37 results about "Urban security" patented technology

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.

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 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 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 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 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.

The invention discloses an underground karst hidden danger intelligent detection system and method based on an electric shock effect, and the method comprises the steps: transmitting an electromagnetic pulse signal to a detection region through an electromagnetic transmitting module, receiving the electromagnetic pulse signal through an electromagnetic collection module, and feeding back the electromagnetic pulse signal to an electromagnetic collection base station; the earthquake acquisition module receives earthquake wave signals and feeds back the earthquake wave signals to the earthquake acquisition base stations, and the intelligent control host carries out synchronous time synchronization with the two base stations, so that the process of simultaneously acquiring two kinds of data can be realized only through one-time electromagnetic pulse excitation without additional excitation equipment, the data acquisition is ensured to be under the same time-space condition, and the data acquisition efficiency is improved. And accurate data support is provided for subsequent non-destructive, high-efficiency and high-precision identification of urban underground hidden dangers. According to the method, the independently constructed model is adopted during data processing, whether the result of the abnormal body exists or not is judged and output, manual participation is not needed, the recognition accuracy is good, and therefore instant data support is provided for city safety management.

The invention discloses an unmanned aerial vehicle detection remote monitoring system, which belongs to the technical field of unmanned aerial vehicle detection remote monitoring and comprises a region planning module, a path optimization module, a behavior judgment module and a decision response module. According to the unmanned aerial vehicle detection remote monitoring system, by collecting the image data of the unmanned aerial vehicle and the data of the public security camera, the urban security monitoring data can be quickly obtained and processed, so that a worker can conveniently and timely master the real-time condition of urban security through the user side; and meanwhile, the system has multiple functions of path dynamic optimization, anomaly grading judgment, multi-source data fusion, alarm recording and manual recheck, and effectively solves the problems of multiple blind areas, low patrol efficiency and lagging anomaly response of traditional urban security and protection monitoring.

This invention relates to the field of smart city safety monitoring technology, and discloses a method for multi-source data fusion monitoring of smart city safety risks. The method includes: collecting heterogeneous data from IoT sensor terminals, emergency management systems, citizen alarm platforms, and video surveillance analysis platforms, and encoding them into standardized event description records; performing spatiotemporal alignment within a unified spatial grid and time window framework; calculating uncertainty metrics for each data source based on data timeliness, completeness, and historical reliability; calculating inter-source conflict degree based on the degree of probability distribution overlap, and calculating fusion weights by combining uncertainty factors and conflict attenuation factors to obtain a fused safety state probability distribution; calculating a comprehensive risk score and outputting the safety monitoring results.

The application discloses a kind of city target protection system and method based on radar monitoring and data acquisition, it is related to city safety protection technical field, the present application includes radar data acquisition module, multi-source data preprocessing module, multi-source data fusion module, target identification and classification module, safety state evaluation module, early warning decision module, protection execution module, data storage and backtracking module, remote monitoring interaction module;The present application fuses millimeter wave / laser radar and multiple types of sensors, both monitor moving targets, and monitor static structures, solve the one-sidedness of single sensor data problem.Through quantitative formula, realize "data fusion-safety evaluation-early warning determination", avoid subjective judgment, and the safety index S can accurately reflect the target state.From data acquisition to the response time of protection execution is short, early warning information is pushed in real time, solve the problem of poor real-time performance of manual inspection.

The invention discloses an urban community waterlogging toughness portrait construction method and system based on a rainwater overload rate, and relates to the technical field of urban safety toughness portrait construction. The method comprises the following steps: acquiring post-raining state scene data of a target area, and preprocessing the post-raining state scene data; calculating surface runoff generation features and surface confluence features of the target area in real time based on the preprocessed post-rain state scene data; and calculating a rainwater overload rate of the target area according to the surface runoff generation characteristics and the surface confluence characteristics, and constructing a waterlogging toughness portrait of the target area based on the calculated rainwater overload rate. By means of the method, the problems that existing waterlogging toughness evaluation is static and dynamic portraying is difficult can be effectively solved, the rainwater overload rate serves as a core toughness index in a breakthrough mode, the dynamic toughness portraying of an underlying surface system of an urban community is achieved, and therefore the precision of the constructed urban community waterlogging toughness portraying is improved.

This invention discloses an AI-based intelligent system for identifying security risks in smart cities, belonging to the field of smart city security technology. The system includes: a data acquisition and fusion module, a spatiotemporal anomaly identification module, a physical verification module, an AI prediction and early warning module, and a response and linkage module. By integrating video surveillance, IoT sensors, geographic information, and meteorological data, this invention constructs a security data resource pool covering the entire city, breaking the limitations of traditional security systems that rely on a single data source. Based on a spatiotemporal curvature attention model, the system can deeply extract spatiotemporal features from video streams, accurately identify abnormal behaviors and scenes, and generate a highly reliable list of anomaly samples. This combination of multi-source data fusion and intelligent analysis not only improves the comprehensiveness of anomaly detection but also significantly reduces the false alarm rate, providing more reliable technical support for urban security.

The application provides a city safety risk information retrieval method based on text2sql, an electronic device and a storage medium. The process includes data integration and preprocessing, text2sql model construction and training, natural language query processing, SQL statement generation and execution, result processing and display. This method realizes the full automation of the operation of the user directly and accurately obtaining scattered risk information and obtaining intuitive visual results in natural language, to solve the three core problems of high technical threshold of non-professional user database retrieval, difficulty of multi-source heterogeneous data integration management, and insufficient accuracy of SQL generation caused by semantic gap and context loss in professional field query, which has important wisdom city theoretical innovation research significance and wide industrialization use value.

This invention discloses an intelligent detection system and method for underground karst hazards based on the electroseismic effect. It employs an electromagnetic emission module to transmit a single electromagnetic pulse signal to the detection area, and an electromagnetic acquisition module to receive the electromagnetic pulse signal and feed it back to an electromagnetic acquisition base station. Simultaneously, a seismic acquisition module receives seismic wave signals and feeds them back to a seismic acquisition base station. The intelligent control host synchronizes with both base stations, enabling simultaneous acquisition of two types of data with only a single electromagnetic pulse excitation, eliminating the need for additional excitation equipment. This ensures data acquisition under the same spatiotemporal conditions, providing accurate data support for subsequent non-destructive, efficient, and high-precision identification of urban underground hazards. During data processing, a self-constructed model is used to determine the presence of anomalies, eliminating the need for manual intervention and achieving good accuracy, thus providing real-time data support for urban safety management.

The invention provides a water gauge scale identification and detection method based on an unmanned aerial vehicle carrier, which belongs to the field of urban safety and comprises integration of an unmanned aerial vehicle and an edge detection server, unmanned aerial vehicle cruise path planning, real-time video stream pushing and real-time algorithm detection and analysis. According to the system, on one hand, pictures such as a water gauge field environment and a water gauge state can be returned to a background client in real time, on the other hand, scales of the water gauge are stored in a local database in real time, whether the scales exceed the preset water gauge scales of the point location or not is judged, and if yes, warning information is transmitted to related personnel. To a certain extent, the problem that time and labor are consumed when the numerical value of the water gauge is read manually is solved, and efficient operation and maintenance of real-time monitoring of the field environment are improved.

The embodiment of the invention provides an urban safety prediction processing method and device based on big data and a storage medium. The method comprises the following steps: acquiring urban operation data from a multi-source heterogeneous security data fusion layer; identifying the urban operation data through a hidden danger identification model established by adopting a space-time neural network to predict geographic position information and time information of a hidden danger event; performing event evolution on the hidden danger event through an event evolution prediction model established by adopting a Markov decision network; on the basis of the geographic position information and the event information of the hidden danger event and the event evolution of the hidden danger event, predicting the deduction of a disposal scheme for the hidden danger event through a resource constraint model; and according to the deduction of the disposal scheme and the current resource information, an emergency resource scheduling scheme matched with the deduction of the disposal scheme is generated, so that an accurate urban safety solution from prediction to decision making to scheduling is realized.

The invention relates to the technical field of land subsidence risk evaluation, in particular to a land subsidence risk evaluation method and system based on InSAR observation and machine learning fusion. According to the method, large-range and high-precision earth surface deformation data and multi-source geographical and geological data obtained by fusing a time sequence InSAR technology are fused, and a machine learning algorithm is introduced, so that more objective and accurate evaluation on the land subsidence danger is realized; and a vulnerability evaluation system is further constructed in combination with regional economic development, traffic distribution and municipal facility levels, a risk quantitative evaluation result is comprehensively formed, and the accuracy, spatial resolution and reliability of land subsidence risk evaluation are significantly improved. According to the method, a high-precision risk distribution diagram can be generated, risk space differentiation characteristics can be visually reflected, and scientific basis and decision support can be provided for urban safety planning, infrastructure protection and disaster prevention and control strategy formulation.

The application relates to the technical field of smart cities and public safety, and discloses a multi-end cooperative city service safety monitoring video data processing method; the method comprises the following steps: determining the emergency weight value of a plurality of monitoring points of city service safety monitoring; if the emergency weight value is higher than a preset threshold value, the response quality deviation index of the monitoring points is acquired, the target monitoring points with significant differences are determined and are prioritized, and the resource allocation tilt scheme is determined in combination with the current bandwidth occupation ratio of the target monitoring points, the calculation unit load peak value and the video encoding bit rate upper limit; if the calculation unit load peak value of the target monitoring points in the resource allocation tilt scheme exceeds the limited condition, the promotion quota of the image processing and encoding control related parameters is generated based on the real-time load and bandwidth state of the target monitoring points, and is applied to the target monitoring points, so that the adjusted burst event response optimization configuration is obtained, and the response efficiency of the city safety monitoring system under a burst event is improved.

This utility model relates to the field of traffic facility technology and discloses a prefabricated crash barrier and a prefabricated crash barrier system, including: a base, connecting ribs, crash barrier pipes, and column kits; the base has an upward-facing connecting groove, the crash barrier pipe has a pre-embedded section and a sleeve section connected in the vertical direction, the pre-embedded section is disposed in the connecting groove, and the outer peripheral wall of the pre-embedded section and the groove wall of the connecting groove form a first receiving cavity for filling cement mortar, the connecting ribs pass through the groove wall of the connecting groove to connect the base and the pre-embedded section, the sleeve section passes through the groove opening of the connecting groove and protrudes upward relative to the base, and the column kits are sleeved on the sleeve section. This utility model occupies less space, has a simple and beautiful appearance, and is suitable for urban safety protection.