Application of Intelligent Automation Technology in Instrumentation and Measurement

Abstract: The application information of intelligent automation technology in instrumentation and measurement is provided by excellent flowmeter and flowmeter manufacturers. The application of intelligent automation technology is fully infiltrating the instrumentation industry. 1. Application in the improvement of instrument structure and performance First, intelligent automation technology has opened up broad prospects for the application of instrumentation and measurement related fields. Use wisdom. For more flowmeter manufacturers to select models and price quotations, you are welcome to inquire. The following are the details of the application of intelligent automation technology in instrumentation and measurement. The application of intelligent automation technology is fully infiltrating the instrumentation industry. 1. Application in the improvement of instrument structure and performance First of all, intelligent automation technology has opened up broad prospects for the application of instrumentation and measurement related fields. Using intelligent software and hardware, each instrument or meter can accurately analyze and process current and previous data information at any time, and appropriately abstract the measurement process from low, medium and high levels to improve the existing measurement system. It expands the functions of traditional measurement systems, such as the use of intelligent technologies such as neural networks, genetic algorithms, evolutionary computing, and chaotic control, to enable instruments to achieve high-speed, high-efficiency, multi-function, and high mobility and flexibility. Secondly, micro-chip technologies such as microprocessors and microcontrollers can also be used in different instruments and meters of a decentralized system to design fuzzy control programs, set critical values ​​of various measurement data, and use fuzzy inference technology of fuzzy rules to understand the behavior of things. Various fuzzy relationships make various types of fuzzy decisions. The advantage is that there is no need to establish a mathematical model of the controlled object, nor a large amount of test data. It is only necessary to summarize the appropriate control rules based on experience, apply the off-line calculation of the chip, on-site debugging, and generate accurate according to our needs and accuracy. Analysis and punctual control actions. Especially in sensor measurement, the application of intelligent automation technology is more extensive. Using software to realize signal filtering, such as fast Fourier transform, short-time Fourier transform, wavelet transform and other technologies, is an effective way to simplify the hardware, improve the signal-to-noise ratio, and improve the dynamic characteristics of the sensor, but it needs to determine the dynamic mathematical model of the sensor, and high-order The real-time performance of the filter is poor. Using neural network technology, high-performance autocorrelation filtering and adaptive filtering can be realized. Make full use of the powerful self-learning, self-adapting, self-organizing capabilities of artificial neural network technology, associative, memory functions, and black-box mapping characteristics between input and output of nonlinear complex relationships, regardless of applicability and fast real-time performance. Both will greatly exceed the complex functional formula, and can make full use of multi-sensor resources to comprehensively obtain more accurate and credible conclusions. Among them, real-time and non-real-time, fast-changing and slow-changing, fuzzy and deterministic data information may support each other or may contradict each other. will be difficult. So neural network or fuzzy logic will be the most worthy method of choice. For example, gas sensing arrays are used for mixed gas identification. In the signal processing method, a combination of self-organizing mapping network and BP network can be used to first classify and then identify components, and convert the whole-process fitting of the traditional method into segmental fitting. It can reduce the complexity of the algorithm and improve the recognition rate. For another example, the difficulty of detecting and identifying food taste signals was once a major obstacle for research and development units. Now, wavelet transform can be used for data compression and feature extraction, and then the data is input into the fuzzy neural network trained by genetic algorithm, which greatly improves the recognition rate of simple and complex flavors. For another example, in the evaluation of the quality of fabrics, the processing of tactile signals by flexible operators, the fault diagnosis of machines, and intelligent automation technology have also achieved a large number of successful examples. 2. The combination of applied instruments and measurement technology and computer technology in the structural design of virtual instruments has not only greatly improved the measurement accuracy and the level of intelligent automation, especially the rapid development of computer hardware softening and software modularization of virtual instruments, and Its unification and optimized performance configuration with the networked system resource program have created more and more favorable conditions for the rapid improvement of the intelligent level of instruments.

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