What the data for this category looks like
Internal data for urban water utilities comes from municipal pipe network monitoring nodes, water plant automation systems, drainage pump station data platforms, and internal inspection work order systems. Data update rhythms fall into two categories:
- Equipment operation data is pushed every 10 to 60 seconds.
- Inspection work order and employee scheduling data is synced in batches every early morning.
Data documents use structured JSON format, with fixed fields including pump station number, real-time water level (unit: meter), pipe network pressure (unit: megapascal), water turbidity (unit: NTU), equipment operation status, work order number, processing progress, and other fixed fields. No additional dynamic fields are included. Field types and value ranges are predefined by the water utility system.
What constraints these characteristics impose on HTTP interfaces and external systems
The low-latency push requirement for real-time equipment data means interface response times must stay within a reasonable range. This avoids scheduling delays caused by data backlogs. The fixed field structure requires strict matching of request and response parameter formats. Fields cannot be added or removed arbitrarily, otherwise downstream systems will fail to parse the data. The large volume of batch sync data for inspection work orders requires pagination parameters to avoid overloading single requests. In addition, external interfaces of water utility systems usually require identity authentication. The Authorization parameter must be set correctly, otherwise normal calls cannot be made. The units of fields returned by the interface must align with internal business systems, otherwise data anomalies will occur.
How to Set Configurations
| Config Item | Suggested Value | Rationale |
|---|---|---|
Authorization | Bearer {API key provided by the water utility system} | External interfaces of water utility systems typically use Bearer token authentication to verify call legitimacy, and comply with the authentication configuration specifications of FastGPT v4.17.0 |
variables | {"stationId": "001", "dataType": "realTime"} | Used to pass fixed query parameters to the water utility system interface, limit the query scope and data type, and adapt to the fixed structure of the request format |
responseData | Return only specified core fields | Filter redundant non-business data, reduce HTTP transmission load, and improve interface call efficiency |
| Single request data limit | First 100 entries | Balance the transmission efficiency of batch work order data and interface load, avoid overloading single requests |
| Interface timeout period | 30-60 seconds | Adapt to the low-latency requirements of real-time data interfaces. Batch interfaces can be appropriately extended to 60 seconds to avoid request failures caused by network fluctuations |
Three Common Mistakes
- Symptom: A
messages is emptyerror is returned when calling the water utility interface. Cause: Necessary query parameters are not configured invariables, causing the interface to fail to identify the request scope and return empty data. - Symptom: FastGPT prompts that the model stream response is empty. Cause: The
Authorizationparameter is not set correctly. The water utility interface returns an empty response body after authentication failure, which FastGPT identifies as an invalid stream. - Symptom: The number of data entries returned by the batch work order interface does not match expectations. Cause: Pagination parameters are not configured. The interface only returns the default number of entries, and does not pull full content.
How to Verify Correct Configuration
- Call the test interface, check if the response body contains the core fields of the water utility system, such as pump station number, real-time water level, etc., to confirm that the data format matches expectations.
- Check if the format of the
Authorizationparameter is correct. Use an interface debugging tool to verify that authentication passes, with no 401 or 403 errors. - Simulate a batch request to confirm that the number of returned data entries matches the configured single request limit, with no truncation or overflow.
- Check the FastGPT running logs to confirm that the parameters of the interface request match the configuration, with no missing or formatting errors.
The values provided here are common starting points, and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.