The current situation of extensive ventilation management for the fully mechanized heading face cannot dynamically control air velocity and make reasonable dust migration distribution, resulting in serious disaster of dust and environmental pollution in the roadway. In this paper, the fluid mechanics, finite element numerical simulation, and underground measurement were combined to analyze the distribution of dust concentration under the variable airflow state at the duct outlet to obtain the massive correlation data of duct outlet parameters and dust concentration. For the pedestrian breathing-height in the backflow side and driver location, the double-objective BP prediction model for dust concentration under airflow adjustment was established, and the performance parameters and prediction accuracy of the BP prediction model were analyzed by using the relative error and fitting analysis. In Ningtiaota coal mine, located in Northern Shaanxi province of China, the self-developed control device is installed on the duct outlet with underground test and application verification to refine the model. The results indicated the dynamic control of airflow optimizes dust concentration distribution. The dust concentration at the pedestrian breathing-height in the backflow side and driver location was significantly decreased after the installation of adjustment device. Dust concentration at the pedestrian breathing-height and driver location was decreased by 31% and 34%, respectively, compared with the results before adjustment, which achieved the safe, environment-friendly, and energy-saving ventilation and the dust removal function in fully mechanized heading face.
Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation.
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