What This Category’s Data Looks Like
Production ledger data for electric motor and generator manufacturing originates from electromagnetic design databases, copper wire and insulation material batch records, winding equipment, dynamic balancing machines, electrical test benches, and ERP/MES systems. Data updates once per finished product or batch of materials, and syncs whenever a process type changes. Ledger documents include winding process cards, insulation treatment records, dynamic balancing reports, withstand voltage insulation test records, and type and factory test reports. Core fields include winding turns, insulation thickness, dynamic balance offset, test voltage, and temperature rise values. Additional fields include batch numbers, equipment numbers, and test timestamps. Units include turns, millimeters, grams, kilovolts, degrees Celsius, and other standard industrial units.
Constraints for HTTP Interfaces and External Systems
This category’s ledger data draws from multiple dispersed sources and connects to multiple types of industrial equipment and business systems. HTTP interfaces must support multi-source data pulling and field mapping. Data updates frequently, and single batch data volumes are large. Interfaces must accommodate long request processing cycles to avoid data synchronization interruptions from timeouts. Ledger fields have clear industry-specific units and precision requirements. Interface parameters must strictly match field definitions to prevent data parsing errors. Each motor’s ledger must bind a unique batch identifier. Interface calls must carry batch-related variables to enable accurate data association.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Request timeout | 300–600 seconds | Motor test benches handle large data volumes per batch, and must accommodate response delays from industrial equipment |
Authorization | Bearer plus external system authentication token | Standard authentication validates interface legitimacy when connecting to enterprise systems such as ERP/MES |
variables | Include batch number, equipment number, and test type | Matches batch association and field filtering requirements for motor production ledgers |
responseData | Extract winding turns, insulation thickness, dynamic balance offset | Only retain core business fields required for production ledger summary |
| Retry attempts | 2–3 times | Addresses temporary occasional fluctuations in industrial equipment interfaces |
| Loop nesting configuration | Initiate requests in cycles per batch | Adapts to the business logic of generating independent ledger reports for each motor |
Three Common Configuration Errors
- Symptom: An HTTP module call returns a connection error, but Postman can normally retrieve results. Cause: The
Authorizationparameter is not configured correctly, or required fields specified by the interface are not included invariables. - Symptom: Chinese garbled characters appear in ledger data stored in MySQL. Cause: UTF-8 encoding format is not specified during the
responseDataprocessing stage, leading to abnormal parsing of Chinese fields. - Symptom: HTTP requests automatically interrupt after 300 seconds. Cause: The request timeout configuration item is not adjusted. The default timeout duration is insufficient to cover the pulling process for large batch data.
How to Verify Proper Configuration
- Initiate a single HTTP request, and verify that the returned
responseDataincludes core business fields such as winding turns and insulation thickness. - Copy the complete request parameters from Postman, replace them with FastGPT configuration content, initiate a call, and verify consistency of returned results.
- Configure the
variablesparameter to carry the specified batch number, initiate a cyclic call, and verify that multi-batch data generates summary ledgers in sequence. - Adjust the request timeout to 600 seconds, initiate a request for large batch data, and confirm that no automatic interruptions occur.
The values provided here are common starting points, and should be evaluated against local deployment samples.