Experimental Detection Data Collection Based on Intelligent Access Adaptation

Abstract

The real-time collection and processing of experimental and testing data are crucial for ensuring product quality and promoting technological innovation in the fields of industry and scientific research. However, traditional data collection methods suffer from issues such as insufficient standardization, inconsistent interfaces, and delayed data processing, which limit the effective utilization of data. This study investigated a method for collecting experimental detection data based on intelligent access adaptation, aiming to address the aforementioned challenges. The research involved the design of a RESTful API (Representative State Transfer Application Programming Interface) and a unified interface in JSON (JavaScript Object Notation)
format, and the construction of a real-time data stream processing platform based on Apache Kafka. ElasticSearch and Pandas libraries were used to intelligently adjust data formats, and gRPC (Google Remote Procedure Call) protocol and Protobuf (Protocol Buffer) data format was used to optimize data transmission efficiency. In regard to the effectiveness of the proposed method, the experimental group reduced data collection latency by about 28.7% compared to traditional methods, demonstrated by the reduction in the page loading time from 738 milliseconds to 526 milliseconds, and a performance score of 92 points, significantly higher than the control group’s 68 points. In terms of classification accuracy, the experimental group achieved an accuracy rate of over 96% in five data acquisition scenarios: electricity, meteorology, magnetic field, distance, and velocity. The recognition precision of the electricity category reached 0.988. The testing of interface compatibility showed that the experimental group exhibited total compatibility, while the control group had multiple compatibility deficiencies. The system throughput monitoring shows that the throughput of the experimental group is closer to the target value, indicating greater processing efficiency.

Keywords: intelligent access; data collection; real-time processing; experimental testing; Internet of Things; data adaptation

S. Yang, Z. Zhou, W. Gao, Q. Xue, J. Zhang, "Experimental Detection Data Collection Based on Intelligent Access Adaptation", Engineering Intelligent Systems, vol. 34 no. 1, pp. 27-38, 2026.