Abstract
To form a unified configuration and information management platform, FCCMS (financial center configuration management system) will integrate and sort information based on various configuration data and relationships as well as integrate processes and permissions. However, the most serious issue that data centers are currently facing is how to effectively manage these infrastructures. For various infrastructures, the data center currently uses a decentralized operation and maintenance management model. When an infrastructure fails due to inexperienced configuration management, this mode is not conducive to quickly locating and resolving the problem. A detection method of RFCO (random forest algorithm based on clustering optimization) is proposed, and an appropriate tree is selected from RF to integrate, so as to achieve the best effect. In this paper, the target matching algorithm based on FSL (few-shot learning) is deeply studied, and the target detection model is applied to the target matching and positioning task by using the ML method. The performance of the algorithm is tested by experiments on relevant datasets to verify the effectiveness of the algorithm in various scenarios.
Highlights
In China, the business volume of major commercial banks has increased significantly year after year as banking business has become more integrated with local market demand and with the global financial environment
Many excellent algorithms have appeared in the field of computer vision, and these algorithms have made remarkable achievements in the fields of image classification, target location, target detection, etc
DL has begun to rise. e related algorithms based on this have found a breakthrough for FSL [8, 9]
Summary
In China, the business volume of major commercial banks has increased significantly year after year as banking business has become more integrated with local market demand and with the global financial environment. E results of a study [13] comparing and analyzing four financial ratio indicators show that the current ratio and debt ratio have the best early warning effect. E neural network model is used in literature [15] to study enterprise financial crisis early warning and is compared to the multivariate discriminant analysis model. E financial crisis prediction model based on moderate financial indicators and a genetic algorithm is studied in the literature [16], and the results show that this type of model has a high prediction accuracy. Literature [17] compares an SVM-based early warning model to a neural network model and a multivariate discriminant analysis model. By introducing DenseNet, the number of parameters is reduced and the training complexity of target detection network is optimized
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