This document is written for FastGPT v4.17.0.
What the data for this category looks like
Data for this category primarily comes from ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, customer drawings and technical requirements. Daily generated documents include foundry process cards, batching lists, heat records, mold histories, first article inspection records, material test reports, and non-conforming product review forms.
Data update frequency is per heat, per batch, or daily. Fields include heat number, alloy batching values, furnace temperature readings, mold cycle identifiers, defect types, and more. Most entries are structured data directly uploaded by equipment or manually entered, with no unified bulk export format.
Constraints imposed on HTTP interfaces and external systems
Data for this category is multi-source, heterogeneous, and its update frequency fluctuates with production tempo. This creates multiple constraints for HTTP interface and external system integration.
First, different data sources such as melting furnaces, MES, and ERP have varying field formats. Interfaces must support custom field mapping to match the data structures of local systems.
Second, the high-frequency update requirement per heat or batch means interface call timeout settings must adapt to short-cycle business rhythms, to avoid blocking production processes.
In addition, data must be associated with unique identifiers such as heat and mold cycle. Interfaces must support carrying these identifier parameters to complete accurate queries and synchronization. This ensures inspection records correspond one-to-one with production links, facilitating subsequent non-conforming product traceability.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {pre-configured API access key} | Follows general API authentication standards to secure cross-system integration |
variables | Pass business unique identifiers such as heat number, mold cycle number | Matches the characteristic of this category's data being linked to production links, enabling accurate data synchronization |
responseData | Return structured batching data, furnace temperature readings, defect records | Adapts to the core business requirements of foundry inspection and audit, making it easy for external systems to parse directly |
| Single call timeout | 10–30 seconds | Adapts to the production tempo per heat, avoiding production process blocking due to network fluctuations |
| Metadata field mapping rules | Map heat ID, alloy ratio, and similar fields according to external system field names | Compatibility with format differences across data sources, reducing data conversion costs |
Three Common Mistakes
- Phenomenon: The interface returns a
401 Unauthorizedstatus code, and heat data cannot be pulled. Cause: TheAuthorizationparameter is not configured correctly, or an expired API key is used. - Phenomenon: Synchronized batching data does not match actual heat records, with some fields empty or values deviated. Cause: The correct heat number is not passed via
variables, causing the interface to return data for the wrong batch. - Phenomenon: Interface calls time out, and production processes are blocked. Cause: The single call timeout setting is too long, not adapted to the short-tempo rhythm of foundry production, or there is network link delay.
How to Confirm Proper Configuration
- Call the test interface, check if the returned
responseDataincludes preset business fields such as heat number and alloy batching values. - Pass the specified
variablesparameter, confirm that the interface only returns inspection data for the corresponding heat, with no redundant or incorrect entries. - Simulate high-frequency calls, verify that the interface's response within a short period meets expectations, with no timeouts or errors.
- Check the
Authorizationparameter configuration, confirm that a valid API access key is used, with no format errors.
The values provided here are common starting points and should be evaluated against the reader's own samples.