Big Data in Smart City •Prof. Deren Li •State key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing 6th International Workshop on Early Warning and Crisis Management in the Big Data Era Wuhan University 21 April 2015, Novosibirsk, Russia Contents §1 Smart city and its application §2 Big data in smart city §3 Cloud computing and data mining §4 Smart city operating center §5 Conclusions and future works § –A smart city is built upon the infrastructure of the digital City. It integrates the real world and the digital world with the internet of things, and perceives the states of everyone and everything in the real world. Then the sensed data is transferred to the cloud computing center for computation and understanding, providing intelligent service for economic development, city management and publics. –The smart city is a key component of the smart earth. 1 Smart city and its application * What is a smart city? Smart city=digital city+internet of things +cloud computing Cyber physic space Cyber space Do everything on web See everything on web Do every The development of the smart city 1993 1998 2006 2009 The "information superhighway" starts the information era of the cities Digital city Informational-ized city Intelligent city “The build of the digital comfortable community” marks the construction of the digital city and digital earth IBM proposed a new concept: “smart city”, marking the digital city into a new stage. The new information technologies such as internet of things and cloud computing integrates the digital city system. The smart city is based on the information infrastructure and the digital city, It pays more attention on the integration of the digital city with the real city through ubiquitous sensor networks, puts more emphasis on the intelligent control and the automatic feedback. It is a more advanced stage of the digital city, and a high-degree integration of the industrialization and information technology. –Urbanization Low-carbon –Industrialization Green –Informationizing Sustainability –Realize the Chinese dream！ – The motivation and goal of the smart city in China Smart City Digital city infrastructure Basic geodata 3D model data Street view images Panoramic images Ortho- images Thematic data Data Layer Registration Center Public platform Meta data Catalog management DATA distribution Geo-information public platform Data center Service sharing Secondary development interface Public application of ITS, tourism and heritage management Government application of land, planning, urban management Demo application –Realize the interoperability between human and human, human and machine, machine and machine. Internet of things Appliance Mobile • Audio • Video Communi- cation • Multi-purpose • Office • Education • Retail • Hospitality • Healthcare • Grocery • Airports • Stadiums • Data Centers Commercial / Institutional Industrial • Process - Semi Fabs - Chemical - Pharma - Refining • Pulp/Paper • Discrete • Batch Legislature Public Infra- structure Public Safety • Docs •Records • Voting • Polling • Other • Equip. & personnel - Water -Wastewater - Other • Equip. & personnel - Police, - Fire -Regulatory Devices: Voting booths information cards, Scanners, Public infrastructure, etc. Lab • Water treatment • Building environ. • General environ. Residential Vehicles Personnel Material • Land • Air • Sea • People • Machines • Weapons • Supplies • Other Devices: Weapons, Vehicles, Soldiers, Unmanned drones, etc. Security / Surveillance • Radar/Satellite • Environment • Generation • Transmission Supply Demand • Distribution (Utility) • Residential • Industrial • Commercial Devices: Central plants, Distributed resources • Single family • Multi-family Entertainment • Audio • Video • Gaming Devices: TVs, VCRs, DVRs, Dishwashers, Washer/Dryers, Refrigerators, Lights, Computers, etc. Devices: HVAC, Vertical Transport, Fire & Safety, Lighting, Security & Access, etc. • Laundry • Kitchen •Environmental • Information • Lighting • Security • Climate Buildings Consumer Industrial Health Power Retail Transportation Government / Security •Complex machines • Simple / standard machines POS Infrastructure • In-store • Remote • Display equipment • Dist. centers • Shopping centers Devices: POS terminals, Tags, Cash registers, Payment terminals, Signs, etc. Signage / Display Lab Misc. Infrastructure • Process equip. • Product diagnosis • Facility mgmt. • Infrastructure • Cars/trucks • Aircraft • Watercraft • Construction Vehicles Infrastructure • Fueling stations • Nav. systems • Traffic control & Hway sys. Devices: Vehicles, Roads, Gas stations, Signage, etc. Machine Control Electronics/Semicon Industrial Process Control • Complex machines • Simple / standard machines • Semicon - Semi tools - Wafer hand - Test systems • Electronics - T&M - Comp • Placement & board assbly • SCADA • Instrument/ sensor • CDS Devices: Pumps, Valves, Vats, Conveyors, Pipelines, Motors, Drives, Switches, Machines, etc. • POC • Lab diagnostics • Point-of-Care • ER • Mobile POC • Implants • POC • POC • Lab facilities Devices: MRIs, PDAs, Implants, Surgical equip., Pumps, Monitors, Telemedicine, etc. Lab Hospital Doc. Office/ Care facility Home / In Vivo The ubiquitous internet of things 9 trillion wireless devices serving 7 billion people by 2020（Predicted by the international authoritative organizations） The ubiquitous web infrastructure pic Core: Fiber Optic Cable Transmission： Metropolitan area network Access: Local area network User: fixed, nomadic, mobile applications City functions Smart city functions Human settlement Smart security \ eco protection\ energy \ urban managers\ smart urban planning \ community \ home … Economic development Smart manufacturing \ industrial internet \ logistics ... Social interaction Smart transportation \ shopping \ community integrated management … Cultural enjoyment Smart outdoor streaming media \ education \ travel … Applications of the smart city §2 Big data in smart city • During the construction and application of the smart city, the ubiquitous sensor networks may collect and generate TB, PB or even EB level amount of data in real time, bringing our world to the era of “big data”. http://www.leiphone.com/wp-content/uploads/02-3/-2/02-33-21-22.jpg The construction of thesmart city is moving into the era of big data The era of big data is coming In Feb, 2011, Science noted the arrival of big data era Obama announced that the U.S. government officially launched the "big data research and development program“. He regarded big data as the oil of the world is future world. The significance of this program is comparable to the last century’s "information superhighway plan" Eric, from American Academy of Engineering, said: “We are in an exciting era that we can use big data to make prediction and modeling, visualization and discovery of new laws” Geospatial Information Resource Network Service Model Integrated information web Spatial data resources Processing resources Data requirement service Integrated service Sensor resources Distributed registration service center Knowledge discovery service Processing Sensor planning service Sensor registration Geo-knowledge resources Processing registration Computing resource Network resource Storage resource Spatial information resources Spatial information registration Data registration ZY-3 Products - Palm Island,Dubai ‹#› 15 http://y2.ifengimg.com/a/2014_34/2910db1a0083e2a.jpg GF-1影像, 2m • 巴黎 GF-2,0.8m,45km Paris • 华盛顿 GF-2,0.8m,45km Washington,DC ‹#› 18 E:\fusion\jt\jpg\xx_FUSION-A_000144647_004_005_005_RGB_L1_法拉利主题公园.jpg High RS，1m ‹#› 19 D:\_wangmi\work\2015\03\福州会议报告\北京南站.jpg High RS，0.5m C:\Users\think\Desktop\Beijing_CN_BeijingnanRailStation_IK_29Jul2012_0.jpg A typical high-resolution remote sensing images: Beijing West Railway Station, GeoEye, resolution 0.4 m 3D city modeling with 4+1 tilt cameras Ground / underground 3D integration (Wuhan) Massive spatial data scheduling and management（100TB） I show China: Live Maps in 300 cities (300TB) ØFusion of panoramic images and LiDAR 测点1 Integration of GIS and videos Five levels of monitoring networks: Street, district, municipal, provincial, national China has built the world's largest video surveillance network National multi-level network monitoring project will be built up this year In 2005, the State Council started the construction of the “Safe City” Built in 660 cities The number of lenses are over 20 million Invest more than 320 billion yuan The world's first large-scale urban construction monitoring network 数据来源要明确(茂胜) The challenges of big data – cannot afford to save A Case Study of Tianjin：the future storage cost: In 2015, the data volume of the monitoring data in Tian jin will be: The Storage device costs 58.32 billion yuan The HD cameras generates 3.6GB of data per hour The video storage volume is for 3 months The 4TB storage server is over 50000 yuan In 2015, Tianjin will install 600,000 cameras Equals to the GDP of Tibet in 2013. 4665.6PB IMS Research forecasted in 2011 : The storage volume for the new monitoring equipment in 2012 will reach to 3300 PB. The rapidly growing storage volume and investment is an important factor that restricts the development of city surveillance system 画面需要美化 The situation is even more severe in the era of big data The U.S murderer Iris Abrazan stabbed more than 20 people (5 were dead) in three states. Traditional Database Searching result Large-scale Database Searching result The rapid growth of data has led to a large amount of false alarms that the manual handling cannot follow up The rapid growth of data has exposed the shortages of the traditional warning techniques – The big data in the application of the smart city Smart transportation (hundreds of millions of people and cars ) arrest1 003-10 基站5 Anti-theft in residential quarter Roads Campus security Building sites The subway Cell phone user Monitoring center mobile-Surveillance fixed-3 fixed_1 emergency1 The application of the smart city Smart security（365 days x 24 hours） D:\MEDICAL MOBILE\Logos e Imagenes\Vega GPS Watch\Brazalete Vega.png Smart provision for the aged people （ 200-250 million people in China） The application of the smart city Features of big data •Volume: TB, PB, EB level of data waiting to be processed. •Velocity：The data stream waiting to be response should be processed in seconds or even milliseconds is continuously generated. •Variety: Data sources and types are various. Text, pictures, videos and other structured and unstructured data are exist; •Veracity: Because of the noise, loss, inconsistency and ambiguity, the uncertainty should be taken into consideration. •Value: Big data contains great values. It offers an unprecedented possibility to quantify and understand the world. The ultimate goal of big data is to find the great values within them. • This "5V" also translated as: volume, speed, diversity, authenticity and value. 3 Cloud computing and data mining §3.1 Cloud computing §3.2 Data mining § § Jim Gray: the 4th Paradigm 第 35 页 C:\Users\ping\AppData\Roaming\Tencent\Users\646222892\QQ\WinTemp\RichOle\6K1J0}J07BZNT97H09@)@MV.jp g 3.1 Cloud computing Socialization, intensification and specialization •Cloud computing, of which the computing resources (including computing power, storage capacity, the interoperability) is dynamic, scalable, virtualized and provided as a service, allows the public to participate in and supports information services. Virtual service Server clusters and virtualization Professional service causes the server to diverse More jobs are undertaken by servers The emergence of servers Browser/Server Client/Server The amount of server is increased dramatically … Server management Service management Searching server Video server Security server Mail server Data server Changes of Internet resource configuration Information service based on cloud computing The computing resources and servers on the Internet is increased, such as Google, Amazon, Hotmail… Providing network services independently Infrastructure service providers , such as Amazon Web services Software service provider Such as Google map and Apple Platform service providers, such as force.com和Google APP Engine Software services, such as salesforce Software integration services, such as programmable web The inner ecosystem of cloud computing center –Remote sensing data, processors and analysis methods are at the remote cloud computing platforms. Users can get the final result after they choose the data and processors, requiring no local computing environments any more. Remote sensing cloud Remote sensing cloud ——OpenRS-Cloud An instance of calculating the inundating area An example of remote sensing cloud service Service chain is established Service chain runs Service inquiry Service registration Output A well-designed processing module 1 is registered on the web Service 1 registration Service 2 registration A well-designed processing module 2 is registered on the web Service inquiry Get the software module from the processing service center Establish an abstract service chain based on project requirements and the existing processing module. Service chain is established Service chain runs The abstract service chain is mapped to the BPEL execution services chain 洪水淹没3 –The GNSS signals that the mobile phones receive, as well as the other positioning information, can be transferred to the cloud computing center. These information is computed in real-time to achieve the goal of indoor / outdoor continuous positional and navigation. Positioning cloud Geographical conditions monitors, disaster reporters, forest investigators, geological survey team, land investigators, urban management staff, traffic police and other civil servants and vehicle networking users. C:\Users\admin\AppData\Local\Microsoft\Windows\Temporary Internet Files\Content.IE5\9MHCFYIX\MC900432629[1].png Smart phone WLAN C:\Users\admin\AppData\Local\Microsoft\Windows\Temporary Internet Files\Content.IE5\5QY0H0OS\MC900349993[1].wmf Wireless signal Cellular network & Wireless digital television signals Bluetooth RFID/ NFC Accelerometer Gyro Phone camera Electronic compass Sensors C:\Users\admin\AppData\Local\Microsoft\Windows\Temporary Internet Files\Content.IE5\5QY0H0OS\MC900434857[1].png GNSS C:\Users\admin\AppData\Local\Microsoft\Windows\Temporary Internet Files\Content.IE5\5QY0H0OS\MC900034307[1].wmf § Satellite positioning § Wireless signal positioning § Sensor positioning § Integrating positioning C:\Documents and Settings\Administrator\桌面\06103322466783.jpg Positioning cloud Navigation from outdoor into indoor •Public service platform of Beidou positioning cloud 北斗位置云公共服务平台（带标题） Positioning cloud Chinese Beidou system begins to run and provides services ★In Dec 27, 2012, Chinese Beidou system begins to run and provides services for China and the surrounding countries. ★The State Council Information Office held a conference for Beidou system, officially announced the space control signal interface file. C:\Documents and Settings\zhangyt\桌面\_DSC6412.png 中巴双方代表中巴双方代表中巴双方代表中巴双方代表中巴双方代巴双方代表 Positioning accuracy: 10 meters (both in horizontal and vertical directions) Speed measurement precision: >0.2 m/s Timing Accuracy: 50 ns (one-way) Short message communication Wide Area Differential & ground-based enhancement 未命名 Service covering area The demo validation of Beidou ground-based enhancement system in Hubei oOverall objective： nTo provide high-precision navigation and positioning service capabilities in Hubei Province by the ground-based enhancement system. oDetailed objectives： nEstablish 6 frame network reference stations that uniformly distribute in Hubei, with an average side length of 220km. nEstablish 24 regional reference stations, with an average side length of 60km, to provide the regional tri-band centimeter-level precision positioning service. nEstablish a precise positioning service system in Hubei, to provide services for surveying/mapping, meteorology and transportation industries. Performance analysis of real-time precise positioning Positioning model Ambiguity fixing success rate Initialization time (in seconds) Inner precision / m (Average) Out precision / m (Average) Plane Height Plane Height GPS double frequency +BDS triple frequency 100% 5.76 0.004 0.018 0.010 0.036 GPS double frequency +BDS triple frequency 80% 27.46 0.003 0.015 0.011 0.042 BDS triple frequency 83% 16.40 0.007 0.020 0.013 0.052 BDS double frequency 40% 50.78 0.003 0.015 0.014 0.045 GPS double frequency 44% 40.28 0.006 0.021 0.012 0.048 Performance analysis of high-precision navigation 武汉市区近景-4 It can be used for intelligent transportation vehicle control and intelligent driving In Nov. 2013, the center has built the first oversea Beidou CORS station at Chonburi, Thailand. D:\X.SHEN\Downloads\图片1.png Test in one station GPS Beidou GPS+Beidou Available satellites 6-8 13-14 19-22 Satellites in use 6-8 13-14 19-22 Accuracy analysis of Thailand Beidou CORS station Test in three stations Content Target Carrier Car, max speed: 80km/h Positioning accuracy Plane 2cm, Height 5cm Navigation accuracy Plane 0.5m（Lane-level accuracy navigation） Accuracy and performance of Beidou is better than that of GPS in the low latitude area of ASEAN such as Thailand. 第 49 页 Accuracy analysis of Thailand Beidou ground enhancement system HRMS（m） 3.55 1.65 1.60 RMS（m） 7.84 3.44 3.03 Definition of data mining •A process that automatically discover and extract implicit, non-obvious patterns, rules and knowledge from the massive, multi-source big data . •Data mining is more difficult than data processing and information extraction because it requires intelligent reasoning based on big data and knowledge base. •The purpose is to find out the laws of nature and society changes, people’s behavior and preferences, trends of the social thinking and public opinion, in order to infer the market reaction to various aspects of products, services or policies, etc. Process of data mining Data acquisition Data storage Data computing Data tranmisstion and distribution Data mining Data presenation Data security Data collection, compilation and integration Low-cost storage and distributed processing Hundreds of parameters are needed for multiple simulation and computation Process the high dimensional data such as multimedia data after dimensional reduction. Need visual calculation results Sharing and access control, security and privacy protection Data warehouse Choose data Choose Transform Mining Fusion 抽取信息 融合信息 Transformed data Data fusion, sampling, multi-resolution analysis De-noising, feature extraction, normalized Dimension reduction Classification/clustering Visulization / Validation Data mining algorithm 52 Preparation Discovery Linear analysis, Nonlinear Analysis, Linear regression, Factor analysis, Univariate curve, Bivariate statistics, Time series analysis Artificial neural networks, Decision tree method, Genetic algorithms, Law reasoning Multivariate graphical analysis and find out the relationship between variables Find out laws and create models Interpretation Reveal the intrinsic relationship between data to find out relationship between the file access pattern of government / department and obtain correlation between different webpages. To assign a category according to the classified public attributes and description. Derive and forecast using historical data. Include customer clustering, web pages clustering, to provide specific services for the website users. Find out the time sequence of data. Find out the internal affair model that “some items follow others” Find out the web site pages that are mostly visited, Understand behavior of network users in order to improve the design of pages and site structure sdmkd1 Pyramid of Spatial Data Mining 20141006_113153 Methods of space-time data mining and knowledge discovery •Statistical methods and spatial statistics •Inductive methods •Clustering methods •Spatial multi-measurement, multi-temporal analysis methods •Exploratory data analysis •Rough set approach •Data field and cloud model •Image analysis and pattern recognition •Neural networks, evidence theory, genetic algorithms, mathematical morphology ... Inductive learning of banks operating income analysis and site evaluation bankflow The bank, road network and census site map of Atlanta banks2 bankrule Relationship between bank income and geographical factors evalue Speculate about the “well-managed”and “weak-managed” banks according to rules and exceptions. predict The new bank site evaluation map Night light remote sensing analysis for socio-economic information Image of night light in east Asia (taken in 2012 by DMSP/OLS) Beijing CBD Tokyo Ginza Wuhan Chuhe&Hanjie •The visible and near-infrared brightness of the earth surface obtained by remote sensing satellites (such as DMSP, NPP) can be used to characterize the urban range, GDP, population distribution and other socio-economic factors •Economic growth, urbanization, humanitarian disasters are likely to be reflected as the brightness changes of remote sensing images within a period of time. Night light remote sensing for economic statistics •Traditional GDP investigation takes an administrative unit as the statistical unit, which is not accurate enough. •To obtain the grid GDP data by allocating the GDP of an administrative unit into different grids via economic statistics, light images, population distribution and land cover types, etc. • • • • • • • • Global GDP grid in 2006 obtained by data mining GDP of southwest California GDP near Maxico city Assess the humanitarian disaster by time-series night light remote sensing images •A sudden decrease of night light may be caused by a reduction in power supply, or a large-scale resident migration, indicating a possible humanitarian disaster occurs. 卢旺达大屠杀前后的夜光变化 Typical night light fluctuation of peace countries and countries in war Possibilities of breaking a war with different night light fluctuation extent •By analyzing the night light fluctuation in 169 countries, we find that a sudden decrease in night light indicates a war is broken. While a sudden increase in night light show the reconstruction begins. Assessment of Syria civil war by time-series night light remote sensing images •Since Mar. 2011, the Syrian civil war has killed at least 100,000 people. Most of the reports on Syria cannot reflect the whole picture of the war. •Night light remote sensing images provide a way to assess the situation in Syria. •The following images show that the Syrian civil war has led to a significant reduction in the night light in Syria. • • • 绘图1 üBy developing new space-time analysis techniques, the night light images are clustered to find out the space-time model of the Syria cival war. • • • • • (a) 3 space-time distributions of night lights (b) 3 corresponding trends of night lights (by DMSP-F18) Allepo provice • Three models show significant regional characteristics: üThe night lights of the neighboring countries of Syria keep stable (in green). üThe night light of Syria reduce significantly, including two modes, i.e. red and blue. üAllepo province, in which the war is the most intense, shows a dramatic reduction (in red) in night lights. Assessment of Syria civil war by time-series night light remote sensing images Assessment of Syria civil war by time-series night light remote sensing images（2011-2015） ctn_153_interfg Master image City subsidence monitoring by data mining from The multi-temporal SAR data 24 Interferograms from ERS images of Shanghai Shangai Globale 20 - 20 0 Velocity has been saturated for visualization purpose. Much higher rates have been detected in Shanghai urban center. Reference point, assumed motionless More than 20.000 PS identified Shanghai subsidence velocity by the differential interferometry PS method Result of PS InSAR from Prof. Rocca legend_21_153 Benchmark Reference point CTs Distribution of benchmarks and CTs in the test site Shanghai subsidence velocity by the Coherent Target Analysis (CTA) method Result of CTA in Shanghai Cross Validation of PS and CTA results Benchmark Leveling(mm/y) CT(mm/y) Diff. at CTs PS (mm/y) Diff. at PSs 0- 64 -39.13 -39.15 0.02 -32.43 -6.695 0-113A -15.88 -12.61 -3.27 -20.02 4.145 0-120 -17 -17.18 0.18 -14.75 -2.25 0-139 -30.38 -29.25 -1.13 -25.43 -4.945 0-155 -21.25 -20.96 -0.29 -16.13 -5.12 0-192 -13.38 -16.37 2.99 -16.57 3.195 0-221 -14.75 -18.66 3.91 -12.46 -2.29 0-222 -11.13 -9.58 -1.55 -11.07 -0.055 0-223 -11.38 -10.33 -1.05 -5.05 -6.325 0-225 -11.63 -7.3 -4.33 -6.53 -5.095 0-289 -6.63 -13.37 6.74 -1.77 -4.855 • Difference between leveling and PS Average：4.088mm/year，STD.：3.73 mm/year • Difference between leveling and CT Average：2.315 mm/year，STD：2.50 mm/year Comparison of results from PS-InSAR and CTA in the overlay area of Shanghai test site Landslides on the river bank Building subsidence Dismounted construction Preliminary results: deformation distribution at the Three Gorges Dam v_meanAMP Monitoring landslide in Badong, Three Gorges Area Data set: Envisat images 34 scenes from Aug, 2003 to June, 2007 Two deformation regions are identified with PS-InSAR Agricultural data mining based on remote sensing big data lThe world's main producing areas (China, USA, Brazil, Argentina, Malaysia, Indonesia, Australia, Canada, India, Thailand, etc.) lSoybeans, cotton, corn, palm oil, rapeseed, sugar cane, wheat, rice. l Growth monitoring, yield estimation. lThe average yield estimation accuracy is better than 97%, which is released half month earlier than USDA. Wind, Shanghai Evening Post, Sina Finance, Mandarin Finance and Futures Daily relay in real time. lLater release the first-hand data of global nonagricultural macro-economic data. Customer: agricultural financial market participants, government departments H:\logo.png Products H:\logo.png C:\Users\xiangtao\Desktop\大豆.png C:\Users\xiangtao\Desktop\甘蔗.png C:\Users\xiangtao\Desktop\棉花.png C:\Users\xiangtao\Desktop\油菜.png C:\Users\xiangtao\Desktop\玉米.png C:\Users\xiangtao\Desktop\棕榈油.png Video Data Mining * Safe behavior intelligent analysis * Automatic video data understanding * Automatic video data compression 1 2 5 Analog video monitoring Digital video monitoring Web video monitoring Intelligent video monitoring 1990 All use analog technology Acquisition: Analog signal Storage: Digital signal 2000 Digital acquisition Digital storage Digital transmission Motion detection Behavior detection Intrusion alarm Sensor networks Face Recognition Behavior recognition Abnormal alarm Automatic intelligent monitoring 2005 2010 Video processing technique Analog video Digital video compression Intelligent video technology, such as computer vision, image analysis, etc. Year Intelligent recognition system is the recent trend of development of monitoring systems Processing flow in intelligent surveillance video analysis system • Abnormal event detection Target classification Human abnormal behavior detection Moving target tracking Moving object detection Basic technique Advanced technique lGet key information in video to focus, observe, and analyze suspects. Climb over the wall l u The main functions and features Human behavior recognition for video investigation Aggregation Running Climb over the wall Wandering Behavior analysis under multiple cameras 和前面的挂钩再引入技术问题 再将研究内容和特色 •In the large-scale network monitoring system in the city, the TV wall in the monitoring center can display dozens of monitor screens simultaneously. It is easy to miss abnormal events if only rely on human eyes. •Research shows that professional monitors will miss 95% of the behavior after 22 mins if watch 2 screens. Traditional monitoring systems In July, 2005, a severe bomb explosion was occurred in London subway. The clues were found after an time-consuming investigation on the large number of monitoring videos. The crime could not be prevented beforehand. Cannot prevent the crimes. Defects Video data mining n Intelligent video analysis Traditional monitoring systems Video data mining •In the large-scale network monitoring system in the city, the TV wall in the monitoring center can display dozens of monitor screens simultaneously. It is easy to miss abnormal events if only rely on human eyes. •Research shows that professional monitors will miss 95% of the behavior after 22 mins if watch 2 screens. nThe computer can understand the content of the videos by digital image processing and analysis. nDetect, separate, and track moving targets in dynamic scene videos automatically. Effectively identify the behavior of the target. 4 SCOC：Smart City Operation Center The municipal leaders establish the SCOC and assign the chief operation officer (COO). This is very important for smart city. COO SCOC management office CIO joint Conference Committee IT operation / maintenance center Big data center City monitoring and command center Smart service center （Data open platform, operating company） Functions of SCOC *Participate and review the top-level design of smart city； *Plan and review the target, framework, tasks, and operations manager system of the information development for various industries; *Develop relevant policies, regulations and standards; *Responsible for urban integration, information resources sharing, and integrated monitoring of urban running; *Collaboration between different departments; *Promote society-oriented big data applications and trading system. ——Build SCOC on the cloud-based open architecture Overall architecture Develop smart applications png-0236 Building & Energy Sensor network Public information platform Intelligent water supply Municipal landscaping Smart transportation Smart medical treatment Flower seedlings Urban lighting health Smart scenic spot Spatial data Public video collection and sharing Smart energy Drinking water source protection and water quality monitoring Waste management Smart residential quarter Smart city png-0292 pic_b Wireless network Keep stability D:\我的文档\Downloads\摄像头.jpg Keep livelihood Keep growth Smart government affair Smart livelihood Smart industry PaaS SaaS DaaS IaaS Application layer Software development and runtime layer Infrastructure layer Network Computer room Server Storage Sensors Smart urban manager Public information platform Smart police Smart tour …… Core: Public Information cloud services platform IT cooperation in the era of smart city is related to big data: data acquisition, processing, storage, cleaning, mining, decision making, and control / use services. 020 Big data ecology in smart city Big city monitoring and dispatching based on big data E:\002-共享平台\08-售前工作\S四川成都温江\图\图片2.png C:\Users\Administrator\Desktop\u=4271549917,3938238956&fm=21&gp=0.jpg C:\Users\Administrator\Desktop\u=3628877895,707633455&fm=21&gp=0.jpg C:\Users\Administrator\Desktop\windows.jpg Smart service center City governance mode changes from management to service, traditional government IT information architecture will be replaced by "cloud-end" interaction in smart city 1）Smart service platform for the public 2）Application service platform for enterprises 3）Big data trading platform and operating company by government-enterprise cooperation 背景 SCOC - heart of the smart city •Resource pool of big data in city •Hub of internet of things in city •Urban monitoring and operations command center •Overall perception of urban operations data •Cross-functional, cross-regional, cross-system collaborative and efficient emergency response. •Social enterprises and public service platform •Reduce urban informatization construction and operation and maintenance costs. •Minimize costs, improve urban efficiency. European authorities later reducing administrative costs of $ 250 billion after using big data tecunique (McKinsey) SCOC makes innovation to create a new e-government platform Governance: “Facts based on truth” ---√ “Subjective will” ---X “Influence of interest groups” ---X City running： Visualization, control, intelligence, predictable and quantifiable assessment, and continuous optimization. The government will become more open, accountable and more efficient, thus minimizing the risk of administration. Enterprises：Reorganize production resources by big data, improve business modes and obtain greater income. Public: Smart service will run through “birth, medical treatment, education, employment, marriage and child rearing, pension, funeral mourning” to enhance the well-being of urban residents. 政府治理：“基于实证的事实”---√ “意识形态”---X “利益集团的影响”---X 城市运行：可视化、可控化、智能化、可预测及可量化评估与持续优化。政府将因之变得更加开放和负责，并更有效率，从而最大程度地降低行政风险 企业能够通过大数据重组生产资源，改良商业模式，获得更大效益 市民：智慧服务将贯穿“出生、医疗、教育、就业、婚育、养老、殡丧”等人生全过程，提升城市居民幸福感 •Smart city is the integration of real world and digital world. It is based on the digital city, internet of thins and cloud computing. •Smart city has broad prospects in economic restructuring and development, urban management and public intellectual service, so that a more coordinated development between man and nature can be achieved. •The realization of smart city should based on a more perfect information infrastructure, to ensure the various applications of smart city is useful and affordable. •New opportunities and challenges are brought by big data in smart city. We should make innovations in techniques to stimulate the development of digital services industry to realize the various applications in smart city. •The realization of smart city is complex. Top-level design and overall planning should be made to found the SCOC according to the characteristics of different cities. • 5 Conclusions and future works 14 Thank you！ 20140325_092311