13151results about "Closed circuit television systems" patented technology

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 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 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 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 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 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 monitoring equipment fault real-time detection, root cause diagnosis and closed loop processing method and system. According to the method, by collecting multi-dimensional operation data of equipment, a dynamic weighted health degree model is constructed to realize early-stage accurate discovery of a fault; performing intelligent alarm grading by combining time sequence prediction and health degree change; realizing automatic root cause diagnosis by utilizing topological correlation analysis, log semantic analysis and case similarity matching; and closed loop processing and model self-optimization are realized through a work order system. According to the method, the problems of lagging fault discovery, difficulty in positioning, slow repair and disjunction in assessment in the prior art are effectively solved, and the operation and maintenance efficiency and the service quality are remarkably improved. The abstract drawing is Figure 1.

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.

A system comprising security devices and a computing device. The security devices may each be configured to detect an event in response to video frames and audio and communicate the video frames, the audio and a notification of the event. The computing device may be configured to execute one or more steps, the steps comprising performing a classification of the notifications of the events into a plurality of event levels and displaying a list of the notifications of the events according to the classification. Each of the plurality of event levels determined to be high priority events are added to a top of the list as the high priority events are detected. Each of the plurality of event levels determined to be a lower priority than the high priority events are added to the list below the high priority events as the events with the lower priority are detected.

The invention relates to the technical field of logistics storage, and discloses an edge-cloud collaborative 5G logistics storage anomaly detection device and method. The device comprises a logistics data acquisition module, an edge preprocessing module, a lightweight model reasoning module, a cloud collaborative analysis module and a dynamic strategy generation module. The logistics data acquisition module acquires multi-source heterogeneous logistics storage data; the edge preprocessing module performs cleaning and dimension reduction on the related data to generate a standardized edge feature vector; the lightweight model reasoning module constructs a model based on an AutoML framework to perform real-time reasoning; the cloud collaborative analysis module fuses the data to generate a global abnormal event positioning map; and the dynamic strategy generation module generates an instruction and an alarm signal according to the result and issues the instruction and the alarm signal through 5G network slicing. According to the invention, multi-source heterogeneous data can be efficiently processed, real-time abnormal detection, accurate positioning and rapid response are realized, the intelligent level and operation efficiency of logistics warehouse management are improved, and the cost and the cargo loss risk are reduced.

The invention provides an intelligent video monitoring system and method based on deep learning, and belongs to the technical field of video monitoring, and the system comprises a heterogeneous sensing array, a cognitive driving feature fusion module, a dynamic evolvable detection model, a causal reasoning tracking engine, a meta-knowledge enhancement behavior analysis module and a quantum-classical hybrid computing architecture. The heterogeneous sensing array comprises a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array and a millimeter wave radar, and is configured to generate multi-physical field sensing data; and the cognitive driving feature fusion module adopts a space-time-frequency spectrum joint coding technology, and integrates a three-dimensional convolutional network and a graph attention mechanism to realize cross-modal feature interaction. According to the invention, through multi-dimensional technical innovation, the intelligent video monitoring system with autonomous evolution capability is constructed, the perception capability, reasoning precision, resource efficiency and safety are obviously superior to those of a traditional scheme, and the high-standard requirements of smart cities, security and other scenes are met.

Some aspects include a method for operating an autonomous robot, including: capturing, with a first sensor disposed on the robot, data of an environment of the robot; generating, with the processor, a map of the environment based on at least the data of the environment; localizing, with the processor, the robot within the environment; capturing, with a second sensor disposed on the robot, data of a floor surface; determining, with the processor, a floor type of areas of the environment based on the data of the floor surface; and determining, with the processor, settings of the robot based on at least the floor type of the floor surface, wherein the settings comprise at least an elevation of each of at least one component of the robot from the floor surface.

The invention discloses a video monitoring data storage management system based on big data, and particularly relates to the field of data analysis, comprising multi-source heterogeneous data acquisition, cross-layer feature fusion calculation, comprehensive index fusion, collaborative decision generation and adaptive regulation and control execution. Through multi-level data synchronous acquisition and cross-domain feature fusion, a three-dimensional tensor structure is constructed to eliminate dimensional difference, global state perception of coding, transmission and storage is realized, local optimization limitation caused by a traditional data island is solved, a dynamic weight adjustment model automatically matches an optimal strategy based on a three-order collaborative management coefficient calculated in real time, and a dynamic weight adjustment model is improved. The joint elastic regulation and control of coding parameters and network protocols and the dynamic reconstruction of storage resources are realized, and the defect of response lag of a static threshold mechanism under burst traffic is overcome.

Utility locators for providing information about hidden or buried pipes or other cavities are disclosed. A locator may be configured to provide positional information associated with the buried utility based on signals received from a line trace signal and a sonde signal and present the positional information on a visual display or other output device.

The invention relates to the technical field of electric power operation, and discloses a power plant employee violation intelligent identification method and system based on an AI large model, and the system comprises a visual collection module, an edge calculation node, a personnel positioning and tracking module, a three-dimensional scene reconstruction module, a multi-modal analysis module, a grading early warning module, a self-adaptive adjustment module, and an inspection robot. According to the invention, accurate identification of personnel behaviors in a complex industrial environment is realized, the problems of high false alarm rate, response lag and the like of a traditional monitoring system are effectively solved through a specially designed self-adaptive adjustment mechanism and a grading early warning strategy, and the technical scheme has the advantages that through innovative integration of technologies of machine vision, edge calculation, multi-modal analysis and the like, the safety of the system is improved. An intelligent safety management and control system with an autonomous optimization capability is constructed, and breakthrough progress is made in core indexes such as identification precision, response speed, resource utilization efficiency and the like.

The invention is applicable to the technical field of safety monitoring and early warning, and provides a new energy station operation site safety monitoring and early warning method, which comprises the following steps: collecting multi-source heterogeneous data in real time through an edge computing unit deployed in an operation site; analyzing the video stream in real time by using a preset artificial intelligence visual analysis model, identifying personnel violation behaviors, equipment abnormal states and environmental risk factors, performing cross validation in combination with an electronic access control state and a work ticket permission state, and generating a primary alarm signal when it is detected that preset condition information is not matched; risk grade evaluation is carried out based on the primary alarm signal and environmental risk factors, and a dynamic early warning instruction is generated and synchronized to a station level platform; and the station control layer triggers video review of the associated area, a linkage access control system locks the dangerous area, early warning information is pushed to the target terminal, and an emergency processing plan is generated. And the real-time response speed and the active protection capability of a high-risk operation scene are effectively improved.

The invention discloses a transformer substation external damage prevention intelligent monitoring method and system based on cloud computing and artificial intelligence, and the method comprises the steps: data collection: collecting environment data and equipment state data of a transformer substation in real time through high-definition cameras and various sensors disposed around the transformer substation; data transmission: transmitting the collected data to a cloud computing platform for storage and processing; intelligent analysis: performing real-time analysis on the data transmitted to the cloud computing platform by using big data analysis and an artificial intelligence algorithm, and identifying potential external damage behaviors; performing early warning and response, triggering an early warning mechanism when an abnormal behavior is detected, and notifying operation and maintenance personnel; and data visualization and report generation: generating an operation and maintenance report, an alarm report and a risk analysis report for decision reference of operation and maintenance personnel and a management layer. Through the real-time monitoring and intelligent identification technology, the system can accurately capture abnormal behaviors inside and outside the transformer substation, effectively avoid external damage events, and significantly reduce safety risks.

The invention relates to the field of industrial cameras, and particularly provides a panoramic splicing type industrial camera device for a complex scene and a splicing method, and the panoramic splicing type industrial camera device comprises a multi-camera array, a synchronous control module, an image processing module, a calibration module and a communication module. The multi-camera array is distributed in an annular or matrix mode. The synchronous control module realizes sub-microsecond synchronization (error is less than 0.8 microsecond) based on an LVDS hardware trigger architecture, and eliminates splicing dislocation of a high-speed moving target. The image processing module carries an FPGA hardening assembly line and completes image distortion correction, optimization ORB feature matching and multi-band fusion in real time. The calibration module integrates a multi-mode sensing unit, and dynamically adjusts exposure, focal length and white balance through improved Kalman filtering and a deep reinforcement learning decision algorithm. In a strong interference environment, the panoramic stitching precision reaches 0.01 mm, the defect detection sensitivity is 30 [mu] m, the power consumption is only 12 W, and the system is suitable for various industrial scenes.

The invention relates to a bridge crane operation dangerous area personnel intrusion detection method and system, and the method comprises the steps: obtaining the real-time position and speed data of a hoisted object through a sensor, analyzing the motion trail of the hoisted object through a deep learning algorithm, and obtaining the prediction result of the motion trail of the hoisted object; calculating a dangerous area boundary by adopting a dynamic model according to a hoisting object motion trail prediction result in combination with load mass and rope length data, and determining a dynamic dangerous area range; acquiring image data of an operation area through an image acquisition device, and identifying personnel positions in the image to obtain personnel position data; and when it is judged that the personnel enter the dynamic dangerous area range according to the personnel position data, an early warning signal is generated, a three-dimensional visualization technology is adopted to superpose the early warning signal to the field real-time image, visual early warning information is obtained, and the visual early warning information is displayed through a display device. According to the invention, dynamic and accurate prevention and control of the dangerous area are realized through multi-dimensional technology fusion.

The invention relates to the technical field of community video monitoring, and discloses a community personnel abnormal behavior video monitoring method based on human factor engineering, and the method comprises the steps: dividing community monitoring video data based on a preset activity region, and obtaining the monitoring data of each sub-region, including a personnel track, environment interaction and the like; determining the abnormal level of each sub-region according to the movement frequency of the personnel trajectory data and the interaction strength of the environment interaction data; key monitoring nodes are calculated in combination with abnormal levels and the like, and the scanning sequence and the scanning duration are adjusted and determined according to a dynamic path; then generating observation anchor points and redundant anchor points at the boundaries of the sub-regions; and finally, associating and integrating to generate a monitoring path. According to the method, through multi-dimensional data processing and dynamic optimization, the accuracy, flexibility and environmental adaptability of community abnormal behavior monitoring are improved, and the method is suitable for community safety management.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

The invention discloses a video monitoring system and device based on quantum encryption communication, and relates to the technical field of secure transmission of relay chain video data. The video monitoring system and device based on quantum encryption communication comprise the following modules: a data acquisition module for acquiring transmission data through front-end equipment and performing standardized preprocessing on acquired multi-modal data; the quantum encryption communication module is used for evaluating the security of the video data in a transmission process and dynamically adjusting a transmission strategy based on an evaluation result; the relay chain transmission module is used for analyzing the reliability of a relay chain link and optimizing a link load distribution mode; and the parallel chain data processing and collaborative analysis module comprehensively judges the collaborative response capability of the node in the data transmission process and generates an optimization report based on a judgment result. The problem that in the prior art, when high-intensity interference and data hijacking risks exist, the safety and reliability of a transmission link are difficult to guarantee is solved.

The invention relates to the technical field of industrial intelligent inspection driven by artificial intelligence, and discloses a multi-modal property management scene risk point detection system based on artificial intelligence, and the system comprises a multi-modal data collection module which is used for synchronously obtaining visible light images, infrared thermal imaging and Internet of Things sensor data in building facilities; the domain self-adaptive defect generation module is based on a decoupling type generator and a StyleGAN2-ADA framework; a multi-modal feature fusion module; an attention enhancement detection module; a lightweight model compression frame; a real-time detection module; and a continuous learning module. High-fidelity defect samples are generated through the decoupling generator and the StyleGAN2-ADA, the problems of sample scarcity and class imbalance are solved, samples are generated through the cyclic generative adversarial network to cover multiple defect types, the acquisition period of the defect samples is shortened, and the requirement for training real samples is reduced.

The invention provides a network camera monitoring identification method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a monitoring data stream which is outputted by a network camera and comprises a video frame sequence and a corresponding time sequence metadata sequence, and then carrying out the spatial-temporal context coding processing of the monitoring data stream; the method comprises the following steps: generating a context feature cube containing spatial position information and time evolution information, then executing a normal behavior mode learning operation based on the context feature cube, and generating a reference feature library containing typical scene feature templates and feature evolution rule description; and dynamically matching and comparing the context feature cube of the current time period with the reference feature library, calculating a feature matching deviation value and generating an abnormal confidence score, and finally generating a monitoring early warning instruction containing abnormal occurrence time, a space coordinate range and a confidence level identifier according to the abnormal confidence score and corresponding space-time position information. And the accuracy and the early warning effect of monitoring and identification of the network camera are effectively improved.

The invention relates to the technical field of video analysis, in particular to an AI-based video analysis and management system, which comprises a channel evaluation module, a track extraction module, a cross-source fusion module, a content screening module and a scheduling generation module. In the method, key frame optimization is realized by combining frame activity index evaluation in an object identification process, track continuity breakpoint repair is realized based on a displacement sequence and a direction change trend in a track extraction process, and track matching fusion is realized through region and direction cross mapping in cross-channel analysis. Effective channels are identified based on track keeping features in content screening, and a structured identification directory is constructed in combination with label mapping in a scheduling process, so that target identification and track splicing among different channels have a full-process dynamic completion and consistency verification mechanism, the cross-video-source target identification precision and stability can be effectively improved, and the target identification efficiency is improved. And thus, more efficient content screening and structured recognition can be realized in a multi-source video environment.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.

The invention provides a coastal water level monitoring method based on a dynamic threshold algorithm, a medium and a system, and belongs to the technical field of coastal water level monitoring. The coastal water level monitoring method based on the dynamic threshold algorithm comprises the steps that data are collected through a water level sensor array, and a water level basic matrix and a floating matrix are constructed; a dynamic warning value is calculated by applying a tidal kinetic equation and a hydrometeorological coupling equation set, efficient resource scheduling is realized by utilizing a calculation jump matrix, self-learning optimization and water level prediction are performed based on a hydrological space-time network model, and water level monitoring and prediction data are output. Early warning of different levels is triggered according to the fact that the water level reaches the minimum range or the maximum range of the dynamic warning value, warning information is pushed to the disaster management platform through the edge computing gateway, a comprehensive warning report is finally generated for emergency decision making, and intelligent monitoring and accurate early warning of the coastal water level are achieved.

The invention relates to the technical field of alarm network control, and discloses an alarm controller remote linkage method, device and equipment and a storage medium, and the method comprises the steps: obtaining the multi-level monitoring layout information of a target monitoring area, carrying out the self-linkage and interactive linkage analysis, forming a linkage layout total mode, collecting and analyzing the data of each monitoring unit, and carrying out the remote linkage of the alarm controller. Obtaining regional monitoring feature distribution; the method comprises the steps of carrying out alarm live analysis based on historical data, analyzing response measures according to a total mode, issuing a remote instruction, switching a linkage object to a response mode, obtaining expansion information and scheduling subsequent measures, and can realize intelligentization and quick response, improve the emergency processing capability and efficiency of a safety monitoring system, and improve the safety of the safety monitoring system. The problems that in the prior art, the alarm form is fixed, and adaptability is not meticulous and comprehensive enough are solved.