What Data for This Category Looks Like
Computer equipment yield rate and market data is sourced from internal enterprise IT asset management systems and public API endpoints of compute scheduling platforms. The latest calculated results are synchronized every 10 minutes. Single data entries use structured JSON format, containing device unique identifier, cumulative compute output over runtime cycles, unit energy cost, net revenue over the calculation cycle, yield rate metric, and statistical timestamp. Field types are string, float, float, float, float, and ISO-formatted time string, respectively. The yield rate metric is a normalized value, and the units for net revenue and output metrics are general compute settlement units.
What Constraints Do These Characteristics Impose on the "Model Integration and Configuration" Link
The 10-minute data update interval requires that model invocation scheduled trigger intervals do not exceed 10 minutes. Otherwise, expired market and yield rate data will be retrieved. Structured multi-field JSON data requires precise field mapping rules to ensure the model correctly associates device identifiers with yield rate metrics. Normalized yield rate metrics have no fixed range. Input normalization parameters must be configured to avoid numerical drift affecting inference accuracy. For bulk pulls of multiple device data, the maximum data volume per request requires configuring batch request split thresholds to prevent single requests from exceeding platform API limits. Fixed-format statistical timestamp parameters require configuring request parameter format validation rules to avoid data pull failures caused by format errors.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
CRON_EXPRESSION | */10 * * * * | Matches the 10-minute update rhythm of the data source to ensure access to the latest yield rate and market data |
FIELD_MAPPING_RULES | Map in the order device ID → compute output → energy cost → yield rate | Matches the fixed field order of structured JSON data to ensure the model correctly reads target analysis metrics |
INPUT_NORMALIZATION_RANGE | 0-1 range | Adapts to the normalized numerical format of yield rate metrics, unifies the input range to avoid numerical drift during inference |
BATCH_REQUEST_SPLIT_SIZE | 20 items per shard | Adapts to the single data volume limit of the platform's bulk API, prevents single requests from exceeding processing time limits |
REQUEST_TIMEOUT_SECONDS | 600 seconds | Covers the average response duration of bulk multi-device data pulls, matches common timeout scenario thresholds |
MODEL_TEMPERATURE | 0.3-0.5 | Adapts to the stable output requirements of yield rate market broadcasts, avoids excessive randomness leading to inconsistent analysis results |
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, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: No model temperature setting entry appears after selecting variable references in the conversation component, making the
MODEL_TEMPERATUREparameter unadjustable. Cause: The "Advanced Variable Configuration" switch is not enabled, causing hidden model parameter settings to not be displayed. - Phenomenon: Only 1 request group appears during bulk model invocation, and single request processing time exceeds 600 seconds, triggering platform timeout errors. Cause: The
BATCH_REQUEST_SPLIT_SIZEparameter is not configured, merging multiple device data into a single request, exceeding the platform request time limit. - Phenomenon:
408 Request Timeouterror code is returned when pulling device data, logs show request duration exceeds 60000ms. Cause: TheREQUEST_TIMEOUT_SECONDSparameter is not adjusted, the default timeout duration is insufficient to cover the response time of bulk data pulling.
How to Confirm Configuration Is Complete
- View the platform's scheduled task monitoring panel, confirm that the trigger interval of data pull requests matches the data source update rhythm, and adjust the corresponding scheduled configuration parameters as needed.
- Submit a test request for single-device data, verify that the model output matches the mapping relationship of input fields, and confirm that the association logic of field configuration items is correct.
- Simulate a bulk data pull request for multiple devices, check the request response duration and error logs, adjust timeout and shard configuration parameters until no abnormal errors occur.
- Enter the platform's request log panel, extract the
requestidfield from the corresponding request, and verify that the request parameters in the logs match the configured items.
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.