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Research and Application of Intelligent Universal WMS System Research on Inventory Classification Based on K-means clustering algorithm and ABC Classification Method 8

Research and Application of Intelligent Universal WMS System...

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8th International Conference on Information Systems Engineering (ICISE)

作者： Yang, Tao Yan, Hairong Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9798400709173

WMS(Warehouse Management System) is an important tool for warehouse management, which enables the management, control and optimization of inventory products and orders through information technology. In order to improve the flexibility and ease of use of the WMS system, and to be able to apply to many types of warehouses and a variety of different industry operating scenarios, this paper designs a generalized WMS system to better meet the different needs of customers for warehouse management. Through actual research and demand analysis, we designed the core functions of inventory management, purchasing management, sales management, financial management, etc. The architecture and technology of the whole system adopts the current popular programming language and system framework. In addition, in order to improve the efficiency of the warehousing process, the WMS system introduced in this paper also combines the ABC classification method of K-means clustering algorithm, the WMS system of inventory and orders, etc. for a more reasonable classification, to achieve intelligent management of the WMS system, to improve the autonomy and self-adaptability of the warehouse.

关键词： WMS(Warehouse Management System) warehouse management intelligent management clustering algorithm K-means algorithm

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Design Student Management System Based on Vector Influential clustering algorithm Analysis

Design Student Management System Based on Vector Influential...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Zhang, Ying Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350315455

With the gradual development and maturity of emerging technologies such as Internet technology and big data mining (DM), the degree of campus networking is getting higher and higher. In the era of rapid development of information technology, the Internet has become a very important and meaningful information channel in people's lives and studies. The student management system (SAS) is an indispensable part of the school education system management and service work. The college SAS is a comprehensive system with distinctive features that fully reflects the school's management level and teaching quality. With the continuous advancement of education reform, computer-based, through the use of vector influence (VI) clustering technology to classify the information of college students, and divide the corresponding target data set based on the corresponding user category in the rule database. Therefore, based on the analysis of the VI clustering algorithm (CA), this article conducts research on the SAS and its related content. This article adopts experimental analysis method and data analysis method, which intends to use experiments to analyze the clustering effect and performance of the SAS based on the VI CA. As indicated by the trial results, the base mistake inside the class of the framework for these four levels is 0.12, 0.17, 0.21 and 0.48, separately. The framework has generally little mistakes in the examination of understudy execution, and the bunching impact is somewhat great, which can address the issues of understudies the essential necessities of execution investigation. © 2023 IEEE.

关键词： Big data mining clustering algorithm student management system student performance vector influence

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Application of the K-medians clustering algorithm for Test Analysis in E-learning 6th

Application of the K-medians Clustering Algorithm for Test A...

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6th Computational Methods in Systems and Software, CoMeSySo 2022

作者： Aljarbouh, Ayman Tsarev, Roman Robles, Adrián Segura Elkin, Sergei Gogoleva, Irina Nikolaeva, Irina Varyan, Ivetta University of Central Asia Naryn Kyrgyzstan Moscow Russia V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia International Academy of Science and Technologies Moscow Russia University of Granada Ceuta Spain PT Taplink Digital Global Kuta Indonesia Institute of Economics of the Ural Branch Russian Academy of Sciences Yekaterinburg Russia Yakut State Agricultural Academy Yakutsk Russia M.K. Ammosov North-Eastern Federal University 58 Belinsky Street Yakutsk677027 Russia N.M. Emanuel Institute of Biochemical Physics Russian Academy of Sciences Moscow Russia Plekhanov Russian University of Economics Moscow Russia

ISBN: (纸本)9783031214349

E-education allows significantly automating the learning process. One of the key elements of e-learning is testing. The use of tests allows an objective and unbiased assessment of student knowledge. However, this automated approach requires high-quality tests that are relevant to the theoretical material and practical skills acquired by the student while taking an e-learning course. This paper proposes the use of the K-medians clustering algorithm, which makes it possible to divide the test questions into separate clusters in order to analyze them and further improve the tests, and possibly the entire e-course as a whole. The paper presents fragments of program code that can be used by other researchers in solving clustering problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： clustering algorithm clustering analysis E-learning K-medians clustering Manhattan distance

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E-Commerce Precision Marketing and Consumer Behavior Models Based on IoT clustering algorithm

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JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2022年第5期24卷 1-21页

作者： Guo, Shujuan Zhai, Rongbing Anhui Xinhua Univ Business Sch Hefei Peoples R China

This article aims to study e-commerce precision models and consumer behavior models based on clustering algorithms, and at the same time conduct detailed research on the Gaussian mixture distribution algorithm, consumer behavior and model construction, and precision marketing strategies in the clustering algorithm. First, a lot of analysis and demonstration of precision marketing strategies and the construction of consumer behavior models are carried out, and then the clustering algorithm -based electronic some experiments were carried out on the application of commercial precision marketing methods and consumer behavior models. The experimental results show that the precision marketing method using the clustering algorithm is more in line with the development of modern e-commerce. The application of the algorithm in the precision marketing methods of enterprises and consumer behavior models has promoted the vigorous development of enterprises, making the sales volume of enterprises reach 9.8%.

关键词： Behavior Model clustering algorithm E-Commerce Precision Marketing Methods

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B2C E-Commerce Logistics Network Optimization Based on Constrained clustering algorithm 4

B2C E-Commerce Logistics Network Optimization Based on Const...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Xiaoliang, Xiong Wuhan Technologe and Business University Hubei 430065 China

ISBN: (纸本)9798350305258

With the improvement of residents' consumption level, attention to quality has increasingly become an important reference standard for consumers to choose e-commerce, making e-commerce gradually shift from "price war" to "service war". The research on B2C e-commerce logistics network optimization based on constrained clustering algorithm is a research work in the field of information technology and engineering. The main goal of this research is to develop a new algorithm to optimize the performance of B2C e-commerce logistics network. This research also focuses on developing an effective method to identify the constraints that affect the performance of B2C e-commerce logistics network. In order to optimize the performance of B2C e-commerce logistics network, constraint clustering method is used to identify all constraints that affect the performance of B. In B2C e-commerce logistics network optimization, constrained clustering algorithms can also optimize delivery paths. By preset constraints such as starting point, ending point, and necessary passing point, constrained clustering algorithms can optimize delivery routes, reduce transportation time and costs while ensuring the same quality of consumer service. In addition, constrained clustering algorithms can also predict logistics demand, formulate more reasonable logistics plans, and dynamically adjust according to actual situations. © 2023 IEEE.

关键词： B2C e-commerce clustering algorithm Logistics network

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Research on Fast and Corresponding Multi-Stream Data Management System of Power Pretraining Based on Central Weighted Data Stream clustering algorithm 3

Research on Fast and Corresponding Multi-Stream Data Managem...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Liu, Han Chen, Xia Guo, Qing Yu, Jiangbin Liu, Xiaofei State Grid Shandong Electric Power Company Beijing China State Grid Information and Communication Industry Group Co. Ltd Jinan China

ISBN: (纸本)9798350341546

To solve the anomaly detection problem of power big data stream, this paper combines the clustering algorithm of power big data stream with power big data stream. In view of the existing stream data clustering algorithm is not easy to store all the data, power data easy to lose and other problems and the stream data clustering algorithm for offline stage clustering algorithm real-time response requirements from the data integrity, security and low time complexity of the stream data clustering algorithm to improve the CLU Stream data clustering algorithm. At the same time, a central-weighted data stream clustering algorithm is proposed. In the online phase, Redis cluster is used to buffer stream data, and node time attenuation strategy is designed. Increases the percentage of valid heartbeat messages. The offline stage clustering algorithm is optimized. The optimal distance method is used to determine the initial clustering center and reduce the number of iterations. Finally, the proposed central-weighted data stream clustering algorithm is used to detect the abnormal behavior of users. The experimental results show that the algorithm can detect the abnormal behavior of users well. © 2023 IEEE.

关键词： Center weighting clustering algorithm multi-stream data management system power data

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Design of Multi-source Big Data Cross Source Scheduling System Based on Optimal K-means clustering algorithm

Design of Multi-source Big Data Cross Source Scheduling Syst...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Gong, Lili Zhang, Guang Sun, Xiang Liaoning Vocational College of Economics Shenyang China No.12 Middle School of Wuhan Hubei China

ISBN: (纸本)9798350313413

Traditional big data scheduling methods cannot effectively integrate massive data in the network, which easily leads to 'rigid' operation of the system. In response to the above issues, this project intends to study a multi-source collaborative scheduling method for multi-source heterogeneous big data. K-average connections store data in binary format, and mine the index nodes of the data to achieve organizational indexing of the data. On this basis, the scheduling framework was used to obtain cross source parameters, and the location of the cross source scheduling center was determined through direct tracking, achieving the smooth application of the design of a multi-source big data cross source scheduling system based on the optimal K-means clustering algorithm. The scheduling time of the Design System 3 is relatively long, but its scheduling time does not change much as the amount of data increases. Through simulation and comparison, the experimental results showed that compared with traditional big data scheduling methods, the new scheduling method can effectively suppress the phenomenon of system rigid operation. © 2023 IEEE.

关键词： clustering algorithm Cross Source Scheduling Multi-source Big Data Optimal K-means

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Analysis of Network Marketing Performance Evaluation System Based on BP Neural Network clustering algorithm

Analysis of Network Marketing Performance Evaluation System ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Quan, Xiaoyan Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350315455

With the rapid development of Internet technology, corporate marketing has become an essential way for the survival of companies in the market. Improving corporate marketing and providing support for companies effectively is extremely important. In response to these needs, this paper attempts to introduce the BP neural network clustering algorithm, through the construction of a three-layer index system for enterprise network marketing, to clarify the specific set of enterprise network marketing indexes;on this basis, through the BP neural network clustering method, enterprise network marketing is carried out. Comprehensive multi-level fuzzy evaluation of performance. Finally, the actual enterprise network marketing evaluation is conducted according to the specific calculation of index calculation. The results of the simulation experiment show that 0.435 of estimated vale, 0.096 of standard deviation, and 4.545 of Z-value of the BP neural network clustering algorithm is effective and can support enterprise network marketing performance evaluation and analysis. © 2023 IEEE.

关键词： BP Neural Network clustering algorithm Enterprise Multi-level fuzzy Network Marketing Performance Evaluation

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An improved LBG clustering algorithm based on Bayesian information criterion 14

An improved LBG clustering algorithm based on Bayesian infor...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Wan, Sisi Wu, Gongyu Department of Network Security No. 30 Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China Department of Generic Technology Research Southwest China Institute of Electronic Technology Chengdu China

ISBN: (纸本)9798350301359

Cluster analysis is an unsupervised classification method, which is the pretreatment part in data mining. And It is also the key to discovering unknown knowledge. However, the traditional clustering algorithm has some problems such as some algorithms need to predetermine k values, some algorithms can spontaneously search for k values but are not accurate enough and some algorithms can accurately search k values but the computational efficiency is too low. In order to solve the challenge of traditional clustering algorithm. An improved Linde-Buzo-Gray (LBG) clustering algorithm based on Bayesian information criterion was proposed. First, according to the traditional LBG thoughts to cluster and the rough value of k is obtained. After that the Bayesian information criterion was used to optimize each cluster and the exact k value was obtained. The theoretical analysis and simulation results show that the improved algorithm can search for the correct k value with high efficiency for clustering, which is more accurate than several traditional clustering methods. © 2023 IEEE.

关键词： Bayesian information criterion clustering algorithm Data mining knowledge discovery LBG algorithm

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Novel Adjustable Potential Assessing Method of Customer Loads Based on the clustering algorithm of Electricity Consumption Patterns 3

Novel Adjustable Potential Assessing Method of Customer Load...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Luo, Zhe Cui, Jia Yang, Zhiwei Gao, Mingze Hu, Zhen Tian, Aixin Shenyang University of Technology School of Electrical Engineering Liaoning Shenyang China State Grid Jibei Electric Power Company Limited Beijing China

ISBN: (纸本)9798350393224

Tapping the regulation resources on the 'load' side, upgrading the traditional source-load movement to source-load interaction, and improving the regulation capability of the power system are the new and important way to ensure the safe and economic operation of the new power system. This paper proposes a method for assessing the adjustable potential of users based on the clustering algorithm of power usage patterns. Firstly, the regularity of user electricity consumption and the classification method of electricity consumption pattern are studied. The abnormal data is excluded from the original data of user load. And the KNN algorithm is utilized to compensate the load data to ensure the integrity of the data. Secondly, the K-Means big data analysis algorithm is proposed to get the typical electricity consumption pattern of users. Thirdly, the baseline load on the regulation day is classified based on the affiliation function. Finally, based on the feasibility of the conversion of different power usage patterns, the calculated average error between the most likely regulation power of users and the actual value provides adjustable power data support for flexible load aggregation regulation. © 2023 IEEE.

关键词： adjustable potential assessment clustering algorithm power usage pattern

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