Big Data Mining Technology Research Articles

Design and research of intelligent watt hour meter fault early warning system based on data mining technology

With the rapid development of information technology and the continuous improvement of communication technology, electric energy meters are innovating and developing towards networking, informatization and intelligence. This research constructs a model design of electric energy failure early warning system based on big data mining technology under the background of big data. It analyzes and studies the intelligent electric energy meter failure early warning system. Through the analysis and comparison of the factors affected, comprehensive performance and application prospects of the intelligent electric energy meter failure early warning system under different data mining technologies, the research results show that the application of big data mining technology makes the heavy work of data collection and data analysis integrate big data mining technology, it can be popularized more widely and applied to more scenarios, thus reducing the manual workload, it makes the work efficiency more significantly improved, and can promote the reform of China’s smart grid more quickly.

The correlative factors of enterprise culture and enterprise human resource based on data analysis and federated learning

Under the current big topic of enterprise management, how to cultivate, use, and maintain talents is the critical problem of modern human resource management. Based on big data mining, cloud computing, and other related model optimization technologies, this study explores the crucial significance of enterprise culture construction and enterprise human resource management in achieving the business objectives of the entire enterprise. The establishment of this model also means that the enterprise’s profit margin and business indicators have been improved and developed under a certain model. The case results demonstrate that the optimized competency model is significant to studying corporate culture management system behavior, policy analysis, and improving corporate human resource management policies.

Research on monitoring and energy management systems for energy storage stations on the power generation side

Abstract Addressing the challenges of limited operational efficiency, inadequate battery safety, inflexible system scalability, inadequate AGC/AVC functionality, and low levels of intelligent operation and maintenance encountered by energy storage power stations integrated with new energy generation sources, this article delves into the pivotal role of these power stations on the generation side. It meticulously examines the network architecture, functional framework, and pivotal technologies underpinning the system. Furthermore, the article proposes a comprehensive monitoring and energy management system tailored specifically for energy storage on the generation side. The system uses micro-service architecture and container technology to realize the processing and storage of massive data. Through complete isolation of the information network and control network and millisecond communication with GOOSE, the charging and discharging response speed of the energy storage station is greatly improved. The power allocation algorithm based on device status recognition completes the system’s high-precision power control of PCS, and this system uses big data analysis and data mining technology to conduct a comprehensive statistical analysis of battery health status, power station operation efficiency, and equipment operation status and energy consumption. The design of the system has favorable technical and application guidance for the subsequent energy management design and engineering application of the energy storage power station.

Research and Application of University English Writing Teaching Reform Based on Data Mining

Based on big data mining and learning analysis technology, this paper systematically summarizes the theoretical basis of network-based college students' English writing skills training mode and discusses the reform and application of college English writing teaching. By mining network data, establishing corresponding writing training database and writing corpus, and at the same time strengthening writing teaching methods with the help of existing network corpus. Students learn through reading, imitation and innovation in the course, and receive psychological and motivation training. The research shows how data mining ability can test the data production ability of front-line authors, and test the ability of front-line teachers to cultivate students' autonomous learning and self-adaptation. It is emphasized that this teaching mode updates the knowledge structure of front-line teachers and cultivates new teachers who combine knowledge with technology. By providing innovative methods to optimize the teaching process, it is expected to effectively improve students' writing ability.

Construction framework of smart tourism big data mining model driven by blockchain technology

In the era of sharing economy, the tourism market is increasingly characterized by personalized demand, mobile consumption and product segmentation. This paper aims to apply big data mining technology in the field of smart tourism. Firstly, it focuses on image summary selection and collaborative filtering technology based on big data mining. It then demonstrates the integration of blockchain in smart tourism, emphasizing the use of decentralized structures and smart contracts to achieve data security and transparency, and describes the testing process of smart tourism platforms, including data preparation and platform operational efficiency testing. Finally, the research results of this paper are summarized, and the development potential and practical application value of smart tourism are demonstrated. The results show that in the smart tourism big data mining model, the minimum support for the data set is 10 % and 20 %, respectively. Moreover, with the increase of the number of nodes in the same data set, the running time decreases gradually. It can be seen that smart tourism big data mining has strong scalability.

Public attention and psychological trends towards waste reduction: A large-scale data analysis based on social media

In the digital era, using social media big data to capture the true views and psychological trends shown by the public regarding waste reduction could considerably improve the formulation of targeted waste reduction policies and guide residents to participate in environmental governance from the source side. This study used big data mining technology to trawl 617,771 pieces of waste reduction text from a typical social media site (Sina Microblog). A machine learning algorithm model was used to identify the psychological and cognitive focus of the public and their preferences based on large-scale text data. The temporal and spatial differences in public attention trends, hot topic trends, and sentiment trends were also investigated. The results showed that the public attention level was related to the release of policies by government and that public attention in the southeast coastal areas was higher than that in the northwest inland areas. Moreover, waste reduction had a “working attribute” because the public paid more attention to waste reduction during working hours (i.e., 9:00–12:00 and 15:00–18:00) than during leisure hours. In terms of individual heterogeneity, males were initiators of the topic, whereas females were followers. In particular, participation by young people in waste reduction discussions was higher than for other groups. The topic analysis showed that public attention followed a cooperative construction pattern that had multiple entities, including the individual, community, and government. Overall, the public sentiment score towards waste reduction increased year by year during the study period, with positive sentiment posts accounting for over 70% of the total number of blog posts, and that the vast majority of residents had a positive attitude towards waste reduction. This study expanded current research knowledge by exploring the public attitude response to waste reduction from a social media perspective. The study will help the government to effectively intervene in public behavior tendencies toward waste reduction from the psychological perspective and provided important implications about how the government can enhance its use of social media to effectively guide public opinion and improve policies.

Research on the application of big data mining technology based on neural network

In the development of social construction, all walks of life have the application of data mining technology, with the continuous development of science and technology, data mining technology can not only use the hidden law to solve the problem of time, but also can grasp more valuable data in the increasingly competitive social environment. Originally, after understanding the application research status of big data mining technology, based on the application of big data mining technology with neural network as the core, empirical analysis was carried out with Guanhealth big data intelligent analysis system as an example, in order to verify the unique value of data mining technology.

Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems

With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.

Analysis of Higher Vocational English Learners’ Behavioral Characteristics and Teaching Content Optimization Strategies Based on Big Data Mining

Abstract In the context of the big data era, the study of the massive data accumulated in the information construction of higher vocational colleges and universities can provide convenience for the teaching and management of colleges and universities. By improving the K-means clustering algorithm and Apriori algorithm in big data mining technology, the article mines the English learning behaviors and laws of higher vocational English learners and explores the correlation between learners’ behavioral characteristics and teaching performance. Finally, through empirical testing, this paper proposes an optimization strategy for teaching content in higher vocational English education. In the comparative analysis of reading and writing pre-test and post-test scores between the experimental class and the control class, the pre-test score of reading comprehension of the students in the experimental class is 31.25, and the post-test score is 32.84, and the average score of reading comprehension has increased by 1.59, which can be obtained that the English reading comprehension of the students has been improved after teaching with the teaching content optimization strategy proposed in this paper.

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

Comparison of the Visibility Grading Forecast Method Based on Meteorological Factors and Environmental Factors

The main visibility forecast factors were identified with the support of data from routine meteorological observations from the Mianyang Airport and the Mianyang Environmental Monitoring Station from 2015 to 2018, and a visibility grading forecast model was established and tested by dint of the multiple linear regression and the KNN algorithm based on big data mining technology, and the variation characteristics of visibility in winter at the Mianyang Airport were studied. The results showed that (1) in addition to having a significant positive correlation with wind speed, the visibility in winter at the Mianyang Airport has a significant negative correlation with relative humidity, dew point temperature, AQI, PM2.5 concentration, PM10 concentration, and CO, and it has the strongest correlation with relative humidity, and the correlation coefficient is −0.76. (2) The multivariate linear regression model and the KNN model were adopted for grading forecasting experiments on visibility, and the results revealed that both models could be used for visibility grading forecasts. The multiple regression model secures an accuracy of over 70% for forecasts of level 1–5 visibility. In terms of the KNN model, the forecast accuracy is the best when K = 3 or K = 5, notably for level-2, level-4, and level-5 visibility. The forecast accuracy rate is 100% for level-2 visibility, but the forecast for level-1 visibility is poor. (3) The minimum value of the average daily visibility of the Mianyang Airport in winter appeared at 09 : 00 and the maximum value appeared at 17 : 00. The level-1 visibility occurred and developed before 09 : 00 and faded and vanished between 08 : 00 and 15 : 00.

Research on Public Management of Health Examination in Centers for Disease Control and Prevention

In the face of the social environment with the rapid development of population aging, ordinary social residents may suffer from chronic diseases such as diabetes, hypertension, cerebral thrombosis, coronary heart disease, rheumatic heart disease, respiratory diseases, etc., so regular physical examination for middle-aged and elderly groups and young groups has become an important direction of the CDC's public management. Relying on big data mining technology, C/S (Client Server) architecture, SQL server database and other software development technologies, we will build an information management system for the health examination of the Centers for Disease Control and Prevention. We will strengthen the systematic input and management of personnel registration, examination photography, project sampling, examination results, and review results, effectively improving the public management quality of the health examination of the Centers for Disease Control and Prevention.

Car Purchase Based on Customer Specifications using Big Data Analysis: A Review

-This paper presents an in-depth and technical analysis of car purchases based on customer specifications, highlighting the crucial role of big data and data mining technology in the automobile manufacturing industry. By employing web crawlers and advanced algorithms, we extract valuable insights from customer reviews and sales data to optimize production and enhance customer satisfaction. The paper explores various data mining techniques and their impact on analyzing customer specifications, leading to tailored recommendations and improved market competitiveness. Key Words: big data, data mining, car purchase, customer specifications, web crawlers.

Smart city maturity models: A multidimensional synthesized approach

AbstractSmart cities are one of the consequences of digital transformation, and there have been many attempts to assess the smartness of cities with various frameworks. Among these frameworks, smart city maturity models (SCMMs) evaluate the existing conditions of cities and provide guidelines for progressing through the subsequent stages of maturity. However, most maturity models follow the instructions of the first model, published by the International Data Corporation, and there are many similarities across the models. These maturity models have advantages and disadvantages, while previous studies have not addressed the differences. Therefore, this article fills this knowledge gap by systematically reviewing the existing SCMMs. The findings suggest that some trending topics, such as resiliency concerning global pandemics and cultural aspects are neglected in SCMMs. Moreover, the validation techniques of the models are not rational. Finally, given the theoretical nature of most models, they cannot be applied to multiple regions.This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence Technologies > Machine Learning

Big Data-Driven Threat Intelligence Analysis and Early Warning Model Construction

With the development of big data technology, its application in various fields is becoming increasingly widespread, especially in the field of threat intelligence analysis and early warning. By using the processing power of big data and data mining technology, patterns and laws of threats can be discovered from the massive and multifaceted data, thus realizing effective early warning of threats. However, big data-driven threat intelligence analysis and early warning model construction is a challenging process, which involves various aspects such as understanding and processing of big data, methods, and tools for threat intelligence analysis, design and implementation of early warning models, and validation and evaluation of early warning models. This paper aims to comprehensively describe this process to provide guidance and reference for big data-driven threat intelligence analysis and early warning.

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

Impact assessment of air pollutants and greenhouse gases on urban heat wave events in the Beijing-Tianjin-Hebei region.

The production and quality of human life have been impacted by the extreme heat wave events caused by global warming and urbanization. This study analyzed the prevention of air pollution and the strategies of emission reduction based on decision trees (DT), random forests (RF), and extreme random trees (ERT). Additionally, we quantitatively investigated the contribution rate of atmospheric particulate pollutants and greenhouse gases to urban heat wave occurrences by combining numerical models and big data mining technology. This study focuses on changes in the urban environment and climate. The main findings of this study are as follows.The average concentrations of PM2.5 in the northeast of Beijing-Tianjin-Hebei in 2020 were 7.4%, 0.9%, and 9.6% lower than those in the corresponding years of 2017, 2018, and 2019, respectively. The carbon emissions in the Beijing-Tianjin-Hebei region showed an increasing trend during the previous 4years, which was consistent with the spatial distribution of PM2.5. In 2020, there were fewer urban heat waves, which was attributable to a reduction of 75.7% in emissions and an improvement of 24.3% in the prevention and management of air pollution. These results suggest that the government and environmental protection agencies need to pay attention to changes in the urban environment and climate to diminish the negative effects of heatwaves on the health and economic growth of the urban population.

AdaBoost-driven multi-parameter real-time warning of rock burst risk in coal mines

The stope dynamic disaster, which occurs around the mining space and is represented by stress-type rock burst and fracture-type rock burst, seriously affects the safety production of coal mine. How to effectively forewarn rock burst risk to reduce the disaster caused by rock burst is an urgent problem to be solved in stope. In this paper, the spatiotemporal parameters that affect the occurrence of stope dynamic disaster are collected, and the big data of stope state is established. Due to the complex temporal and spatial parameters of rock bursts with different intensities occurrence and their varying degrees, it is difficult for some machine learning methods to excavate their internal relationships and make accurate warning. In this paper, (1) a big data platform is used to record and integrate the multiple parameters of stope dynamic disaster, and data preprocessing technology is used to de-noise and standardize them. (2) Based on the fused historical data, a strong classifier is iteratively found by the classification algorithm AdaBoost to identify the existence of the rock burst risk, so as to achieve the purpose of accurately and timely warning of rock burst risk. The research of this paper uses big data mining technology and machine learning method to carry out intelligent perception warning of the rock burst risk in real time. The experimental results show that the proposed method has a good effect and is of great significance to the prevention and control of rock burst disaster in stope.

Enterprise marketing strategy using big data mining technology combined with XGBoost model in the new economic era.

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

Rapid screening of sensory attributes of mackerel using big data mining techniques and rapid sensory evaluation methods.

The present study aimed to investigate the potential of big data mining technology in conjunction with rapid sensory evaluation methods for the swift screening of sensory attributes of three kinds of frozen mackerel. Specifically, two rapid sensory evaluation methods, namely ideal profile method (IPM) and check-all-that-apply (CATA), were implemented and compared with the conventional descriptive analysis method. The results revealed that eight sensory attributes based on consumer network evaluations demonstrated significant consistency during the training process (p < .05). Notably, the application of web-based sensory attributes yielded highly comparable results between IPM and traditional descriptive analysis (0.915). Moreover, the results of the IPM preference map were in closer agreement with those of traditional descriptive analysis. While traditional sensory evaluation boasts high accuracy and a greater ability to detect nuances, the evolution of sensory evaluation technology has shifted its focus toward consumers. Rapid sensory evaluation analysis technology supports the collection of information directly from consumers, even by untrained or semi-trained groups, thereby presenting broad prospects for product qualitative analysis.