What the Data for This Category Looks Like
Data related to dedicated equipment yield rates comes from connections to domestic and overseas securities market market data gateways, and local collection modules built into dedicated equipment. Real-time market data updates every 10 seconds. Daily yield summary documents generate after 17:00 each day. Each daily report covers all collected targets for that day. Documents use standardized JSON format, including fields such as device_id, collect_time, symbol_code, symbol_type, daily_return, trading_volume, check_status. The unit for trading_volume is shares. The unit for daily_return is ten-thousandths. The check_status field only has two enum values: normal and abnormal.
Constraints Imposed on Workflow Orchestration
Data generation time is fixed after 17:00 daily. Workflow trigger nodes must run at a fixed daily time. This avoids pulling incomplete temporary data early. High-frequency real-time market data updates require data pull nodes to support batch pull logic. This prevents overload from single calls. Standardized JSON fields must strictly match parsing rules. Any missing field causes errors in subsequent broadcast content. Data collected in parallel across multiple devices must aggregate by device_id. This requires workflows to support batch data processing and group validation. Abnormal data filtering must complete early in the workflow. This ensures the final broadcast only includes valid entries.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
trigger_type | Scheduled trigger, 17:30 daily | Dedicated equipment daily yield data is generated after 17:00 daily. Running 30 minutes in advance ensures complete data pull |
data_parse_schema | Specify field mapping: device_id, collect_time, symbol_code, daily_return, check_status | Structured data returned by dedicated equipment requires fixed field parsing to prevent subsequent node errors from missing fields |
batch_process_size | 10 items per batch | Single batch processing volume matches the single data return limit of dedicated equipment, to avoid interface call timeouts |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Aggregating data across multiple devices requires extended parsing and validation time, to prevent workflow interruption from mid-run timeouts |
output_filter_mode | Filter by check_status=normal | Invalid data from abnormally collected dedicated equipment must be excluded, to ensure accurate broadcast content |
global_var_scope | Workflow global | Device authentication token must be passed to all nodes, to avoid repeated configuration of authentication information |
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 settings.
Three Common Mistakes
- Phenomenon: The classify node cannot select a deployed AI model in the configuration interface, but the workflow runs normally after publication. Cause: The local preview environment does not sync with globally deployed AI model configurations. The deployed configuration is automatically called after publication.
- Phenomenon: Code execution nodes in the workflow cannot carry device authentication tokens, causing market interface calls to fail. Cause: The token is not configured as a variable with
global_var_scopeset to workflow global, so nodes cannot read authentication information. - Phenomenon: Code execution nodes in SaaS version workflows return
500 Internal Server Error. Cause: The code does not adapt to the interface return format of dedicated equipment, causing parsing failure.
How to Confirm Proper Configuration
- Review workflow trigger configurations, confirm
trigger_typeis set to a fixed daily time, and verify the time is later than the dedicated equipment data generation time. - Run a test workflow, check that the output results only include entries matching the
check_statusrequirement, with no abnormal data. - Review global variable configurations, confirm the authentication token is set to workflow global scope, and can be called by all nodes.
- Check field mappings in the data parsing node, confirm all required fields returned by dedicated equipment are included, with no omissions.
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.