What the data for this category looks like
Dairy product financing daily report data is primarily sourced from industry monitoring databases, official dairy enterprise announcements, and publicly disclosed information from financial media. Full data integration and update for the previous day is completed each early morning. Each daily report document includes standardized fields: 融资日期, 企业主体名称, 融资金额（单位：万元人民币）, 融资轮次, 投资方名单, 核心业务品类, 披露渠道. The 核心业务品类 field is subdivided into specific sub-categories such as liquid milk, yogurt, cheese, infant formula milk powder, and others. The 投资方名单 field may include multiple institutional or individual entities. Entries with undisclosed financing amounts are left blank.
What Constraints These Characteristics Impose on Model Access and Configuration
The multiple sub-categories and multiple entity fields present in dairy product financing daily reports require targeted entity extraction rules to be configured during model access, to accurately identify sub-categories such as liquid milk and cheese. The daily update rhythm requires configuration of a daily scheduled data source pull task, to avoid data synchronization delays. The scenario where some entries do not disclose financing amounts requires configuration of null value filtering or placeholder replacement rules, to prevent abnormal model output. The presence of multiple entities in the 投资方名单 field requires configuration of parameter thresholds for multi-entity extraction, to ensure complete extraction of associated investor information. Format differences across different disclosure channels require configuration of format adaptation rules for data source preprocessing, to unify field structures.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Scheduled Sync Interval | 86400 seconds | Matches the daily update rhythm of dairy product financing daily reports, ensuring that the latest previous day's data is pulled daily |
Entity Extraction Classification Threshold | 0.75–0.85 | Adapts to the recognition needs of dairy product sub-categories, balancing classification accuracy and recall rate |
Null Value Handling Rules | Replace with "Unspecified" placeholder | Addresses scenarios where some entries do not disclose financing amounts, avoiding empty results or abnormal prompts from model output |
Multi-entity Extraction Limit | 10 items | Covers the number of investors in most financing cases, preventing extraction of too many irrelevant associated entities |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the field parsing duration of a single daily report document, avoiding parsing task failure due to large data volume |
Similarity threshold | Label by business scenario | Filters duplicate financing entries to ensure the uniqueness of daily report data. The specific threshold is adjusted based on actual data duplication rate |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and actual testing on local samples should be conducted before finalizing values.
Three Common Mistakes
- A model test returns the
429 当前分组上游负载已饱和，请稍后再试error. The cause is that a reasonable request concurrency limit is not configured, leading to too many model invocation requests initiated in a short period, which triggers the platform's load protection mechanism. - Knowledge graph access for dairy product financing daily reports fails. The cause is that entity association mapping rules are not configured, causing entities such as dairy enterprises and investors in the graph to fail to bind correctly to corresponding fields in the daily report data.
- Local Ollama model access test fails. The cause is that the model's API address and port are not configured correctly, or the enable switch for the corresponding model is not turned on in the FastGPT model management interface, leading to failure to establish a stable connection.
How to Confirm Configuration Is Complete
- Manually import a test dairy product financing daily report document, check whether the field extraction results parsed by the model include required fields, and whether sub-category recognition conforms to the classification logic of the dairy industry.
- View the running logs of the scheduled synchronization task, confirm whether the daily early morning data source pull and parsing tasks are completed normally, with no timeout or error records.
- Initiate a model invocation test, check whether the returned results include complete investor lists and financing round information, and that there are no abnormal null values or incorrect classifications.
- Adjust the model's request concurrency parameters, observe the platform's load status, and confirm that the
429error protection mechanism is not triggered.
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.