Narrow Results

This work focuses on an intelligent management framework for vocal music media resources, with use of deep neural networks and web crawlers. The web driver module is used to simulate the Google browser access mode to collect and preprocess the data of the China vocal music network webpage project, and the delay code is added to solve the problem of Internet Protocol (IP) restrictions on crawling webpages. After parsing, the content of the project description is obtained and temporarily stored in the local file through the panda’s module to realize the automatic collection of project data. Membership functions of four basic types of emotions are designed. By utilizing the analytic hierarchy process, the experience and wisdom of many music experts are gathered. The weights of activity and the evoking force in determining the music emotion membership value are discussed, trying to make the membership function closer to reality. Through the application of the music emotion analysis model, the music emotion analysis technology is combined with the recommendation algorithm, and the music recommendation algorithm combined with the emotion analysis is realized. The experimental test proves that the algorithm has higher accuracy than the traditional music recommendation algorithm.

Nowadays, intelligent industrial aquaculture management has been a more universal demand in digital society. Deep learning-based vision computing technique can provide much potential for such applications. As a result, this paper proposes a deep vision computation-driven realtime multitarget tracking approach for this purpose. It is a two-stage method, in which object detection is used in the first stage and target tracking is used in the second stage. First, the improved YOLOv7 model is utilized to train and identify the preprocessed data set to achieve accurate detection of fish targets. Then, a tracking algorithm, named SORT, is utilized to conduct an in-depth analysis of fish images to achieve continuous tracking of fish targets. Thus, further management affairs can be realized upon the basis of such conditions. Experimental results show that the improved YOLOv7 model achieves a high accuracy of 92% on the target detection task, and the Sort algorithm maintains a high degree of tracking accuracy and low tracking errors between consecutive frames. By combining these two methods, the daily behavior of fishes can be accurately detected and tracked in real time. In addition, the research also explores how to combine tracking results with breeding decisions to promote the development of breeding management in an intelligent direction.

This paper explores future energy systems primarily powered by innovative floating reactors. These reactors exhibit high potential, particularly for supplying power to remote areas beyond the reach of traditional power grids. In this study, a novel artificial intelligence (AI) system is introduced to optimize the management of floating reactors in remote, distributed environments, where the reactors must move between different sites and energize their local grids. This research marks the first application of AI in providing management decisions for those reactor types in such distributed assembled environments. Specifically, an intelligent management system has been developed to coordinate the reactor’s operations across various sites. The proposed system leverages fuzzy logic tools, which enable it to account for multiple uncertain factors when making management decisions. The system’s performance is tested through simulated scenarios and benchmarked against a round-robin fixed time method. The results demonstrate the system’s efficiency in delivering both economic and operational benefits.

In order to improve the effect of engineering cost accounting, this paper applies the Internet of Things technology to the engineering cost accounting system, and combines the chaotic data processing method with the Internet of Things technology. Moreover, this paper uses the Internet of Things technology to construct the engineering cost accounting system, uses the Internet of Things technology to collect various data in the real-time process of the project, and builds the engineering cost accounting system based on the actual situation. In addition, this paper combines improved intelligent algorithms to improve system performance to enable the system to collect data, manage data, process data, transmit data, and output data. It can be seen from the research results that the project cost evaluation system constructed in this paper is rated above good, which is higher than the existing project cost evaluation methods. The experimental research shows that the project cost accounting system based on the Internet of Things technology proposed in this paper has a good engineering data processing effect.