Multi-source Heterogeneous Data Research Articles

Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management

In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.

Multi-source heterogeneous data fusion model based on fuzzy mathematics

Sensors as the sensing end of intelligent control can be used to collect various data instead of human beings. In the context of technological development, the variety of sensors leads to multiple and structurally unequal data sources, and fusion of these data becomes a problem for consideration. The study constructs an intuitionistic fuzzy transformation method to handle data with various attributes with the help of fuzzy mathematical concepts, which characterizes the data based on the hesitancy and ideal solutions under Gaussian distribution. Simulations of classical classification data show that the intuitionistic fuzzy transformation method can effectively differentiate the affiliation of data points in the dataset, and the results of 800 simulations show that the qualitative accuracy of the algorithm can reach 89%, while the causes of abnormal data are explored and it is found that the attributes of the dataset based on Gaussian distribution are too close to each other as the cause of misclassification; the algorithm is also optimized from multi-dimensional considerations, and a An optimization operator based on the distance method of superior and inferior solutions was constructed and simulated for several optimization paths. The results show that the study uses an optimization scheme that is significantly better than the existing fuzzy operator, and 800 times can improve the accuracy rate up to 95.23%, which is 14.01% higher than that of a single attribute. This indicates that the intuitionistic fuzzy algorithm of this study has some rationality and is able to fuse the data of multiple attributes of the sensor for determination and provide the necessary basis for decision making.

Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment

The continuous and reliable operation of the transformer is the basis to ensure the normal operation of the power system. Relevant departments collect multi-dimensional and multi-source heterogeneous parameter data during the operation, maintenance and repair of transformers. The effective information contained in the parameter data can directly reflect the current operating status of the transformer. On the basis of support vector machine and grey wolf algorithm, an improved GWO-MCSVM algorithm based on nonlinear convergence factor and Tent chaotic mapping is proposed. The algorithm parameters are optimized through training samples, and the results are evaluated and verified in the algorithm itself, so as to improve the accuracy of the status assessment results. Finally, the accuracy of assessment results of the algorithm proposed in this paper, existing genetic algorithms and particle swarm optimization algorithms are compared by evaluating multiple sets of measured samples. By comparison, the effectiveness of the algorithm proposed in this paper for transformer condition assessment has been verified.

Construction and application of knowledge graph for grid dispatch fault handling based on pre-trained model

With the construction of new power systems, the power grid has become extremely large, with an increasing proportion of new energy and AC/DC hybrid connections. The dynamic characteristics and fault patterns of the power grid are complex; additionally, power grid control is difficult, operation risks are high, and the task of fault handling is arduous. Traditional power-grid fault handling relies primarily on human experience. The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling. Therefore, this mode of operation is no longer suitable for the requirements of new systems. Based on the multi-source heterogeneous data of power grid dispatch, this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model, constructs a knowledge graph of power-grid dispatch fault processing and designs, and develops a fault-processing auxiliary decision-making system based on the knowledge graph. It was applied to study a provincial dispatch control center, and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.

An integrated risk assessment model for the multi-perspective vulnerability of distribution networks under multi-source heterogeneous data distributions

The reliability of distribution network transmission corridors are vulnerable to different risk factors in both internal operation parameters and surrounding environmental conditions, and thus pinpoint evaluations of these impacting factors can further enhance their safety. To this end, this paper proposes an integrated assessment method, that is the multi-dimensional assessment with integrated fuzzy association (MDATFA). In this model, healthiness and importance indices of transmission lines are both defined to reflect the self-robustness and potential damages, respectively, and therefore to enable a two-dimensional assessment to incorporate the multi-source heterogeneous inputs. Firstly, healthiness indices for environmental characteristics are analyzed. On the one hand, discrete input features are processed by the established association rule mining model with rare variables (ARMrv) to mine and diagnose rarely occurred elements, and their healthiness contribution values can then be calculated. On the other hand, continuous features are handled by the designed hierarchical probabilistic fuzzy systems (HPFS). The weight assignments of these two expert models are adjusted by a gating network. Secondly, the importance indices for electrical characteristics are calculated. Under the condition that electrical signal constraints are satisfied, the amount of load loss and lost users can be solved by the built distribution network fault recovery model which is according to heuristic rule and second-order cone (HRSO). Finally, the validity and robustness of this operational condition assessment model is verified by an empirical analysis in an actual 39-bus power distribution system.

A multimodal dual-fusion entity extraction model for large and complex devices

In the context of large and complex devices with a multi-source heterogeneous data environment, the extraction of network device configuration related entity information from diverse modalities of the Internet of Things data is a crucial and fundamental step towards establishing a domain knowledge graph for global Zero Touch Provisioning of network devices. In this paper, we present a novel multimodal dual-fusion entity extraction model that serves as a foundation for an intelligent and efficient network device configuration process. Firstly, the multimodal data is encoded, followed by the ViLBERT pre-training model to obtain more feature information of entities for multimodal front-end fusion. Next, the attention weights of each modal feature are learned through a multilayer neural network classifier and probabilistic graphical model, facilitating multimodal back-end fusion and reducing information redundancy. Finally, entity recognition is accomplished by employing a cohesive memory module that extracts the essential parameters for device configuration. The simulation results demonstrate that the proposed model performs exceptionally well on the MSCOCO2017 public dataset and the SFZK-Dev data network device dataset, with F1 values of the comprehensive evaluation index of model quality at 94.65% and 96.94%, respectively, indicating high stability. Additionally, the link prediction metrics HITS@1 achieved accuracy levels of 58.21% and 68.04%.

Retracted: Intelligent Prediction Model of CET-4 Passing Rate Based on Multisource Heterogeneous Data Mining

Retracted: Intelligent Prediction Model of CET-4 Passing Rate Based on Multisource Heterogeneous Data Mining

WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic

ABSTRACT The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.

Creating Historical Building Models by Deep Fusion of Multi-Source Heterogeneous Data Using Residual 3D Convolutional Neural Network

ABSTRACT There are a large number of historical buildings in the world, but many of them are seriously damaged. A fundamental task to revitalize them is to record their details and redraw them, but it is a highly inefficient task. This paper aimed to create three-dimensional (3D) models automatically for those damaged historical buildings while respecting the 1964 Venice Charter, the ICOMOS recommendations, and local ordinances. After reviewing the advantages and disadvantages of various measurement tools, we implemented a machine learning algorithm, the residual 3D convolutional neural network (CNN) method, to combine heterogeneous data from multiple sources including a 3D laser scanner, an unmanned aerial vehicle, a total station, and a camera, in a historical building information model (HBIM) to increase its accuracy. Those data, preprocessed by four steps including data registration, reducing noises, filtering data, and data integration, were trained and fused in a 3D CNN model, which consisted of one input layer, five 3D convolutional and pooling layers, and two connected layers. A Shanghai historical building conservation project was taken as a pilot case to verify its productivity and accuracy in developing the HBIM, on which the architectural features were thoroughly recorded, and the damaged details were recovered.

Federated Generative Model on Multi-Source Heterogeneous Data in IoT

The study of generative models is a promising branch of deep learning techniques, which has been successfully applied to different scenarios, such as Artificial Intelligence and the Internet of Things. While in most of the existing works, the generative models are realized as a centralized structure, raising the threats of security and privacy and the overburden of communication costs. Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. Particularly, our generative model framework can solve the problem of distributed data generation on multi-source heterogeneous data in two scenarios, i.e., feature related scenario and label related scenario. In addition, in our federated generative models, we develop a synchronous and an asynchronous updating methods to satisfy different application requirements. Extensive experiments on a simulated dataset and multiple real datasets are conducted to evaluate the data generation performance of our proposed generative models through comparison with the state-of-the-arts.

Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach

The analysis of passenger travel choice behaviours under train delays has become a crucial topic in research on urban rail transit operation management. In this paper, we focus on analysing travel choices of affected regular passengers under train delays by utilizing the data collected through an automatic fare collection (AFC) system along with train delay log records. Along this line, we propose a data-driven four-stage framework for studying regular passengers’ responses under delays, consisting of data profiling, regular passenger screening and travel patterns extraction, affected regular passenger identification, and affected passenger behaviour prediction modelling. Using a real-world case of the Shenzhen Metro in China, we conduct extensive experiments for method validation and feature insights analysis. The proposed framework could provide a microscopic view of passenger travel behaviours under train delays for fine prediction and exhibit a possibility for multi-source heterogeneous data mining in passenger behaviour analysis and train delay-related tasks.

A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area.

"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article, a data-driven approach is proposed to analyzing the trajectory of urban electricity CEs based on empirical mode decomposition, which suggests combining macro-energy thinking and big data thinking by removing the barriers among power systems and related technological, economic, and environmental domains. Based on multisource heterogeneous mass data acquisition, effective secondary data can be extracted through the integration of statistical analysis, causal analysis, and behavior analysis, which can help construct a simulation environment supporting the dynamic interaction among mathematical models, multi-agents, and human participants.

A compatible carbon efficiency information service framework based on the industrial internet identification

Green and low-carbon is a new development model that seeks balance between environmental sustainability and high economic growth. If explainable and available carbon emission data can be accurately obtained, it will help policy regulators and enterprise managers to more accurately implement this development strategy. A lot of research has been carried out, but it is still a difficult problem that how to accommodate and adapt the complex carbon emission data computing models and factor libraries developed by different regions, different industries and different enterprises. Meanwhile, with the rapid development of the Industrial Internet, it has not only been used for the supply chain optimization and intelligent scheduling of the manufacturing industry, but also been used by more and more industries as an important way of digital transformation. Especially in China, the Industrial Internet identification and resolution system is becoming an important digital infrastructure to uniquely identify objects and share data. Hence, a compatible carbon efficiency information service framework based on the Industrial Internet Identification is proposed in this paper to address the problem of computing and querying multi-source heterogeneous carbon emission data. We have defined a multi cooperation carbon emission data interaction model consisting of three roles and three basic operations. Further, the implementation of the framework includes carbon emission data identification, modeling, calculation, query and sharing. The practice results show that its capability and effectiveness in improving the responsiveness, accuracy, and credibility of compatible carbon efficiency data query and sharing services.

Construction of a rural tourism information service management system for multi-source heterogeneous data processing.

This study offers an integrated service management system for rural tourist information based on a cloud platform to address the three main issues of high platform concurrency, difficulty storing and managing data, and trouble sharing data functions. Three levels-data, process, and architecture-are considered in the analysis and design of the platform. The Hadoop data storage system makes possible the collection, storage, administration, and exchange of data functions for large amounts of heterogeneous data from many different sources by utilising Netty data transmission technology, hybrid data storage technology, and the Web Foundation. The results demonstrate that the system's response time is low, and the CPU consumption time and the average utilisation rate meet the actual needs. They resolve issues with the current rural tourism platforms application, such as the difficulty of data collection, the low rate of reuse, the low rate of sharing, the lack of timely updates, and severe island phenomena.

Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.

Increasing evidence indicates that metabolites are closely related to human diseases. Identifying disease-related metabolites is especially important for the diagnosis and treatment of disease. Previous works have mainly focused on the global topological information of metabolite and disease similarity networks. However, the local tiny structure of metabolites and diseases may have been ignored, leading to insufficiency and inaccuracy in the latent metabolite-disease interaction mining. To solve the aforementioned problem, we propose a novel metabolite-disease interaction prediction method with logical matrix factorization and local nearest neighbor constraints (LMFLNC). First, the algorithm constructs metabolite-metabolite and disease-disease similarity networks by integrating multi-source heterogeneous microbiome data. Then, the local spectral matrices based on these two networks are established and used as the input of the model, together with the known metabolite-disease interaction network. Finally, the probability of metabolite-disease interaction is calculated according to the learned latent representations of metabolites and diseases. Extensive experiments on the metabolite-disease interaction data were conducted. The results show that the proposed LMFLNC method outperformed the second-best algorithm by 5.28 and 5.61% in the AUPR and F1, respectively. The LMFLNC method also exhibited several potential metabolite-disease interactions, such as "Cortisol" (HMDB0000063), relating to "21-Hydroxylase deficiency," and "3-Hydroxybutyric acid" (HMDB0000011) and "Acetoacetic acid" (HMDB0000060), both relating to "3-Hydroxy-3-methylglutaryl-CoA lyase deficiency." The proposed LMFLNC method can well preserve the geometrical structure of original data and can thus effectively predict the underlying associations between metabolites and diseases. The experimental results show its effectiveness in metabolite-disease interaction prediction.

A novel LASSO-ATT-LSTM model of stock price prediction based on multi-source heterogeneous data

Stock price volatility is influenced by many factors, including unstructured data that is not easy to quantify, such as investor sentiment. Therefore, given the difficulty of quantifying investor sentiment and the complexity of stock price, the paper proposes a novel LASSO-ATT-LSTM intelligent stock price prediction system based on multi-source data. Firstly, establish a sentiment dictionary in the financial field, conduct sentiment analysis on news information and comments according to the dictionary, calculate sentiment scores, and then obtain daily investor sentiment. Secondly, the LASSO (Least absolute shrinkage and selection operator) is used to reduce the dimension of basic trading indicators, valuation indicators, and technical indicators. The processed indicators and investor sentiment are used as the input of the prediction model. Finally, the LSTM (Long short-term memory) model that introduces the attention mechanism is used for intelligent prediction. The results show that the prediction of the proposed model is close to the real stock price, MAPE, RMSE, MAE and R2 are 0.0118, 0.0685, 0.0515 and 0.8460, respectively. Compared with the existing models, LASSO-ATT-LSTM has higher accuracy and is an effective method for stock price prediction.

Research on a methodology for intelligent seismic performance evaluation and optimization design of buildings based on IFC and ontology

Seismic loss prediction and performance assessment for buildings have already been hot spots for resilience after earthquakes with the emergence of the concepts of digital twins. However, because a large amount of multisource heterogeneous data and a real-time accurate prediction model are required to implement the process, the popularization and implementation of these technologies are hampered. Based on performance-based seismic design (PBSD) theory, this study introduces BIM and ontology and other emerging technologies to carry out methodology research on intelligent earthquake damage prediction and seismic performance evaluation for individual buildings. First, based on FEMA P-58 (Seismic Performance Assessment of Buildings), this paper uses BIM technology to integrate and transmit component-level engineering information. Ontology method is used to express the assessment content and logic, and a multidimensional semantic ontology model based on IFC is proposed to form the linked data foundation to organize, store, associate and interact the multisource heterogeneous data required for assessment in a unified way to realize the semi-automated seismic performance assessment for individual RC frame building. On this basis, the seismic optimization design under the guidance of the “investment - benefit” criterion is regarded as the problem of seeking the balance between the initial construction cost and the expectation of seismic loss. The multi-objective genetic algorithm (NSGA-II) is used to realize the optimization iteration at the component level. According to the results of the case study, the new method in this paper significantly improves the quality of seismic performance assessment and seismic design optimization.

Circular Economy Model for Elderly Tourism Operation Based on Multi-source Heterogeneous Data Integration

ABSTRACT The emerging senior tourism industry has attracted increasing attention in China due to the continuous development of social, economic and technological factors. However, the senior tourism market in China still faces significant shortages, and further market development is needed. To address this issue, a senior tourism business model based on the circular economy, artificial intelligence (AI) technology and multi-source heterogeneous data integration has been proposed in this study. The model uses AI technology to integrate and analyze multi-source heterogeneous data, providing insights into the senior tourism market and facilitating precise marketing. Additionally, the model combines senior tourism products with sustainable development by adopting a circular economy model, achieving both economic and environmental benefits. The application of this model is expected to enhance the competitiveness and sustainable development of the Chinese senior tourism market, providing new ideas and references for the development of the industry. The integration of multi-source heterogeneous data is a significant feature of this model, which facilitates a more in-depth understanding of the senior tourism market. By analyzing various data sources, such as social media, search engines, and online reviews, the model can better identify senior tourists’ preferences and behaviors, thereby improving the precision of marketing strategies. Furthermore, by integrating environmental data, such as carbon footprint and energy consumption, the model can assess the sustainability of senior tourism products, contributing to the development of eco-friendly senior tourism.

A novel coal-rock cutting state identification model based on the Internet of Things

The sudden change in the interface between coal-rock mass can lead to the increased abrasion of picks and the failure rate of mining machinery. The safe and efficient coal-rock identification technology is the key to realize the intelligent control of coal mining machinery. To realize the real-time perception and accurate recognition of coal-rock cutting state information, a novel coal-rock cutting state identification model based on the Internet of Things (IoT) is explored. Specifically, by using the virtual prototype technology, multi-source heterogeneous data from acquisition, processing and identification of coal-rock cutting state information are fused and analyzed. First, the paper analyzes the physical and mechanical characteristics of coal-rock mass, and takes the cutting drum of bolter miner as the research object to theoretically analyze its load, which provides a foundation for the research on coal-rock cutting state identification. Second, a rigid-flexible coupling virtual prototype model of the cutting drum and coal-rock models under different cutting conditions are established. The simulation process is implemented by employing the discrete element method (DEM) to ensure the real-time transmission of motion information and state characteristic signals. Ultimately, the dynamic information of drum load during the coal-rock cutting process is obtained. Finally, LVQ (Learning Vector Quantization) and PSO-BP (Particle Swarm Optimization-Back Propagation) neural networks are created, and the variation coefficient, waveform factor, and peak factor of load curves of cutting drums in the coal-rock mass with different firmness coefficients are input into the neural networks as feature vectors for state recognition. The experimental results show that LVQ and PSO-BP neural networks can be used for coal-rock cutting state identification, and PSO-BP network has faster convergence speed and higher recognition efficiency, which provides a new scheme for coal-rock cutting state identification to improve the safety and efficiency of coal mining machinery.

Cloud-Edge Computing Technology-Based Internet of Things System for Smart Classroom Environment

In view of the main challenges faced by the realization of smart classroom environment in classroom teaching, such as inconvenient use of equipment, inability to comprehensively obtain multi-source heterogeneous data in real time, and lack of effective support for smart teaching, an IoT system archi-tecture based on cloud-edge computing technology is proposed. The system architecture consists of four parts: sensor layer, edge computing layer, core network layer and platform application layer, which are respectively used to obtain multi-source heterogeneous data, configure edge nodes on demand, transfer information and provide various educational services. A question-naire survey was adopted to investigate the teachers and students in the smart classrooms of Central China Normal University where the proposed system was implemented. The results showed that the overall satisfaction of students and teachers is high. Among them, 100.00% of teachers and 97.14% of students agreed that the system can effectively collect educational data and give students feed-back; 96.00% of teachers and 91.43% of students believed that it can pro-mote classroom teaching and bring pleasant experience to teachers and stu-dents. In brief, the proposed solution is completely practicable and can pro-vide a reference for the research, construction and application of smart classrooms.