What data for this category looks like
Data sources for this category include concentrate and auxiliary material analysis reports, DCS control systems, electrolytic cell voltage and current monitoring, laboratory test results, and energy and environmental monitoring systems. Daily generated documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data updates occur per batch of raw materials, per furnace, or per electrolytic cell batch. Some monitoring indicators are updated in real time. Data fields include slot identification, real-time voltage and current values, raw material ratio parameters, composition test results, and environmental emission indicators. Corresponding units include volts, kiloamps, tons, cubic meters, and others.
What constraints these characteristics impose on the HTTP Interfaces and External Systems link
Multiple data sources require interfaces to support flexible data source configuration to adapt to request formats of different systems. Production data with varying update rhythms require interfaces to support custom trigger cycles. Interfaces for real-time monitoring data must adapt to short-interval polling requirements. Batch data interfaces must support triggering per production cycle. Document fields include parameters with specific units, so interface requests must carry field mapping rules to ensure incoming data from external systems match FastGPT knowledge base document fields. Exception report drafting requires association with historical production records, so interfaces must support passing context parameters to associate production data for corresponding batches. Additionally, the sensitivity of production data requires interface authentication configuration to comply with security specifications.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {app_key} | Complies with security transmission specifications for production data, prevents unauthorized access to core production data |
variables | Configure variables by production batch, cell number, monitoring cycle | Adapts to the business logic of generating process documents and exception reports per batch or cell |
responseData | Extract specified fields and map to knowledge base document fields | Matches the specific fields and unit requirements of documents in this category, avoids data format mismatches |
| Single request timeout | 30–60 seconds | Adapts to the fetch rhythm of real-time monitoring data and batch data, avoids prolonged connection occupancy |
| Interface trigger rule | Trigger upon completion of production batch, upon cell condition abnormality | Matches the production update frequency and exception reporting requirements of this category |
Three common errors
- Phenomenon: An error
Key is error. You need to use the app key rather than the account keyis returned when calling the interface. Cause: The login key of the FastGPT account is mistakenly used as the interface authentication key, and the app key of the corresponding application is not used. - Phenomenon: Connection failure or no return data occurs when calling an external interface with header authentication. Cause: Authentication header parameters are not correctly added in the FastGPT interface configuration, and the authentication requirements of the external system are not matched.
- Phenomenon: Uploaded process documents are not correctly associated with corresponding production batch data, and document fields in the knowledge base are missing or have incorrect units. Cause: The
variablesparameter mapping production batch, cell number and other business fields are not configured, resulting in incorrect data matching.
How to confirm configuration validity
- Call the test interface, check if the returned
responseDataincludes expected fields and units, and verify that data mapping is correct. - Trigger an interface call per production batch, check if the FastGPT knowledge base generates documents corresponding to batching lists or smelting records, and verify that the trigger rule is effective.
- Call the interface with an incorrect account key, confirm that an authentication error prompt is returned, and verify that the
Authorizationconfiguration is effective. - Simulate cell condition abnormality trigger conditions, check if the interface automatically initiates a call, and verify that the trigger logic matches business requirements.
The values provided are common starting points and should be measured against the relevant samples for the specific deployment.