118 results about "Urban security" patented technology

The invention relates to the technical field of security knowledge maps, and discloses a city security knowledge map construction method based on an AI large model and a related device, and the method comprises the steps: carrying out the vectorization feature extraction of multi-source city security event data, obtaining a multi-dimensional feature matrix, carrying out the feature analysis through a BERT encoder, and obtaining a multi-dimensional feature matrix; performing key value pair generation and feature processing on each group of query vectors in the obtained grouped query vector set; performing spatial-temporal correlation calculation on the event association feature matrix to obtain an initial security knowledge graph, and performing graph neural network learning and event classification on the initial security knowledge graph to obtain an event type division result; performing situation assessment index calculation on the event type division result to obtain security situation early warning data; and performing knowledge graph updating on the security situation early warning data to obtain an urban security knowledge graph, thereby realizing accurate classification and situation evaluation of security events and dynamic updating of the urban security knowledge graph.

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 multi-mode city security video abnormal behavior real-time detection system and method, and belongs to the technical field of artificial intelligence and city security, and the system comprises an edge node module which is used for collecting a local video stream and sensor data, and executing local model training and real-time abnormal detection; the central coordination server module is used for federal model parameter aggregation, global model distribution and dynamic resource coordination; the multi-modal data fusion module is used for realizing cross-modal feature fusion through space-time alignment and an attention mechanism; the dynamic federation strategy module is used for calculating a load and a network state according to an edge node and adaptively adjusting a model parameter aggregation frequency; and the alarm linkage module triggers a local sound-light alarm when an abnormal behavior is detected, and encrypts and transmits event information to a command center. According to the method, the problems of privacy leakage, bandwidth pressure and multi-modal data collaboration in the prior art can be solved, and on the premise that data privacy is guaranteed, calculation overhead is minimized, and heterogeneous equipment is adapted.

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.

The invention belongs to the technical field of smart city safety management, and discloses a smart park safety shunting control method, which comprises the steps of collecting data through a multi-modal sensor network to construct a space-time tensor, predicting people flow distribution through tensor decomposition and calculating a residual error, generating a path strategy by adopting a multi-agent game, and performing intelligent park safety shunting control. A shunting instruction is dynamically adjusted in combination with Lyapunov optimization, and closed-loop control is realized by cooperatively updating model parameters through residual feedback; the system comprises a multi-modal sensor network module, a spatio-temporal data fusion module, a tensor decomposition and prediction module, a multi-agent game planning module, a Lyapunov optimization control module and a parameter collaborative updating module. According to the method, the space-time tensor is constructed through multi-modal sensing and data fusion, people flow prediction is realized in combination with tensor decomposition, individual and system targets are balanced and coordinated by using a game, and based on a Lyapunov dynamic optimization shunting strategy, parameters are adaptively adjusted through a residual feedback closed loop, so that the stability of park safety management and control is improved.

The invention discloses an urban safety risk assessment method and system based on a digital standard, and the method comprises the steps: obtaining urban safety risk monitoring data and historical data, constructing a risk index event library according to the historical data, carrying out the abnormal screening and classification of the monitoring data, and obtaining risk indexes, matching the risk indicator with the risk indicator event library to determine a first risk event, predicting an uncertain risk event according to the first risk event, and constructing a city safety risk assessment model according to the first risk event, the uncertain risk event and the risk event weight, and inputting to-be-assessed city safety risk monitoring data and historical data into the city safety risk assessment model to obtain a city safety risk assessment result. The method not only can improve the efficiency and accuracy of urban safety risk assessment, but also has good interpretability, and can be directly applied to an urban safety risk assessment system.

The invention discloses a digitalization method of an urban safety risk assessment standard, which comprises the steps of identifying risk factors for historical accident time sequence data, performing correlation analysis to obtain a correlation function, further constructing an urban network topology structure, determining a prior probability of a node according to the similarity of the node and pre-disaster data, and determining the probability of the node according to the prior probability of the node. Generating a simulated accident scene, adding a disturbance item to simulate an emergency risk, combining with the prior probability of the nodes to obtain a risk propagation path, constructing a risk assessment model, assessing the space-time influence range of the nodes on the propagation path, and performing weighted summation on the assessment results of the nodes to obtain an urban safety risk assessment result. According to the method, a network topology structure is constructed by multi-stage analysis of historical accident data, so that risk propagation simulation is more accurate, urban risk assessment can be accurately quantified, and meanwhile, the method has relatively good interpretability.

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 provides a building change detection method and system based on a multi-source live-action three-dimensional model, and the method comprises the steps: obtaining the triangulation network topology data of a two-stage live-action three-dimensional model in a to-be-detected region, and carrying out the preprocessing and standardization processing; a plane-elevation layered registration technology is adopted for automatic registration, and coordinate unification without manual intervention is achieved; calculating and identifying a three-dimensional change area of the two-stage live-action three-dimensional model by using a spatial index difference, and optimizing the three-dimensional change area by adopting a multi-scale expansion-corrosion combination algorithm; filtering the optimized three-dimensional change area by using three-dimensional feature dual channels, and verifying the real building change; and generating a four-dimensional spatio-temporal result, and outputting a visual model including the elevation change value, the material type and the three-dimensional reconstruction parameters. According to the method, the problem that the subtle change of the building in a large-scale real scene model can be read by a machine is solved, and a high-reliability three-dimensional quantitative analysis tool is provided for urban illegal building monitoring 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 an urban mobile pump truck dispatching system research and development method based on a digital twinborn technology. According to the method disclosed by the invention, by integrating a third-party navigation system, real-time data acquisition, a multi-target optimal scheduling algorithm and a perfect visual emergency response mechanism, efficient and intelligent scheduling, real-time monitoring and optimal management of the urban mobile pump truck are realized, and accurate scheduling and efficient rescue of the mobile pump truck are realized; dangerous cases such as road ponding and underpass ponding are handled, the efficiency and response speed of urban drainage flood prevention are improved, urban waterlogging disasters are effectively handled, and urban safety is guaranteed.

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 utility model discloses a smart city safety monitoring device, which belongs to the technical field of smart city safety and comprises a bottom plate, four supporting rods are assembled on the upper end face of the bottom plate, a mounting plate is assembled at the upper ends of the four supporting rods, and a mounting box is assembled on the lower end face of the mounting plate. A rotating rod penetrates through one side of the upper end face of the mounting plate, a first gear is assembled outside the rotating rod, one side of the first gear is in meshed connection with a second gear, a threaded lead screw penetrates through the upper end face of the mounting plate, a sliding shaft sleeve is slidably arranged outside the threaded lead screw in a meshed mode, a sliding block is assembled outside the sliding shaft sleeve, and a clamping assembly is assembled on one side of the sliding block. The smart city safety monitoring device has the advantages of being easy and convenient to operate, capable of improving safety and rapid to mount and dismount, maintenance personnel do not need to climb at high altitude, the monitoring camera can descend only by rotating the rotating rod, the risk of high-altitude climbing is avoided, the occurrence possibility of accidents is reduced, and the safety of the maintenance personnel is improved. Meanwhile, the time cost in the maintenance process is reduced, and the working efficiency is improved.

The utility model relates to a sewage well anti-falling device for urban safety engineering, which comprises a well body and a well cover positioned at the top end of the well body, an anti-falling component is mounted on a step surface of the well body, the anti-falling component comprises a support ring and an anti-falling plate, and the support ring is movably connected with the anti-falling plate through a plurality of clamping components; the clamping component is movably installed on the supporting ring and comprises a fixing block, a bolt rod and a fixing rod, one end of the bolt rod and one end of the fixing rod are both fixedly connected with the bottom face of the fixing block, and the other end of the bolt rod and the other end of the fixing rod both extend downwards to be in sliding fit with the supporting ring. The fixing block is pulled upwards, the fixing block drives the fixing rod and the bolt rod to slide upwards along the second inserting hole and the first inserting hole correspondingly, the bottom end of the bolt rod is pulled out of the clamping hole to be located in the first inserting hole, the anti-falling plate is disassembled from the supporting ring, operation is convenient and fast, and the purposes of reducing construction danger, reducing damage to a well body and improving the safety of the well body are achieved. And the service life of the well body is prolonged.

The invention relates to the technical field of urban waterlogging monitoring. The invention relates to a smart city waterlogging monitoring method based on Internet of Things data. The method comprises the following steps: S1, installing a water level monitoring device in a low-lying area in which water is easy to accumulate in a city, and establishing a water level collection module; s2, enabling a water level collection module to collect water level data collected by a water level monitoring device through the Internet of Things, and obtaining a city three-dimensional map by the water level collection module; water level data of a smart city is collected in real time through the water level monitoring device, an emergency scheme is generated through the water level collection module, water level prediction is carried out according to the position of emergency processing personnel, and then the corresponding emergency scheme is sent according to the predicted water level data. Therefore, emergency personnel can carry corresponding tools to go to the waterlogging position by referring to the emergency scheme, the city safety is improved, waterlogging monitoring and early warning are timely and accurately carried out, threats of waterlogging to lives and properties of people can be reduced, and urban losses caused by waterlogging are reduced.

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 relates to the technical field of street lamp control, in particular to a self-adaptive dimming LED street lamp for a low-carbon environment-friendly smart city and a control method of the self-adaptive dimming LED street lamp. The method comprises the following steps: acquiring road-related information of ambient light intensity, pedestrian flow and traffic flow in real time, preprocessing the road-related information, and processing and analyzing the road-related information; the ambient light intensity, the pedestrian flow and the traffic flow are used as input variables, and the LED street lamp brightness is used as an output variable; and adjusting the brightness of the LED street lamp according to the output variable, communicating with a smart city security system, and detecting the state of the street lamp in real time by the smart city security system. The system comprises an information and transmission acquisition module, a light control module and a road early warning module. According to the invention, the problems of low intelligent degree, insufficient remote control and monitoring capability and limited energy-saving effect are solved; the LED street lamp is remarkable in energy-saving effect, the service life of the LED lamp is prolonged, the maintenance cost is reduced, and the city safety and attractiveness are 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.