Accessing Multi-Source Geological Data through Network in MORPAS Software System

MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical, geochemical and remote sensing data. It overlays geological database management, geological background and geological abnormality analysis, image processing of remote sensing and comprehensive abnormality analysis, etc.. It puts forward an integrative solution for the application of GIS in basic-level units and the construction of information engineering in the geological field. As the popularization of computer networks and the request of data sharing, it is necessary to extend its functions in data management so that all its data files can be accessed in the network server. This paper utilizes some MAPGIS functions for the second development and ADO (access data object) technique to access multi-source geological data in SQL Server databases. Then remote visiting and congruous management will be realized in the MORPAS system.

A multi-source data fusion modeling method for debris flow prevention engineering

The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.

Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Multi-Source Data

In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is presented for estimating vehicular queue length using data from both point detectors and probe vehicles. The methodology applies the shockwave theory to model queue evolution over time and space. Using probe vehicle locations and times as well as point detector measured traffic states,analytical formulations for calculating the maximum and minimum( residual) queue length are developed. The proposed methodology is verified using ground truth data collected from numerical experiments conducted in Shanghai,China. It is found that the methodology has a mean absolute percentage error of 17. 09%,which is reasonably effective in estimating the queue length at traffic signalized intersections. Limitations of the proposed models and algorithms are also discussed in the paper.

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.

Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

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.

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.

Extracting a Heterogeneous Social Network of AcademicResearchers on the Web Based on Information Retrieved from Multiple Sources

The majority of academic researchers present the results of their scientific activity on the Web. This trace can be used to derive useful information of their past, present activity and forecast the future intentions. Hence, social network of academic researchers can be of important value for scientific community. This information can be retrieved from various data source currently available on the Web. From each of them a separate net-work can be built. In this paper we present a method which can be used to combine multiple single-relational networks into a single network which will combine all relations, hence it will be multi-relational.

Ensuring Security, Confidentiality and Fine-Grained Data Access Control of Cloud Data Storage Implementation Environment

With the development of cloud computing, the mutual understandability among distributed data access control has become an important issue in the security field of cloud computing. To ensure security, confidentiality and fine-grained data access control of Cloud Data Storage (CDS) environment, we proposed Multi-Agent System (MAS) architecture. This architecture consists of two agents: Cloud Service Provider Agent (CSPA) and Cloud Data Confidentiality Agent (CDConA). CSPA provides a graphical interface to the cloud user that facilitates the access to the services offered by the system. CDConA provides each cloud user by definition and enforcement expressive and flexible access structure as a logic formula over cloud data file attributes. This new access control is named as Formula-Based Cloud Data Access Control (FCDAC). Our proposed FCDAC based on MAS architecture consists of four layers: interface layer, existing access control layer, proposed FCDAC layer and CDS layer as well as four types of entities of Cloud Service Provider (CSP), cloud users, knowledge base and confidentiality policy roles. FCDAC, it’s an access policy determined by our MAS architecture, not by the CSPs. A prototype of our proposed FCDAC scheme is implemented using the Java Agent Development Framework Security (JADE-S). Our results in the practical scenario defined formally in this paper, show the Round Trip Time (RTT) for an agent to travel in our system and measured by the times required for an agent to travel around different number of cloud users before and after implementing FCDAC.

融合多源异构气象数据的光伏功率预测模型

高精度光伏功率预测对提高电力系统运行效率具有重要意义。光伏功率受多种因素影响,其中云层的变化是最主要的不确定因素。传统光伏功率预测方法没有充分考虑云的3维结构和气象要素对光伏功率的影响。因此,该文提出一种融合多源异构气象数据的多源变量光伏功率预测模型(MPPM)。MPPM的核心包括时空条件扩散模型(STCDM)、注意力堆叠LSTM网络(ASLSTM)和多维特征融合模块(MFFM)。STCDM模型通过对2维卫星云图进行精确预测,消除了云层边界处的模糊现象。ASLSTM模型则提取了3维天气研究与预报模式(WRF)气象要素特征。MFFM模块将2维卫星云图特征和3维WRF气象要素特征进行融合,以得到未来1 h光伏功率预测结果。该文分别利用STCDM模型和MPPM模型开展卫星云图预测实验和光伏功率预测实验。实验结果显示,STCDM模型预测1 h内卫星云图的结构相似性指数(SSIM)达到0.914,MPPM模型预测1 h内光伏功率的相关系数(CORR)达到0.949,优于所有对比算法。

基于多源数据与随机森林方法的城市建成区提取——以郑州市为例

基于夜间灯光数据的阈值分割法在城镇建成区提取研究中被广泛应用,但由于夜间灯光数据分辨率低、灯光溢出和阈值分割法无法顾及区域差异等问题,一定程度上影响了该方法的提取精度。以郑州市为例,以LJ1-01与NPP/VIIRS两种夜间灯光影像为主要数据源,结合Landsat8中分辨率遥感影像、网络城市兴趣点(POI)及路网数据,利用随机森林分类方法对郑州市2018年建成区进行提取,参考土地利用数据,对RF分类法与NTL、VANUI、BANUI、PANUI、RANUI指数等阈值法进行对比实验和精度评价,评估基于多源数据的随机森林分类方法在城市建成区提取中的优势。实验表明,RF比阈值法提取的建成区更接近真实建成区且提取精度更高,具有更好适用性;LJ1-01数据提取的效果和精度总体优于NPP/VIIRS数据;在采用RF分类时,各类特征的重要性在不同夜光数据源中表现差异较大。

融合多源数据的湖北省城市洪涝灾害韧性综合评估

【目的】洪涝灾害是湖北省主要自然灾害之一,为了评估湖北省城市洪涝灾害韧性水平,以加快韧性城市建设、助力推进省域协同发展,【方法】从洪涝灾害的暴露度、敏感性、适应力和恢复力4个维度,构建湖北省城市洪涝灾害韧性评价指标体系,采用G1法(序关系法)和熵权法对评价指标进行赋权,收集气象、遥感影像、基础地理、社会经济等多源数据,应用GIS空间分析技术,对湖北省城市洪涝灾害韧性进行评估。【结果】结果显示:湖北省城市洪涝灾害韧性整体处于中等韧性水平。其中,高韧性区域主要分布在神农架林区、宜昌市和武汉市,较高和中等韧性水平区域大多位于荆门市及其以西的鄂西地区,其余地区洪涝灾害韧性水平较低,总体呈西强东弱的分布态势。【结论】融合多源数据的洪涝灾害韧性评估方法能准确高效地评估研究区洪涝灾害韧性水平,研究成果可为湖北省制定区域洪涝灾害管理及韧性城市规划提供参考。

基于云数据中心的多源异构数据治理技术研究

目前常规的多源异构数据治理方法主要通过对数据属性进行判断,从而实现分区域数据清洗,由于缺乏对非线性数据的分析,导致治理性能不佳;对此,提出基于云数据中心的多源异构数据治理技术;采用关系型数据库中的ETL功能对数据进行清洗,对数据转换模式以及数据清洗规则进行定义;引入互信息系数对数据相关程度进行判定,并进行非线性数据相关性分析;以云数据中心作为载体,对多源异构数据治理体系进行构建;在实验中,对提出的数据治理技术进行了治理性能的检验;最终的实验结果表明,提出的数据治理技术具备较高的查准率,对云数据中心多源异构数据具备较为理想的数据治理效果。

面向水库型小流域的多源水利三维建模方法研究

针对大型河道三维可视化建模中采用激光雷达、声呐等传感器获取水下地形建模方式操作复杂且成本较高的缺陷,在利用基础测绘成果构建实景地形模型的基础上,采用多源三维模型融合方法对月塘水库小流域进行实景建模。首先采用倾斜摄影三维建模技术对重点水利工程进行实景建模,并利用专业建模软件对重要控制单元进行构件级建模;然后利用已有河道断面数据结合河流面状矢量数据,实现流域内胥浦河河道实景三维建模;最后通过人机交互方式对多源实景模型进行微调与相互印证,实现月塘水库小流域多源三维模型精准融合构建。研究成果可为河道型水库的预报调度与洪水风险分析提供可视化模型基础与决策支撑。

基于多源数据的长江流域1982—2022年骤旱事件时空演变

骤发性干旱(简称骤旱)是一种突发性高且强度大的极端干旱现象,会对农业生产和生态系统构成严重威胁。近年来,长江流域骤旱频发,然而其骤旱时空演变格局及规律尚不明晰。本研究基于GLEAM、GLDAS和ERA5-Land数据,以标准化蒸发胁迫比及其变化值作为识别指标,开展1982—2022年长江流域骤旱识别,全面分析长江流域骤旱空间分布和时间演变特征;并鉴于2022年旱情的严重性和特殊性,重点分析该年长江流域骤旱事件。研究结果表明:①在空间分布上,长江流域上游的金沙江水系和中下游的大型水库湖泊骤旱发生频率最高且强度最大;②在时间演变上,骤旱发生频率、平均持续时间和强度均在长江流域整体上呈现出非显著上升趋势,而有显著变化趋势的区域在2001年前后表现出明显的趋势反转现象;③2022年夏季受极端高温热浪影响,长江流域遭遇大规模骤旱事件,具有波及范围广、持续时间长的特点,且骤旱在空间上呈现出从上游向下游传递的态势。

水稻作物产量预测模型的研究——基于多源数据回归模型

水稻是我国重要的粮食作物之一,在我国不同农业生产区域都有种植,实现水稻产量的精准预测对于稳定我国粮食安全具有重要意义。为了改良传统产量预测方法,实现水稻产量高效预测,基于主成分分析法提取主成分,再将主成分作为BP神经网络模型的输入,对水稻种植面积较大的黑龙江省、江苏省、湖南省和湖北省2011-2020年的数据进行预测分析。研究结果表明:水稻产量与月最高土壤温度、月最低土壤温度、月土壤平均温度、月大气最高温度、月大气平均温度、月平均大气湿度为极显著相关,与月降雨量显著相关,与月大气最低温度相关性较弱。主成分分析与BP神经网络组合模型下,水稻产量的预测精度明显高于传统BP神经网络模型,R^(2)达到0.86,MAPE仅为0.97%,RMSE为0.93,预测值与试验值之间拟合程度较高,模型验证结果表明模型预测结果准确稳定。研究结果对于更加科学、合理地预测水稻产量具有重要的指导意义。

基于1DCNN融合多源表型数据的杨树干旱胁迫评估方法

目前关于不同杨树品种抗旱性的研究主要集中在利用传统测量方法获取形态结构和生理生化表型参数进而分析杨树的抗旱性,依据多源成像传感器提取的表型参数指标确定杨树干旱胁迫等级的方法较为少见。为了阐明杨树耐旱的表型机制、筛选抗旱性树种和明确杨树抗旱等级,本文以杨树不同性别的喜水和耐旱品种为研究对象,在杨树苗期进行梯度干旱胁迫处理,通过热红外以及RGB多源成像传感器获取杨树冠层温度参数与颜色植被指数表型数据,并建立基于1DCNN的多任务分类模型划分杨树苗期品种抗旱等级与干旱胁迫等级等2个分类任务,探究杨树性别与生长时间对杨树干旱胁迫响应机制的影响。结果表明,以27组数据变量降维后的4个特征作为模型变量,与传统机器学习算法SVM、RF、XGBoost相比,本文提出的1DCNN多任务分类模型在杨树品种抗旱等级分类与单株干旱胁迫等级分类2个任务中的模型分类精度皆达到最优,分类准确率分别为81.8%和62.3%;引入杨树的性别和生长时间后共6个特征作为模型的输入变量后,杨树苗期品种抗旱等级与干旱胁迫等级的分类精度显著提高,1DCNN多任务分类模型在2个分类任务中的准确率分别达到93.5%与76.6%,模型分类准确率分别提高11.7个百分点与14.3个百分点。研究结果表明,通过热红外与RGB成像传感器获取多源表型数据,并建立1DCNN多任务分类模型对实现杨树干旱胁迫等级评估的可行性,同时表明杨树的性别和生长时间作为模型输入变量能够有效提升模型的分类精度,可为筛选杨树抗旱性品种提供新的思路与方法。

基于多源数据的高速公路差异化收费路段识别

在全面推广高速公路差异化收费背景下,针对如何选取差异化收费路段的问题,建立一种基于多源数据的差异化收费路段识别方法.首先,基于高速公路门架数据、收费站出入口流量、普通国省道交通流量自动观测站数据等多源数据,从路段沿线社会经济发展水平、网络化水平、交通运行特征、交通可转移性、交通负荷均衡性5个维度,构建高速公路差异化收费路段识别指标体系.然后,基于k-means聚类和分类回归树算法(Classification and Regression Trees,CART)建立高速公路差异化收费路段识别模型.最后,以云南省130条高速公路路段为例,提取高速公路差异化收费路段细分规则及关键特征指标.研究结果表明:影响高速公路差异化收费路段细分的关键特征指标分别为路段成网率、收费站节点度、出行成本偏差率、路段全天小时拥挤度、可替代路径数量;根据决策树细分规则可将差异化收费路段划分为差异化上浮型、不适合差异化、差异化下浮型3种,符合高速公路实际收费运营情况.

基于相对熵权重组合的多阶段贮存数据融合评估方法

目的针对目前仅使用加速贮存数据获取产品性能参数退化信息导致贮存寿命评估可信度低的问题,融合自然贮存数据和加速贮存数据2阶段的数据结果。方法采用基于最小二乘和相对熵的方法,确定不同阶段数据的权重比例,最后形成融合2阶段数据的评估方法,并在扭杆和控制放大器2个产品上进行自然贮存数据与加速贮存数据的融合分析。结果实现了2个产品贮存寿命的高置信度评估,扭杆自然贮存数据和加速贮存数据的权重比例分别为0.263和0.737,控制放大器自然贮存数据和加速贮存数据的权重比例分别为0.776和0.224。结论融合评价的方法能够结合2部分数据的退化特点,给出一个更有信服力的评价结果。

中国地面自动气象站小时降水质量控制方法

高时空分辨率自动气象站降水观测作为重要数据来源,已被广泛应用于强对流天气监测、模式评估、预报复盘等研究工作。仪器故障、特殊天气条件下观测设备的局限性等因素是自动气象站降水数据不确定性的主要来源,这些问题在无人值守气象站尤为突出。该研究基于2021—2023年中国自动气象站实时观测降水量数据、高时空分辨率雷达数据和高灵敏性降水类天气现象数据,发展适应于中国自动气象站小时降水数据的多源数据协同质量控制方法(multi-source data collaborative quality control,MDC)。通过综合定量指标与典型个例分析,对MDC的应用效果进行全面评估。结果显示:MDC判识正确率为99.92%,错误数据命中率较现行业务提升39.3%。基于多源降水观测数据时空一致性,MDC显著提升了晴空降水、融雪性降水和虚假零值降水等异常数据的甄别能力,有效弥补了传统方法的不足。