What the data for this category looks like
Construction machinery leasing yield and market trend data comes from the national construction machinery leasing alliance public quotation database, manufacturer after-sales leasing ledgers, second-hand equipment transaction platform transaction records, and construction bidding platform shift pricing standards. Shift unit prices are updated daily, leasing rates are updated every 7 days, second-hand equipment transaction data is synchronized in real time, and monthly revenue calculation data is generated monthly.
Data is stored as structured CSV or JSON returned by APIs. Fields include: unique equipment code, model, service life, location, current shift pricing standard, reference monthly leasing duration, monthly realizable revenue per unit, and data update timestamp. Shift pricing is measured in yuan/hour, and monthly revenue is measured in yuan/unit.
What constraints these characteristics impose on forms and interactions
Dispersed data sources lead to differing field naming across channels. This requires the form to support custom field mapping rules to unify cross-channel data formats.
Differing update frequencies across data types require the interaction flow to distinguish between static configuration fields and real-time pull fields, and set differentiated refresh trigger logic.
Multi-region business dimensions require the form to support multi-level region filter components, to quickly narrow query scope.
A large number of associated parameters per single device requires the form to support batch import and batch verification, to reduce operational costs from single-item entry.
Structured data requires industry-specific numerical verification rules to prevent invalid data from entering subsequent processes.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
field_mapping_mode | Match standard fields grouped by equipment model | Adapt to differing field naming across multiple data sources, unify data formats |
refresh_interval | Real-time data: 3600 seconds, static data: 604800 seconds | Match the update rhythm of shift pricing and leasing rates, avoid unnecessary pull requests |
batch_import_max_rows | 500–1000 rows per batch | Balance import efficiency and system load, adapt to the typical ledger scale of the industry |
data_validation_range | Shift pricing: 0–600 yuan/hour, monthly revenue: 0–60000 yuan/unit | Cover the reasonable numerical range of typical industry equipment, filter abnormal data |
search_trigger_fields | Equipment model, location | Match the core dimensions of user queries, reduce redundant returned data |
The parameter values provided on this page are common recommendations for starting point configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Empty fields are returned after calling the text content extraction node, and the specified reply is triggered without correctly waiting for user supplementary input. Cause: The waiting logic for the
empty_content_triggerparameter is not configured, causing the workflow to terminate directly without pausing to wait for new input. - Phenomenon: The number of returned results after reranking does not match the configuration, only the single result with the highest similarity is returned. Cause: The value of the
rerank_top_kparameter is not set correctly, it was mistakenly set to1, and the expected number of returned results was not used. - Phenomenon: A field format error occurs after form submission, prompting "numerical value exceeds reasonable range". Cause: Industry-specific
data_validation_rangerules are not configured, and the general verification interval is used, causing reasonable numerical values to be incorrectly judged as abnormal.
How to confirm the configuration is complete
- Import test data containing 3 to 5 different models of construction machinery, verify that field mapping is correct with no missing or misaligned fields.
- Trigger a real-time data query, check whether the update time of returned results matches the data source's update rhythm, adjust the
refresh_intervalparameter until the expected behavior is achieved. - Submit test data containing abnormal numerical values, verify that the
data_validation_rangerules correctly block invalid input. - Configure the text content extraction node, input vague equipment information, confirm that the specified reply is triggered and the workflow pauses to wait for user supplementary input.
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.