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With the rapid development of social economy in recent years, people’s living standards are also improving. The use of automobiles is becoming increasingly frequent, and people’s requirements for the safety, comfort, and energy saving of automobiles are also getting higher. This paper mainly studies the thermal straightening control system after the rolling of variable-section automotive leaf springs through edge computing based on the Internet of Things. This paper presents the basic concepts of IoT edge computing and the role they play in various aspects. The percentage of IoT development trends in 2011 was 6.7%. By 2020, the development trend percentage of IoT reached 68%, an increase of 61.3%. It can be seen that the development of the Internet of Things is very rapid. It can be seen that the straightening accuracy of the thermal straightening control system based on edge computing after the rolling of variable-section automotive leaf springs reaches 78%, and it is 29% higher than the traditional system straightening accuracy, which is only 49%. The safety of the thermal straightening control system of the variable-section automotive leaf spring after rolling based on edge computing reaches 95%, which is 33% higher than the safety of the traditional system. The thermal alignment control system for variable-section automotive leaf springs after rolling based on the edge computing of the Internet of Things is not only safer than the traditional system but also much higher in comfort and alignment accuracy than the traditional system. It can be seen that the thermal straightening control system for variable cross section automotive leaf springs after rolling based on IoT edge computing is more conducive to the development of the automotive industry.

For the pattern recognition problem, this paper proposes a feature selection method based on complementarity analysis. Analyse the separability of single feature, search the feature combination with the smallest probability of mixing region and in the mixed region with the greatest separability to reduce the probability of classification error. Compared with other feature selection algorithms, data testing result shows that the feature selection method based on complementarity analysis has a lower error recognition rate than other methods, which has verified the superiority and the advanced nature of the method.

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