What the data for this category looks like
Data sources for this category include DCS/APC control systems, laboratory information management system (LIMS), tank farm metering systems, crude oil evaluation databases, equipment management systems, and energy management systems. Routine documents cover operating procedures, production plans, material balance reports, distillate outlet quality reports, interlock ledgers, shift handover records, and change management documents. Data update frequencies fall into three categories: real-time, per shift, and daily. Fields include process parameters, laboratory test indicators, material inventory, equipment status, and more. Units include degrees Celsius, ppm, cubic meters, kilograms per cubic meter, and more.
What constraints do these characteristics impose on HTTP interfaces and external systems
Multiple heterogeneous data sources and varied update cycles require HTTP interfaces to support data pulling across three cycles: real-time, per shift, and daily. The interfaces must also adapt to authentication rules of different external systems. Diverse document structures and field units require interfaces to return standardized structured responseData to enable unified parsing. The logistics coupling characteristics of refining units require interfaces to support batch data transmission, avoiding delay accumulation from single-item calls. High data correlation means interface requests must carry corresponding context parameters to ensure returned results match production scenarios. Reasonable timeout and retry mechanisms must also be configured to avoid disrupting continuous production processes.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer ${custom API_KEY} | Follow general external system authentication specifications to ensure the legitimacy of interface calls |
variables | {"pullCycle": "real-time", "targetModule": "DCS"} | Adapt to the refining business requirement to pull control system data in real time |
responseData | Map and categorize by process parameters, laboratory test indicators, and material metering | Enable unified parsing of heterogeneous external system return content |
| Timeout period | 600 seconds | Adapt to data pulling delay requirements in continuous production scenarios, avoid process interruptions |
| Batch request limit | First 50 entries | Balance data transmission efficiency and interface load, match the scale of single-batch refining data |
| Retry count | 3 times | Address temporary network fluctuations, ensure successful pulling of critical production data |
This document was written based on FastGPT v4.17.0.
Three Common Mistakes
- The interface returns a
401 Unauthorizedstatus code, and logs show authentication failure. The cause is incorrect configuration of theAuthorizationparameter, or the API_KEY permission scope does not cover the target external system. - The interface pulls data with empty fields that do not match actual production data. The cause is that the data source or update cycle specified in the
variablesparameter does not match the target system, failing to adapt to the shift-based update rhythm of the refining business. - After container deployment, the interface listening port does not match the configuration, and it always starts with the default port. The cause is incorrect mapping between container ports and host ports, or the port parameter in the configuration file was not loaded correctly, preventing external systems from calling the interface.
How to Confirm Correct Configuration
- Call the test interface, check if the returned
responseDataincludes expected process parameters, laboratory test indicators, and material metering fields. - View external system logs to confirm that the
Authorizationauthentication information was correctly identified. - Observe the data pulling cycle to confirm it matches the pullCycle parameter configured in
variables. - Simulate network fluctuation scenarios to check if the interface automatically retries requests according to the configured retry count.
The values provided are common starting points and should be measured against the reader's own samples.