66 results about "Urban security" patented technology

The invention discloses an urban safety risk assessment method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: constructing a multi-source data collection network, and obtaining data from a government department database, Internet of Things equipment, a social media platform and a traffic monitoring system in real time; preprocessing the collected multi-source data, wherein the preprocessing comprises data cleaning, format standardization, unstructured data semantic analysis and sentiment analysis; a cross-department data security sharing mechanism is established, and the traceability and security of data exchange are ensured through a block chain technology; constructing a dynamic risk assessment model, analyzing multi-source data relevance based on a deep learning algorithm, and dynamically adjusting the weight of each risk factor; and generating a visual risk assessment report, and pushing the visual risk assessment report to related departments in real time through an early warning system. According to the invention, through multi-technology fusion and a dynamic optimization mechanism, the accuracy, real-time performance and cooperation efficiency of urban safety risk assessment are significantly improved.

The invention discloses a region denial method and system for an unmanned aerial vehicle. Precise defense is realized through a four-stage cooperation mechanism. The method comprises the following steps: S1, a multi-source detection stage; s2, an intelligent identification stage; s3, a dynamic response stage; and S4, a closed loop feedback stage. The system architecture comprises a control center, a composite detection array, an intelligent analysis engine and a multi-mode countering unit, and a complete automatic link from situation awareness to effect execution is formed. Through cooperation of soft and hard killing means, detection and recognition integrated design and an intelligent decision engine, the unmanned aerial vehicle interception efficiency in a complex electromagnetic environment is remarkably improved, an echelon defense system covering electron suppression to precise destroy is constructed, and the method is particularly suitable for important place protection, city security and other high-value target protection scenes.

The invention discloses an urban underground pipe network multi-dimensional state perception and risk assessment method and system, and relates to the field of urban pipe network monitoring. The method comprises the following steps: S1, multi-source data acquisition: acquiring physical, chemical and environmental perception differentiation data of a gas pipe network and a drainage pipe network through a multi-source heterogeneous sensor network; s2, edge side data processing: preprocessing the data and dynamically weighting the data, and executing local anomaly recognition; s3, performing multi-parameter coupling analysis to obtain a coupling risk index; s4, carrying out three-dimensional dynamic evaluation, and carrying out differentiated evaluation from dimensions of probability, consequence and vulnerability; and S5, intelligent decision making is carried out, and grading early warning and linkage control are triggered. The system comprises a sensor layer, an edge calculation layer, a cloud analysis layer and an early warning platform. The problems of single dimension, response lag and the like of traditional monitoring are solved, full-life-cycle intelligent management of a pipe network is realized, and safe operation of a city is guaranteed.

According to the severe weather adaptive road violation real-time monitoring method and system provided by the invention, the image quality is dynamically judged by using the visibility index, and accurate traffic target identification and violation behavior detection are realized by combining image enhancement and a gating feature fusion mechanism. The system comprises a video acquisition module, a visibility evaluation module, an image enhancement module, a feature extraction and fusion module, a target detection module and a violation recognition module, can maintain high-accuracy recognition performance in various severe weathers (such as rain and fog), and is suitable for the field of intelligent traffic and urban safety management.

The invention relates to the technical field of safety management, in particular to a city safety emergency management system design scheme based on a three-dimensional map, and the scheme comprises a facility space construction module, a risk identification module, a disaster type area identification module, a linkage early warning marking module and an avoidance path analysis module. According to the invention, risk scoring is carried out on facility node coordinates, function levels, the number of accommodated people and service radiuses in a city three-dimensional map, the risk distribution condition of each facility node is visually reflected, and the node risk levels are displayed in a three-dimensional manner by setting the identification map layer through column height, color marking and flicker frequency. Visual focusing and spatial clustering of risk nodes are realized, a commander is helped to quickly identify a high-risk area, response grading of a disaster situation interaction area is effectively refined by performing composite situation grade evaluation on a composite disaster type area and marking an early warning icon, an avoidance passage path is automatically screened and planned, and traffic congestion and secondary risks are avoided in real time.

The invention relates to the technical field of urban safety monitoring, and discloses an unmanned aerial vehicle and bee colony coordinated urban safety monitoring and early warning system, which comprises a bee colony initialization module used for acquiring historical risk heat data, dividing honeycomb units and determining the number of unmanned aerial vehicles in each layer; the route planning module is used for generating a whistle ring layer grid route and an inspection layer clustering route and managing the standby state of a near inspection layer; the risk modeling module is used for constructing a risk projection matrix, generating a spatial risk level and calculating a preliminary risk index; the bee gathering response module is used for activating the micro unmanned aerial vehicle based on the initial risk index, forming a collaborative bee gathering unit and calculating a secondary risk index; the early warning level judgment module is used for fusing the risk index, the target density and the speed information and determining an early warning level; and the scheduling processing module is used for calculating ground resource priorities and completing guidance processing. According to the invention, an efficient risk perception and linkage disposal system of urban air-ground cooperation is realized.

The invention relates to the technical field of intelligent pipe networks, and discloses an urban pipe network management system and method based on digital twinborn and cross-system collaboration.The urban pipe network management system comprises a multi-source data acquisition layer, a dynamic twinborn modeling layer, a data sharing and exchanging system and an intelligent decision-making layer, and the dynamic twinborn modeling layer comprises layered digital twinborn bodies; the data sharing and exchanging system is used for acquiring data of the multi-source data acquisition layer and performing data exchange on the acquired data and the dynamic twin modeling layer to form a core data center of an intelligent pipe network; and the intelligent decision-making layer performs data extraction and exchange with the data sharing and exchange system to form a layout optimization module, an anomaly detection and response module and an anomaly processing flow for the intelligent pipe network. According to the invention, the operation efficiency and safety of the city pipe network are improved, the development of the smart city is promoted, the emergency response time of the pipe network is shortened, and the efficiency of the city safety management level based on digital twinning and cross-system collaboration is improved.

The invention discloses a monitoring video processing method and device in a public security scene and a medium, and the method comprises the steps: obtaining the current network state information of a video transmission network in real time in the process of carrying out normal compression coding on a monitoring video stream collected by a monitoring device in real time and transmitting the monitoring video stream to a receiving control end in real time; when the current network state information satisfies a degradation coding condition, determining each monitoring video stream to be processed as a degradation video stream, and respectively identifying a target monitoring area and a non-monitoring area in each degradation video stream; and performing at least one round of degradation coding processing on each degradation video stream according to the target monitoring area and the non-monitoring area in each degradation video stream until a degradation processing ending condition is met, according to the technical scheme of the embodiment of the invention, a reliable video transmission guarantee scheme is provided for public security scenes such as urban security and protection through intelligent area identification and dynamic hierarchical coding technologies.

The invention discloses a monitoring, alarming and early warning method and system based on numerical analysis and coupling analysis. The method comprises the following steps: acquiring monitoring data of each industry based on urban safety development evaluation from a plurality of data sources and preprocessing the monitoring data; calculating the preprocessed monitoring data by using an alarm logic formula based on static numerical analysis and dynamic numerical analysis, and determining whether to give an alarm or not according to a calculation result; according to an alarm logic formula, determining an association relationship and an interaction factor of multi-source alarm, and according to the association relationship and the interaction factor, performing association coupling early warning analysis and spatial coupling early warning analysis on each monitoring data; through the preset big data analysis model, urban safety event prediction and early warning prompt are carried out based on the coupling early warning analysis result, and through an alarm analysis mechanism and an early warning analysis mechanism which are widely applied and fully verified, the alarm accuracy and the early warning effectiveness in a cross-industry, cross-organization and cross-region monitoring scene are effectively improved.

The invention belongs to the technical field of wireless communication safety monitoring and intelligent supervision, and particularly relates to a distributed low-cost hotel view-stealing and photo-stealing intelligent supervision system which comprises a wireless probe and a sky-wing cloud wireless analysis management and control platform. The wireless probe is connected to a hotel room router, collects space electromagnetic signals and terminal and AP wireless data, and pre-processes and reports the space electromagnetic signals and the terminal and AP wireless data; the cloud platform is constructed based on OpenStack, network mapping is updated through an ANO adversarial online automatic learning algorithm, wired / wireless attributes and event characteristics are subjected to correlation analysis by means of a VAE-CWGAN model, risks are identified, and an alarm is issued. The system realizes lightweight deployment, low-cost operation and maintenance, real-time intelligent supervision and room-level accurate positioning, can detect hundreds of wireless security incidents and dozens of cameras, is suitable for the fields of secrecy, national security and public security, and assists in security guarantee of smart civilized cities.

The invention belongs to the technical field of meteorological monitoring, and provides an urban road extreme weather real-time forecasting and risk early warning method and system based on artificial intelligence and digital twinning. The system comprises a data acquisition layer, a data processing and computing layer, a business application layer and a user interaction layer. According to the method, multi-source data of radar, meteorology, geography, municipal administration and the like are fused, the artificial intelligence technology is utilized to carry out accurate forecasting of short and temporary rainfall, the urban hydrological hydraulic model based on the digital twin technology is driven to carry out rapid simulation, and high-precision, dynamic and visual forecasting and early warning of the urban inland inundation risk are achieved. According to the method, the problems of poor precision, slow timeliness and weak decision support of a traditional early warning mode are effectively solved, the technical support of turning from passive response to active prevention and control can be provided for city safety management, and the capability of coping with extreme weather events of a city is remarkably improved.

The invention discloses a city safety abnormal event image generation method, system and device and a medium, belongs to the technical field of computer vision and smart cities, and aims to solve the technical problem of how to improve the generation quality and diversity of city safety abnormal event images. According to the technical scheme, the method comprises the following steps: constructing and preprocessing a city safety abnormal event data set: collecting monitoring images containing normal scenes and various abnormal events, constructing a labeling graph by labeling abnormal region positions and types of the monitoring images, and dividing a training set and a test set according to a preset proportion; heterogeneous feature fusion is executed, and comprehensive condition features are generated: an original monitoring image is coded into latent layer features, downsampling is carried out on a labeled image, and the labeled image and Gaussian noise features are spliced and fused in a channel dimension; performing field adaptation on the pre-training model based on an intelligent fine tuning framework; integrating multi-scale and space attention mechanism to enhance feature extraction; performing condition-guided image generation; and generating performance is quantified by adopting a systematic evaluation system.

The invention discloses a city safety monitoring and early warning method and system, and the technical key points are that the method comprises the following steps: S1, multi-source data collection: collecting heterogeneous city safety monitoring data through monitoring terminals distributed in all scenes of a city, the heterogeneous data comprising real-time video stream data, structured numerical data and semi-structured log data; s2, data preprocessing and fusion: inputting the heterogeneous data acquired in the step S1 into a multi-source data fusion model based on an attention mechanism, filtering noise data through a data cleaning module, and extracting spatial-temporal features of each data source through a feature extraction module; s3, performing dynamic risk assessment; s4, grading early warning and collaborative linkage are carried out; and S5, performing closed-loop optimization. According to the method, the multi-source data fusion precision can be improved by more than 35%, the weights of different data sources are automatically identified by constructing a heterogeneous data fusion model based on an attention mechanism, the problem of data islands is solved, and a reliable basis is provided for risk assessment.

The invention relates to the technical field of data analysis, and particularly discloses an urban safety performance dynamic evaluation and early warning system based on multi-source real-time data fusion, comprising: a data processing module performing feature extraction on a multi-source real-time data pool to obtain a standardized spatial-temporal feature data set; the weight strengthening module strengthens the weight of the scene sensitive index in the urban safety performance index system to obtain the dynamic strengthening weight of the urban safety performance index system; the performance scoring module is used for calculating a dynamic safety performance score based on the dynamic strengthening weight of the urban safety performance index system; the conduction analysis module analyzes the multi-index linkage risk, obtains the risk conduction probability among all the indexes, and generates a dynamic risk thermodynamic diagram based on the risk conduction probability among all the indexes; the grading early warning module fuses the dynamic safety performance score and the dynamic risk thermodynamic diagram to obtain a current city safety performance evaluation value, and triggers an early warning response in combination with a grading early warning mechanism; and comprehensive, accurate and timely urban safety assessment and early warning are realized.

The invention discloses a city security patrol unmanned aerial vehicle fusing edge agent decision, relates to the crossing field of unmanned aerial vehicle technology and city security technology, and provides the following scheme that the city security patrol unmanned aerial vehicle comprises a vehicle body and further comprises rotor wing assemblies arranged on the periphery of the exterior of the vehicle body in an array mode and used for achieving flight attitude control of the unmanned aerial vehicle; the sensing module is arranged at the top of the machine body. According to the invention, localized processing and decision-making of security and protection data are realized, dependence on a remote control center is not needed, abnormal event identification and processing instruction generation time is shortened to millisecond level, processing lag caused by communication delay is effectively avoided, patrol paths and decision-making strategies can be automatically adjusted according to on-site environments, waiting for remote instructions is not needed, and operation is simple and convenient. The method is suitable for complex and changeable urban security protection scenes, the edge computing power of the unmanned aerial vehicle is fully utilized through cooperative interaction between the edge intelligent agents, idle equipment resources are avoided, the algorithm precision is continuously optimized through an autonomous learning function, and the long-term security protection patrol effect is improved.

The application provides a kind of data-driven-based city safety management performance dynamic evaluation method and device, related to city safety management technical field, obtains the multilevel city safety field data of city to be evaluated;The multilevel city safety field data is encoded and classified, and the city safety material data set is formed;Three-dimensional index of city safety management performance evaluation is constructed;The corresponding index integral of each process performance index, result performance index and key performance index is calculated respectively;According to the preset process performance index weight, the preset result performance index weight, the corresponding index integral, the city safety management performance evaluation grade result is determined;Through the added Assess performance evaluation link and city safety management performance evaluation grade result, the city safety management performance evaluation model is constructed;According to city safety management performance evaluation model, dynamic evaluation is carried out, and the evaluation result is obtained.

The invention discloses an Internet of Things video monitoring scene adaptive adjustment method combining big data mining and deep learning, and belongs to the technical field of Internet of Things video monitoring. The method comprises the following steps: firstly, acquiring multi-source data such as a video stream and a pan-tilt attitude, and performing association fusion and preprocessing to form an effective data set; a typical scene is obtained through clustering of an improved D-CURE algorithm, scene classification is achieved in combination with a random forest, and initial monitoring parameters are output; then, parameters are dynamically corrected by using a WLSTM model, and video coding and transmission collaborative optimization are completed by relying on DHC-Net; and finally, continuously optimizing the system through multi-dimensional evaluation and iterative feedback. The problems that traditional monitoring fixed parameters are poor in adaptability, data mining is insufficient and the like are solved, the monitoring image quality and transmission stability are improved, the operation and maintenance cost is reduced, and the method is suitable for a multi-scene intelligent monitoring system for urban security and traffic control and the like.

The invention relates to an interactive control system and method for smart city security and large screen, and relates to the technical field of computers, and the system comprises a data collection module which is used for collecting heterogeneous security data and injecting spatio-temporal information to generate a standardized data packet; the data fusion module is used for receiving the data packet and carrying out lightweight structure processing on the video data to reduce the data volume; the large-screen display module is used for visually presenting the structured information; the interaction control module is used for capturing a touch operation on a large screen and performing permission verification, encryption and priority scheduling on an instruction; and the security equipment control module is used for receiving and translating the instruction and issuing the instruction to front-end equipment for execution. According to the method, the technical problems of difficulty in heterogeneous data fusion, low interaction efficiency and unsafe control are solved, and unified presentation of a global situation, efficient and visual interaction control and safe and reliable instruction execution are realized.

The present application provides a method and system for building change detection based on multi-source real-scene three-dimensional models. The method includes: obtaining the triangulation topology data of two-phase real-scene three-dimensional models within the area to be detected, and performing preprocessing and standardization processing; using plane-elevation hierarchical registration technology for automatic registration to achieve coordinate unification without manual intervention; using spatial index difference calculation to identify the three-dimensional change areas of the two-phase real-scene three-dimensional models, and optimizing the three-dimensional change areas by using a multi-scale dilation-erosion combination algorithm; using three-dimensional feature dual-channel filtering for the optimized three-dimensional change areas to verify real building changes; generating four-dimensional spatio-temporal results and outputting a visualization model including elevation change values, material types, and three-dimensional reconstruction parameters. The present application overcomes the machine-readable problem of subtle changes in buildings in large-scale real-scene models, and provides a highly reliable three-dimensional quantitative analysis tool for urban illegal construction monitoring and urban safety management.

The invention belongs to the technical field of geological disaster risk assessment and urban safety disaster prevention, and particularly discloses an urban karst surface collapse risk assessment method and a storage medium, and the method comprises the steps: screening out dominant factors from a plurality of susceptibility evaluation indexes through a geographic detector, and carrying out the quantification of each dominant factor; quantizing the plurality of induction factors, and inputting the quantized dominant factors and induction factors into a support vector machine model to obtain a risk evaluation result; quantifying the plurality of vulnerability evaluation indexes, performing combined weighting by using an analytic hierarchy process and an entropy evaluation method to determine the weight of each vulnerability evaluation index, and calculating the vulnerability index of the research area based on the weights; and based on the risk evaluation result and the vulnerability evaluation result, superposition is carried out through the same risk level discrimination matrix to form a risk evaluation result, and the research area is divided into a plurality of risk levels. Evaluation indexes can be scientifically screened, human activity induction factors are fused, and high precision and strong interpretation are achieved.

The present invention discloses a method and system for constructing an early warning model for a city safety risk assessment benchmark. The method comprises updating historical city safety data, determining a risk threshold, determining risk data based on the risk threshold, obtaining a historical data matrix from the city database, determining an assessment benchmark value based on the historical matrix and the risk data, determining a risk estimate based on the risk data and the risk threshold, optimizing the city safety risk assessment benchmark early warning model based on risk estimate deviations, and inputting the city safety data to be assessed into the optimized city safety risk assessment benchmark early warning model for risk assessment and risk warning. This method not only improves the efficiency and accuracy of city safety risk assessment benchmark management but also exhibits good interpretability and can be directly applied to city safety risk assessment benchmark early warning model systems.

The invention provides an underground pipe gallery inspection method and device based on inertial navigation and electronic equipment, and belongs to the technical field of urban safety inspection, and the method comprises the following steps: obtaining original inertial data when an inspection carrier moves in a pipe gallery, synchronously collecting micro-dynamic feature data of the inspection carrier, and generating a state feature group; judging whether the inspection carrier is in a high-confidence static state or not based on the original inertial data and the state feature group to obtain a judgment result; executing a corresponding inspection strategy according to the judgment result so as to suppress the accumulative error of inertial navigation, and continuously calculating the position, speed and attitude of the inspection carrier so as to output the current navigation positioning result of the inspection carrier; and evaluating the uncertainty of the current navigation positioning result in real time, and generating risk early warning information of the inspection operation based on the uncertainty. According to the invention, the positioning precision and the operation safety controllability of long-distance autonomous inspection of the underground pipe gallery are improved.

The invention discloses a multi-modal fusion body-equipped intelligent system in a complex scene and a use method, and belongs to the technical field of artificial intelligence and robots. Aiming at the problems of perception lag, task splitting and incapability of understanding an open instruction of an existing intelligent agent in a dynamic complex environment, a complete perception-decision-execution closed loop is constructed by integrating core modules such as multi-modal perception, open vocabulary target detection, dynamic semantic mapping, natural language instruction analysis and multi-modal decision. According to the system, vision-language embedding, cross-modal attention fusion and an intelligent obstacle avoidance mechanism are adopted, multi-dimensional understanding and self-adaptive path planning of the environment are achieved, and the performance of the model is optimized through a two-stage training strategy. According to the method, the accurate response, multi-task cooperative execution and dynamic obstacle avoidance capability of the intelligent agent to a natural language instruction in an unknown scene are remarkably improved, and the method can be widely applied to the fields of military reconnaissance, disaster rescue, urban security and protection and the like and has high practicability, adaptability and expandability.

The application discloses a ground collapse detection and early warning scheme based on real-time three-dimensional imaging, and a method thereof comprises the following steps: acquiring background noise data of a target underground structure through a seismometer array installed at a target underground structure detection site; then, surface wave extraction and dispersion curve calculation are carried out based on the background noise data measured by the seismometer; imaging of the underground three-dimensional structure is carried out through a seismic interference imaging technology, the change of the wave velocity of the underground medium is monitored, so that the abnormal area of the velocity structure is identified and positioned, and a reliable basis is provided for identifying the precursor information of the ground collapse. The technical scheme provided by the application can realize real-time monitoring and early warning of the ground collapse risk which is prone to occur in cities in the field of city safety risk monitoring, early warning and management and control, improve the city ground collapse early warning and emergency disposal capacity, and greatly reduce the collapse risk hidden danger and guarantee the safety of people's lives and property.

The invention discloses a fine-grained urban crime pattern mining method and system. The method comprises the following steps: combining and organizing crime data into a three-dimensional data structure according to areas, time periods and situations; constructing a first objective function containing a multi-factor matrix on the basis of the structure, and constructing a second objective function containing a spatial-temporal correlation matrix in combination with spatial-temporal correlation; superposing two objective functions and setting constraint conditions to convert the two objective functions into an optimization problem; iteratively solving by adopting an alternating optimization mode until the objective function is converged; and obtaining each matrix estimation value after convergence, and mining crime modes under different regions, time periods and situation combinations. Wherein the construction of the space-time correlation matrix, the setting of the optimization constraint and the alternative iteration solution all comprise specific sub-steps. According to the method, multi-dimensional data are integrated, time-space correlation characteristics are described, the problems of incomplete modes and insufficient precision are solved, an accurate basis is provided for urban crime prevention and control, a collaborative guarantee method of all units of the system is integrated, and refined urban safety management is assisted.

The invention discloses an AI water level early warning linkage gate control method and system and a medium. The method comprises the steps that S10, a waterlogging area water level gauge image is obtained; step S20, extracting scale features from the water level scale image by using a water level scale identification algorithm, and calculating a water level height; and S30, executing a corresponding gate control strategy according to the calculated water level height. The system can help urban safety and flood control, quickly discover and prevent problems, timely notify a responsibility unit when an alarm occurs, and automatically link a control scheme for automatic control, so that the early warning problem can be handled in the first time; an emergency department is helped to quickly deal with sudden safety accidents, prevent the safety accidents and timely and automatically deal with emergencies, life and property safety of people is guaranteed, and a large amount of manpower and material resource cost is saved.

The invention discloses an urban safety monitoring quantitative prediction method and system based on big data analysis, and the method comprises the steps: determining a plurality of monitoring indexes of urban safety development, collecting the multi-source heterogeneous historical data of each monitoring index, and carrying out the preprocessing; performing data coupling and feature extraction on the preprocessed multi-source heterogeneous historical data, and selecting an analysis model according to data features and data industry features; carrying out training and cross validation on the analysis model based on the multi-source heterogeneous historical data of each monitoring index, and generating an urban safety monitoring quantitative prediction model; quantitative prediction is carried out based on multi-source heterogeneous real-time data of each monitoring index by using an urban safety monitoring quantitative prediction model, and different analysis models are selected to adapt to various data quality and data industry characteristics, so that the accuracy of quantitative prediction of the monitoring indexes is improved; the operation situation of the monitoring point can be effectively and quantitatively predicted, and early warning can be carried out based on prediction.

The invention relates to the technical field of city safety management, and particularly discloses a city safety grading evaluation and risk early warning system which comprises a multi-dimensional data acquisition module, a city function classification module, a three-dimensional coupling evaluation module, a risk conduction blocking quantification module, a dynamic threshold early warning module and a data dynamic updating module. According to the method, disaster accidents and ecological safety risks are integrated, K-means clustering adaptation function partitioning is adopted, a comprehensive score is calculated in combination with an AHP and an entropy weight method, the risk blocking resistance efficiency is quantified through an improved SIR model, and an early warning threshold value is dynamically adjusted based on a coupling coordination degree index and a real-time working condition. According to the method, multi-dimensional risk unified assessment, early warning and accurate matching of regional features are realized, the actual effect of anti-risk measures is quantified, the comprehensiveness, accuracy and practical operability of urban safety assessment and early warning are effectively improved, and data support is provided for urban safety management.

The application belongs to the technical field of meteorological monitoring, and provides a city road extreme weather real-time forecasting and risk early warning method and system based on artificial intelligence and digital twinning. The system comprises a data acquisition layer, a data processing and calculation layer, a business application layer and a user interaction layer. The application fuses radar, meteorological, geographical, municipal and other multi-source data, uses artificial intelligence technology to accurately forecast short-term precipitation, and drives a city hydrology and water power model based on digital twinning technology to simulate quickly, thereby realizing high-precision, dynamic and visual forecasting and early warning of urban waterlogging risk. The application effectively solves the problems of poor accuracy, slow timeliness and weak decision support of traditional early warning methods, can provide technical support for changing from passive response to active prevention and control for city safety management, and significantly improves the ability of cities to cope with extreme weather events.