## 1 Introduction  
Opencast mining is an important method for extracting mineral resources. The production process includes drilling, blasting, mining, loading, transportation, and unloading. During these operations, a significant amount of rock crushing will generate highly concentrated composite dust. The small composite dust particles float in the air for extended periods, posing a threat to workers' health, hastening mechanical wear, diminishing visibility in the workspace, and polluting the ecological environment [1]. Dust pollution is a significant environmental challenge encountered by opencast mines. Therefore, it is crucial to predict the dust concentration in opencast mines and to provide guidance for effective dust prevention and control measures. Dust concentration prediction methods can generally be categorized into four types: qualitative and semi-quantitative predictions, linear regression predictions, machine learning predictions, and combined predictions [2]. Currently, the prediction of dust concentration primarily evolves from traditional methods like life tables, macro measurements, and empirical analogies to more advanced methods such as time series analysis, linear regression, and grey theory. In recent years, this has expanded to include combination methods like artificial neural networks, random forests, support vector machines, and machine learning optimized by biological intelligent algorithms. For example, scholars like [PERSON] have proposed a new method for predicting dust concentration by combining meteorological parameters and production intensity. This method utilizes weather forecast data and mine production intensity data as model inputs, and integrates various machine learning techniques to develop a new model for predicting daily dust concentration in opencast mines. The results indicate that incorporating production intensity significantly enhances the model's performance [3]. [PERSON] et al. introduced and formulated a functional model for predicting dust concentration using a power function algorithm. The model can forecast the distribution state of dust without relying on empirical data and can establish the distribution characteristics of PM10, PM4, and PM2.5 dust particles [4]. [PERSON] et al. focused on monitoring dust concentration in various working areas of the mine, characterizing dust from different sources, and assessing personal dust exposure. AERMOD software ([[https://www.weblakes.com/software/air-dispersion/aermod-view/](https://www.weblakes.com/software/air-dispersion/aermod-view/)]([https://www.weblakes.com/software/air-dispersion/aermod-view/](https://www.weblakes.com/software/air-dispersion/aermod-view/))) was utilized to forecast the dust concentration in various locations within the mine and its surrounding areas [5]. [PERSON] et al. introduced an enhanced machine learning approach to optimize the estimation of dust concentration. The method combines random forest-Markov chain (RF-MC) to establish a model. After applying the Markov algorithm correction, the root mean square error is significantly reduced, and the Pearson correlation coefficient and mean absolute error are also significantly improved [6]. [PERSON] et al. developed a CNN-BiLSTM attention multivariate hybrid model, which incorporates a convolutional neural network (CNN), bidirectional long short-term memory neural network (BiLSTM), and attention mechanism, to predict PM2.5 concentration in the following 24 h. The model fitting effect is good, and the error rate is low [7]. The aforementioned research demonstrates how the existing prediction algorithm is utilized in the context of opencast mining environments. Machine learning, deep learning, and various algorithms discussed in the literature can achieve superior predictive outcomes compared with conventional experimental techniques. However, they also have inherent drawbacks, including a strong reliance on data accuracy, low efficiency, extended timeframes, and a tendency to become stuck in local optima [8].