What the data for this category looks like
This category's data primarily comes from store POS terminals, central cash register systems, and ingredient purchase ledger systems. Data settlement is completed 1 to 2 hours after daily business operations conclude, generating structured data files. Each file corresponds to one individual store, and includes fields such as store identifier, business date, total daily revenue, ingredient procurement costs, labor costs, net profit, table turnover count, and customer unit price. Revenue, costs, and net profit are measured in Chinese Yuan. Table turnover count is measured in counts. Customer unit price is measured in Yuan per person. Most data files use CSV format. The number of rows per file varies based on the number of stores, with no fixed length.
What constraints do these data characteristics impose on model integration and configuration?
The data characteristics of this category create three core constraints for model integration and configuration. First, data is updated at fixed daily times. A scheduled pull task must be configured to avoid invalid requests outside business hours. Second, fields include multiple unit types. Corresponding rules between fields and units must be clearly defined in the model prompt to prevent the model from confusing measurement standards for values. Third, most data files use CSV format, and the number of rows per file varies widely. A CSV-adapted parsing component must be configured, and dynamic batch processing parameters must be set to adapt to file sizes across different numbers of stores. Additionally, bulk pulling of multi-store data must support custom store ranges to meet the needs of different operating entities.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
scheduleTriggerCron | 0 30 22 * * ? | Matches the daily 22:00 settlement cycle for this category's data, pulling 30 minutes later to avoid unready data |
parseFileType | csv | Adapts to the mainstream storage format of this category's data |
fieldMappingConfig | Store Identification: Store ID, revenue: Daily Total Revenue, cost_food: Food Cost, cost_labor: Labor Cost, profit: Net Profit, table_turnover: Table Turnover Count, unit_price: Guest Unit Price | Unifies field naming rules to avoid deviations when the model identifies fields |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing duration of large multi-store CSV files to prevent mid-run timeout interruptions |
batchPullThreshold | 50 stores per request | Balances request efficiency and resource usage, adapting to the single-file scale of most stores |
maxContextLength | 8000–12000 characters | Supports aggregated broadcast content for multi-store data, meeting the requirement for complete information transfer |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: An external model call returns a
401 Unauthorizederror. Cause: The model's API key and dedicated endpoint are not configured correctly, causing the authentication flow to fail. - Symptom: Interface calls fail after binding the WeChat Work channel, returning a
403 Forbiddenstatus code. Cause: WeChat Work's API call permissions or trusted domain names are not configured, causing platform requests to be blocked. - Symptom: After configuring a timestamp in the prompt, the generated content's timestamp does not match the actual date. Cause: The system time variable node is not bound in the workflow, or the variable trigger timing is later than the prompt rendering timing.
How to confirm the configuration is complete
- Manually trigger a data pull task, check if the parsed structured data includes all preset fields, and confirm that field units match the actual collected data rules.
- Run a single-round model conversation test, input simulated store data, and check if the model's generated broadcast content accurately maps fields and corresponding values.
- Check the scheduled task's run logs to confirm that the daily pull task executes successfully during the preset time period, with no timeouts or abnormal errors.
- Verify the external channel binding status, check if any permission verification prompts are returned, and confirm that call permissions have been properly configured.
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.