23731results about "Closed circuit television systems" patented technology

The invention relates to the technical field of camera linkage alarm, and discloses a camera linkage alarm method and system for intelligent environment monitoring, and the method comprises the steps: synchronously collecting the video stream data of a plurality of cameras and the multi-parameter monitoring data of an environment sensor; executing multi-path feature analysis and cross-modal feature fusion to obtain environment-vision joint features; environment scene self-adaptive classification is executed, and environment-vision joint features are analyzed through an anomaly judgment model to obtain an abnormal event judgment result; executing multi-camera collaborative verification and vision-environment data association analysis to obtain multi-dimensional abnormal event description information; and selecting a multi-camera linkage response strategy and coordinating the plurality of cameras to carry out three-dimensional monitoring on the abnormal area to obtain a linkage response execution result, so that a false alarm mode can be identified and an abnormal judgment rule can be dynamically adjusted, continuous optimization of the performance of the plurality of cameras is realized, and the false alarm rate and the missing report rate of the plurality of cameras in long-term operation are reduced.

A personal tactical system including a load-bearing garment, a pouch with one or more batteries enclosed in the pouch, at least one power distribution and data hub, and at least one camera. The camera is incorporated into or removably attachable to the load-bearing garment, the pouch is removably attachable to the load-bearing garment and the one or more batteries are operable to supply power to the at least one power distribution and data hub. The at least one power distribution and data hub is operable to supply power to at least one peripheral device. A plurality of personal tactical systems is operable to form an ad hoc network to share images and other information for determining object direction, location, and movement.

The invention discloses a mountainous area high pier cast-in-place safety detection method based on multi-source data fusion, and relates to the technical field of intelligent building safety monitoring, and the method comprises the steps: deploying a sensor network, a multi-angle holder video monitor and an unmanned aerial vehicle, covering a mountainous area high pier for comprehensive monitoring, and collecting multi-source data; transmitting the multi-source data to an edge computing node in real time through a wireless network, and preprocessing the multi-source data to form a standardized multi-source data set; applying a spatio-temporal data fusion method, combining historical data and a comprehensive data model to predict a security risk, and generating a risk factor; threshold and rule abnormity identification is carried out on the risk factors, the safety condition is evaluated in real time, early warning is triggered in real time, and disposal suggestions are provided. According to the method, the safety detection efficiency and accuracy in the mountainous area high pier cast-in-place process are remarkably improved through multi-source data fusion and a quantum enhancement multi-modal algorithm, the triggering speed of safety early warning is effectively increased, and the safety guarantee of a construction site is remarkably enhanced.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent construction site management method and system based on the Internet of Things. The method comprises the following steps: acquiring construction site environment monitoring data in real time based on multiple Internet of Things sensor nodes; performing real-time environment state change mining on the construction site environment monitoring data, performing global environment perception modeling, and constructing a construction site digital environment perception map; obtaining a construction site omnibearing monitoring video and construction site construction equipment state monitoring data; carrying out target dynamic optical flow tracking on the omnibearing monitoring video of the construction site one by one, carrying out dynamic operation state evolution, and constructing a real-time behavior portrait model of each worker; and performing operation state equipment temperature change trend analysis and dynamic load change evolution according to the state monitoring data of the construction site construction equipment, and generating comprehensive state characteristics of each piece of equipment. The construction site management efficiency and the construction site construction safety are improved.

The invention discloses a video monitoring safety control system and method based on the Internet of Things, particularly relates to the technical field of video monitoring safety control, and is used for solving the problem of low video monitoring control efficiency. Multi-source data are collected through an Internet of Things sensor network, the time sequence consistency is ensured by adopting a clock synchronization protocol, the data are preprocessed, the accuracy is ensured, then correlation analysis is carried out by utilizing a Bayesian network and principal component analysis, historical information and scene modes are extracted, risk levels of all areas are determined, and the risk levels of all the areas are determined. And determining a resource scheduling priority according to the risk level. And by simulating scene change, the risk early warning capability of the system is improved. According to the system, camera resources are dynamically scheduled through a collaborative optimization algorithm, monitoring resources of a high-risk area are preferentially allocated, redundant monitoring is reduced, power consumption and bandwidth use are optimized, and therefore the response speed and precision of video monitoring in a complex environment are improved, and the safety of video monitoring is improved.

The invention provides a vehicle-mounted video monitoring system and a vehicle-mounted video monitoring method integrating 360-degree look-around perception and multi-mode intelligent analysis. According to the system, video images around a vehicle are collected through a vehicle-mounted 360-degree all-round camera, high-precision vehicle position information is obtained through a vehicle-mounted multi-sensor fusion positioning technology, videos and position time are stored in an associated mode, and a space-time video database is constructed. The system utilizes a multi-mode perception fusion technology to intelligently analyze a video, extracts semantic information, and supports a user to quickly retrieve a target video clip through natural language interaction. Meanwhile, the system can monitor abnormal driving behaviors and vehicle surrounding environment risks in real time and trigger early warning when necessary. According to the invention, advanced technologies such as 360-degree looking-around perception, multi-sensor fusion positioning, multi-modal intelligent analysis, natural language interaction and the like are innovatively and deeply fused with vehicle-mounted video monitoring, so that the limitation of a traditional vehicle-mounted monitoring system is broken through, the intelligent and humanized level is greatly improved, the development direction of a vehicle-mounted video monitoring technology is guided, and the vehicle-mounted video monitoring system has a wide application prospect. The method is of great significance in guaranteeing driving safety of intelligent automobiles and promoting development of intelligent traffic.

The invention provides a tunnel construction monitoring analysis method and system based on AI video monitoring, and relates to the technical field of artificial intelligence. A tunnel construction original video stream data set is acquired through a multi-view video acquisition device, and a spatio-temporal feature sequence is generated through spatio-temporal feature fusion processing; the spatiotemporal feature unit comprises a pixel region movement track, a personnel wearing identifier and a construction equipment contour feature, then extracting dynamic features of the spatiotemporal feature sequence to obtain a dynamic spatiotemporal feature set, and executing construction personnel statistical processing based on the dynamic spatiotemporal feature set; generating a hole entering personnel quantity statistical result, a personnel distribution statistical result of each construction part and a personnel identity matching result, performing work efficiency comparative analysis according to the personnel statistical result and a preset construction progress plan, and generating a construction progress deviation index and resource allocation optimization strategy; and when the progress deviation index exceeds the first threshold value, early warning is triggered, and a strategy is output to the management terminal, so that effective monitoring and management of tunnel construction are realized.

The invention, which relates to the technical field of community safety supervision, discloses an artificial intelligence-based community safety environment supervision system comprising a data acquisition module, a data processing and analysis module, an intelligent decision module, an early warning response module and a system management module. The data acquisition module is used as a sensing layer of the system; the data processing and analysis module specifically comprises a feature extraction unit and a behavior recognition unit; the intelligent decision module is used for receiving the risk assessment result output by the data processing and analysis module; and the early warning response module generates the scheme according to the intelligent decision module. According to the community safety environment supervision system based on artificial intelligence, intelligent supervision of a community safety environment is realized through a complete closed loop of data acquisition, data processing, intelligent decision making, early warning response and system management; all the modules are in close cooperation, full-process automation from data collection to emergency response is ensured, and the efficiency and accuracy of community safety management are greatly improved.

The invention relates to the technical field of traffic management, in particular to a video monitoring traffic intelligent optimization system which comprises a time sequence fragment evaluation module, a path priority adjustment module, a traffic scheduling optimization module and a buffer strategy adaptation module. According to the invention, the transmission efficiency and quality of the video monitoring data are improved by analyzing the video stream in real time and adjusting the frame rate and the compression rate, the importance of each time sequence segment in the video stream is evaluated, the frame rate is adjusted according to the importance, more resources are ensured to be obtained by important events, unnecessary data transmission is reduced, network congestion is effectively reduced, and the transmission efficiency of the video monitoring data is improved. The network transmission path is monitored in real time, and the data transmission proportion is adjusted, so that the video stream passes through the network path with higher quality more preferentially, the stability and speed of video transmission are enhanced, the priority and bandwidth allocation are dynamically adjusted, and the video stream with high priority is protected to ensure that the video quality of a key monitoring area is not influenced by network fluctuation.

The invention discloses a forest fire detection and disappearance method and system, and relates to the technical field of forest fire prevention and data processing, and the method comprises the steps: carrying out the fire inspection of a planned forest region through a plurality of inspection unmanned planes, and transmitting an image and airborne sensor data back to a command center in real time; identifying a fire behavior by using an artificial intelligence method, and performing fire behavior judgment and fire hazard grade division; the command center screens fire points according to the thermal images and updates the positions of the fire points in real time; the fire scene spreading trend is judged according to the returned data, and a basis is provided for the dispatching task of throwing the fire extinguishing bombs by the fire extinguishing unmanned aerial vehicle; using an MAPPO multi-agent reinforcement learning algorithm to train an optimal dynamic fire point collaborative disappearance regulation strategy; and after the open fire is extinguished, the inspection unmanned aerial vehicle continues to inspect to identify a re-ignition point or a smoke point, and secondary large-area fire is prevented. According to the method, efficient collaborative fire extinguishing, dynamic environment adaptation and resource optimization configuration can be achieved, and robustness is kept when part of intelligent agents fail.

In some embodiments, a method may involve receiving, by an application executing on a computing system operated by a first user, data representing at least a first image acquired by a camera at a property and causing the computing system to output a first indication of an event at the property, the first indication including at least the first image. Based at least in part on an input by the first user indicating that the event is of a first type and preference data indicating that notifications of events of the first type are to be sent to a second user, causing, by the application, a notification concerning the event to be sent to an endpoint device associated with the second user, the notification causing the endpoint device to output a second indication of the first event.

The invention relates to the technical field of video stitching, and discloses a monitoring video stitching method, device and equipment and a storage medium, and the method comprises the steps: carrying out the video framing of a monitoring video collected by a monitoring terminal, and obtaining a plurality of target video frames; performing human shape detection to obtain a human shape detection result; aligning and cutting a face region in the human shape detection result to obtain a standardized face image, and extracting a face feature vector; performing motion prediction and trajectory extrapolation to obtain a multi-target human shape tracking result; inputting the multi-target human shape tracking result into a distributed computing framework for task allocation and cross-node feature matching to obtain a global human shape tracking result; and performing double-flow network processing on the plurality of target video frames according to the global human shape tracking result, calculating to obtain video stitching parameters, and performing stitching operation on the plurality of target video frames to obtain a stitched video, thereby effectively improving the quality and visual effect of the stitched video.

The embodiment of the invention provides an end-to-end automatic driving control method and device based on multi-camera fusion, and multi-view target detection and tracking are realized through spatial transformation and coordinate mapping by combining front wide-angle camera information and left and right wide-angle camera information. A multi-view feature fusion network architecture is designed, the multi-view feature fusion network architecture comprises three sub-networks of feature extraction, dynamic weight distribution and feature fusion, and the fusion weight is dynamically adjusted based on image definition, detection confidence and view overlapping degree. A geometric consistency constraint between visual angles and a reconstruction loss function are introduced, a deep neural network model is constructed, abnormal conditions such as camera shielding are effectively handled, and an accurate control instruction is output. According to the method, the defects of the traditional technology in the aspects of multi-view information fusion, shielding processing and the like are overcome, and the sensing ability and the control reliability of the automatic driving system are remarkably improved.

The invention relates to the field of electric power inspection, in particular to an unmanned aerial vehicle assisted porcelain bottle defect off-line inspection method and system. The method comprises the steps that the unmanned aerial vehicle collects real-time environment data and carries out imaging and data acquisition on porcelain bottles along the line, and images and three-dimensional model data of the porcelain bottles are obtained; performing primary processing and analysis to obtain a primary analysis result; the preliminary analysis result and the real-time environment data are analyzed, when the suspected defect is displayed, the flight route and the inspection strategy of the unmanned aerial vehicle are adjusted, key review is conducted on the porcelain bottle area with the suspected defect, and a review analysis result is obtained; adjusting the flight route and the inspection strategy of the unmanned aerial vehicle, and continuing to inspect according to the new flight route and the new inspection strategy; when the preliminary analysis result is displayed to be intact, continuing to inspect according to the original flight route; and transmitting all analysis results and the flight path adjustment instruction back to the ground control station in real time until the inspection is completed. According to the invention, the problems of changeable unmanned aerial vehicle inspection environments and task requirements can be effectively solved.

The invention relates to the technical field of camera remote control, and discloses a camera remote control method and system based on portable WiFi. The method comprises the following steps: acquiring position information, coverage range and signal intensity of camera nodes, and constructing a distributed network topology structure; according to the distributed network topology structure, scene analysis is carried out on video content collected by a camera, and an optimal coding parameter configuration table is calculated; dividing the video frame into different priority regions according to the optimal coding parameter configuration table, and executing differential compression processing to form a video data packet with a priority mark; according to the method, video data packets with priority marks are distributed to a multi-level transmission queue, data transmission is scheduled according to the current bandwidth state of the portable WiFi, and an ordered transmission stream is generated, so that the computing load and energy consumption of each camera node are effectively balanced, stable video transmission and control response can still be kept under the condition of network fluctuation, and the video transmission efficiency is improved. And the video lagging rate and the control delay are obviously reduced.

The invention discloses an intelligent definition method and system for industrial edge data acquisition, a medium and equipment. The method comprises the following steps: acquiring operation data of field equipment in real time; performing protocol analysis and cleaning on the data to generate standardized data points, dynamically detecting the communication state and adaptively adjusting the acquisition frequency; classifying and aggregating the standardized data points into a running state feature set and calculating feature indexes; edge side anomaly detection is carried out based on the feature set, and a protocol container library is synchronously called to match a device communication protocol to generate matching information; an acquisition strategy optimization instruction is generated in combination with the abnormal result and the matching information, and transmission characteristic indexes and instructions are packaged; and structuring storage feature indexes according to equipment types and time dimensions to form a historical operation database, and managing a storage period by adopting a sliding window mechanism. According to the invention, protocol adaptive analysis, dynamic acquisition adjustment and edge intelligent analysis are realized through software definition, the hardware dependence and field debugging risk are reduced, and the data acquisition efficiency and the intelligent level are improved.

The invention discloses an intelligent power plant joint inspection system and method based on video AI and a robot, and the method comprises the steps: dividing an intelligent power plant into a plurality of subareas, evaluating the danger levels of the plurality of subareas according to historical data, and obtaining the danger levels of the plurality of subareas; setting an inspection cycle according to the danger levels of the plurality of partitions, and setting a plurality of robots to acquire operation data according to the inspection cycle to obtain inspection data of the plurality of partitions; acquiring a plurality of monitored videos, and performing video analysis on the acquired monitored videos by using an AI technology to obtain an abnormal position; according to the abnormal position, distributing an inspection robot of a corresponding partition to carry out fixed-point inspection to obtain fixed-point inspection data; and performing fault detection on the periodic inspection data and the fixed-point inspection data by using the fault recognition model, and performing fault early warning according to a fault detection result. The invention relates to the technical field of power plant inspection, and solves the technical problems of unreasonable inspection resource distribution and untimely fault early warning in an existing combined inspection system.

The invention provides an airport terminal video digital twinning method, device, equipment and medium, and the method comprises the steps: firstly obtaining data collected by a plurality of sensors, then constructing a high-precision airport terminal static three-dimensional model, and taking a world coordinate system as a reference, a mapping relation among a two-dimensional image coordinate system, an airport terminal static three-dimensional model coordinate system and a camera coordinate system is constructed, an airport terminal space initial layout is obtained, two-dimensional layout estimation is used as a key point, image textures are reconstructed, registration from the two-dimensional image textures to an airport terminal static three-dimensional model under a single camera is achieved, and the registration result is obtained. And finally, calculating a suture line in a fusion rendering process, and fusing a multi-camera two-dimensional image with the constructed static three-dimensional model to realize the three-dimensional panoramic digital twinning of the terminal building. Through the scheme of the invention, three-dimensional live-action visualization under the fusion of multiple cameras, structured data and the terminal indoor model in a large scene of the terminal is realized, comprehensive real-time monitoring is realized, and safe operation of an airport is guaranteed.

A system, in particular an auto-commissioning system comprising an image capturing means, such as a camera, arranged to capture an image of an area, and a processing means configured to perform the steps of controlling the image capturing means so as to capture a sequence of images over time, processing said captured sequence of images over time to detect at least one scene element in said area, selecting at least one application from a list of applications, such as a traffic monitoring application or a parking monitoring application, based on a result of the processing and configuring the at least one application based on the captured sequence.

The invention discloses a remote Internet of Things control management system and method based on fire fighting, and belongs to the technical field of Internet of Things. The system comprises an image recognition module which obtains a video stream of a fire scene through a monitoring camera, and carries out the real-time analysis and recognition of an image through image processing and a deep convolutional neural network; the gas recognition module is used for monitoring the gas concentration of a fire scene in real time and recognizing gas components by using a gas recognition algorithm; the data fusion analysis module is used for carrying out feature fusion on the data acquired by the image recognition module and the gas recognition module, and carrying out analysis and modeling on the fused data; the decision support module is used for performing fire risk assessment according to an analysis result of the data fusion analysis module; based on historical data and the scene model, performing simulation and prediction of a fire development trend; and the remote internet-of-things control module is used for providing data for checking fire scene conditions in real time, so that a user can remotely control all equipment and devices of the fire extinguishing system.

The invention discloses a power plant specific area personnel behavior identification and early warning system, and relates to the technical field of industrial safety intelligent monitoring, the power plant specific area personnel behavior identification and early warning system comprises a data acquisition module, an edge calculation module and a central processing platform, the edge computing module carries out lightweight processing and localized decision making on original data, the load of the central processing platform is reduced, and the central processing platform realizes full-process safety management and control of a high-risk area through multi-modal data fusion, dynamic authority management and trajectory analysis. According to the invention, lightweight AI models are embedded in various cameras of a work card authentication terminal and a visual perception unit, preliminary reasoning is directly completed at the work card authentication terminal and the cameras, so that the work card authentication terminal and the cameras have edge computing capability, and independent edge servers or industrial personal computers are deployed near data sources of various regions of a power plant. And serving as a regional edge computing node.

The invention relates to the technical field of artificial intelligence, and discloses an abnormal behavior real-time early warning method and system based on intelligent security, and the method comprises the steps: determining a monitoring environment feature set corresponding to a security monitoring region, and formulating a behavior early warning threshold value corresponding to security monitoring equipment; collecting dynamic behavior information of personnel in the security and protection monitoring area, analyzing the behavior modal analysis dimension corresponding to the security and protection monitoring equipment, and evaluating the area security situation corresponding to the security and protection monitoring area; analyzing physical feature attributes corresponding to the personnel in the security and protection monitoring area, analyzing action mode features corresponding to the personnel in the security and protection monitoring area, and calculating behavior risk entropies corresponding to the personnel in the security and protection monitoring area; analyzing the monitoring adaptation degree of the security and protection monitoring equipment in the security and protection monitoring area; and formulating an abnormal behavior early warning strategy corresponding to the security and protection monitoring area, and executing abnormal behavior real-time early warning in the security and protection monitoring area to obtain an early warning result. According to the invention, the real-time early warning efficiency of abnormal behaviors under intelligent security is improved.

The invention discloses a VR-based unmanned aerial vehicle inspection method and system, a storage medium and a program product, and the method comprises the steps: calculating a low-polygon terrain model, and carrying out the subdivision smoothing processing, and obtaining a three-dimensional terrain image; matching and registering the panoramic image sequence and the three-dimensional topographic image to obtain a point cloud map; generating a virtual mining area environment map; calculating image quality parameters; when the image quality parameter is lower than a preset threshold value, generating a semi-real and semi-virtual fusion display picture; transmitting the semi-real and semi-virtual fusion display picture to a VR helmet for display, and synchronizing the attitude information of the unmanned aerial vehicle to the VR helmet; when it is determined that a fatigue feature appears according to the brain wave data, the unmanned aerial vehicle is controlled to be switched to a preset autonomous flight mode; constructing a virtual anti-collision boundary; and obtaining an optimized flight path according to the virtual anti-collision boundary, and controlling the unmanned aerial vehicle to inspect according to the optimized flight path. The continuity and reliability of the inspection task are improved, and the safety risk of the inspection task is reduced.

The invention discloses an underground coal mine video monitoring method, which relates to the technical field of image communication, and comprises the following steps of: acquiring historical video data, extracting historical key feature data for analysis, establishing a feature baseline range of an underground coal mine normal operation state, constructing an anomaly rule base for different anomaly types, and establishing a feature baseline range of the underground coal mine normal operation state; establishing a mapping relation between historical key feature data and abnormal rules, acquiring real-time video data and audio data of the underground coal mine, extracting real-time key feature data, comparing the real-time key feature data with a feature baseline range, matching corresponding rules in an abnormal rule base based on the established mapping relation, outputting an abnormal analysis result, and if it is judged that an abnormality exists, outputting the abnormal analysis result. If yes, triggering a corresponding alarm according to the exception rule, associating an exception handling process, and realizing intelligent safety monitoring by collecting multi-period underground video extraction features, establishing baseline and differentiation judgment conditions, constructing an exception library and a triggering mechanism, comparing real-time data to judge exception and performing graded response handling.

The invention relates to the technical field of video monitoring, and discloses a camera cooperative monitoring method and system based on multi-modal perception, and the method comprises the steps: detecting an abnormal event signal in an environment through a non-visual sensor node, triggering the activation of a camera, and carrying out the visual capture of a target region; the camera performs relay tracking based on target feature matching to generate a continuous motion track; performing multi-view collaborative shielding on privacy sensitive information in the tracking target, generating desensitized monitoring data, and uploading the desensitized monitoring data; and optimizing the energy consumption of the camera in a non-event triggering period, and updating the behavior recognition model based on the desensitization data. According to the method, the contradiction between privacy protection and monitoring efficiency is solved, cross-regional model evolution and energy consumption reduction are realized, and the problems of response lag, data redundancy, privacy disclosure and high energy consumption of traditional monitoring are systematically avoided.

The invention relates to the technical field of remote monitoring, in particular to a mobile terminal remote control monitoring system and method, and the system comprises a video dynamic coding module, a content importance analysis module, a cognitive load adjustment module and an audio optimization and alarm module. According to the method, the change of the network bandwidth can be responded in real time by dynamically adjusting the video coding parameters, the stability and adaptability in data transmission are remarkably enhanced, the dynamic content and color change are accurately captured by analyzing the video frames, and key events are immediately positioned and extracted, so that the emergency response speed and precision are greatly improved, and the data transmission efficiency is improved. Cognitive resources are dynamically distributed by utilizing video stream complexity evaluation, resource configuration is adjusted according to actual requirements, monitoring quality is guaranteed, resource use efficiency is optimized, audio processing is optimized, real-time alarm strategies are adjusted, clear transmission of sound information and alarm timeliness are guaranteed, and the overall response capability and effect are improved.

The invention discloses a three-dimensional panoramic intelligent monitoring method and device for a coal conveying gallery, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: obtaining initial point cloud data and an initial multispectral image of the coal conveying gallery through a rotary laser radar, a high-dynamic-range camera and an infrared thermal imager; inertial measurement units are arranged in the devices to collect vibration data, and the vibration data are used for carrying out vibration compensation on point cloud and images to generate target point cloud data and target multispectral images; thirdly, performing three-dimensional modeling and multispectral texture mapping on the coal conveying gallery based on the data to obtain a target three-dimensional model; and finally, mapping equipment, personnel and environment data into the three-dimensional model in real time, displaying each piece of state information, and realizing comprehensive monitoring. According to the application, high-precision three-dimensional panoramic monitoring can be realized in a complex vibration environment of the coal conveying gallery, and the safety management level and the operation and maintenance efficiency of the coal conveying gallery are effectively improved.

The invention relates to the technical field of big data, and discloses an audio and video monitoring and early warning method and system based on multi-modal model driving, and the method comprises the steps: S1, collecting audio and video signals in a monitoring scene, and extracting the energy distribution characteristics of the audio and video signals, wherein the energy distribution characteristics at least comprise the frequency energy distribution of the audio signal and the brightness and color change rate of the video signal; s2, multi-modal feature extraction of energy distribution features is carried out based on audio and video signals, and environment background feature vectors are constructed in combination with environment background information; s3, dynamically adjusting anomaly detection thresholds of the audio signal and the video signal based on the environmental background feature vector; s4, comparing the audio and video signal energy distribution characteristics extracted in real time with an anomaly detection threshold value, and when the audio and video signal energy distribution characteristics exceed the range of the anomaly detection threshold value, determining an abnormal event and obtaining early warning information; and S5, sending the early warning information from the edge equipment to a monitoring center through a low-bandwidth communication protocol.

The invention discloses a video image accelerated scheduling method based on an airport multi-level video networking architecture, and the method comprises the steps: dividing a video monitoring system into a plurality of levels based on the actual layout and safety monitoring demands of an airport, and constructing the multi-level video networking architecture; based on a multi-level video networking architecture, video streams are analyzed and classified, the video streams are classified and divided into different types of video streams according to characteristics and importance, and then the different types of video streams are optimized; based on a real-time scheduling algorithm, dynamically adjusting the transmission sequence and the transmission rate of the optimized video streams according to the priority and the time slice distribution condition of the video streams; a distributed cache mechanism is arranged in each hierarchy, and high-frequency access or important video data is temporarily stored, so that the data can be quickly acquired from the cache when needed; based on a distributed cache mechanism, a load balancing strategy for dynamically allocating video processing resources is implemented, and the video processing resources are dynamically adjusted and allocated according to the actual load condition of each level.