Multi-source Heterogeneous Data Research Articles

Research on the Digital Twin System of the Centring Process for High-Precision Lens

In the manufacturing process of optical lenses, the lack of monitoring and detection of the central processing process leads to low processing efficiency and difficulty in ensuring product consistency. We propose a digital twin system for alignment processing to address this issue. The system adopts a hierarchical architecture based on the digital twin five-dimensional model, aiming to improve the fidelity and interactivity of the virtual model of the centring lathe by combining dimension-driven virtual models with integrated data and physical models of the turning mechanism. We have successfully achieved the semantic and physical fusion of multi-source heterogeneous data during centring processing using information models and OPC UA-based data interaction methods. In addition, we adopted the VMD-GRU method based on feature fusion for real-time monitoring of critical components of the centring lathe. Finally, we validated the feasibility and effectiveness of the digital twin system for the central lathe through development examples. The application of this system is expected to promote the digital and intelligent development of high-precision optical component processing, providing references including references for related manufacturing fields. In summary, we propose a digital monitoring and detection system for the centring process of optical lens manufacturing. The application of this system will help improve product consistency and processing efficiency while reducing risks and costs in the manufacturing process.

Establishing a dynamic and static knowledge model of the manufacturing cell management system: An active push approach

The workshop operation management (WOM) system contains a large amount of multi-source heterogeneous data, but these data often lack effective correlation, posing challenges in extracting valuable information for achieving unified system modelling at the knowledge level. Due to the lack of knowledge active support in the actual WOM system, managers could only passively rely on their own past experiences. To bridge this gap, the knowledge active push method for optimal operation management of the manufacturing cell (OOMMC) is studied based on knowledge graph (KG). Firstly, a dynamic knowledge model is established according to the knowledge application scenario of manufacturing cell, in which the Local Tangent Space Alignment (LTSA) + k-means algorithm is used for active abnormal warning. Secondly, a static knowledge model based on user needs using feature vocabulary and naive Bayesian classifier is established, where managers can obtain knowledge from the KG based on professional noun keywords or simple query questions. These two models complement and promote each other: the dynamic knowledge model using a service-based collaborative filtering method to push the nearest knowledges in the KG to users, effectively inspiring them to retrieve more relevant knowledge. While the static knowledge model evaluates the effectiveness of previously pushed knowledge by setting the knowledge demand index and providing feedback to the system to ensure accuracy of knowledge push. Finally, an active push simulation system is designed and developed to verify the feasibility and effectiveness of the proposed architecture and key enabling technologies.

Design and Implementation of Power Big Data Platform

In recent years, with the advancement of the national construction of smart grid and the development of power grid enterprise integration system, the traditional power data platform has some defect, such as insufficient scalability, repeated implementation of two sets of logic of off-line data warehouse and real-time data warehouse, highly correlated storage of data warehouse during computation, and difficulty in platform migration, which restrict the deeper mining and analysis of power big data. Therefore, this paper adopts a technical architecture incorporating distributed calculating, micro-service, flow-batch integration and lake-warehouse integration, combining the current popular Web front-end and back-end technologies such as Vue, SpringCloud and Flask, and utilizing big data components such as Hadoop, Flink, Hudi and Kafka as well as Docker containerization technology. We explore and build a power grid system operation oriented big data platform that assembles real-time computing, multi-source heterogeneous data storage, and develop APIs for authority management, information management, data analysis and visualization, machine learning and other data supporting services that assist power enterprises in managing electrical equipment and users’ power consumption.With the help of this platform, power enterprises can intelligently track the equipment status, view the offline and real-time visual charts to analyze the power consumption by users, so as to have better basis in the power dispatching, maintenance and scheme adjustment of electrical market.

Applied Research on Intelligent Collection System of Positioning Information for Multi-source Heterogeneous Distribution Communication

Abstract In order to better realize the intelligent perception of fault location information of power distribution communication access networks in different scenarios, this paper carries out the construction of a power distribution communication network system through industrial Ethernet technology combined with EPON technology under fiber optic communication technology. Based on GIS technology and combined with OTDR technology, we constructed a fault location system for the fiber optic cable of the power distribution communication network so as to realize the acquisition of fault location information. From the multi-source state information, the joint Kalman filtering model is used to excavate the characteristics of the grid operation state, and then the fusion of multi-source heterogeneous data of power distribution communication is realized. To verify the effectiveness of the distribution communication network system constructed in this paper for locating fault information and intelligently collecting power consumption, system tests were conducted. The results show that the relative error of the node voltage estimation of the joint Kalman filter algorithm is below 0.015%, which improves the accuracy of fault identification in the distribution network. The system constructed in this paper for information transmission connectivity testing PON port light-emitting power range of 2~6dm, receiving optical power greater than -25dm, each PON port without power overload. This shows that the distribution automation communication network constructed in this paper meets the information collection requirements, and the construction of an intelligent distribution communication network can meet the requirements of distribution automation and can also accurately collect the fault location information of the distribution network.

Ecological restoration for mega-infrastructure projects: a study based on multi-source heterogeneous data

PurposeThis research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.Design/methodology/approachBased on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.FindingsThis research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.Practical implicationsManagers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.Originality/valueUnlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

Bridging National Policies with Practical Rural Construction and Development: Research on a Decision Support System Based on Multi-Source Big Data and Integrated Algorithms

While national policies play a crucial role in shaping local development, effective governance is essential for rural revitalization. However, the successful implementation and impact of these policies in rural areas can vary due to unique local circumstances, limited information, and a lack of sophisticated decision making tools. Closing the divide between overarching national policies and practical rural development is an immediate necessity. This study begins by creating a comprehensive five-dimensional evaluation system encompassing industrial economy, public utilities, transportation and logistics, policy and institutions, and resources and the environment. It then summarizes four typical development modes—the suburban fusion mode, the characteristic industry-oriented mode, the humanistic and ecological resource-based mode, and the balanced development mode with less distinct characteristics—through an analysis of the Chinese government’s policy framework for rural construction. Subsequently, it introduces a decision support system for rural construction and development founded on multi-source heterogeneous big data and integrated algorithms. This system was tested using 782 townships as samples for classification, evaluation, and decision support. The results leverages insights into current rural development trends to efficiently align with national policies and provide customized implementation recommendations tailored to local resource characteristics. This contributes to the practical execution of rural revitalization strategies and the advancement of scientific rural decision making.

An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data

An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data

Deep feature interactive network for machinery fault diagnosis using multi-source heterogeneous data

Infrared thermal images have been applied for monitoring health condition of machines due to the noncontact and nonintrusive manner. While fault diagnosis performance of those deep neural networks (DNNs) that use infrared thermal images is restricted by the information learned from single sensor. In this study, multi-source heterogeneous data (i.e., infrared thermal images and vibration signals) are used for machinery fault diagnosis. A new DNN, i.e., deep feature interactive network (DFINet) is proposed for machinery fault diagnosis, where a novel interactive feature extraction module is developed for adaptive feature fusion on multi-source heterogeneous data. Firstly, the private and public features of multi-source heterogeneous data are extracted separately by measuring the distribution discrepancy between heterogeneous features in the feature interactive module. The feature splicing is implemented to interactively fuse common fault features of heterogeneous data and to preserve private unique features. A global feature fusion module is further proposed for adaptive fusion of superficial local features and deep abstract features learned by different feature interactive modules. The experimental results on a rotor test-bed and gearbox test-bed indicate that DFINet is promising for fusion and feature extraction on multi-source heterogeneous data in machinery fault diagnosis.

A model of integrating college students’ consumerism education and ideological and political education by applying multivariate data fusion

Abstract This paper explores the relationship between college students’ consumption views and their consumption views under Civic Education in the context of multiple datasets. Secondly, a multi-agent, multi-source heterogeneous data collection model is used to collect data on consumption view and Civic Education. A CNN model is designed to study the correlation between consumption view and Civic Education. The CNN and LSTM multi-grain data classification models are used to fuse the features of consumer outlook and Civic Education, respectively. Then, build an online learning platform for college students’ consumerism. Finally, the platform’s fusion effect and learning results for college students were analyzed and studied. The average JS index of data fusion was 93.32%, and the fusion effect was good. The total score of students’ pre and post-test consumerism scores increased from 86 to 100, and the effect of college students’ consumerism education was positive.

Threshold effect of technological innovation on carbon emission intensity based on multi-source heterogeneous data

It is of much importance to clarify the impact of technological innovation on carbon emission intensity for the low-carbon transformation of China's economy. This study, based on the panel data of 30 Chinese provinces and municipalities from 2010 to 2020, measures and analyzes the carbon emission intensity and the level of technological innovation, establishing a spatial econometric model to study the spatial spillover effect and a panel threshold model to analyze the nonlinear influence of technological innovation level on carbon emission intensity. The findings are as follows: First, the overall carbon emission intensity in China shows a decreasing trend from 2010 to 2020, with the average dropping from 3.09 in 2010 to 1.98 in 2020; Second, the spatial autocorrelation results reveal that the level of technological innovation and carbon emission intensity in China are obviously aggregated in the global spatial distribution pattern. Third, the regression results of the spatial econometric model show that the direct effect of technological innovation on carbon emission intensity is significantly negative at the level of 1%, that is, the improvement of the technological innovation in a certain area has a significant inhibitory effect on carbon emission intensity. Fourth, based on the level of economic development, there is a significant three-threshold effect of the level of technological innovation on carbon emission intensity in China, and the influence of the level of technological innovation on carbon emission intensity varies in the direction of existence and coefficient values within different threshold intervals. As economic development reaches the third interval, the technological innovation level has the most significant inhibition on carbon emission intensity. These findings enriches the research of the nonlinear relationship between technological innovation and carbon emission intensity, clarifies the spatial spillover effect and threshold effect between among them, and provides inspiration for better promote the low-carbon transformation of economy.

Value and path optimization of multi-data fusion algorithm to help sports tourism high-quality development

Abstract This paper begins by analyzing the high-quality development of sports tourism and then characterizes the massive data in sports tourism with multi-source heterogeneous and heterogeneous data. The parallel data fusion platform is Hadoop, and the multi-data feature extraction algorithm is LSTM. To complete multi-source data fusion, a random forest model enhances the algorithm’s classification performance. It is verified that the information weight value H in the weight of high-quality development of sports tourism gradually increases and stabilizes at 9.87. The multi-source data fusion algorithm can help in the in-depth fusion and common sharing of data resources in sports tourism and promote the high-quality development of sports tourism.

Evaluation model of aluminum electrolysis cell condition based on multi-source heterogeneous data fusion

Evaluation model of aluminum electrolysis cell condition based on multi-source heterogeneous data fusion

Structure-Aware Subspace Clustering

Subspace clustering has attracted much attention because of its ability to group unlabeled high-dimensional data into multiple subspaces. Existing graph-based subspace clustering methods focus on either the sparsity of data affinity or the low rank of data affinity. Thus, the quality of data affinity plays an essential role in the performance of subspace clustering. However, the real-world data are generally high-dimensional, complex, and heterogeneous multi-source data, so that the data affinity learned by these methods cannot be completely dependent. Moreover, since these approaches always ignore the intrinsic structure of data, their grouping effect is relatively low. In this paper, we propose a novel unsupervised algorithm, called Structure-Aware Subspace Clustering (SASC), to address the above issues. SASC considers local and global correlation structures simultaneously to capture the intrinsic structure. Further, it integrates the captured structure into representation learning to gain a relatively precise data affinity. It is powerful to promote an all-around grouping effect and enhances the robustness and applicability of subspace clustering. Experiments on various benchmark datasets, including bioinformatics, handwritten digit, object image, and speech signal, demonstrate the effectiveness of the proposed algorithm.

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph.

The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.

Fusion-based Representation Learning Model for Multimode User-generated Social Network Content

As mobile networks and APPs are developed, user-generated content (UGC), which includes multi-source heterogeneous data like user reviews, tags, scores, images, and videos, has become an essential basis for improving the quality of personalized services. Due to the multi-source heterogeneous nature of the data, big data fusion offers both promise and drawbacks. With the rise of mobile networks and applications, UGC, which includes multi-source heterogeneous data including ratings, marks, scores, images, and videos, has gained importance. This information is very important for improving the calibre of customized services. The key to the application's success is representational learning of fusing and vectorization on the multi-source heterogeneous UGC. Multi-source text fusion and representation learning have become the key to its application. In this regard, a fusion representation learning for multi-source text and image is proposed. The convolutional fusion technique, in contrast to splicing and fusion, may take into consideration the varied data characteristics in each size. This research proposes a new data feature fusion strategy based on the convolution operation, which was inspired by the convolutional neural network. Using Doc2vec and LDA model, the vectorized representation of multi-source text is given, and the deep convolutional network is used to obtain it. Finally, the proposed algorithm is applied to Amazon's commodity dataset containing UGC content based on the classification accuracy of UGC vectorized representation items and shows the feasibility and impact of the proposed algorithm.

RETRACTED ARTICLE: COVID-19 Forecast and Bank Credit Decision Model Based on BiLSTM-Attention Network

The COVID-19 pandemic has caused drastic fluctuations in the economies of various countries. Meanwhile, the governments’ ability to save the economy depends on how banks provide credit to troubled companies. Therefore, the impact of the epidemic on bank credit and inclusive finance are worth exploring. However, most of the existing studies focus on the reform of the financial and economic system, only paying attention to the theoretical mechanism analysis and effect adjustment, scant data support, and insufficient scheme landing. At the same time, with the rise and rapid development of artificial intelligence technology in recent years, all walks of life have introduced it into real scenes for multi-source heterogeneous big data analysis and decision-making assistance. Therefore, we first take the Chinese mainland as an example in this paper. By studying the impact of the epidemic on bank credit preference and the mechanism of inclusive finance, we can provide objective decision-making basis for the financial system in the post-epidemic era to better flow credit funds into various entities and form a new perspective for related research. Then, we put forward a model based on Bi-directional Long Short-term Memory Network (BiLSTM) and Attention Mechanism to predict the number of newly diagnosed cases during the COVID-19 pandemic every day. It is not only suitable for COVID-19 pandemic data characterized by time series and nonlinearity, but also can adaptively select the most relevant input data by introducing an Attention Mechanism, which can solve the problems of huge calculation and inaccurate prediction results. Finally, through experiments and empirical research, we draw the following conclusions: (1) The impact of the COVID-19 pandemic will promote enterprises to increase credit. (2) Banks provide more credit to large enterprises. (3) The epidemic has different impacts on credit in different regions, with the most significant one on central China. (4) Banks tend to provide more credit to manufacturing industries under the epidemic. (5) Digital inclusive finance plays a (positive) regulating effect on bank credit in COVID-19 pandemic. Inspired by the research results, policymakers can consider further solving the information asymmetry and strengthening the construction of a credit system, and more direct financial support policies for enterprises should be adopted. (6) By adopting the COVID-19 prediction model based on the BiLSTM-Attention network to accurately predict the epidemic situation in the COVID-19 pandemic, it can provide an important basis for the formulation of epidemic prevention and control policies.

Data Recognition for Multi-Source Heterogeneous Experimental Detection in Cloud Edge Collaboratives

This article proposes a multisource heterogeneous experimental data recognition method based on CRNN and DBNet in a cloud-edge collaborative environment in an attempt to address the issues of low efficiency and a high error rate that come with traditional manual data detection and recognition. Firstly, a recognition architecture for experimental detection data of intelligent measurement systems is designed based on a cloud-edge collaborative environment to improve the efficiency of data processing. Then, the improved DBNet network is used in the edge computing center to detect the text, and the correction module is used to correct the deviation of the detected text to ensure the standardization of the text. Finally, in the central cloud, the end-to-end indefinite length character recognition (CRNN) algorithm is used to analyze and identify the text order, rules, and other information of the image after the correction is completed, extract the test detection data, and convert the detection data into row data according to the two-dimensional table structure, and conduct structured storage and management through the relational database. An experimental analysis of the proposed method is conducted based on the deep learning framework, and results show that its accuracy rate and recall rate are close to 96% and 94%, respectively, with an average accuracy of 95.09%. This fully demonstrates the proposed method is effective and, therefore, applicable to power equipment experimental detection data recognition.

Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study.

Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.

Fault diagnosis strategy of industrial process based on multi-source heterogeneous information and deep learning

With the progress of technology, modern industrial monitoring data not only includes traditional process data but also includes video data. In order to make full use of these multi-source heterogeneous data to achieve better accuracy of fault classification, feature extraction and feature fusion of different modalities is vital important. For feature extraction, because the contribution rate of process data at different times is different to fault classification, in order to obtain the temporal features that have a great impact on fault classification, the Batch Normalization Long Short-Term Memory based on Self-Attention mechanism (BN-LSTM-SA) is used for feature extraction of process data. Since temporal and spatial features are important indicators of video classification, the Two Stream Shifted Windows 3D Convolution Transformer (TSSCT) model is used to extract temporal and spatial features. Cross Multi-head Self-Attention(CMSA) model is designed for feature fusion, which fully utilize the correlation and complementarity of multi-source heterogeneous information and can reasonably allocate the weight of different modalities. Experiments show that our proposed method is superior to the unimodal model or the existing multimodal fusion model and prove the effectiveness of the proposed method.

A distributed multi-vehicle pursuit scheme: generative multi-adversarial reinforcement learning

Multi-vehicle pursuit (MVP) is one of the most challenging problems for intelligent traffic management systems due to multi-source heterogeneous data and its mission nature. While many reinforcement learning (RL) algorithms have shown promising abilities for MVP in structured grid-pattern roads, their lack of dynamic and effective traffic awareness limits pursuing efficiency. The sparse reward of pursuing tasks still hinders the optimization of these RL algorithms. Therefore, this paper proposes a distributed generative multi-adversarial RL for MVP (DGMARL-MVP) in urban traffic scenes. In DGMARL-MVP, a generative multi-adversarial network is designed to improve the Bellman equation by generating the potential dense reward, thereby properly guiding strategy optimization of distributed multi-agent RL. Moreover, a graph neural network-based intersecting cognition is proposed to extract integrated features of traffic situations and relationships among agents from multi-source heterogeneous data. These integrated and comprehensive traffic features are used to assist RL decision-making and improve pursuing efficiency. Extensive experimental results show that the DGMARL-MVP can reduce the pursuit time by 5.47% compared with proximal policy optimization and improve the pursuing average success rate up to 85.67%. Codes are open-sourced in Github.