What data for this category looks like
Carbon steel data primarily comes from domestic steel spot trading platforms, publicly available statistics from industry associations, and futures market contract data. There are two update schedules: spot data updates after daily market close, while futures data updates in real time during trading sessions, with settlement prices updated at a fixed time each day after market close. Data is returned in structured documents or via API interfaces, including fields such as carbon steel product identifier, origin, daily trading price, daily settlement price, daily cumulative trading volume, and daily open interest. The unit for price is yuan/ton, trading volume is measured in tons, and open interest is measured in trading lots.
Constraints imposed on model integration and configuration by these characteristics
The multi-update schedule, structured fields, and industry-specific identifiers of carbon steel data impose multiple constraints on model integration and configuration. First, support must be provided for both scheduled batch pulling and real-time streaming access to data sources, with corresponding pull cycles and trigger mechanisms configured. Second, the data fields include industry-specific identifiers, so field mapping rules must be configured to bind native fields returned by interfaces to standardized fields required by the model. Third, category filtering rules must be configured to only pull data for target carbon steel product categories, avoiding redundant data that interferes with model output. Finally, unit validation logic must be configured to ensure the units of incoming data meet preset standards, preventing abnormal data from entering the model.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
data_sync_cron | 0 18 * * * | Aligns with the post-market close update schedule for carbon steel spot data, ensuring daily synchronization of the latest spot data |
real_time_pull_interval | 300 seconds | Matches the in-session update frequency of carbon steel futures data, balancing data timeliness and API call costs |
field_mapping_rule | Bind fields as product identifier → product_type, trading price → trade_price, settlement price → settle_price | Matches native fields from carbon steel data with standardized input fields required by the model |
category_filter_key | Filter key is product_type, matching values are Q235螺纹钢, 普通线材 | Only pull data for target carbon steel product categories, avoiding non-target category data that interferes with model output |
data_unit_validate | Enabled | Ensures incoming data uses units of yuan/ton, tons, and lots, preventing abnormal data from entering the model |
model_input_chunk_size | 800–1200 characters | Adapts to the high number of fields in carbon steel data, avoiding single inputs that exceed model context limits |
The parameter values provided on this page are common 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 configuration errors
- Symptom: Model calls return a
404 Not Founderror, or prompt "model unavailable". Cause: API address and authentication key for model integration are not configured correctly, or an incompatible model type was selected. - Symptom: Incoming carbon steel data fields are empty, or field values do not match actual data. Cause:
field_mapping_ruleis not configured correctly, or the filter rule binds incorrect field key-value pairs. - Symptom: Scheduled synchronization tasks restart indefinitely, or logs prompt "token encoder error". Cause: Model token encoding parameters are not configured correctly, or model dependency packages are missing in the deployment environment.
How to verify successful configuration
- Manually trigger a data pull, check if returned carbon steel data fields are complete and comply with preset mapping rules.
- Check model call logs to confirm no authentication errors or interface unavailable prompts are returned.
- Run a model inference task, check if the output includes standardized content related to carbon steel yield rates.
- View scheduled synchronization task run records to confirm tasks execute normally per preset cycles.
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.