What This Category’s Data Looks Like
Commercial property yield-related data primarily comes from in-house property ERP systems, rent collection ledgers, public area energy consumption settlement documents, and business type rent reference documents released by surrounding business district property management. Update cadences vary: rent collection and energy consumption settlement data is updated monthly, while surrounding business district market data is updated weekly. Most documents are structured CSV or Excel format, containing fields such as property unique identifier, business category classification, rentable area, actual rent income, energy consumption expenditure, operation and maintenance cost, etc. Corresponding units are no unit, classification item, square meter, yuan, yuan, yuan respectively.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Commercial property yield-related data comes from multiple heterogeneous sources with inconsistent update cadences. Workflow orchestration must support time window alignment for multi-source data. This prevents pulling invalid data across cycles. Many structured fields exist, and field differences vary across business categories. Branch nodes must be configured to apply field mapping rules for each business category. This prevents missing fields or type mismatches. In batch processing scenarios, a single workflow handles datasets for dozens to hundreds of properties. Nodes must support parameter passing for batch input. Single batch data volume must be limited to avoid execution timeouts. The data pull link must adapt to API call frequency limits for different data sources. This prevents triggering rate limits and blocking.
How to Set Configurations
| Configuration Item | Recommended Value | Basis for This Value |
|---|---|---|
WORKFLOW_MAX_RUN_TIMES | 1000 times | The number of properties processed in a single batch for commercial property usually falls within the thousands, which matches the maximum run count limit of batch execution nodes |
BATCH_INPUT_MAX_SIZE | 100–500 entries | The volume of data in a single batch for commercial property should not be too large, to avoid node execution timeouts or memory overflow |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Commercial property operation bill files usually contain multiple pages of details. A longer parsing duration ensures complete data reading |
VAR_ARRAY_ASSIGN_FORMAT | Standard JSON array format, for example ["Property A","Property B"] | Complies with the syntax requirements of FastGPT global array variables, ensuring nodes correctly recognize array content |
API_RESPONSE_MERGE_MODE | Independent storage mode | Prevents results from multiple rounds of API calls from overlapping, ensuring each variable result is independent and complete |
NODE_CONNECTION_TIMEOUT | 300 seconds | Commercial property data pull and calculation links usually require long processing durations. Sufficient timeout time prevents abnormal node interruptions |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Mistakes
- Node status always shows "Running" and cannot proceed to the next step. Cause: No timeout parameters configured that match the business data volume, or the number of batch input entries exceeds the
BATCH_INPUT_MAX_SIZElimit, resulting in node execution blocking. - Global string array variables cannot be recognized by subsequent nodes. Cause: Did not use standard JSON array format for assignment, or did not pass parameters according to the syntax required by
VAR_ARRAY_ASSIGN_FORMAT. - The last variable result returned by the API call overlaps with the output content of the previous round. Cause:
API_RESPONSE_MERGE_MODEwas not set to independent storage mode, causing results from multiple rounds of calls to be automatically merged and overwritten.
How to Confirm Proper Configuration
- Execute a test workflow for a single property. Verify that input and output fields of each node match the configured mapping rules.
- Run batch sample data. Confirm that the workflow can complete all node executions normally, with no timeouts or blocking. Adjust the values of relevant configurations based on actual data volume.
- Check the global array variable assignment log. Confirm that the format complies with the array variable syntax requirements of FastGPT.
- Call the test API. Confirm that the return results of each round are stored independently, with no content overlapping. Adjust relevant rate limit configurations based on API call frequency.
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.