What data for this category looks like
Daily data for iron and steel smelting and continuous casting is linked to heat and casting batch dimensions, covering heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab inspection sheets. Data sources include hot metal and scrap charging data, L2 process control systems, laboratory test results, MES production management systems, energy medium systems, and equipment status monitoring platforms. Complete smelting and continuous casting records are generated per heat. Composition test data is synchronized in real time. Scheduling and energy consumption data are aggregated per shift. Core fields include heat ID, hot metal charging weight, scrap addition batch, smelting end temperature, continuous casting pulling speed, and slab defect detection results. Each field follows the unit specifications of the corresponding industrial system.
Constraints for HTTP interfaces and external systems
The strongly linked data structure of heat and casting batches requires interfaces to aggregate multi-source system data by unique heat ID. Data cannot be retrieved using only a single field. Scenarios with mixed update frequencies require interfaces to support short polling for real-time composition data, as well as batch retrieval of historical data over shift ranges for tender preparation. Field differences across multiple system data sources require interfaces to complete cross-system field mapping and format unification. The strongly coupled nature of high-temperature continuous processes requires interfaces to have idempotent design to avoid risks of continuous casting interruptions or heat downgrades caused by repeated requests. Bulk data requirements in tender scenarios require interfaces to support filtering data by time range or casting batch, improving data preparation efficiency.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {Enterprise-specific API Key} | Verifies caller identity when connecting to multiple industrial systems, preventing unauthorized access that could lead to data leaks or process interruptions |
variables | {"target_heat_ids": ["HEAT20240501001", "HEAT20240501002"], "time_range": "2024-05-01 00:00:00/2024-05-01 23:59:59"} | Specify target heat IDs or time ranges per tender requirements to accurately retrieve historical smelting and continuous casting data |
responseData | ["heat_id", "molten_steel_weight", "final_temperature", "casting_speed", "defect_rate"] | Only return core fields required for tender quotations, reducing data transfer volume and improving interface response speed |
| Interface timeout threshold | 600 seconds | Aggregating data for a single heat requires waiting for responses from multiple systems, so sufficient time is reserved for data retrieval and format organization |
| Interface retry count | 3 times | Adapts to occasional network fluctuations in industrial systems, reduces interface call failure rates, and ensures uninterrupted tender data preparation workflows |
Three common errors
- Phenomenon: The interface returns
400 Bad Requestand theresponseDatafield is empty. Cause: Multi-source system data is not associated by heat ID, and only incomplete fields from a single system are retrieved, failing to cover all complete smelting and continuous casting parameters required for tenders, which prevents the generation of compliant tender documents. - Phenomenon: Interface call times out, returning
504 Gateway Timeout. Cause: The timeout threshold is set too short, not matching the multi-system data aggregation time required for iron and steel smelting and continuous casting processes, resulting in request termination before data retrieval is complete, interrupting tender data preparation. - Phenomenon: Quotation preparation tasks are triggered repeatedly, leading to conflicts in downstream scheduling processes. Cause: Idempotency verification is not configured, and the interface generates multiple duplicate tender documents after receiving repeated requests, posing a risk of disrupting continuous casting operations.
How to confirm correct configuration
- Call the interface with a test heat ID, and check if the returned
responseDatafields include parameters required for tenders such as smelting temperature and pulling speed. - Simulate occasional network fluctuations in industrial systems, verify that the interface automatically retries according to the configured retry count, and does not generate duplicate tasks.
- Compare the interface returned data with original records from MES and laboratory systems to confirm that field mapping is accurate.
- Check the FastGPT v4.17.0 interface call logs to confirm that the
Authorizationparameter is correctly carried and identity verification is passed.
The values provided here are common starting points and should be measured against the reader's own samples.