What the data for this category looks like
Data for this category originates from hot metal and scrap charging data, L2 process control systems, laboratory test results, manufacturing execution systems (MES), energy medium systems, and equipment condition monitoring modules. Data update cycles include per-heat, real-time, and shift-based periods. Daily associated documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Data fields include heat number, hot metal temperature, carbon, silicon, manganese composition, converter endpoint control parameters, continuous casting pulling speed, slab thickness judgment results, and more. Units include degrees Celsius, percentage, meters per minute, millimeters, and other standard units.
What Constraints These Characteristics Impose on HTTP Interfaces and External Systems
The update cycles of real-time data and heat-level data vary widely for this category. This requires HTTP interfaces to support requests with different time granularities. Data from multiple system sources requires interfaces to support different field mapping rules, to match the fixed field formats of documents such as heat plans and composition reports. High-temperature continuous production processes cannot be interrupted. Interfaces must support timeout retries and abnormal data filtering to avoid disrupting the coordination of steelmaking and continuous casting rhythms due to interface failures. Fields in documents such as slab judgment sheets must be strictly aligned. Information returned by interfaces must include specified heat identifiers and composition parameters to ensure the accuracy of subsequent process scheduling.
How to Configure
This configuration guide is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {replace with FastGPT application API key} | Complies with HTTP authentication specifications to ensure interface requests are properly identified |
variables | {"heat number": "heat number to query", "data type": "smelting record/continuous casting process"} | Accurately binds business query dimensions for iron and steel smelting and continuous casting to narrow the search scope |
responseData | Return matching process clauses, standard document fragments, and associated heat data | Filters redundant information to meet the lightweight information acquisition needs of production scenarios |
| Single request timeout | 30–60 seconds | Adapts to the latency range of real-time data pulling and historical document queries to avoid blocking production processes |
| Request retry count | 2 times | Addresses temporary network fluctuations to reduce the impact of interface request failures on production scheduling |
Three Common Configuration Mistakes
- The interface returns a
401 Unauthorizedstatus code and cannot retrieve process specification data. The cause is that theAuthorizationparameter is not configured with a correct API key, or the key permission is not bound to the corresponding application. - The
responseDatafield returned by the interface is empty, with no matching target process content. The cause is that business identifiers such as heat number or data type are not passed in thevariablesparameter, resulting in an overly broad search scope that fails to hit target documents. - The interface request times out, or returns a
504 Gateway Timeoutstatus code. The cause is that the single request timeout setting is too short, which does not adapt to the latency requirements of pulling data from multiple systems in steel smelting and continuous casting scenarios.
How to Verify Correct Configuration
- Call the interface with a known valid heat number and data type, and check if the returned
responseDataincludes fragments of the corresponding process specification or technical standard. - Verify the format of the
Authorizationparameter, confirm that it starts withBearerfollowed by the correct API key, with no extra spaces or characters. - Simulate temporary network fluctuations by sending multiple requests, and confirm that the interface can return valid data normally after retries.
- Compare the knowledge base retrieval results in the FastGPT application with the
responseDatareturned by the interface, and confirm that the content of both is consistent.
The values provided are common starting points and should be measured against the reader's own samples.