Multi-source heterogeneous data fusion technology for electric power based on big data mining

The study of the prediction of stock market volatility is of great significance to rationally control financial market risks and increase excessive investment returns and has received extensive attention from academic and commercial circles. However, as a dynamic and complex system, the stock market is affected by multiple factors and has a comprehensive capability to include complex financial data. Given that the explanatory variables of influencing factors are diverse, heterogeneous and complex, the existing intelligent algorithms have great limitations for the analysis and processing of multi-source heterogeneous data in the stock market. Therefore, this study adopts the edge weight and information transmission mechanism suitable for subgraph data to complete node screening, the gate recurrent unit (GRU) and long short-term memory (LSTM) to aggregate subgraph nodes. The compiled data contain the metapaths of three types of index data, and the introduction of the association relationship attention dimension effectively mines the implicit meanings of multi-source heterogeneous data. The metapath attention mechanism is combined with a graph neural network to complete the classification of multi-source heterogeneous graph data, by which the prediction of stock market volatility is realized. The results show that the above method is feasible for the fusion of heterogeneous stock market data and the mining of implicit semantic information of association relations. The accuracy of the proposed method for the prediction of stock market volatility in this study is 16.64% higher than that of the dimensional reduction index and 14.48% higher than that of other methods for the fusion and prediction of heterogeneous data using the same model.

As the complexity of intelligent public transportation systems continues to increase and the degree of interconnection of equipment at all levels gradually deepens, building a digital twin system will generate a large amount of multi-source heterogeneous data. Effective processing and in-depth mining of data can provide real-time and intelligent decision-making optimization for the construction of digital twin systems, thereby efficiently handling complex emergencies. This paper conducts a systematic review on the processing methods and technologies of multi-source heterogeneous twin data in traffic dynamic scenarios. First, the content and classification of multi-source heterogeneous twin data in the operation process of intelligent bus systems are clarified; secondly, the multi-source The data processing methods and technologies applied in each stage of data collection, data storage, data fusion and data analysis in heterogeneous twin data processing are analyzed, and the advantages, disadvantages and applications of various methods and technologies are analyzed; finally, corresponding to the multiple This paper summarizes the methods and technologies for processing multi-source heterogeneous twin data, and points out the challenges and development trends faced by the current multi-source heterogeneous twin data processing methods and technologies.

The complexity and diversity of equipments presented in the development of modern industrial technology brings the wide application of deep learning in fault diagnosis. However, the multi-source heterogeneous data collected from different sensors applied in industrial production cannot be used by traditional neural networks directly, which registers a difficulty to fault diagnosis based on deep learning. In addition, with only one of the data used, traditional deep learning fault diagnosis methods ignores the correlation between heterogeneous data, which may result in the loss of useful information and the accuracy of fault diagnosis. To solve the above problems, a fault diagnosis framework based on deep learning for multi-source heterogeneous data fusion is proposed. In the feature learning stage, stack self-encoder (SAE) and convolution neural network (CNN) are used to extract deep features from one-dimensional vibration data and two-dimensional image data respectively, and then feature fusion fault diagnosis is carried out. It solves the problem that traditional neural network can't make full use of multi-source heterogeneous data, and improves the accuracy of fault diagnosis. What's more, the bearing data of Case Western Reserve University are used to verify the validity of the diagnosis method proposed in this paper.

With the growing importance of the fuzzy spatiotemporal data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy spatiotemporal data. In this paper, we first propose a fuzzy spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy spatiotemporal data, and use the fuzzy spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy spatiotemporal data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority.

Aiming at the low cleaning rate of the traditional multi-source heterogeneous power grid big data cleaning model, a multi-source heterogeneous power grid big data cleaning model based on machine learning classification algorithm is designed. By capturing high-quality multi-source heterogeneous power grid big data, weight labeling of data source importance measurement, data attributes and tuples, and constructing Tan network based on the idea of machine learning classification algorithm, the data probability value is finally used to complete the classification and cleaning of inaccurate data. Experiments show that the model based on machine learning classification algorithm can effectively improve the imprecise data cleaning rate compared with the traditional model to solve multi-source heterogeneous imprecise data cleaning.

In medical domain, huge amounts of data are generated at all times. These data are usually difficult to access, with poor data quality and many data islands. Besides, with a wide range of sources and complex structure, these data contain essential information and are difficult to manage. However, few existing data management frameworks based on Data Lake excel in solving the persistence and the analysis efficiency for medical multi-source heterogeneous data. In this paper, we propose an efficient Multi-source Heterogeneous Data Lake Platform (MHDP) to realize the efficient medical data management. Firstly, we propose an efficient and unified method based on Data Lake to store data of different types and different sources persistently. Secondly, based on the unified data store, an efficient multi-source heterogeneous data fusion is implemented to effectively manage data. Finally, an efficient data query strategy is carried out to assist doctors in medical decision-making. In-depth analysis on applications shows that MHDP delivers better performance for data management in medical domain.

This paper proposes an optimization algorithm for constructing education knowledge graphs based on multi-source heterogeneous data fusion (MEGO). The algorithm first cleans and extracts features from multi-source education data, and uses deep learning models to mine deep features of different types of data, then dynamically adjusts the weights of each data source through the attention mechanism to achieve efficient fusion of multi-source data, finally, the graph structure is optimized with the help of graph convolutional networks to remove noise and redundant information. The experiment selects <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0 0, 0 0 0}$</tex> multi-source heterogeneous education data from 10 universities in a certain region within 5 years, and compares them with the traditional TransE algorithm and the KG-Basic algorithm based on a single data source. The results show that the accuracy of the MEGO algorithm is 92.3%, which is 8.7% higher than the TransE algorithm and 12.5% higher than the KG -Basic algorithm, the recall rate is 89.6%, which is 7.2% and 10.8% higher than the two comparison algorithms respectively, the F1 value is 90.9%, which is 7.9% and 11.6% higher respectively, the graph construction time is only 12.5 minutes, which is 35.2% shorter than TransE and 42.8% shorter than KG-Basic. In summary, the MEGO algorithm can effectively improve the quality and construction efficiency of the education knowledge graph, and provide solid technical support for multi-dimensional and precise education quality evaluation.

In order to fully utilize the features of multi-source heterogeneous data and effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery, a multi-source heterogeneous data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencoder (MSCAE) is proposed. Firstly, multi-scale information learning is integrated into the convolutional autoencoder (CAE) to consider the temporal and spatial feature information of the diagnostic object simultaneously. To improve the training and diagnostic efficiency of MSCAE, a quantum particle swarm optimization (QPSO) module is used to perform hyperparameter optimization on it using chaos initialization and dynamic weight strategy (DWS). Besides, the sparse attention mechanism is introduced into the MSCAE model to improve the recognition rate of key fault features hidden in the original heterogeneous signals. Finally, the confusion matrix and visualization techniques are used to achieve fault classification. The experimental results demonstrate that after 100 experiments, the proposed method has an average diagnostic accuracy of 98.5 % and strong robustness to noise, providing a new method for rotating machinery fault diagnosis based on multi-source heterogeneous data.

With continuous innovation of the “Internet+” and development of related industries, huge amounts of data need to be integrated on the basis of spatial data analysis and processing. Therefore, efficient integration of multi-source heterogeneous spatial data and data interoperability requirements has become more and more urgent. The design of a multisource heterogeneous vector spatial data integration scheme on WebGIS has been proposed by GeoJSON as the universal transformation format, based on the analysis of the existing multi-source heterogeneous spatial data conversion method. Finally, a prototype of the online prototype system was developed with. Net platform to verify the feasibility and practicability of the scheme. The scheme has the characteristics of high efficiency, easy to expand and low cost, and provides a new way to realize the data integration of “Internet+”.

With the acceleration of intelligent transformation of energy system, the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration. In view of the heterogeneous characteristics of physical sensor data, including temperature, vibration and pressure that generated by boilers, steam turbines and other key equipment and real-time working condition data of SCADA system, this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning. By constructing a multi-level fusion architecture, the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data, simulation calculation results and expert knowledge. The data fusion module combines Kalman filter, wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference. Simulation results show that the data fusion accuracy can be improved to more than 98%, and the calculation delay can be controlled within 500 ms. The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model, supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring, system response time is less than 2 seconds, and data consistency verification accuracy reaches 99.5%.

The invasive pest Hyphantria cunea poses a serious threat to forestry and agricultural ecosystems. To mitigate this threat, accurate and efficient prediction of its distribution is critical for enabling proactive management and targeted control strategies. However, existing traditional statistical models and machine learning approaches exhibit limited predictive performance due to their inability to effectively capture complex spatiotemporal dependencies and process multi-source heterogeneous data. Furthermore, although deep learning has demonstrated progress in pest prediction, current models still suffer from critical limitations including inadequate feature fusion and insufficient critical temporal feature capture. To address this issue, this study proposes a novel Synergistic Fusion Bidirectional Long Short-Term Memory network (SF-BiLSTM). Centered on the synergistic fusion of multi-source heterogeneous data, its architecture consists of three core stages. Firstly, a Bidirectional LSTM (BiLSTM) is utilized to capture complex spatiotemporal dependencies within environmental sequences. Subsequently, dual-path temporal feature refinement is performed: one path employs mean pooling and layer normalization for feature compression, while the other integrates an attention sub-network to focus on critical time steps and conduct weighted fusion. Finally, an adaptive weighted fusion of the dual-path features is achieved through a weight learning network, yielding the final risk prediction. Experiments on the Shandong Province dataset from 2019 to 2024 demonstrate that the SF-BiLSTM model achieves outstanding comprehensive performance, with a Precision of 0.9667 and an F1-score of 0.9496, significantly outperforming traditional machine learning methods such as Gradient Boosting, KNN, and all baseline models. This model successfully reveals the spatiotemporal patterns and risk levels of Hyphantria cunea outbreaks in Shandong Province, providing reliable decision support for precise regional pest prevention and control.

To improve the effect of college English teaching, combines multi-source data (MSD) fusion technology to build a college English blended teaching system. Moreover, through the research on the functions of smarter classrooms and the analysis and exploration of blended teaching, this paper designs a college English blended teaching system based on MSD fusion. In addition, this paper analyzes the bandwidth-tunable optical routing module of the interference-coupled series double-ring involved in multi-data fusion. Finally, this paper studies the working principle and structural design of the module, and constructs a college English blended teaching system structure based on MSD fusion. From the evaluation results, it can be seen that the college English blended teaching system based on MSD fusion proposed in this paper can effectively improve the quality of English teaching.

In recent years, the demand for urban travel is increasing and the travel modes are diverse. Online car Hailing has become an important way to meet the travel needs of residents. The online car-hailing platform receives tens of thousands of travel requests every day. However, a large portion of the thousands of orders are unfinished, that is, canceled by passengers. This not only reduces the income of drivers but also affects the order dispatching efficiency of the online car-hailing platform. To predict the cancellation probability of online car-hailing orders(OCP), the relationship between multi-source heterogeneous data and OCP is first introduced, in which the presence of idle taxis is the main factor for passengers to cancel their orders during the waiting period. Secondly, a deep learning model based on the Seq2Seq structure is designed to predict OCP in real-time. The model consists of an attribute fusion module, encoder layer, and decoder layer. Finally, a full experiment is carried out using the Didi Chengdu online car-hailing order data set to verify the effectiveness of the algorithm.KeywordsTaxi order cancellationUrban travelFeature fusionDeep learning

Higher education continues to emphasize theoretical knowledge, leaving students ill-equipped to translate technological advances into viable business solutions. This gap often results in innovation and entrepreneurship projects with limited technological depth or poor execution, ultimately hindering the overall growth index. Therefore, this paper studies the strategies for cultivating college students’ innovative thinking and improving their entrepreneurial abilities in the artificial intelligence environment. Using a deep reinforcement learning algorithm, we encode both structured data (e.g., course grades) and unstructured data into a unified state space. Through the interaction between the agent and the environment, the reward mechanism is used to optimize and fuse actions, combined with the deterministic policy gradient to fuse multi-source heterogeneous data. The K-means clustering algorithm is used to cluster the results of multi-source heterogeneous data fusion, and college students are divided into different characteristic groups. The Bayesian network combined with the expectation maximization algorithm is used to evaluate the innovation and entrepreneurship abilities of college students with different characteristics by initializing the prior probability and iteratively optimizing the posterior probability of the latent variables. According to the evaluation results of college students’ innovation and entrepreneurship abilities, a user-strategy feature matrix is constructed based on the collaborative filtering algorithm of deep learning Euclidean embedding. Through the multi-layer perceptron to learn the non-linear interaction, corresponding strategies for cultivating innovative thinking and improving entrepreneurial abilities are provided for students. The experimental results show that when this method is used to cultivate college students’ innovative thinking and improve their entrepreneurial abilities, the innovation and entrepreneurship growth index is as high as 0.9, providing a reference for colleges and universities to cultivate high-quality innovation and entrepreneurship talents.

In this paper, a smart campus analysis model based on multi-source data fusion technology is constructed with the study of college students’ behavior as the starting point. The K-means algorithm is utilized to mine the campus data of real college students from multiple sources, and through the fusion of multi-source data, a real, intelligent, multi-level student portrait is portrayed in the three dimensions of consumption, academics, and socialization, so as to provide a rational decision-making basis for the comprehensive development of the ideological and political education and student management of the university with the “three-whole-education” approach. The results show that multi-source data fusion technology can significantly reduce the storage space required for data. University X has a low borrowing interest for Civics and Political Science books. Sports are loved by 70% of students, but half of them don’t exercise regularly. Outside spending is a habit among some students, and 20% of them may exhibit abnormal behavior. These individual behavioral characteristics will have a significant impact on the performance of Civics.