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.

The Identification and Geological Significance of Fault Buried in the Gasikule Salt Lake in China based on the Multi-source Remote Sensing Data

The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.

Multi-Scale PIIFD for Registration of Multi-Source Remote Sensing Images

This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.

Accuracy Analysis on the Automatic Registration of Multi-Source Remote Sensing Images Based on the Software of ERDAS Imagine

The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.

Retrieval of urban land surface component temperature using multi-source remote-sensing data

The components of urban surface cover are diversified,and component temperature has greater physical significance and application values in the studies on urban thermal environment.Although the multi-angle retrieval algorithm of component temperature has been matured gradually,its application in the studies on urban thermal environment is restricted due to the difficulty in acquiring urban-scale multi-angle thermal infrared data.Therefore,based on the existing multi-source multi-band remote sensing data,access to appropriate urban-scale component temperature is an urgent issue to be solved in current studies on urban thermal infrared remote sensing.Then,a retrieval algorithm of urban component temperature by multi-source multi-band remote sensing data on the basis of MODIS and Landsat TM images was proposed with expectations achieved in this work,which was finally validated by the experiment on urban images of Changsha,China.The results show that：1） Mean temperatures of impervious surface components and vegetation components are the maximum and minimum,respectively,which are in accordance with the distribution laws of actual surface temperature; 2） High-accuracy retrieval results are obtained in vegetation component temperature.Moreover,through a contrast between retrieval results and measured data,it is found that the retrieval temperature of impervious surface component has the maximum deviation from measured temperature and its deviation is greater than 1 ℃,while the deviation in vegetation component temperature is relatively low at 0.5 ℃.

Red Tide Information Extraction Based on Multi-source Remote Sensing Data in Haizhou Bay

[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.

Federated Learning for 6G:A Survey From Perspective of Integrated Sensing,Communication and Computation

With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.

Effects of Heterogeneous Vegetation on the Surface Hydrological Cycle

Using the three-layer variable infiltration capacity （VIC-3L） hydrological model and the successive interpolation approach （SIA） of climate factors, the authors studied the effect of different land cover types on the surface hydrological cycle. Daily climate data from 1992 to 2001 and remotely-sensed leaf area index （LAI） are used in the model. The model is applied to the Baohe River basin, a subbasin of the Yangtze River basin, China, with an area of 2500 km^2. The vegetation cover types in the Baohe River basin consist mostly of the mixed forest type （-85%）. Comparison of the modeled results with the observed discharge data suggests that： （1） Daily discharges over the period of 1992-2001 simulated with inputs of remotely-sensed land cover data and LAI data can generally produce observed discharge variations, and the modeled annual total discharge agrees with observations with a mean difference of 1.4%. The use of remote sensing images also makes the modeled spatial distributions of evapotranspiration physically meaningful. （2） The relative computing error （RCE） of the annual average discharge is -24.8% when the homogeneous broadleaf deciduous forestry cover is assumed for the watershed. The error is 21.8% when a homogeneous cropland cover is assumed and -14.32% when an REDC （Resource and Environment Database of China） land cover map is used. The error is reduced to 1.4% when a remotely-sensed land cover at 1000-m resolution is used.

OBH-RSI:Object-Based Hierarchical Classification Using Remote Sensing Indices for Coastal Wetland

With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction:GF-FloodNet

Deep learning algorithms show good prospects for remote sensingflood monitoring.They mostly rely on huge amounts of labeled data.However,there is a lack of available labeled data in actual needs.In this paper,we propose a high-resolution multi-source remote sensing dataset forflood area extraction:GF-FloodNet.GF-FloodNet contains 13388 samples from Gaofen-3(GF-3)and Gaofen-2(GF-2)images.We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it.Compare with otherflood-related datasets,GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels,but also consists of multi-source remote sensing data.We thoroughly validate and evaluate the dataset using several deep learning models,including quantitative analysis,qualitative analysis,and validation on large-scale remote sensing data in real scenes.Experimental results reveal that GF-FloodNet has significant advantages by multi-source data.It can support different deep learning models for training to extractflood areas.There should be a potential optimal boundary for model training in any deep learning dataset.The boundary seems close to 4824 samples in GF-FloodNet.We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o.

面向多源异质遥感影像地物分类的自监督预训练方法

近年来,深度学习改变了遥感图像处理的方法。由于标注高质量样本费时费力,标签样本数量不足的现实问题会严重影响深层神经网络模型的性能。为解决这一突出矛盾,本文提出了用于多源异质遥感影像地物分类的自监督预训练和微调分类方案,旨在缓解模型对于标签样本的严重依赖。具体来讲,生成式自监督学习模型由非对称的编码器-解码器结构组成,其中深度编码器从多源遥感数据中学习高阶关键特征,任务特定的解码器用于重建原始遥感影像。为提升特性表示能力,交叉注意力机制模型用于融合异源特征中的信息,进而从多源异质遥感影像中学习更多的互补信息。在微调分类阶段,预训练好的编码器作为无监督特征提取器,基于Transformer结构的轻量级分类器将学习到的特征与光谱信息结合并用于地物分类。这种自监督预训练方案能够从多源异质遥感影像中学习到刻画原始数据的高级关键特征,并且此过程不需要任何人工标注信息,从而缓解了对标签样本的依赖。与现有的分类范式相比,本文提出的自监督预训练和微调方案在多源遥感影像地物分类中能够取得更优的分类结果。

基于模型定义的光学遥感卫星星上处理系统设计

本文提出了一种基于模型定义的光学遥感卫星星上处理系统设计方法,构建了“硬件资源-算子模块-通用处理核-典型应用”的星上处理任务行为模型范式。在硬件层面,采用星上异构嵌入式计算平台,将多处理器一体化设计,通过标准化高速数据互连,完成高速信号传输和数据处理;通过网络拓扑实现良好的扩展性,支持设备规模及数据处理复杂性的变化;在软件层面,针对星上智能处理任务中频繁数据读写操作、大量重复计算操作、卷积神经网络的通用计算与加速等需求,设计了星上平台指令集、星上算法通用算子和星上智能网络组件组成的可配置算子模块,并基于该模块可快速实现特定算法的硬件IP核。经仿真试验验证,本文方法可根据卫星平台和星上处理任务需求,按需适配最佳软硬件解决方案,并有效提高了计算资源利用率;提出的结合云检测的实时流水压缩编码方案,显著提升了压缩性能;设计的轻量化目标检测识别方法,计算资源效率达到91.5%;以高分一号、高分七号原始数据率为例进行分析,相比常规方案整体计算资源利用率分别提高了16.51%、17.77%。实现了星上处理系统研制过程模型化、工作模式可定义、逻辑资源共享化,是适应卫星小型化、快速部署的一种合理优化选择。

集合卡尔曼滤波与随机森林算法在异源遥感降水数据同化融合中的应用

为减小异源遥感降水产品的非均质误差,提出集合卡尔曼滤波(EnKF)联合随机森林(RF)的数据同化融合算法,选取长江流域5种遥感降水产品(ERA5、TerraClimate、GPM、TRMM和PERSIANN-CDR),在分析星地降水数据一致性的基础上,进行EnKF-RF数据同化与融合处理,并利用独立气象站点评估其精度。结果表明,异源产品在长江流域降水量捕捉精度为TRMM>GPM>TerraClimate>PERSIANN-CDR>ERA5;各产品经EnKF同化后的精度纳什效率系数N NSE增至0.93~0.96,均方根误差RRMSE降至89.48~176.03 mm,较未同化前的N NSE提升11.46%~22.34%、RRMSE减小96.35%~122.60%;RF融合进一步改进了单一源降水产品可靠性,融合后产品精度N NSE达0.99、RRMSE为43.56 mm;异源降水数据的EnKF-RF同化融合策略减少了单一源降水产品的误差,在长江流域乃至全球尺度具有较大应用潜力。

面向异构图像压缩感知的阶数自适应多假设重构

大数据时代到来,使得图像传感应用面临大维度处理与大容量传输的挑战,压缩感知技术及相关算法在一定程度上解决了该问题。然而,现有压缩感知算法存在对异构图像集泛化性不足的问题,需要为此类图像集设计高泛化性的压缩感知重构算法。因此,基于泛化性较高的多假设预测机制,提出一种阶数自适应多假设重构算法。首先通过窗口自适应线性预测器对各块进行预处理,根据预处理获得的相关性指标,改变多假设搜索窗口的大小,并依据相似度对搜索窗口内的预测块进行排序,结合自适应的搜索窗口挑选不同数量的高相似预测块,生成多假设预测的重构图像。选取自然图像集以及X光胸片和脑磁两个异构图像集进行实验,在不同采样率下对比所提算法与传统的多假设压缩感知重构算法以及两种新近提出的基于多假设预测的算法性能。实验结果表明,所提算法具有良好的性能提升。在自然图像集下,相比两种新近提出的基于多假设预测的重构算法,所提算法保持了一定的恢复质量,且运行时间分别减少了17.5%,28.7%。此外,相比两种新近提出的算法,在胸片图像集下,所提算法分别获得了1.16 dB,1.43 dB的平均PSNR提升,以及36.1%,21.5%的平均运行时间减少;在脑磁图像集下,所提算法分别获得了1.64 dB,1.97 dB的平均PSNR提升,以及平均28.6%,26.1%的运行时间减少。整体而言,所提算法具有较低的时间复杂度、较高的恢复质量,综合性能更佳。

基于空间异质运算的结构信息提取辅助遥感影像分类研究

以Landsat-8和高分一号数据为例,采用仅有光谱特征、3种纹理特征(概率统计、灰度共生矩阵、空间异质运算)辅助光谱特征的方法提取影像空-谱信息,并通过支持向量机分类器进行基于像元的地物分类。结果表明:1)纹理特征辅助光谱特征的地物分类精度明显优于仅使用光谱特征的分类,可提高8.62%~24.36%;2)相较于概率统计、灰度共生矩阵方法结果,空间异质运算结果分类精度在GF-1影像中分别提高了13.31%和2.03%,在Landsat-8影像中分别提高了11.62%和7.79%;3)对于线状地物,相较于概率统计、灰度共生矩阵方法结果,空间异质运算结果分类精度在GF-1数据中分别提高了29.31%和0.80%,在Landsat-8数据中分别提高了11.90%和6.64%,有效减小了分类误差。因此,空间异质运算提取的空间结构信息辅助光谱特征的分类方法能显著改善遥感图像的分类精度,为空间结构信息辅助遥感影像地物分类及线状地物的提取提供一种新的思路和方法。

田块尺度水稻农情遥感监测平台设计与试验

为解决我国水稻种植过程中由于药肥施用不当和缺乏系统化管理所导致的单产低、农业面源污染严重等问题,并针对现有农情系统数据源单一的现状,以多源数据的协同监测为核心,基于WebGIS和Ant Design搭建前端框架,整合多源时空地理数据和分布式数据存储方法,采用Python、HTML、Javascript+CSS、ArcGIS Server、Mapbox Studio以及PostgreSQL等技术,构建一个前后端分离(F/B Separation)、云端实时更新的田块尺度水稻农情监测平台,以实现水稻生长参数反演、产量预估、田块参数查询、时空数据可视化与统计分析等功能。以江西兴桥镇与井冈山国家农业科技园为试验区,应用该系统的案例分析表明2022年兴桥镇水稻田块分布破碎,且镇内东北区域水稻产量高于西南,水稻田产量介于6750~8250 kg/hm^(2);同时发现试验区内田块水稻的长势与历史药肥施用量存在明显关联,药肥施用策略显著影响田块水稻长势。综上,本平台在多源数据协同作用下,能较好地满足大区域下田块水稻监测所要求的准确性、全面性,并在一定程度上实现水稻长势与产量的归因分析,可作为实现田块尺度水稻农情多源精细监测的有效示例。

异构群智感知PPO多目标任务指派方法

现有移动群智感知系统的任务指派主要面向单一类型移动用户展开,对于存在多种类型移动用户的异构群智感知任务指派研究相对缺乏.为此,本文针对异质移动用户,定义其区域可达性,并给出感知子区域类型划分.进而,兼顾感知任务数量和移动用户规模的时变性,构建了动态异构群智感知系统任务指派的多目标约束优化模型.模型以最大化感知质量和最小化感知成本为目标,综合考虑用户的最大任务执行数量、无人机的受限工作时间等约束.为解决该优化问题,本文提出一种基于近端策略优化的多目标进化优化算法.采用近端策略优化,根据种群的当前进化状态,选取具有最高奖励值的进化算子,生成子代种群.面向不同异构群智感知实例,与多种算法的对比实验结果表明,所提算法获得的Pareto最优解集具有最佳的收敛性和分布性,进化算子选择策略可以有效提升对时变因素的适应能力,改善算法性能.

归属感视角下安置区微观空间异用特征及优化策略

提高安置民归属感是推动安置区与城区融合发展的重要环节。文章以济南市唐冶片区唐官安置区为例,分类调查安置区中安置民的生活性行为和生产性行为两类异用行为,并提出单一生活空间、单一生产空间、生产-生活空间、生活-生活空间四类微观公共空间类型。其次,通过建立微观公共空间质量与归属感评价体系,运用层次分析、皮尔逊相关分析、多元线性回归分析等方法,探究影响安置民归属感的影响因素。最后,从空间共建和社区治理两个角度提出针对性优化策略,以期为安置区空间环境建设提供参考。

低碳感知差异下制造商嵌入区块链的两产品供应链决策

鉴于消费者低碳感知敏感的特点,制造商同时生产低碳产品和普通产品来满足消费者差异化需求,构建并对比分析制造商未采用区块链和采用区块链的供应链决策模型。研究发现,在一定区块链成本阈值内,制造商实施区块链不仅有利于改善自身利润,还提高了零售商利润和消费者剩余,对比两种模式,低碳产品估值削弱因子越高,普通产品估值折扣因子越低,区块链成本阈值低时,制造商会优先采用区块链技术;碳感知敏感消费者占比越大,制造商和零售商的利润越大,而且制造商的利润变化程度始终高于零售商,但碳感知敏感消费者占比对区块链成本阈值无影响;制造商采用区块链增加消费者对低碳产品的需求,降低对普通产品的需求,同时提高低碳产品的零售价和批发价。

基于异构并行的DAS高密度数据实时解调技术

针对分布式光纤声波传感(Distributed optical fiber acoustic sensing,DAS)系统中高密度数据实时解调的需求,提出了基于中央处理器(Central processing unit,CPU)和图形处理器(Graphic processing unit,GPU)的异构并行计算架构,完成了实时解调双通道外差型DAS系统传感数据,可满足同时对两个通道共5000个等效阵元实时解调处理需求。此系统每秒需处理的数据量高达400 MB,相较于仅使用CPU运算的225.5 s运算时间,采用异构并行计算架构的运算时间优化到了468.2 ms,运算速度提升了482倍,且该方案仍有巨大的算力冗余空间,可为后续DAS系统整体实时性能的提升提供算力支持。