## 1 Introduction  
With the advancement of computer technology and increased computational capabilities, the field of machine learning has had a profound impact on industry, particularly within the context of Industry 4.0 [1]. Industry 4.0 standards encourage the use of intelligent sensors, devices and machinery, enabling smart factories to continuously collect data relevant to production. By processing the gathered data, machine learning techniques can generate executable intelligence, thereby improving production efficiency without significantly altering the required resources [2]. Traditional machine learning [3] is typically associated with offline machine learning, also known as batch machine learning. It involves training on historical data to obtain machine learning models. To update the model, it is necessary to retrain it on a new dataset. In contrast, online machine learning refers to the process of continuously training and dynamically updating models as data stream in. This approach allows models to track changes in data in real time, leading to improved predictive performance [4]. In the era of Industry 4.0, the level of automation and informatization of equipment is exceptionally high. Devices are equipped with sensors that enable real-time monitoring of equipment parameters, with the collected data being sent back to monitoring databases, resulting in a continuous stream of data. In the context of streaming data [5], online machine learning addresses the limitation of offline machine learning, which cannot update models in real time. This capability can significantly enhance the accuracy of model predictions. Consequently, online machine learning exhibits unique advantages in industrial applications. Currently, while there is some research in the field of online machine learning, its application in the industrial domain remains relatively limited. The primary challenges [6] include:  
1. Computational efficiency of algorithms: Currently, there are numerous online incremental algorithms, such as online decision tree models and incremental clustering algorithms. These algorithms require setting hyperparameters based on different streaming data. In contrast, online support vector regression models do not necessitate this prior knowledge. [PERSON] et al. proposed accurate online support vector regression, also abbreviated as AONSVR [7]. However, this method exhibits high computational complexity, making it unsuitable for meeting the rapid computation requirements of industrial applications. Subsequent researchers have made various improvements to AONSVR [8; 9; 10], but their primary focus has been on enhancing predictive accuracy, without in-depth investigation into the computational performance of the model. The ORSVR approach introduced by [PERSON] has improved computational performance but obtains a lower predictive accuracy than AONSVR because of its robustness. Therefore, it is essential to enhance computational speed while maintaining predictive accuracy. 2. Concept drift detection [11]: Concept drift is a phenomenon in which the statistical properties of a target domain change over time in an arbitrary way [12]. In online machine learning, where the model is trained on each streaming data point, the model tends to update in the wrong direction gradually when data undergo concept drift. As a result, it is crucial to monitor the streaming data. Concept drift detection algorithms perform real-time monitoring of the data distribution in streaming data. When drift is detected, that portion of data is discarded and it is not used for model training. The system can then issue an alert for human intervention or other necessary actions to address the concept drift. To overcome the challenges outlined above, this paper introduces an online processing parameter prediction and design framework that integrates online machine learning, concept drift detection and Bayesian optimization. The primary contributions of this paper are as follows:  
1.