What Data for This Category Looks Like
Industrial park yield-related data primarily originates from park operation management systems, financial accounting modules, rent collection ledgers, and government industrial support fund disbursement systems. Data is collected daily, with same-day operational data compiled by the following early morning. Documents are presented as structured two-dimensional tables, where each record corresponds to a single park’s daily operational data. Fields include: park unique identifier, number of settled enterprises, total accrued receivable rent amount, supporting commercial revenue amount, government subsidy received amount, total leasable area, and leased area. Monetary amounts use RMB yuan as the unit, area uses square meters as the unit, and the count of settled enterprises uses "enterprises" as the unit.
Constraints on Model Access and Configuration Workflows
The multi-system source nature of industrial park data requires configuration to support field mapping across multiple data sources, preventing data parsing errors caused by differing field names across systems. The daily update rhythm requires scheduled trigger configuration for model calls to align with the park data’s collection cycle, avoiding incomplete, unaggregated data from early calls. The structured document format requires configuring input parsing rules for structured tables or JSON format, aligning with the model’s handling of structured inputs. The large number of fields requires configuring field whitelists or specifying recalled fields, reducing unnecessary data consumption of model context. Fixed units for monetary amounts and areas require configuring unit validation rules, ensuring consistent units for input data.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
api_proxy_url | Fill in the proxy address for custom models, such as https://custom-model.example.com/v1 | Supports access to third-party models other than OpenAI, meeting customized model requirements for park scenarios |
schedule_cron | 0 1 0 * * * (triggers daily at 1 AM) | Park data is summarized by the following early morning, this trigger time ensures complete same-day data is retrieved |
input_parse_mode | structured_table | Park data uses structured table format, this mode improves model parsing accuracy for the data |
field_whitelist | Set to ["园区唯一标识","入驻企业户数","累计应收租金金额","配套商业营收金额","政府补贴到账金额"] | Only retains core fields required for yield calculation, reducing unnecessary data consumption of model context |
data_unit_validate | Enable, configure monetary unit as 人民币元 and area unit as 平方米 | Units for industrial park data are fixed, validation prevents non-standard unit data from being included |
multi_source_config | Configure field mapping rules for the park operation, financial, and support fund systems | Industrial park data comes from multiple heterogeneous systems, unifying field names ensures the model receives standardized data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to conduct testing using applicable samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: After importing a knowledge base containing park data with Chinese-English translations, the model’s responses do not reference corresponding content from the knowledge base, and only output general statements. Cause: The structured parsing switch for the knowledge base is not enabled, or the recalled field range is not specified, preventing the model from matching keywords related to park data.
- Symptom: The sorting of relevant data in the park yield results returned by the model does not align with actual revenue priorities. Cause: The working level of the rerank model is not configured, or the number of returned entries for the rerank model is set incorrectly, resulting in data not being sorted by revenue weight.
- Symptom: When using the Qwen3 model for access, the agent node returns stuck results or no valid output. Cause: The streaming call configuration of the model is not synchronized with the streaming switch of the agent node, forcing non-streaming calls that trigger model compatibility errors.
How to Confirm Configuration Is Successfully Completed
- View the data source synchronization log to confirm that multi-system field mapping rules have taken effect, with no field parsing error reports.
- Trigger a manual model call to check whether the returned results include core data within the configured field whitelist.
- View the scheduled task execution record to confirm that the task has triggered according to the configured cron expression, and that the retrieved data is complete.
- Test the unit validation rule by inputting data with non-standard units, to confirm that the validation rule has taken effect and blocked abnormal data.
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.