What data looks like for this category
Data related to electric power marketing comes from grid marketing business systems, smart meter collection terminals, and online and offline customer service ticket systems. Update rhythms fall into three categories: Power load data updates every 15 minutes. Customer profile tags update daily. Marketing campaign touch records are synchronized in real time. Document structures are mostly structured JSON or CSV. Fields include user ID, power usage time slot proportion, marketing touch channel, current electricity price, and arrears status. Units include kW, yuan/kWh, times, and others.
What constraints do these characteristics impose on HTTP interfaces and external systems
Near-real-time updates for power data require HTTP interfaces to support low-latency responses and batch synchronization. This avoids data lag that affects the timeliness of marketing content. Multi-field structures with specific units require input parameter validation to enforce matching unit formats. This prevents data parsing errors. Real-time synchronization requirements for marketing touch records require interfaces to support high-concurrency writes. Interfaces must also adapt to two authentication modes: OAuth2 for grid systems and API key authentication for customer service ticket systems. Compliance requirements for electric power data require carrying user authorization credential fields in request headers. This prevents unauthorized calls.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
HTTP Request Timeout | 10-30 seconds | Adapts to near-real-time requirements of power marketing data. Excessive timeout causes process blocking, while insufficient timeout misses valid responses |
Request Header Configuration | Include Content-Type: application/json and Authorization: Bearer ${token} | Most grid marketing interfaces and customer service systems use JSON format requests and Bearer Token authentication |
Input Parameter Validation Switch | Enabled | Power data fields include units and specific formats. Validation prevents invalid requests and parsing errors |
Batch Request Threshold | 50-100 items per request | Adapts to batch synchronization requirements of power marketing touch records. Excessive threshold exceeds current limiting limits of external interfaces |
Response Field Mapping | Map user ID→userId, power load→powerLoad, touch time→touchTime | Unifies correspondence between internal data format and external interface fields |
Failure Retry Count | 2-3 times | Adapts to temporary fluctuations in grid interfaces. Too many retries increase load on external systems |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: HTTP interface returns
403 Forbiddenstatus code. Postman calls work normally but FastGPT calls fail. Cause: Failed to carry authentication parameters consistent with Postman in FastGPT's request headers, or the case format of request headers does not meet requirements of external systems. - Phenomenon: Input parameters cannot dynamically match user ranges for different marketing campaigns. Fixed input parameters only cover a single scenario. Cause: Failed to configure dynamic input parameter variables, and failed to use FastGPT's context variables or global variables to replace fields such as user ID and activity ID in input parameters.
- Phenomenon: Garbled characters are returned when connecting to database plugins. All configurations have been adjusted but the issue persists. Cause: Failed to specify character set as
utf8mb4in database connection configuration, or FastGPT's database connection parameters were not updated to include the character set setting.
How to confirm configuration is complete
- Call the test interface, check if the returned response body exactly matches the fields and units returned by the external system, and verify that the mapped internal fields meet expectations.
- Simulate batch requests for near-real-time data, check if the interface completes responses within the preset timeout period, with no blocking or timeout errors.
- Adjust dynamic input parameter variables, replace with different user IDs and activity IDs, verify that variables in the request body are correctly replaced.
- Check system logs, confirm that authentication parameters are correctly carried, and no error codes such as
403 Forbiddenor500 Internal Server Errorappear.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.