The purpose is to improve the financing capability of innovative enterprises in the context of big-intelligence mobile cloud technology. The deep learning (DL) technology is applied to the analysis of enterprise financing capacity. Firstly, the problems of enterprise development are introduced against the background of big-intelligence mobile cloud along with the current situation of innovative enterprise financing and DL neural network (NN), as well as the performance advantages of DL algorithms. Secondly, the financing capability of innovative enterprises is evaluated based on the backpropagation (BP) NN algorithm. Finally, the performance of the backpropagation neural network (BPNN) algorithm is empirically analyzed, and the weight of the evaluation index of innovative enterprise financing capability is calculated. The experimental results show that the algorithm of the deep neural network (DNN) contains more hidden layers and abundant features than a shallow NN, and the feature extraction performance is better. Under the analysis of enterprise financing capability, the evaluation index of innovative enterprise financing capability is established, and the enterprise data are normalized by the maximum and minimum method, thereby facilitating the calculation of the BPNN algorithm. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meanwhile, the error of the sample test is analyzed, finding that the evaluation error of the BPNN algorithm is less than 1.8%, which has high accuracy. Therefore, the BPNN algorithm can effectively evaluate the financing capability of innovative enterprises and can adapt to the financing capability evaluation of an enterprise's actual business under the background of big-intelligence mobile cloud. The results provide a reference for the development of enterprises under the background of big-intelligence mobile cloud.
As the structural carrier of mineral resources, underground mine is a typical artificial large layered underground infrastructure. The safety of mining systems remains a critical concern for nations worldwide. Based on the environmental characteristics of underground mines, the accompanying safety issues are evident. Conventional personnel evacuation drills for mine disasters often fail to create effective disaster evolution memories for people. When a real accident occurs, people cannot escape efficiently in a panic state, which reduces survival probability. To solve this problem, an escape space connection algorithm is developed based on the physical information and management rules in this study, and it is used to drive the extended reality escape system by the game engine. Firstly, this study takes the water-inrush accidents of underground layered mines as the engineering research object and background, the characteristics of water-inrush accidents evolution and personnel evacuation are systematically analyzed based on the scenario construction theory. Secondly, this study develops an escape space connection algorithm by integrating the two-dimensional A* algorithm and the connection weights of escape spaces based on the spatial geometric information and escape strategy of layered mines. Thirdly, a distributed extended reality (XR) human-computer interaction system is developed for escape path guidance in real environments based on the spatial structure characteristics of layered mines and the escape space connection algorithm. Finally, application testing is conducted in the experimental mine to analyze the system performance and future application potential. This study provides a comprehensive technical framework for personnel evacuation in layered underground infrastructure during evolutionary accidents, and the theories and systems involved are universal. In addition, this method can be used as a new, low-cost and efficient digital reference system for personnel safety emergency drills in underground infrastructure.
HVAC (Heating, Ventilation and Air Conditioning) system in buildings is a major component of energy consumption, and realizing high-precision energy consumption prediction is of great significance for intelligent building management. Aiming at the problems of insufficient modeling ability of nonlinear features and insufficient portrayal of long time-series dependencies in prediction methods, this paper proposes an HVAC energy consumption prediction model that combines time-sequence convolutional network (TCN), bi-directional gated recurrent unit (BiGRU), and Attention mechanism. The model takes advantage of TCN’s parallel computing and multi-scale feature extraction, BiGRU’s bidirectional temporal dependency modeling, and Attention’s weight assignment of key features to effectively improve the prediction accuracy. In this work, the HVAC load is represented by the building-level electricity meter readings of office buildings equipped with centralized, electrically driven heating, ventilation, and air-conditioning systems. Therefore, the proposed method is mainly applicable to building-level HVAC energy consumption prediction scenarios where aggregated hourly electricity or cooling energy measurements are available, rather than to the control of individual terminal units. The experimental results show that the model in this paper achieves better performance compared to the method on ASHRAE dataset, the proposed model outperforms the baseline by 2.3%, 22.2%, and 34.7% in terms of MAE, RMSE, and MAPE, respectively, on the one-year time-by-time data of the office building, and meanwhile it is significant 54.1% on the MSE metrics.
