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The TFAHP-FAHP algorithm model constructed in this article can preferably avoid subjective assumptions caused by human factor. The new energy vehicle power battery is a core component for improving the performance of electric vehicles, and constitutes one of the most valuable parts of the invention patent for new energy vehicles. Patent pledge financing is a brand new form with great potential. However, as a result of the imperfect system of the patent pledge financing, banks and other financial institutions are exposed to an increased risk in the process of the patent pledge financing, which seriously limits the development of the patent pledge financing for power battery. In view of the above issues, this paper uses the SPSS reliability and validity tests in order to develop a relatively complete and accurate index system. TFAHP algorithm model and fuzzy comprehensive are combined to determine the final correction coefficient. In this study, the income method is used to determine the pledge valuation value of a new energy vehicle power battery patent portfolio, and the AHP algorithm method is used to determine the weight of the value of the invention patent portfolio within the patent portfolio. As a result, in this paper, the pledge value of the patent portfolio is multiplied by a proportion of the invention patent portfolio's value, and then multiplied by a correction coefficient to obtain the final new energy vehicle power battery invention patent portfolio pledge value. Using this reference value, technology-based energy companies can pledge financing for new energy vehicle power battery invention patent portfolios. [ABSTRACT FROM AUTHOR]

The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training set, test set and comparison set according to the data characteristics. At last, this paper used the data preprocessing method to verify the LSTM NN algorithm. The experimental results showed that the LSTM NN algorithm analyzed in this paper can effectively improve the generalization ability of financial market trend prediction models while ensuring the prediction accuracy. Through experimental analysis, this paper found that the average accuracy rate of using LSTM NN algorithm was 2.25% higher than that of using traditional NN algorithm. This research is primarily aimed at developing effective methods for predicting stock market trends in the continuously evolving Chinese securities market. The core objective is to empower investors with precise guidance by enabling them to make well-informed investment decisions. Achieving accurate predictions holds the potential to significantly impact economic operations in a positive way. Therefore, this research direction is of paramount importance, offering substantial value both in academic exploration and practical application. [ABSTRACT FROM AUTHOR]

The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters. [ABSTRACT FROM AUTHOR]

With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS trajectory data in lifelogs is typically categorized into continuous and discrete trajectories. Finding a point of interest (POI) from discrete trajectories is a challenging task to do and has caught little attention so far. This paper suggests an LP-DBSCAN model for mining personal trajectories from discrete GPS trajectory data. It makes use of the hierarchical structure information implied in GPS trajectory data and it is suggested a variable-levels, variable-parameters clustering method (LP-DBSCAN) based on the DBSCAN algorithm to increase the precision of finding POI information. Finally, the Liu lifelog dataset is subjected to a systematic evaluation. In terms of GPS data that are not evenly distributed geographically, the experimental results demonstrated that the proposed algorithm could more accurately identify POI information and address the adverse effects caused by the global parameters of the traditional DBSCAN algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a photovoltaic (PV) dynamic maximum power point tracking algorithm based on improved PSO (particle swarm optimization) optimization in response to the problems associated with low tracking accuracy, poor immunity, and the ease of falling into local optimization, as well as the failure of the traditional MPPT algorithm (maximum power point tracking algorithm) under partial shading conditions. Firstly, three traditional MPPT algorithms are compared and analyzed, followed by simulation testing under standard and partial shading conditions. The advantages and disadvantages of three traditional algorithms are analyzed. Secondly, it is proposed that dynamic inertia weights and learning factors be applied synchronously during the optimization process in order to speed up the tracking speed of particle swarm optimization. In order to evaluate the effectiveness of different algorithms, it is best to simulate them under static and dynamic conditions. In comparison to the standard particle swarm algorithm and three other traditional algorithms, the proposed algorithm is capable of tracking the maximum power point quickly and accurately under conditions of uniform illumination and static and dynamic partial shading. There is a faster convergence speed as well as a greater degree of accuracy at steady state. [ABSTRACT FROM AUTHOR]