327 results about "High frame rate" patented technology

The invention discloses an event information guided image deblurring and high-frame-rate reconstruction method. The method comprises the following steps: step 1, obtaining a blurred image and an event stream corresponding to the blurred image to construct a training data set; 2, constructing a deblurring and frame insertion combined reconstruction network based on a dynamic cross-modal fusion module; step 3, training to obtain a trained joint reconstruction network based on the cross-modal attention mechanism and the UNet architecture; and step 4, inputting a to-be-reconstructed blurred image and a corresponding event stream into the trained deblurring and frame insertion combined reconstruction network based on the dynamic cross-modal fusion module to obtain a final multi-frame reconstruction image. According to the method, a higher weight dynamic state is given to an event mode in a high-speed motion scene, an image data weight is added in a static scene, and a scale feature fusion strategy is adopted for a seriously fuzzy scene, so that the problems of semantic difference and noise interference are reduced, and the image recovery quality of a fuzzy region is improved.

The invention discloses a dynamic monitoring and analyzing method for a thermal management component molten pool based on image recognition, and relates to the technical field of laser powder bed melting additive manufacturing. The method comprises the following steps: continuously capturing an original thermal management component molten pool image and temperature field distribution data during laser powder bed melting additive manufacturing; and optimizing the original thermal management component molten pool image to generate an optimized thermal management component molten pool image. Molten pool images and temperature field data are captured in real time through a high-frame-rate synchronous camera and an infrared sensor, defect types are recognized online in combination with a lightweight classification model, classification results are fed back to a control system to dynamically adjust laser power or scanning speed, interlayer defect accumulation can be inhibited in time, and the defect quality is improved. The defect diffusion problem caused by response lag of a traditional off-line analysis method is avoided, and the forming precision and the process stability of the heat management component are remarkably improved.

The invention relates to the technical field of image recognition analysis, in particular to an indoor safety monitoring system and method based on image recognition, and the system comprises a plurality of modules and a lower setting unit, an image collection module selects a high-frame-rate and high-resolution camera, and is connected with a rear-end processing device through a USB, Ethernet or Wi-Fi communication interface; according to the scheme, after an owner receives an alarm and checks monitoring content, the owner can manually cancel the alarm through visual judgment of a door condition, after the alarm is cancelled, the system automatically enters the dangerous action capture module, and the module focuses on danger assessment of personnel actions and temporarily ignores other processes such as personnel identity recognition within a certain time after being started, so that the system is more convenient to use. During the period, if a new person enters the monitoring area, the system only carries out dangerous action capture, such setting can filter a large amount of alarm information generated due to frequent person contact to a certain extent, reduce interference to owners, and ensure that potential dangerous actions are kept alert at the same time.

The invention discloses an artificial intelligence large model reasoning acceleration method and system based on a GPU and an NPU, and relates to the technical field of large models. The invention discloses an artificial intelligence large model reasoning acceleration system based on a GPU (Graphics Processing Unit) and an NPU (Network Processing Unit). The system comprises a reasoning information shunting module and a reasoning data acceleration module, according to the method, by combining input data features, model structure dynamic control and heterogeneous computing resource collaborative scheduling, highly-adaptive large model reasoning optimization is realized, accurate matching of computing resources and data complexity is realized, and the overall reasoning throughput and response speed are remarkably improved; a dynamic scheduling mechanism based on a strategy output model is adopted, GPU / NPU calculation tasks are reasonably distributed, resource idling and congestion are avoided, the hardware utilization rate is improved, and the method is particularly suitable for multi-task concurrent and high-frame-rate video scenes.

The invention relates to a high-frame-rate rendering method and system based on a display driving chip, and the method comprises the following steps: carrying out the bidirectional scattering distribution processing of sorted scene data based on a preset real-time radiance algorithm, and obtaining scattering scene data; performing time domain reprojection on the scattering scene data through a preset frequency domain analysis technology to obtain time domain scene projection data; performing high-frame-rate pixel coloring rendering processing on the time domain scene projection data based on a preset programmable pipeline technology to obtain coloring rendering data; and performing frame synchronization processing on the coloring rendering data through a cache management mechanism to obtain synchronous rendering data, and inputting the synchronous rendering data into a preset display driving chip to drive a display screen to appear a target rendering picture, thereby solving the problem that a traditional method usually depends on a single rendering algorithm or a hardware acceleration scheme, and is not easy to implement. And smooth dynamic picture display cannot be realized on the premise that the image quality is not sacrificed.

The invention discloses a cross-film video stream non-inductive fusion playing method based on an HLS protocol, and belongs to the technical field of civil aircraft scene monitoring, and the method comprises the steps: 1, high-frame-rate screen recording preprocessing, 2, meta-information and video stream storage, 3, dynamic playlist construction, and 4, client non-inductive playing and rapid positioning. Through dynamic calculation of a playlist and a video frame index positioning technology, real-time loading and playing of any number of video clips with any time span, loading of target TS slices as required and asynchronous preloading of adjacent frames are supported, the smooth experience of zero lagging of cross-clip playing and comparable to real-time monitoring is realized, and a micro-batch preprocessing and on-demand loading mechanism is adopted, so that the real-time monitoring effect is improved. According to the method, the resource consumption of the server is reduced by more than 90%, the delay and resource bottleneck of a large-video multi-fragment scene are solved, the second-level event positioning capability of cross-day recording and broadcasting is provided for a civil aviation scene monitoring system, and the abnormal event analysis efficiency is greatly improved.

The invention provides an image acquisition control method, device and equipment based on machine vision. The method comprises the following steps: selecting a high-frame-rate image sensor acquisition mode according to environment state parameters; adjusting exposure parameters of the high-frame-rate image sensor according to the selected acquisition mode; fusing the optical flow speed and the brightness of the image flow into a weighting factor according to the exposure parameters, and injecting the weighting factor into an image space to generate a scene partition mask; and the scene partition mask is aligned with the exposure images with different exposure parameters, and the exposure images are fused into a target acquisition image. Through the implementation of the scheme of the invention, the environment state parameters are generated in real time, the corresponding acquisition mode is selected, the exposure parameters are optimized, the optical flow and the brightness data are fused to generate the weighting factor, the scene partition mask is created, the multi-exposure image is aligned through the scene partition mask, the optimal exposure part of each area is accurately fused, and the high-quality target image is obtained. Therefore, the image acquisition efficiency is improved.

The invention discloses a high-speed imaging method, system and device based on an SPAD array and a time counting circuit. The method comprises the following steps: acquiring original data of a photon event to obtain an original photon counting matrix of each frame; performing statistical modeling based on the original photon counting matrix to obtain a dynamic threshold matrix; performing threshold filtering based on the dynamic threshold matrix in combination with the original photon counting matrix to obtain a sparse matrix; and performing accumulation and image reconstruction based on the multi-frame original photon counting matrix to output a final image. The high time resolution of the SPAD array and the TCSPC is combined with the BNQSA algorithm, the noise problem of high frame rate imaging under extremely low illumination is solved, the system has the characteristics of high sensitivity, real-time performance and low power consumption, and a breakthrough solution is provided for a weak light scene.

The invention provides a high-frame-frequency optical filter switching mechanism under cold optics, and the mechanism comprises a Dewar box which is composed of a plurality of Dewar walls, the temperature in the Dewar box is lower than-200 DEG C, the vacuum degree in the Dewar box is lower than 10 <-5 > pa, and a first through hole is formed in the surface of at least one Dewar wall in a penetrating manner; the filter wheel is arranged in a low-temperature environment in the Dewar box, the filter wheel is provided with a plurality of optical filters which are arranged in a sector ring shape, the optical filters are configured to realize high-frame-frequency light filtering in the low-temperature environment, and the switching frequency of the optical filters is greater than 100Hz; the servo motor is arranged in a normal-temperature environment outside the Dewar box and is configured to drive the filter wheel to rotate; and the transmission mechanism penetrates through the first through hole, is connected with the servo motor and the filter wheel, and is configured to provide transmission force and reduce heat conduction from the servo motor to the filter wheel at the same time.

The invention discloses a three-dimensional voxel geometry collision detection method based on feature layering and multiple detection strategies, and relates to the technical field of computer graphics and game engines, the method comprises bit compression storage optimization, feature layering extraction and multiple collision detection, through the feature layering and multiple detection strategies, the unnecessary calculation amount is effectively reduced, and the detection efficiency is improved. The overall performance of the algorithm is improved, efficient memory compression is achieved while the detection precision is guaranteed, the requirement for computing resources is reduced, the technology can stably run in the resource-limited environment, unified collision detection processing of various geometries is supported, the universality and flexibility of the system are improved, and the system performance is improved. According to the method, integration and application in different application scenes are facilitated, the real-time rendering capability is optimized, the mobile terminal and the VR equipment are supported to run a complex physical simulation scene at a high frame rate, and the user experience and the interaction effect are improved.

Features can be tracked across frames. The features are first identified in an initial frame using an image processing technique which may take a relatively long time to complete, such as the length of several frames. When the features are being identified within the initial frame, subsequent frames are stored in a fast track buffer and once the features are identified in the initial image, the features can be tracked across the frames in the fast track buffer using a relatively fast process, such a one that is able to process the buffer at a higher frame rate than the frames are received at.

The invention discloses an image edge processing method and device based on FPGA acceleration. The method comprises the steps that image preprocessing is carried out, image data obtained from an external interface are obtained, each pixel RGB value of the image data is converted into a gray value according to needs, and a converted gray image is stored in an FPGA; gaussian filtering processing is carried out on the grayscale image by using the parallel assembly line characteristic of the FPGA to obtain image data after smooth processing; calculating the gradient magnitude and direction of each pixel point of the image to estimate the edge intensity and direction of each pixel point; non-maximum suppression is completed by comparing the gradient magnitude of the current pixel with the gradient magnitudes of two adjacent pixels in the gradient direction; and optimizing dual-threshold processing by using a Tianying algorithm, automatically adjusting according to the features of the image to obtain an optimal threshold, and outputting an edge image. And the edge detection performance can be obviously improved. And the real-time requirement of high frame rate is met on the occasion of needing real-time or efficient image edge detection.

Provided in the present disclosure is a real-time high-resolution binocular camera distortion correction implementation method based on FPGA. The method includes acquiring input video information in a YUV format, and performing YUV channel separation on the input video information; compressing U and V video signals inputted; performing distortion correction on a Y video signal, and performing distortion correction on the compressed U and V video signals, including calculating binocular camera distortion parameters, calculating a coordinate mapping relationship of Y, U, and V channels, and calculating and generating target image data by using an interpolation algorithm; amplifying U and V target image data after distortion correction to fit an input image; and outputting separated Y, U, and V channel target image data according to a raw image format. According to the present disclosure, high resolution and high frame rate videos can be processed in real time.

The invention discloses an AR glasses real-time object scanning and modeling system, and the system comprises a hardware layer which comprises an AR glasses main body, and a high-precision camera, a depth sensor, a processor and a storage module which are integrated on the AR glasses main body; the data acquisition layer is used for carrying out multi-angle shooting on an object at a high frame rate based on the high-precision camera to obtain a two-dimensional image sequence, synchronously obtaining depth information of each point on the surface of the object by the depth sensor, and carrying out accurate time and space alignment on data of the depth information and the depth information; the algorithm layer comprises a feature extraction algorithm, a point cloud generation algorithm and a three-dimensional reconstruction algorithm; and the display layer is used for rendering the constructed three-dimensional model into a display interface of AR glasses in real time and displaying the model and a real scene in a fused manner. Real-time modeling is achieved, details are rich, a display layer renders in real time, various interactions are provided, and operation is convenient and visual. And a hardware synchronous calibration module and a parallel computing technology further improve the system performance.

The invention provides a large-view-field high-resolution compound eye camera array system based on bionic human eye vision. The system is composed of a curved surface support and an industrial camera array arranged on the curved surface support. Reverse extension lines of optical axes of all the cameras intersect at the same point to form a concave surface structure similar to the retina, and the fixed view field overlapping rate of the adjacent cameras is kept. The industrial camera array is used for collecting a target scene image, large-view-field information is obtained by fusing and splicing all camera images, and a high-resolution image is obtained by conducting super-resolution reconstruction on a plurality of related images in a view field center interested area. The system provides three working modes, namely a low-resolution mode, a conventional-resolution mode and a super-resolution mode, which can be switched by adjusting camera parameters. Finally, the system can perceive the dynamic change of the edge of the field of view in a high-frame-rate and low-resolution mode, magnify the details in the center of the field of view in a super-resolution mode, realize efficient non-uniform imaging, and optimize the information acquisition and processing efficiency and the structure compactness.

The invention provides a fast color single-pixel imaging system and method based on an FPGA and a DLP projector, the fast color single-pixel imaging system comprises the DLP projector, a single-pixel photoelectric detector, an AD data acquisition card, a Raspberry Pi and the FPGA, the Raspberry Pi is adopted as an upper computer, the FPGA and the AD data acquisition card are dispatched to work cooperatively, and the DLP projector and the single-pixel photoelectric detector are integrated. The rapid and stable projection of the substrate pattern and the accurate sampling of the corresponding reflected light intensity information are realized, and the efficient reconstruction of the color image of the target scene is further realized. In a data acquisition stage of an imaging system, an HDMI image data output task is executed by the FPGA, and the FPGA outputs an image frame synchronization signal, so that the stability problem and the signal synchronization problem when a substrate pattern is played at a high frame rate (60Hz) are solved by making full use of the advantages of stable and controllable sequential logic and no software level scheduling delay of the FPGA, and the stability problem and the signal synchronization problem when the substrate pattern is played at a high frame rate (60Hz) are solved. And the influence of image tearing, frame loss and frame repetition on image reconstruction is eliminated.

The present invention discloses a key joint motion estimation based method for estimating continuous human postures. A motion estimation block matching algorithm is applied to human key joint tracking, so as to obtain continuous human posture results. Meanwhile, the results are continuously corrected by using a deep neural network based human posture estimator. The present invention may estimate the continuous human postures in a video stream, where the human postures are specifically embodied as coordinate positions of human joints in a video frame. Compared with a posture estimation method completely relying on a deep neural network, the posture estimation method provided by the present invention has the advantages of high frame rate, low hardware requirements, and sequential continuity of recognition results; and compared with a posture estimation method completely relying on a motion estimation algorithm, the present invention may correct a cumulative error, to improve the estimation accuracy.

The invention provides a low-power-consumption display module system based on refresh rate dynamic switching. The low-power-consumption display module system comprises a driving integrated circuit, a storage module and a frame rate control module. Wherein when the high frame rate mode and the low frame rate mode are switched, the driving integrated circuit does not need to burn driving codes again, frame rate switching is achieved by switching a parameter table in real time and synchronously updating the MI PI clock frequency, the switching delay is smaller than or equal to 20 ms, and the Gamma voltage curve deviation corresponding to the high frame rate and the low frame rate is smaller than or equal to 0.1 V. Through the combined design of pre-burning full codes and dynamic parameter switching, remarkable technical advantages are formed in the aspects of frame rate switching speed (smaller than or equal to 20 ms), power consumption control (amplification is smaller than or equal to 35%) and display stability (brightness fluctuation is smaller than or equal to 5%), and the industrial application value is achieved.

Video data transmission with prioritized frame sequences is disclosed for faster processing and play of a segment of a content item. High and low priority video frame transmission sequences are determined. Depending on an indication of the time remaining until play of the video segment, when sufficient time remains for reception of only the high priority frames, for example, I-frames and perhaps some P-frames, then only those are transmitted. These may be played at a lower frame rate before continuing with a subsequent video segment. In response to determining that, before start of play of the segment, sufficient time remains for reception of additional frames, for example, some B-frames, the lower priority frames may be transmitted. In this case, the segment may be played at a higher frame rate. Throughput may be determined according to an adaptive bitrate (ABR) implementation.

The invention relates to a self-supervision diagnosis method for welding quality abnormity, and aims to solve the problems of unstable welding seam state characterization, high abnormity diagnosis false alarm rate, insufficient adaptability and the like caused by multi-view image acquisition time sequence difference, complex illumination interference and characteristic drift in the welding process. According to the core scheme, the method comprises the steps of synchronously collecting multi-view-angle continuous images of a welding area through multiple cameras at a high frame rate, conducting time sequence synchronization, area focusing, denoising and brightness normalization processing on the original images, extracting normalized frame-level features through a pre-trained convolutional neural network, improving feature space semantic stability through a semantic anchor point generation and dynamic alignment mechanism, and improving the accuracy of image fusion. And abnormal detection and multi-stage dynamic loss adjustment are introduced, so that welding quality abnormal judgment and model self-adaptive optimization are realized. According to the method, the consistency and robustness of welding visual representation can be effectively enhanced, and the accuracy of welding quality anomaly detection and the system generalization ability are improved.

The invention provides an Electron master screen optimization method based on polymorphic rendering and state awareness, and is applied to the technical field of data processing. According to the method, aiming at an Electron advertisement screen application, a window focus, a power supply state and user interaction are monitored through BrowserWindows API and Node.jPowerMonitor, and three playing states, namely an active playing state, a low-frequency playing state and a background playing state, are judged. Rendering modes are dynamically switched according to the states, GPU acceleration is + 60 fps high frame rate in the active state, CPU software rendering is + 15 fps low frame rate in the low frequency state, and off-screen rendering is conducted on cached frame data in the background state. And the local redrawing is realized by combining the Offscene Canvas and the WebWorker, so that the invalid calculation is reduced. Resources such as DOM nodes and GPU memories are actively recycled in the background state, quick recovery is achieved after focus obtaining, users do not perceive the whole process, resource occupation and energy consumption are greatly reduced, and operation stability is improved.

The invention belongs to the technical field of infrared detection, and particularly relates to a low-noise high-frame-frequency short-wave infrared imaging system. The system comprises an optical element, a detector, an analog acquisition circuit, a digital driving circuit and an upper computer, the digital driving circuit sends time sequence signals to the detector and the analog acquisition circuit respectively to enable the detector and the analog acquisition circuit to keep time sequence synchronization, and the optical element collects infrared light of a detected target and sends the infrared light to the upper computer. The infrared light of a detected target is refracted to a focal plane of the detector, the detector converts the infrared light into an analog electric signal based on a time sequence signal, and the analog acquisition circuit processes the analog electric signal based on the time sequence signal and outputs an obtained digital signal to the digital driving circuit. And the digital driving circuit performs format conversion on the digital signal and sends the signal obtained by format conversion to an upper computer for image display. According to the invention, a high-frame-frequency imaging function under the condition of low readout noise is realized.

The invention discloses a grayscale image translation motion blur recovery method and system based on optical flow guidance, and belongs to the technical field of image processing. A target area is determined based on a video stream H; calculating a translational motion vector field matrix between every two adjacent frames in the video stream H by adopting an optical flow method, obtaining a translational motion vector located in a target area, further constructing a track representation matrix for reflecting a motion track of a motion target generating translational motion, normalizing the track representation matrix to serve as a blurring kernel, and obtaining a motion vector field matrix; and translational motion blur of the target area is removed in a targeted manner. According to the method, the high-density time sampling information provided by the video stream H with the high frame rate and the low signal-to-noise ratio is utilized, and the complex translational motion track of the blurred image B with the low frame rate and the high signal-to-noise ratio in the exposure period can be accurately captured. The fuzzy kernel synthesized based on the accurate translational motion trails can highly approach a real physical fuzzy process, and an image with a high signal-to-noise ratio and high definition can be reconstructed.

The invention discloses a visible light image and sound wave data-based visual fusion display method and system, and relates to the technical field of image and sound wave fusion display, and the method comprises the steps: data collection: collecting image frames through an industrial visible light camera, collecting sound wave data through an eight-channel microphone array, synchronizing timestamps, and enabling a dynamic scene to be high in frame rate; a preprocessing step: denoising and sharpening the image, filtering interference by sound waves, and carrying out space-time registration; a feature extraction step: extracting image edge texture and sound wave frequency energy features; a fusion calculation step: dynamically adjusting the weight and generating a fusion feature matrix in combination with an attention mechanism; and a visual display step: constructing 2D, 3D and AR interfaces, superposing sound wave information and supporting multi-modal interaction and parameter control. According to the method, the image and sound wave fusion accuracy is improved, scene adjustment parameters are dynamically adapted, multi-dimensional display is visual, interaction is convenient and fast, the exception recognition and response capability is enhanced, and the method adapts to multi-scene requirements.

The invention discloses a video intelligent monitoring system and method based on cross-modal time-frequency fusion, and relates to the technical field of new energy power monitoring and safety management, and the method comprises the following steps: obtaining multi-modal physical data; detecting a waveform characteristic of the electrical signal, and when the waveform characteristic of the electrical signal meets a preset electrical abnormal condition, performing high frame rate sampling and frame rate compensation on the visible light video to obtain video data with enhanced time sequence continuity; converting the video data, the thermal infrared image, the acoustic signal and the electrical signal to a time-frequency domain to obtain a multi-modal video feature, and inputting the multi-modal video feature to a cross-modal fusion network to obtain a fusion feature vector; and performing anomaly type prediction on the fusion feature vector based on a long-short term memory network in combination with a semantic map model to obtain an anomaly type recognition result and a risk level judgment result. According to the method, the problem that the multi-modal data lacks a unified time reference and fusion mechanism can be solved.

The present application relates to the field of image processing. Provided are an image exposure method and apparatus, and an electronic device and a storage medium. The image exposure method provided in the present application comprises: on the basis of a constructed mapping relationship, determining initial exposure parameters of a target image; on the basis of a current image brightness value of the target image, determining the number of frames for convergence of the target image; on the basis of the current image brightness value and the number of frames for convergence, adjusting the initial exposure parameters, so as to determine a first exposure value and a first gain value; and on the basis of a high frame rate and a low frame rate which correspond to the target image, performing frame rate switching and maximum exposure limit processing on the first exposure value and the first gain value, so as to determine a second exposure value and a second gain value, and on the basis of the second exposure value and the second gain value, controlling an image collection device to output a final image. The present application can be compatible with exposure in different environments while ensuring an exposure effect, and can improve the image exposure efficiency.

The invention discloses a high-speed snapshot system and method, and relates to the technical field of image acquisition. The system comprises a trigger detection module, an image acquisition module, a processing and control module, a DDR cache chip and a storage and transmission module. The image acquisition module adopts a high-speed CMOS (Complementary Metal-Oxide-Semiconductor Transistor) image sensor and works in a high-frame-rate mode which is not lower than 120 frames per second under a preset resolution ratio; the processing and control module adopts a system-level chip integrated with an image signal processor and a hardware encoder; the DDR cache chip is connected with the processing and control module through a memory bus and serves as a high-speed data buffer pool. Through cooperative work of a high-frame-rate sensor and a hardware-level encoder and cooperation of independent DDR cache, clear, continuous and frame-loss-free snapshot of high-speed moving targets such as flying insects is realized, and the problems of image blurring and incomplete data caused by insufficient frame rate and limited data processing capability in the prior art are solved. The system is high in integration level, flexible in deployment and suitable for field monitoring and laboratory research.

This invention discloses a high frame rate output system and method based on a large-area CMOS array, belonging to the field of image processing technology. The system includes a bus communication module, a CMOS configuration readback module, a CMOS detector control module, a CMOS detector, an image data processing and buffering module, and a data transmission output module. Through imaging timing pipeline design and ping-pong buffer design, the imaging frame rate is greatly improved. With only one high-speed data transmission interface, the frame rate can reach 1Hz when outputting 100 million pixels in full-frame mode, and can be increased to 5Hz in detector windowing mode.

Changes in tissue stiffness have long been associated with disease. Systems and methods for determining the stiffness of tissues using ultrasonography may include a device for inducing a propagating shear wave in tissue and tracking the speed of propagation, which is directly related to tissue stiffness and density. The speed of a propagating shear wave may be detected by imaging a tissue at a high frame rate and detecting the propagating wave as a perturbance in successive image frames relative to a baseline image of the tissue in an undisturbed state. In some embodiments, sufficiently high frame rates may be achieved by using a ping-based ultrasound imaging technique in which unfocused omni-directional pings are transmitted (in an imaging plane or in a hemisphere) into a region of interest. Receiving echoes of the omnidirectional pings with multiple receive apertures allows for substantially improved lateral resolution.

The invention discloses a traffic light fault real-time identification and alarm method based on improved Yolov5, and relates to the technical field of intelligent traffic, and the method comprises the steps: S1, building a traffic light fault data set in advance, and collecting traffic light images and video data under different scenes, illumination conditions and weathers; step S2, constructing an improved Yolov5 model; and S3, extracting images of a real-time video stream to be detected according to frames, inputting the images into the trained improved Yolov5 model, outputting position information, light color states and fault probabilities of traffic lights, and analyzing and judging fault types in combination with a preset threshold value and a multi-frame time sequence. Real-time video stream processing and multi-frame time sequence analysis are combined, the high-frame-rate real-time detection requirement can be met, time sequence faults can be accurately judged, and the limitation that manual inspection is low in efficiency and poor in real-time performance is broken through; the fault response and handling period is greatly shortened, and powerful technical support is provided for guaranteeing normal operation of the traffic lights and improving urban traffic management efficiency.