Multi-source Data Fusion Technology Research Articles

Enhancing power marketing audit through IoT and multi-sensor information fusion: A substation scenario analysis

The demand for electricity in economic development is increasing, so it is necessary to establish a unified power marketing audit evaluation system, standardize the power marketing audit workflow, and formulate scientific audit evaluation standards to make the power marketing audit more scientific and objective. This paper introduces multi-sensor cooperative sensing from IoT technology and uses multi-source data fusion technology for complete multi-sensor information fusion. The objective is to illustrate how multi-sensor information fusion and Internet of Things (IoT) improvements may improve the power industry's auditing process, especially at substations, resulting in increased efficiency, accuracy, and risk management. We focus on analyzing the environmental characteristics of the power IoT sensing system in the substation scenario and compare the advantages and disadvantages of various types of networking algorithms. Experiments show that our method has a low bandwidth footprint, energy consumption, and high data accuracy.

Research on the "shape-performance-control" integrated digital twin system for boom-type roadheaders

The boom-type roadheader plays a crucial role in coal mining. However, conducting the real-time monitoring of the mechanical performance and comprehensive adaptive cutting in the dynamic cutting process are challenging. To address these issues, a digital twin system that integrates the elements of “shape, performance, and control” for roadheaders is presented in this paper. The system comprises three components: physical space, service space, and twin space. The service space forms the core of the entire system. Within this space, twin models and control models are created using numerical simulation, artificial intelligence and multi-source data fusion technology. These models serve the purpose of predicting the roadheader’s mechanical performance and controlling the swing speed of the cutting arm. The physical space is built using technologies such as robot kinematics, electrical systems, hydraulic transmission, and other relevant techniques. This approach facilitates the transmission of multi-sensor data to twin models. The control model then manages the roadheader’s function based on the output signals from the control model. The twin space is constructed utilizing physical rendering engines, databases, and 3D modelling tools. This space visualizes and stores the movement, performance, and control parameters of the roadheader. The results demonstrate that the average absolute error between the measured data from the test’s three position strain gauges and the predicted data from the twin system is 10.38 MPa. Furthermore, the twin system achieves an average update interval of 0.34 s, allowing real-time stress monitoring of the structural components of the roadheader and preventing damage caused by overload. The proposed control model enables adaptive adjustment of the swing speed of the cutting arm in approximately 0.3 s. This improvement significantly enhances the adaptive cutting capabilities of roadheaders when dealing with complex coal and rock formations.

Retracted: Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

Retracted: Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

Generator condition monitoring method based on SAE and multi-source data fusion

With the increasing number of units involved in power system regulation and the increasing proportion of industrial load, a single data source has been unable to meet the accuracy requirements of online monitoring of unit conditions in the new power system. Based on the stacked autoencoder (SAE) network, combined with multi-source data fusion technology and adaptive threshold, a generator condition monitoring method is proposed. First, a SCADA–PMU data fusion method based on the weighted D–S evidence theory is proposed. Second, the auto-coding technology is introduced to build a stacked self-coding deep learning network model, extract the deep features of the training dataset, and build a generator fault detection model. Finally, by smoothing the reconstruction error and combining it with the trend change in the state monitoring quantity detected by the adaptive threshold, the fault judgment is realized. The simulation results show that, compared with the traditional method based on a single data source, the proposed method has higher robustness and accuracy, thus effectively improving the refinement level of generator condition monitoring.

Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

In order to control the grassland ecological environment, an application method of multisource data fusion technology in the construction of land ecological index is proposed. Due to the high requirements for grassland environmental monitoring, the use of traditional technologies to monitor grassland environmental conditions lacks certain effectiveness, has high investment costs, and consumes a lot of manpower and material resources. The use of sensors to dynamically monitor the grassland environment is conducive to monitoring the environment from a scientific and technological level. By understanding the fusion principle and process of three fusion methods, adaptive weighted average, BP neural network, and D-S evidence theory, the construction of Bashang grassland ecological energy big data platform based on multisource data fusion is proposed. A two-level data fusion model based on grassland environmental monitoring is proposed. Several environmental parameters in the experimental environment were monitored, and the validity of the two-level fusion model was verified by two evaluation indicators, the mean absolute percentage error and the corrosion error. This suggests that a combination of BP neural network and D-S proof theory improves system performance. It provides the possibility for more comprehensive monitoring of grassland ecological environment in the future.

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification.

The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions.

Research on optimization of index system design and its inspection method: data quality diagnosis, index classification and stratification

PurposeThe purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.Design/methodology/approachUsing modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.FindingsBased on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.Originality/valueThe innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Prediction Algorithm of Wind Waterlogging Disaster in Distribution Network Based on Multi-Source Data Fusion

It is very important for power grid development research and related technical improvement to obtain the disaster situation of fine-scale distribution network, such as the transportation condition evaluation of distribution network and the wind waterlogging disaster prediction of distribution network. Among them, the wind waterlogging disaster prediction of distribution network is the main one, and the prediction of its disaster degree often determines whether the distribution network can be prevented before and rescued after the disaster. Therefore, in view of the above problems, combined with the actual transmission situation of the distribution network, after collecting the measured disaster data of the distribution network in relevant areas, combined with the multi-source data fusion technology and neural network modeling technology, this paper analyzes the disaster degree indicators of different distribution networks and constructs the relevant fuzzy matrix through the fuzzy theory to evaluate the disaster degree, which is verified by the measured data. This distribution network disaster loss prediction model can effectively implement the disaster loss prediction of distribution network and compare its prediction results with the other two different common models. The comparison results show that the prediction accuracy of the multi-source data fusion prediction model constructed in this paper is more than 0.95 compared with the other two models, while the prediction accuracy of the other two models is not more than 0.9, which proves that the model constructed in this paper has smaller errors. It has the advantages of higher accuracy and faster convergence speed.

Design of University Educational Administration Management System Based on Sensor Data and Multidimensional Information Fusion

With the rapid development of education informatization, especially the promotion and reengineering of education business processes by big data and artificial intelligence, how to participate in this major change is the main issue that needs to be considered. Therefore, under the guidance of multisource sensor data fusion technology, this paper adopts the traditional cloud computing operation method, that is, centralized data center processing, which is difficult to adapt to the rapidly expanding data scale. We also propose a new perceptual adaptive data processing method. By designing an edge node adaptive data type sensing mechanism based on counting bloom filter, we can automatically identify the ubiquitous University Internet of things data categories and corresponding fields. The simulation results show that this method improves the recognition accuracy of different data categories in university management by more than 1.3%, which can help teachers use educational technology to do well in the teaching work before, during and after class, and promote the practical application of new teaching modes such as inquiry teaching.

Review of bike-sharing system studies using bibliometrics method

Compared to motorized modes, bike-sharing systems (BSSs) are generally recognized as an environmentally friendly mode of transport and mobility. Due to the advantages and benefits of BSSs, they have spread globally in the past decade. The mapping knowledge domain (MKD) technique is an important tool for bibliometric analysis that can directly reflect the development status and trends in a research field and has been extensively applied in natural science, medical science, engineering and technology, humanities and the social sciences. In this paper, we conduct a systematic analysis of the development trend in bike-sharing studies taken from Web of Science (WoS) Core Collection articles published between 2010 and 2020 using the MKD software tool VOSviewer and CiteSpace. The results show that the topics of the cited documents can be divided into three categories: (a) development, operation mode and lessons learned; (b) BSS static rebalancing problem; (c) spatiotemporal characteristics and demand prediction. Next, we conduct document co-citation analysis and keyword co-occurrence analysis to visually explore the research trends in bike-sharing studies. Our results also find that, (a) bicycle rebalancing problem, (b) travel behavioral movements and barrier, (c) impact factors and characteristics of the internal usage demand, (d) innovation and sustainability for BSSs in the future, and (e) built environment and land-use, are the five major research areas and interests for bike-sharing studies. Finally, examining the trends in BSSs studies by identifying keyword bursts allows an on-demand characteristics analysis based on multisource data fusion technology. This study expands the application field of the MKD analysis method and promotes the development of BSSs against a background of new technology innovation.

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the mapping relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shijingshan District, Beijing is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model.

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation.

Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.

Fusion of Geochemical and Remote-Sensing Data for Lithological Mapping Using Random Forest Metric Learning

Multisource geoscience data can provide significant information for mineral exploration in a variety of ways. For example, remote-sensing images record the spectral characteristics of objects, and geochemical data represent the enrichment or depletion of geochemical elements, which reflect the physical and chemical attributes of geological features. In this study, a hybrid model comprising data fusion and machine learning was applied for lithological mapping. This process is illustrated through a case study of mapping several lithological units in the Cuonadong Dome, in the northeastern part of the Himalayas, China. In this process, multisource data fusion technology is first used to provide more abundant information by integrating geochemical data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote-sensing images, retaining both the geochemical patterns and the textural structure of the remote-sensing images. Then, a random forest metric learning (RFML) approach is employed to achieve a high classification performance based on the fused data. RFML adopts metric learning in the classification process of each decision tree calculation, making full use of the advantages of random forest and metric learning. Seven target lithological units were discriminated with 93.0% overall accuracy. This excellent performance demonstrates the effectiveness of the hybrid method in the geological exploration of areas in poor environments that have undergone limited geological research.

Safety Monitoring of Expressway Construction Based on Multisource Data Fusion

China’s terrain is complex, both plain, microhill (heavy-hill) and mountainous terrain; the hidden dangers of highway construction are prominent. Construction site management, production safety management, and construction personnel management are difficult, and it is necessary to borrow advanced technology to establish information, and it is necessary to borrow advanced technology to establish information system to realize the visualization of safety monitoring. In the construction of highways, mountainous terrain is often complicated due to complex terrain, high mountains, and deep valleys. Excavation of the mountain mass is required to form high and steep slopes. For successful projects, safety monitoring is particularly important. Multisource data fusion is one of the computer application technologies. It is an information processing technology that is automatically analyzed and synthesized under certain criteria to complete the required decision-making and evaluation tasks. This paper analyzes high-speed data in the context of multisource data fusion. Study on highway slope construction safety monitoring. BP neural network fusion technology of multisource data fusion technology is used. A high-speed breccia-bearing silty clay slope is taken as the research object. The feedback information about the deployed monitoring system is fully used in the slope design and construction. The construction design parameters are reversed to predict the stability of the slope and ensure the safety of construction and operation of similar slopes of the entire expressway. The research in this paper finds that the maximum deviation between the slope displacement value and the measured value obtained by the slope monitoring based on multisource data fusion in this paper is 7.53%, which is less than 10%, which verifies the feasibility of the method in this paper. The research methods and ideas of this paper can also provide a reference for similar engineering research.

Multi-Sensor Data Fusion Algorithm Based on BP Neural Network

In multi-sensor detection system, the application of multi-sensor accurate detection system parameters is limited due to the existence of measurement noise. Using multi-source data fusion technology can be more accurate, timely detection and data processing system. Data fusion is a basic function in humans and other biological systems. In this paper, in order to make the system adaptive multi-source data fusion, using the BP neural network algorithm is a good way to deal with incomplete test data and test the noise problem. In this paper, the characteristics of three levels of data fusion and the derivation process of BP neural network algorithm are introduced in detail. In order to verify the role of BP neural network algorithm in the process of detection system filtering, a MATLAB simulation experiment is carried out. The experimental results show that the BP neural network algorithm can effectively reduce the measurement error of multi-sensor detection system and improve the detection accuracy.

Evaluation of CLDAS and GLDAS Datasets for Near-Surface Air Temperature over Major Land Areas of China

As one of the most principal meteorological factors to affect global climate change and human sustainable development, temperature plays an important role in biogeochemical and hydrosphere cycle. To date, there are a wide range of temperature data sources and only a detailed understanding of the reliability of these datasets can help us carry out related research. In this study, the hourly and daily near-surface air temperature observations collected at national automatic weather stations (NAWS) in China were used to compare with the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the Global Land Data Assimilation System (GLDAS), both of which were developed by using the advanced multi-source data fusion technology. Results are as follows. (1) The spatial and temporal variations of the near-surface air temperature agree well between CLDAS and GLDAS over major land of China, except that spatial details in high mountainous areas were not sufficiently displayed in GLDAS; (2) The near-surface air temperature of CLDAS were more significantly correlated with observations than that of GLDAS, but more caution is necessary when using the data in mountain areas as the accuracy of the datasets gradually decreases with increasing altitude; (3) CLDAS can better illustrate the distribution of areas of daily maximum above 35 °C and help to monitor high temperature weather. The main conclusion of this study is that CLDAS near-surface air temperature has a higher reliability in China, which is very important for the study of climate change and sustainable development in East Asia.

Building 3D Realistic Modeling Based on Air-ground Multi-source Data Fusion

Three-dimensional point cloud data is a new form of three-dimensional collection, which not only contains the geometric topology information of the object, but also has high simplicity and flexibility. In this paper, the air-ground multi-source data fusion technology is used to study the fine reconstruction of the 3D scene: based on the 3D laser scanning laser point cloud, the 3D spatial information of the ground visible objects is obtained, and the orthophoto obtained by the drone aerial photography is assisted, Obtain the three-dimensional space information of the top of the ground feature, and the ground three-dimensional laser scanner can quickly obtain the three-dimensional surface information of the building facade, ground, and trees. Due to the complex structure of the building and the occlusion of spatial objects, sub-station scanning is required when acquiring point cloud data. This article uses the Sino-German Energy Conservation Center Building of Shenyang Jianzhu University as the research area, using drone tilt photography technology and ground lidar technology to integrate. During the experiment, the field industry adopted the UAV image acquisition strategy of “automatic shooting of regular routes, supplemented by manual shooting of areas of interest”; in the field industry, the method of “manual coarse registration and ICP algorithm fine registration” The example results show that the ground 3D laser point cloud air-ground image fusion 3D modeling effect proposed in this paper is better and the quality is greatly improved, which makes up for the ground 3D laser scanning. In point cloud modeling, a large number of holes are insufficient due to occlusion and missing top information.

An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure.

Dempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules.

Technology for Transformer Fault Diagnosis with Detection and Monitoring Data Fusion

Fault diagnosis is an effective means to assure the safe operation of power system. In this paper, a detection and monitoring of transformer fault diagnosis of multi-source data fusion technology is introduced. Based on the correlation distance, to calculate the relationship between detection and monitoring data and equipment failure, using the weight function to set fault indicators, status online monitoring data over the fault indicators, judging the equipment what kind of failure is happened, improving the precision of fault diagnosis.