What this category of data looks like
Data sources include public quotes from domestic bulk commodity spot trading platforms and monitoring data released by chemical industry associations. Updates are completed once within one hour after the close of each trading day. Documents are provided in structured JSON or CSV format, with fields including general raw material name, origin, implementation standard, daily transaction average price, 7-day average transaction price, single-day fluctuation value, and statistical cycle identifier. The unit is uniformly yuan/ton. Each data entry includes a unique raw material code and update timestamp.
What constraints do these characteristics impose on the model access and configuration workflow
Multiple data sources require configuring multi-source data format adaptation rules to support return structures from different platforms. The daily update rhythm requires scheduled pull tasks to match the trading day cycle, preventing empty data pulls on non-trading days. Fixed fields and standard units require configuring data validation rules to enforce field existence and unit consistency, avoiding chaotic input data formats. The unique raw material code design requires configuring data deduplication and association rules to ensure correct aggregation of multiple entries for the same raw material. Classification by different implementation standards requires configuring standard-based filtering parameters for precise data screening.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_DATA_SOURCE_TYPE | csv | Chemical raw material market data is often released in bulk CSV format, adapting to common industry data source formats |
DATA_PULL_INTERVAL | 86400 seconds | Matches the daily update rhythm of chemical raw material data, preventing repeated pulls or missed updates |
FIELD_MAPPING_RULES | Configure in the format raw material identifier → raw_material_id, daily transaction average price → current_price, unit → price_unit | Accurately map data fields to model input fields to ensure parsing results meet expectations |
DATA_VALIDATION_THRESHOLD | Set based on actual measurement and calibration | Filter abnormal data outside the industry's conventional fluctuation range and retain valid market information |
RECALL_TOP_K | Top 10 entries | Adapts to the scale of single-batch chemical raw material data entries, balancing information density and model processing efficiency |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Reserve sufficient time to complete parsing and validation of bulk chemical raw material data files |
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 specific analysis, and it is recommended to conduct actual tests on your own samples before finalizing the values.
Three common configuration errors
- Phenomenon: Scheduled pull tasks trigger in the early morning of non-trading days, leading to frequent API calls and excess call quota consumption. Cause: No trading day filtering rules are configured, and the scheduled task runs at fixed intervals without matching the update cycle of chemical raw material data.
- Phenomenon: Mixed unit quotation data appears in model output results, such as mixing yuan/kilogram and yuan/ton. Cause: No field mapping and unit validation rules are configured, and the unit format of data fields is not standardized.
- Phenomenon: Duplicate entries appear in pulled chemical raw material data, leading to redundant model input. Cause: No deduplication is performed by associating raw material codes and update timestamps, and no data deduplication validation logic is configured.
How to confirm successful configuration
- View data pull logs to confirm that tasks only trigger at the specified time on trading days, and verify that the trigger time matches the data update rhythm.
- Randomly select a subset of parsed data entries to check whether field mapping aligns with configured rules and whether units conform to industry standards.
- Review call quota consumption records to confirm that valid calls only occur on trading days, with no unnecessary interface requests.
- Simulate abnormal data input to confirm that content outside the industry's conventional fluctuation range is correctly filtered.
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.