In order to implement the training plan for on-site engineers in the field of big data technology in higher vocational education. With the development of artificial intelligence technology, big data technology, cloud ...
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Intelligent manufacturing, as a pillar of the manufacturing industry, provides crucial support for achieving China's strategic goal of becoming a manufacturing powerhouse. The operation and management process of i...
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Intelligent manufacturing, as a pillar of the manufacturing industry, provides crucial support for achieving China's strategic goal of becoming a manufacturing powerhouse. The operation and management process of intelligent manufacturing enterprises requires a large amount of capital investment, and the efficiency of capital utilization directly affects the economic benefits of intelligent manufacturing enterprises. Relevant enterprises continue to improve the performance evaluation mechanism and enhance their performance evaluation ability, so as to effectively promote the realization of corporate strategic objectives and continue to become a solid support for the steady operation of China's economy. On this basis, the performance of intelligent manufacturing enterprises was deeply explored, and a set of performance evaluation indicator framework for intelligent manufacturing enterprises was established by using machine learning and big data technology. Through empirical analysis, machine learning methods are introduced into performance evaluation to establish an assessment mechanism for intelligent manufacturing enterprises that is suitable for China's national conditions. For ridge regression, lasso regression, and elastic network regression in machine algorithm, we have conducted in-depth research and built a set of performance evaluation system using the model of lightGBM. Construct a framework of performance evaluation system for intelligent manufacturing enterprises based on machine learning and big data technology support, and use TOPSIS method to conduct empirical analysis, thus further highlighting the importance of evaluation and feedback mechanism for intelligent manufacturing enterprises.
This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on bigdata analysis to enhance the informat...
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This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on bigdata analysis to enhance the information security protection capability of enterprises. A bigdata analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models. For different types of security threats, the system uses feature engineering and model training processes to extract key risk indicators and optimize model prediction performance. The experimental results show that the constructed risk prediction model has excellent performance on the test set, and its Area Under the Curve reaches 0.95, indicating that the model has good differentiation ability and high prediction accuracy. In addition, in the multi-class risk identification task, the model achieves an average precision of 0.87. Compared with the traditional method, it has remarkably improved the early warning accuracy and response speed of enterprises to various information security incidents. Therefore, this study confirms the effectiveness and feasibility of applying BDT to EIS risk management, and the successfully constructed prediction model provides strong technical support for EIS protection.
In order to study the impact of the vertical specialisation level and competitiveness of China's manufacturing industry on the international competitiveness of the manufacturing industry, this paper combines impro...
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In order to study the impact of the vertical specialisation level and competitiveness of China's manufacturing industry on the international competitiveness of the manufacturing industry, this paper combines improved data mining technology to analyze the rising factors of the international value chain of the manufacturing industry and construct a regression equation. Moreover, this paper analyzes the impact of vertical specialisation on the competitiveness of manufacturing industry by adding control variables, and finally puts forward policy recommendations for improving the international competitiveness of manufacturing based on the results of empirical analysis, thereby promoting the rise of the status of China's manufacturing industry in the division of labour in the international value chain. The research shows that the analysis model of rising factors of manufacturing international value based on big data technology proposed in this paper has good results.
The efficiency of remanufacturing systems is closely related to the modular design. This paper proposes a sustainable design concept aiming at active remanufacturing. This paper adopts the modular method related to pa...
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The efficiency of remanufacturing systems is closely related to the modular design. This paper proposes a sustainable design concept aiming at active remanufacturing. This paper adopts the modular method related to parameter flow to decompose and construct the product's function. The author applies the clustering method to the parameter flow correlation determination to realize the quantitative division of modules. This paper combines sustainable design principles with the life cycle characteristics of products. In this paper, the sustainable design is divided into modules based on the degree of component association. Finally, this paper uses an example to demonstrate that the model in this paper can divide products into modules in a sustainable way.
The problems of applying big data technology in modern scientometric analysis are considered. The importance and relevance of these problems are determined by the ability of bigdata to significantly increase the effi...
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The problems of applying big data technology in modern scientometric analysis are considered. The importance and relevance of these problems are determined by the ability of bigdata to significantly increase the efficiency of scientometric research through in-depth analysis of mega-volumes of heterogeneous data and to identify on this basis new semantic relationships and patterns. The main content of big data technology is disclosed. The article presents a list of data requirements that allow data to be classified as bigdata;the importance of this technology as the advanced means of scientific research in scientometrics is noted;detailed characteristics of methods of scientific research are given. The features of bibliometric, altmetric, webometric, and probabilistic-statistical methods are analyzed. The important place of big data technology in the modern set of methods and means of scientific research in scientometrics is emphasized.
The explosion of data scale makes data processing encounter many difficulties and challenges. The purpose of this paper is to discuss the group computing task assignment and correlation analysis of big data technology...
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The explosion of data scale makes data processing encounter many difficulties and challenges. The purpose of this paper is to discuss the group computing task assignment and correlation analysis of big data technology. This paper designs and proposes an algorithm for task assignment in bigdata sets, which is based on the user's exact perception of a topic and aims to improve the accuracy of calculation. This algorithm is first able to effectively combine the topic accurate perception and extraction model with adaptive fuzzy clustering, and then it can be done by building a model that focuses on specific target groups and users, and calculating the degree between them. The experimental results of this paper show that the accuracy of the music dataset is relatively low, averaging 50%;for the compilation of thematic data sets, the accuracy rate is high, averaging 70%;the final task assignment accuracy reaches 79.9%.
The emergence of emergencies has impacted China's politics, economy, and other aspects to varying degrees, seriously threatening social stability, economic development, and people's happiness. The emergency ma...
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The emergence of emergencies has impacted China's politics, economy, and other aspects to varying degrees, seriously threatening social stability, economic development, and people's happiness. The emergency management platform plays an important role in the handling of emergencies. The establishment of the emergency management platform can help leaders make correct decisions and reduce the impact of emergencies on the safety of people's lives and property. However, some problems are gradually exposed in the process of establishing the emergency management platform. How to make it play its role better has become an important goal of platform informatization. With the advent of the bigdata era, big data technology has become a new way of information construction of emergency management platforms. This article mainly discussed the information construction of the emergency management platform. This article proposed to use big data technology to carry out the information construction of the emergency management platform and use the time series data mining algorithm based on the Artificial Neural Network to achieve the prediction function of the platform. The experimental results in this article showed that the prediction rate was 98.0% when the emergencies of the input platform were 50 and 99.0% when the emergencies of the input platform were 300. It can be seen that the emergency management platform designed in this article has a high ability to predict emergencies, so that emergency management measures can be taken in advance to reduce the occurrence of emergencies.
With the rapid development of science and technology and bigdata, traditional translation has been greatly tested in the era of bigdata. The translation ability requires to be endowed with excellent bilingual teachi...
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With the rapid development of science and technology and bigdata, traditional translation has been greatly tested in the era of bigdata. The translation ability requires to be endowed with excellent bilingual teaching language ability, rich and diverse translation professional knowledge, sufficient basic translation knowledge, skilled IT technology and computer knowledge, etc., to better shape the translation ability. Based on big data technology, this paper analyzes the feasibility and advantages of using big data technology for English translation by designing a public automatic English translation system and studying the teaching mode of the English translation.
Nowadays, financial management theory involves almost all aspects of enterprise management. It is the importance of these aspects in enterprise management that makes them the key research objects of FM theory, and the...
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Nowadays, financial management theory involves almost all aspects of enterprise management. It is the importance of these aspects in enterprise management that makes them the key research objects of FM theory, and the theoretical achievements of financial management (FM) in turn provide an important basis for enterprise managers to make quantitative decisions. With the advent of the era of bigdata, the original FM mode and content can no longer meet the needs of business development. Therefore, the application of big data technology in enterprise FM is deeply studied in this paper. The research shows that by comparing the situation before and after the implementation of technical optimization, it is found that the completion rate of the income target is improved and the deviation rate of financial risk is reduced. bigdata covers a wide range, and the information analyzed is diverse and complex. How to make good use of this data has become the key to enterprise
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