Industrial Big Data Research Articles

Research on Big Data Platform Deployment and Maintenance Course Practical Teaching based on "Job-Curriculum-Competition-Certification + Ideological and Political Integration"

With the rapid development of the big data industry, the demand for big data technical talents has increased sharply. The big data technology major in higher vocational colleges aims to cultivate high-quality skilled talents with capabilities in data collection, processing, operation and maintenance, and technical services. The major integrates the core skills of big data implementation and operation and maintenance positions into professional courses and conducts teaching practice based on the "Job-Curriculum-Competition- Certification + Ideological and Political Integration" model. By constructing an education system that integrates knowledge imparting, ability cultivation, and value shaping, ideological and political education is integrated into course teaching, achieving an organic combination of professional teaching and ideological and political education content. At the same time, a blended teaching mode is adopted, focusing on the comprehensive cultivation of students' knowledge, abilities, and qualities, using competitions to promote learning and training, and improving the quality of talent cultivation.

Research and Implementation of a Classification Method of Industrial Big Data for Security Management

ABSTRACTPurpose/SignificanceWith the extensive adoption of cloud computing, big data, artificial intelligence, the Internet of Things, and other novel information technologies in the industrial field, the data flow in industrial companies is rapidly increasing, leading to an explosion in the total volume of data. Ensuring effective data security has become a critical concern for both national and industrial entities.Method/ProcessTo tackle the challenges of classification management of industrial big data, this study proposed an Information Security Triad Assessment‐Support Vector Machine (AIC‐ASVM) model according to information security principles. Building on national policy requirements, FIPS 199 standards, and the ABC grading method, a comprehensive classification framework for industrial data, termed “two‐layer classification, three‐dimensional grading,” was developed. By integrating the concept of Data Protection Impact Assessment (DPIA) from the GDPR, the classification of large industrial data sets was accomplished using a Support Vector Machine (SVM) algorithm.Result/ConclusionSimulations conducted using MATLAB yielded a classification accuracy of 96.67%. Furthermore, comparisons with decision tree and random forest models demonstrated that AIC‐ASVM outperforms these alternatives, significantly improving the efficiency of big data classification and the quality of security management.

A review on adversarial–based deep transfer learning mechanical fault diagnosis

Mechanical equipment is a vital foundational support for promoting national economic development and is widely utilized in key sectors such as aerospace, shipping, construction machinery, energy, petrochemicals, and robotics. With the advancement of artificial intelligence and industrial intelligence, industrial big data and its intelligent analysis provide robust support for fault prediction and health management of equipment. Building on existing research, intelligent diagnostics for mechanical equipment based on deep learning have gained significant attention and application. However, the success of big data relies on comprehensive fault data, which is challenging to obtain in practical applications where continuous equipment operation is essential. Moreover, mechanical equipment often operates under varying conditions, leading to different data distributions for training and testing. This discrepancy can result in low diagnostic accuracy or even failure of deep learning methods. Deep Transfer Learning (DTL) is an emerging machine learning paradigm that not only leverages the advantages of deep learning (DL) in feature representation but also harnesses the strengths of transfer learning (TL) in knowledge transfer. Consequently, DTL techniques can make deep learning-based fault diagnosis methods more reliable, robust, and applicable, leading to extensive development and research in the field of intelligent fault diagnosis. This paper primarily introduces adversarial-based deep transfer learning (ADTL) models, which are fundamentally based on Generative Adversarial Network (GAN). We provide a detailed discussion of the main applications of ADTL and its recent developments in intelligent fault diagnosis, along with some future challenges and prospects.

Research on the impact of industrial big data on the collaborative governance of pollution reduction and carbon reduction

Research on the impact of industrial big data on the collaborative governance of pollution reduction and carbon reduction

Machine Learning Algorithms for Prediction of Boiler Steam Production

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.

Retraction Note: Securing healthcare big data in industry 4.0: cryptography encryption with hybrid optimization algorithm for IoT applications

Retraction Note: Securing healthcare big data in industry 4.0: cryptography encryption with hybrid optimization algorithm for IoT applications

An Efficient and Expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption Scheme for Satellite Service Systems

Satellite service systems transfer data from satellite providers to the big data industry, which includes data traders and data analytics companies. This system needs to provide access to numerous users whose specific identities are unknown. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows unidentified users with the proper attributes to decrypt data, providing fine-grained access control of data. However, traditional CP-ABE does not protect access policies. Access policies are uploaded to the cloud, stored, and downloaded in plain text, making them vulnerable to privacy breaches. When the access policy is completely hidden, users need to use their own attributes to try matching one by one, which is an inefficient process. In order to efficiently hide the access policy fully, this paper introduces a new efficient and expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption scheme (CP-ABE-FPH), which integrates the 2-way handshake O-PSI method with the ROBDD method. The integration offers advantages: (1) High efficiency and high expressiveness. The access policy using ROBDD is highly expressive but computationally intensive due to its recursive nature. This shortcoming is overcome in CP-ABE-FPH using the proposed O-PSI method, and the access policy is matched quickly and secretly. (2) High flexibility. The decryption process does not require the owner or the Key Generation Center (KGC) to be online, and system attributes can be added at any time. Security analysis shows that the access policy is fully hidden. Efficiency analysis and simulation results show that the proposed scheme is highly efficient in decryption compared with existing schemes.

The Big Role of Data Analytics in the Industrial Internet of Things

An IoT architecture is presented in the article which is a predictive way to maintain a group of buses using J1939 sensor data[1]. The authors adopted a minimally viable prototype (MVP) at the Socie´te´ de Transport de l’Outaouais (STO) garage in Gatineau, Canada, which was able to capture around 1 GB of uncompressed J1939 data per bus every day.[1] Data is a combination of the sensor reading and consists of data for the wheel speed, vehicle distance, driver pedal positions, engine information, oil information, coolant information, and transmission fluid information. The authors are trying to come up with a variant of Consensus Self-Organized Models (COSMO) named ICOSMO, which can self-fit the sensor selection for anomaly detection. The fourth industrial revolution has changed the face of ancient manufacturing by bringing in new technologies such as sensors, AI, IoT, and big data analytics. This equipment is used to convert factories into intelligent and interconnected systems that generate a huge amount of data. The data collected, when properly processed, can be utilized to improve decision making and reliability and safety. In addition, it makes predictive maintenance inevitable, thus minimizing the downtime and optimization of the performance[2]. Industrial big data is identified by the ”5V” elements: volume, velocity, variety, veracity, and value. Due to these characteristics, traditional data processing techniques become inadequate, so these circumstances require novel approaches to the treatment of the numerous and diverse data types that are produced in industry 4.0. The Proposed ICOSMO Infrastructure for IoT Predictive Maintenance consists of the following key activities: 1) finding out which sensor classes are the reasons for the faulty components, 2) adaptation of the sensor class (SC) and the sensor prediction capability (SPC) using fault detection abilities and 3) COSMO sensor configuration has to be rearranged after a certain period of time. ICOSMO has been verified with data from a minimally viable prototype (MVP) deployed on buses at the Socie´te´ de Transport de l’Outaouais in Canada. Furthermore, the interviewees said that the framework also includes techniques for the composition and the characterization of industrial big data, which is essential to make a practical predictive maintenance system that can work in smart manufacturing environments

Environmental impact assessment of the manufacture and use of N- type and P-type photovoltaic modules in China

For a long time, solar power has been considered a clean and non-polluting energy source because it absorbs sunlight without consuming other energy or materials and does not emit pollutants. Nevertheless, the manufacturing and end-of-life stages of solar power systems leave an environmental footprint and produce pollutants. Hence, a global perspective on photovoltaic (PV) systems using life cycle assessment methodology is necessary. Here, we investigated the life cycle impacts of P- and N-type PV modules in terms of their energy consumption, carbon emissions, and human toxicity potential. In addition, the energy payback durations of P- and N-type PV modules were computed. In this study, we used the Life Cycle Assessment Basic Database from the National Engineering Laboratory of Industrial Big Data Application Technology at Beijing University of Technology to create a life cycle assessment model for N-type PV modules. Subsequently, we performed a life cycle assessment of Chinese silicon N-type- and P-type PV modules. The research system encompassed the production processes for metallic silicon materials, solar-grade silicon materials, silicon wafers, cells and modules, utilization, recycling, and other interrelated processes. We found that the production and processing of silicon-to-solar-grade polysilicon feedstock were crucial stages that significantly affected the energy consumption and environment of P- and N-type PV modules in China. The high electricity consumption of this process, combined with the dominance of coal power in China's electricity structure, contributed to the substantial energy consumption and environmental impacts of the PV modules. Therefore, this stage is critical for the low-carbon and environmentally sustainable manufacturing of N-type PV modules in China. Carbon emissions for both the P-type and N-type PV modules were lower only during the cell production phase but higher during the other stages when compared to the P-type and N-type PV modules. The n-type bifacial PV modules yielded the highest return on investment in terms of energy. Different regions and installation types have a substantial impact on the carbon emissions of P-type and N-type PV modules. Projections indicate that the potential to lower carbon emissions from n-type PV modules will increase by 2060.

HOW DOES THE GOVERNMENT PLAY A ROLE IN THE DEVELOPMENT OF THE BIG DATA INDUSTRY? BASED ON THE fsQCA RESEARCH OF 31 PROVINCES AND CITIES IN CHINA

This study employs fuzzy set qualitative comparative analysis to explore factors influencing the development of the big data industry in 31 provinces and municipalities in China. By analyzing eight representative variables across four dimensions—new infrastructure construction, data resources and applications, government services, and resource input—the study aims to uncover configurations and pathways that affect industry growth. The results reveal four effective strategies: combining infrastructure with data application, integrating government services with data application, coupling infrastructure with resource investment, and focusing solely on government services. Core variables such as optical cable length, policy environment, and service environment require governmental intervention to foster development. Therefore, continuous enhancement of new infrastructure construction, supporting the development of small and medium-sized Enterprises in big data, strengthening the big data industry and government service environment, and increasing investments in talent and capital are recommended to drive industry progression.

Has the Digital Economy Improved the Urban Land Green Use Efficiency? Evidence from the National Big Data Comprehensive Pilot Zone Policy

The advancement of the big data industry is playing a pivotal role in urban land management refinement. Recently, China initiated a big data strategy, establishing national big data comprehensive pilot zones (NBDCPZs) across diverse regions. These initiatives present substantial opportunities for enhancing the urban land green use efficiency (ULGUE). Consequently, in this study, we utilized the super-efficiency slack-based measure (SBM) model with undesirable outputs to assess the ULGUEs across 281 prefecture-level cities in China from 2006 to 2021. Subsequently, leveraging the NBDCPZ establishment as a quasi-natural experiment, we employed the difference-in-differences (DID) method to empirically explore the impact of the NBDCPZ policy on the ULGUE for the first time. The findings revealed the following: (1) The implementation of the NBDCPZ policy significantly enhances the ULGUE; (2) the effects are mediated through mechanisms such as fostering technological innovation, mitigating resource misallocation, and promoting industrial agglomeration; (3) the heterogeneity analysis emphasizes the increased policy effectiveness in cities characterized by fewer natural resources, lower economic growth pressures, stable development stages, and moderate digital infrastructure and human capital levels; and (4) further analysis demonstrates the significant positive spillover effects of the NBDCPZ policy on the ULGUEs of neighboring non-pilot cities, with a diminishing impact as the proximity between pilot and non-pilot cities decreases. Overall, this study contributes to the literature on the relationship between the digital economy and land utilization, offering valuable insights for achieving sustainable urban development.

A Deleuzian Perspective on the Right of Data Protection on Social Media

The goal of this article is to explore – from the given theoretical framework – the effectiveness of the European Union’s data protection capabilities, namely through the General Data Protection Regulation. The first and second sections develop the theory of control societies – as well as its historical background – and connects it with the theory of surveillance capitalism as its essential component. The third section delas with some critiques that have arisen in the few years after the GDPR came into force. The conclusion of the paper is that, only a few years after the GDPR came into force, it is still too early to decisively say what effect will it have on the big data industry. However, from the problems that have been elaborated, it seems unlikely that the big data industry will be meaningfully challenged when it comes to data protection.

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Abstract Properly separating fault information from noisy measured signals is crucial for effective bearing health sensing. However, conventional fault information separation methods face challenges such as predefined model parameters and poor noise robustness. Additionally, with the advent of Industry Big Data, multichannel monitoring signals present significant challenges for traditional single decomposition approaches. To address these challenges and fully extract potential fault information, this paper introduces a tensor low-rank and sparse decomposition (tensor LRSD) approach for multichannel signal processing. Inspired by matrix LRSD, we construct a tensor LRSD model that adaptively decomposes the signal into a tensor sparse term containing fault information and a low-rank term representing the intrinsic signal pattern. To further enhance the decomposition performance, a maximum correlation-based selection strategy is designed. This strategy evaluates the correlation between each tensor slice and selects appropriate tensor sparse terms for fault information extraction. Simulation analysis and two experimental studies involving typical bearing failures are implemented to verify the capability and superiority of the presented tensor LRSD approach. The consequences demonstrate that the presented method outperforms conventional techniques, showcasing its capability to effectively separate fault information from noisy signals.

A quantitative causal analysis and optimization framework for inclusions of steel products

Inclusions generated during steelmaking and continuous casting processes could seriously reduce the mechanical properties of steel products and greatly increase the risk of corrosion failure. Much attention has been paid to perceiving the origin and classification of inclusions based on the process knowledge and mechanism model, while the value of industrial big data is underestimated. Machine learning (ML) methods have demonstrated potential in the processing of nonlinear and strong coupling relationships in industrial data. However, existing ML models are frequently unable to analyze causal relationships between manufacturing process, which is essential for the quality optimization. To address the limitation, this paper proposes a quantitative causal analysis and optimization framework. This framework starts with a feature selection module using propensity score matching (PSM) to measure the quantitative and reliable causal relationship between the process parameters and inclusions. Subsequently, a high-performance AutoML method, AutoGluon-Tabular (AGT), is applied to quantify the optimization range and verify the optimization effect. Specifically, AGT is used to estimate the inclusion level in each window of the process parameter to be optimized, choose the best window of which to determine adjustment strategies, and eventually verify the change of inclusion levels after optimization. The comparative experiments based on the real data collected from a steel company demonstrate that the proposed framework reduces the inclusion rate by 8.85%, which is effective, efficient and has guiding significance for the inclusions control of steel products.

Privacy-Preserving and Secure Industrial Big Data Analytics: A Survey and the Research Framework

Privacy-Preserving and Secure Industrial Big Data Analytics: A Survey and the Research Framework

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework.

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.

A K-means-Teaching Learning based optimization algorithm for parallel machine scheduling problem

With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single-operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. Based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching-learning-based optimization) hybrid K-means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high-similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching–learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency.

Judicial Judgment Standard of Generative Artificial Intelligence and Training Data from the Perspective of Fair Use

With the development of generative artificial intelligence, text and data mining has been put into more applications. However, as a technology for collecting and processing a large number of other works, it faces copyright infringement under the current legal system. As a step in the operation of artificial intelligence, text and data mining is to collect and process existing works and information to realize the behavior of knowledge "reproduction", thus promoting the utilization of knowledge and social innovation. Meanwhile, copyright exemption should be provided for its fair use system. The fair use system in the United States can give full play to the autonomy of judges in specific cases and flexibly identify fair use exemptions, while the copyright exception in Europe clarifies that text and data mining activities should be exempted under specific circumstances. Learning from both, China can not only break the closed fair use system, but also build an evaluation system of artificial intelligence infringement and benefit-sharing mechanism, so as to provide scientific norms for fair use and improve the development of artificial intelligence and big data industry in China.

Research on Urban Architecture Design under the Concept of Smart City

After the digital city and smart city concepts, the latest proposed smart city concept is a general summary of the advanced form of urban information development, which plays an important role in the economic transformation of cities, social governance, improvement of residents' quality of life and energy conservation and environmental protection. The smart city puts forward new requirements for the architectural design of the city, and how to reasonably use the smart city concept in the architectural design is still a major issue for architects and professionals to think about. To this end, this paper proposes a state tracking based full working condition modeling method for nonlinear industrial systems. For the problem that the amount of historical data is too large and it is difficult to screen the modeling data, a sliding window is designed to screen the steady-state data, and a fast recursive algorithm of standard deviation in the window is derived; the influence mechanism of unknown disturbances on the system is analyzed, and the data modeling by dynamic regression to the steady state is selected, and a data-driven modeling algorithm that can eliminate the influence of disturbances is proposed; the model information contained in the process industrial big data is used to apply the higher order function to fit each A linear variable parameter transfer function model based on the characteristic parameters is proposed. The identification of an industrial process is shown to be effective. The main points of urban architectural design should be noted under the concept of smart city from four aspects: safety, diversity, environmental protection and emotionality.

Hybrid deep convolution network and differential evolution algorithm for solving non-permutation flow shop scheduling problem

With explosive growth of industrial big data, workshop scheduling faces problems such as high complexity, multi-dimensionality and low stability. Recent years, the wide application of deep learning provides new idea for scheduling problem. In this paper, a hybrid deep convolution network and differential evolution algorithm is proposed to solve the non-permutation flow shop scheduling problem with the goal of minimizing total completion time. Mining relationship between job attributes and process priority by deep convolutional network is core idea of this method. In this paper, differential evolution algorithm is used to obtain the data set for deep learning, and neighborhood search algorithm is used to optimize scheduling solution. Additionally, a method combining k-means algorithm and data statistics is proposed, which provides a reasonable way for priority division. The experimental results show that this method can greatly improve scheduling efficiency.