When the switch is turned on and set to 0, some models retain minor random adjustments, so the two effects are not consistent.
- Phenomenon: The question classification node processes dairy financial report classification tasks slowly, with fluctuating classification accuracy. Reason: No dedicated classification tags are configured for dairy financial report subcategories, or input text length exceeds the node's default processing limit, reducing matching efficiency.
How to Verify Successful Configuration
- Trigger the scheduled task, check if the multi-source data access node has pulled dairy financial report data for the corresponding update cycle, and confirm that the data update time matches the preset trigger schedule.
- Run the structured parsing node, verify that extracted fields cover the preset core analysis dimensions, and confirm that table content parsing results have no missing or misaligned formats.
- Test the output format of the Specified Reply node, confirm that the result has no unnecessary leading line breaks, and that custom line break formats display normally.
- Run the question classification node, verify that classification results match dairy financial report analysis requirements, and confirm that classification speed meets business response needs.
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.