Firstly, intelligent automation technology has opened up broad prospects for the application of instruments and measurement in related fields. By utilizing intelligent software and hardware, each instrument or device can accurately analyze and process current and previous data information at any time, abstracting the measurement process appropriately from different levels of low, medium, and high, in order to improve the performance and efficiency of existing measurement systems and expand the functions of traditional measurement systems. For example, by using intelligent technologies such as neural networks, genetic algorithms, evolutionary computing, and chaos control, the instrument or device can achieve high-speed, efficient, multifunctional, and highly flexible performance.

Secondly, microchip technologies such as microprocessors and microcontrollers can also be used in different instruments and meters of decentralized systems to design fuzzy control programs, set critical values for various measurement data, and use fuzzy reasoning techniques based on fuzzy rules to make various types of fuzzy decisions on various fuzzy relationships of things. Its advantage lies in the fact that there is no need to establish a mathematical model of the controlled object, nor does it require a large amount of test data. It only needs to summarize appropriate control rules based on experience, apply offline calculations and on-site debugging of the chip, and generate accurate analysis and timely control actions according to our needs and accuracy.

The Application Prospects of Intelligent Automation Technology in Instrumentation and Measurement

Especially in sensor measurement, the application of intelligent automation technology is more widespread. Implementing signal filtering using software, such as Fast Fourier Transform, Short Time Fourier Transform, Wavelet Transform, etc., is an effective way to simplify hardware, improve signal-to-noise ratio, and improve the dynamic characteristics of sensors. However, it is necessary to determine the dynamic mathematical model of the sensor, and the real-time performance of high-order filters is poor. By using neural network technology, high-performance autocorrelation filtering and adaptive filtering can be achieved. By fully utilizing the strong self-learning, adaptive, and self-organizing abilities of artificial neural network technology, as well as its association and memory functions, as well as the black box mapping characteristics between inputs and outputs of nonlinear and complex relationships, it will greatly surpass complex function expressions in terms of applicability and fast real-time performance. It can fully utilize multi-sensor resources to comprehensively obtain more accurate and credible conclusions. Real time and non real time, fast and slow changing, fuzzy and deterministic data information may support or contradict each other. At this time, the extraction and fusion of object features until the final decision, making correct judgments, will become a challenge. So neural networks or fuzzy logic will become the most worthwhile methods to choose from. For example, gas sensing arrays can be used for mixed gas recognition. In signal processing methods, a combination of self-organizing mapping networks and BP networks can be used to first classify and then identify components, transforming the traditional method of full fitting into segmented fitting to reduce algorithm complexity and improve recognition rate. For example, the difficulty of detecting and recognizing food taste signals was once a major obstacle for research and development units. Nowadays, wavelet transform can be used for data compression and feature extraction, and then the data can be input into fuzzy neural networks trained with genetic algorithms, greatly improving the recognition rate of simple compound flavors. For example, in the evaluation of fabric quality, the processing of tactile signals by flexible robots, machine fault diagnosis, and intelligent automation technology, a large number of successful examples have also been achieved.