Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Explicit Correlation Model of Multi-source Constraints for Re-design Parts with Complex Curved Surface

In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related to the geometrical and physical parameters, so the final actual size and shape are affected by multiple source constraints, such as geometry, physics, and performance. These parts are rather difficult to be manufactured and new manufacturing method according to performance requirement is urgently needed. Based on performance and manufacturing requirements for complex curved surface parts, a new classification method is proposed, which divided the complex curved surface parts into two categories： surface re-design complex curved surface parts with multi-source constraints（PRCS） and surface unique complex curved surface parts with pure geometric constraints（PUCS）. A correlation model is constructed between the performance and multi-source constraints for PRCS, which reveals the correlation between the performance and multi-source constraints. A re-design method is also developed. Through solving the correlation model of the typical paws performance-associated surface, the mapping relation between the performance-associated surface and the related removal amount is obtained. The explicit correlation model and the method for the corresponding related removal amount of the performance-associated surface are built based on the classification of surface re-design complex curved surface parts with multi-source constraints. Research results have been used in the actual processing of the typical parts such as radome, common bottom components, nozzle, et al., which shows improved efficiency and accuracy of the precision machining for the surface re-design parts with complex curved surface.

3D Geological Modeling with Multi-source Data Integration in Polymetallic Region:A Case Study of Luanchuan,Henan Province,China

The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten -lead-zinc polymetallic belt in China.

Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

Multi-Source Spatial Data Distribution Model and System Implementation

The Multi-source spatial data distribution is based on WebGIS, and it is an important part of multi-source geographic information management system. a new multi-source spatial data distribution model is proposed on the basis of multisource data storage model and by combining existing map distribution technology, The author developed a multi-source spatial data distribution system which based on MapGIS K9 by using this model and taking full advantage of interfacecode separating thinking and high efficiency characteristic of .net, so high-speed distribution of multi-source spatial data realized.

Multi-source data-based 3D digital preservation of large scale ancient chinese architecture:A case report

The 3D digitalization and documentation of ancient Chinese architecture is challenging because of architectural complexity and structural delicacy.To generate complete and detailed models of this architecture,it is better to acquire,process,and fuse multi-source data instead of single-source data.In this paper,we describe our work on 3D digital preservation of ancient Chinese architecture based on multi source data.We first briefly introduce two surveyed ancient Chinese temples,Foguang Temple and Nanchan Temple.Then,we report the data acquisition equipment we used and the multi-source data we acquired.Finally,we provide an overview of several applications we conducted based on the acquired data,including ground and aerial image fusion,image and LiDAR(light detection and ranging)data fusion,and architectural scene surface reconstruction and semantic modeling.We believe that it is necessary to involve multi-source data for the 3D digital preservation of ancient Chinese architecture,and that the work in this paper will serve as a heuristic guideline for the related research communities.

Estimation on principal component of multi-collinearity Gauss-Markov model based on minimum description length

Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem.

Application Research on Two-Layer Threat Prediction Model Based on Event Graph

Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.

A Gauss-Newton Approach for Nonlinear Optimal Control Problem with Model-Reality Differences

Output measurement for nonlinear optimal control problems is an interesting issue. Because the structure of the real plant is complex, the output channel could give a significant response corresponding to the real plant. In this paper, a least squares scheme, which is based on the Gauss-Newton algorithm, is proposed. The aim is to approximate the output that is measured from the real plant. In doing so, an appropriate output measurement from the model used is suggested. During the computation procedure, the control trajectory is updated iteratively by using the Gauss-Newton recursion scheme. Consequently, the output residual between the original output and the suggested output is minimized. Here, the linear model-based optimal control model is considered, so as the optimal control law is constructed. By feed backing the updated control trajectory into the dynamic system, the iterative solution of the model used could approximate to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, current converted and isothermal reaction rector problems are studied and the results are demonstrated. In conclusion, the efficiency of the approach proposed is highly presented.

基于Gauss伪谱法的高空飞行器再入段轨迹优化对传热效应的影响分析

基于Gauss伪谱法和二阶有限差分(Gauss Pseudospectral Method,GPM),研究了再入过程中高速飞行器的传热问题,并依据最内温层升温最小的目的进行飞行轨迹的数值优化。主要思路:构造传热分析模型,并依据传热方程构造每一温层的传热微分方程;将各温层微分方程以及动力学微分方程作为伪谱法中的微分方程约束条件代入,进行轨迹数值优化设计;利用少量LG点构造拉格朗日多项式,再通过一维插值获得大量LG点的值,获得更高精度的拟合结果曲线。以某高超声速飞行器为对象用本方法进行数值计算,结果验证了方法具有一定的可行性。

基于Park-Gauss模型的风场尾流数值模拟

基于Park模型尾流区线性膨胀假设和径向风速呈高斯分布假设,提出一种新型工程尾流模型Park-Gauss模型,对单台风力机尾流场进行数值模拟研究。采用两种初始尾流半径(风轮半径和紧靠风轮下游处的尾流半径),分别对Park模型、2D Jensen模型以及Park-Gauss模型进行对比研究。经过与风场实测数据和风洞试验的比较,结果表明:以紧靠风轮下游处的尾流半径作为初始尾流半径会明显提高尾流场的预测精度;新提出的ParkGauss模型计算精度优于2D Jensen模型和Park模型;Park-Gauss模型可很好模拟尾流区的风速,不仅在精度上与试验结果接近,而且在径向分布上也更符合真实流场。

玄武岩纤维丝束强度的Weibull和Gauss分布统计分析

通过假设检验验证了Weibull及Gauss分布模型对玄武岩纤维丝束强度统计分析的适用性,并用2种分布模型对BF-CSH-01、BF-CMD-02和BF-CAH-01等3种纤维不同长度丝束的拉伸强度分布进行了统计分析。当纤维种类和试样长度变化时,采用2种模型统计的丝束强度的变化规律一致,即3种纤维中BF-CSH-01强度最高,BF-CMD-02的强度分散性最小;同种纤维中,随着试样长度的增加,丝束强度下降,强度分散性减小。2种分布模型相比,采用Weibull模型计算的丝束强度略高于Gauss模型,但强度分散性稍大。

基于随机阈值的Gauss-Brown失效物理模型的动量轮可靠性评估

动量轮是卫星的关键组件,直接影响卫星的可靠性和寿命。动量轮具有小子样、高可靠性、长寿命和高试验代价等特点,通常无法获得大样本的失效寿命数据,这导致其可靠性评估相当困难。失效物理分析能够从本质上解释产品的失效原因,失效物理试验中的性能退化数据可搭建产品性能和失效的桥梁。鉴于此,从失效物理分析的角度出发,对动量轮这种类型产品提出了一种基于失效物理的评估框架,并针对某型动量轮建立了基于随机阈值的Gauss-Brown失效物理模型,以该模型为基础作了可靠性评估,实例表明,该方法所获得的评估结果符合工程实际。

聚集数据的Gauss－Markoff模型中参数估计的相对效率

文献［１］提出了聚集数据的Ｇａｕｓｓ－Ｍａｒｋｏｆｆ模型中未知参数的两种估计。本文又提出了关于最小二乘估计的三种估计新的相对效率，并研究了它们的性质以及与文献［２］讨论过的另一种相对效率的关系。

基于高斯增强模块的相机模型辨别

在多媒体取证中,高通滤波器是卷积神经网络常用的预处理层之一,用于抑制图像内容的影响,只强调高频特征。但与此同时,其他一些包含取证痕迹的有用信息也将被不加区别地剔除。为了解决这一问题,文中提出了一个简单而高效的高斯增强模块(Gaussian Enhancement Module,GEM)来提取“扩展的”高频特征,即在维持原有特征强度的基础上增强高频细节信息。GEM由两个连续的一维低通高斯滤波器组成,以获得一个模糊版本的特征图,并进一步得到相应的扩展高频残差。通过以高频残差作为空间掩膜,它可以自适应地强化脆弱和细微的低级取证特征,并防止在特征传递过程中出现衰减现象。在相机模型辨别数据集上进行实验,通过将该模块插入多个主流骨干网络,GEM仅仅带来非常轻微的模型复杂度的增加,网络性能和鲁棒性却显著提高,表明该模块支持“即插即用”,与特定的网络架构无关。

Exponent及Gauss型遥测数据建模初始化算法

在对某大型航天电子设备的遥测数据建模时,经常遇到含有指数类变化规律的数据,对其进行建模可以为研究设备性能的变化规律、实际与设计的差异等提供手段。针对有指数变化趋势的遥测数据,采用Exponent模型、Gauss模型对遥测数据建模,给出了这种数据模型的表达式,研究了2种模型的参数初始化算法。通过数值实验说明模型参数初始化算法的有效性,为后续利用最优化理论求解模型精确参数提供了良好的初试点。

给定Gauss曲率函数的旋转曲面的设计

旋转曲面的设计在CAD/CAM及CAGD中有重要作用。由于给定Gauss曲率函数的嵌入旋转曲面存在性问题已经得到较好的证明,故使得曲面的设计成为可能。给出了一种给定Gauss曲率函数的嵌入旋转曲面设计算法,该算法通过求解一个二阶微分方程,并适当选取初始条件,得到旋转曲面的位置矢量。最后,通过两个实例表明,该算法是可行的,为旋转曲面的工程设计与曲面造型提供了一个新方法。