This document is written based on FastGPT v4.17.0.
What This Category's Data Looks Like
Core data for this category comes from smelting, electrolysis, laboratory, and scheduling departments. It includes documents such as batching lists, smelting records, electrolytic cell status records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratory equipment, and energy and environmental monitoring devices. Update frequency adjusts in real time when each batch of raw materials, each furnace, or each tank job is completed. Fields include job batches, equipment operating parameters, detection indicators, ledger numbers, and more. Units cover dimensions such as mass, current, and time. There is no unified standardized format.
Constraints for HTTP Interfaces and External Systems
Data for this category comes from dispersed sources. Sources include equipment operation, laboratory testing, environmental monitoring, and other systems. Update frequencies vary widely. Some data, such as real-time cell voltage and current data, updates immediately. Other data, such as batching lists and laboratory reports, generates per batch. Multi-source, heterogeneous data structures require HTTP interfaces to match specific fields like job batches, equipment parameters, and detection indicators. Generic process industry interface templates cannot be reused directly. Timely data updates and serious error consequences require interfaces to support breakpoint resume and failure retry mechanisms. This prevents critical process interruptions from single request exceptions. Differences in department scheduling needs require interfaces to support flexible trigger timing configuration. This adapts to both real-time pull and scheduled synchronization modes.
Configuration Settings
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
Authorization | Bearer {Enterprise-specific API key} | Matches identity verification rules of internal systems for non-ferrous metal smelting enterprises, and ensures production data security |
variables | {"batch_id": "", "slot_id": ""} | Adapts to data query requirements by job batch and electrolytic cell dimension, and accurately pulls corresponding production records |
responseData | Only return batch_no, voltage, current, test_result fields | Filters redundant data, reduces interface transmission volume, and adapts to lightweight transmission needs in production scenarios |
| Request timeout setting | 60–120 seconds | Adapts to average delay from workshop network fluctuations, and avoids interrupting critical production processes |
| Retry count configuration | 3 times | Addresses common workshop network instability scenarios, and reduces the impact of single request failures |
| Trigger timing configuration | Triggered by job completion events | Matches the data update rhythm per furnace or tank for this category, and avoids invalid polling |
Three Common Mistakes
- Scenario: The interface returns status code 400, and the
responseDatafield is empty. Cause: The job batch and electrolytic cell number parameters invariablesare not filled correctly. This causes the system to fail to match corresponding production data, and cannot return valid detection or operation records. - Scenario: The interface call times out, and the failure persists after multiple retries. Cause: The request timeout setting is set to a value lower than 30 seconds. This fails to pull multi-source heterogeneous production data, leading to interruption of critical process data synchronization.
- Scenario: The data pulled by the interface includes redundant environmental protection ledger fields, causing parsing exceptions in downstream accounting systems. Cause: No field filtering rule is configured for
responseData. Full document data is pulled directly, exceeding the field parsing range of downstream systems, and triggering data processing errors.
How to Verify Correct Configuration
- Call the test interface, and check if the returned fields match the preset filtering rules in
responseData. - Simulate network fluctuation scenarios, and verify whether the retry mechanism is triggered and returns data successfully after retries.
- Compare the interface returned data with corresponding records in the local production system, and confirm that the data matching degree meets business requirements.
- Attempt to call the interface with an invalid key, and confirm that the
Authorizationidentity verification interception logic takes effect normally.
The values given are common starting points and should be measured against the reader's own samples.