1685 results about "Real time video" patented technology

The invention discloses a power distribution fusion terminal real-time video reasoning method based on an SDNN, relates to the technical field of intelligent power grid monitoring, and aims to solve the problems of high delay, high bandwidth consumption, low power consumption and the like caused by insufficient utilization of computing power resources of an existing power distribution terminal and dependence on a cloud on video processing in the prior art. And the problem of poor adaptability of the model in a complex power grid scene is solved. Firstly, a power grid special sample library is constructed, and data quality is optimized through time domain key frame extraction and a stratified sampling mechanism; carrying out enhanced training on the lightweight model by adopting transfer learning and multi-modal confrontation; designing an intermediate representation conversion and hierarchical caching mechanism to realize efficient model compiling and heterogeneous hardware deployment; edge-side real-time reasoning is realized based on dynamic frame sampling and statistical threshold detection; finally, a self-adaptive feedback closed loop is driven through reinforcement learning, and incremental updating of the model and dynamic allocation of resources are achieved. The real-time performance of power distribution equipment anomaly detection is remarkably improved, and meanwhile the model memory occupancy rate is reduced.

The invention relates to the field of artificial intelligence and computer vision, and discloses an artificial intelligence physical training exercise system which comprises a data acquisition module, a data preprocessing module, an action modeling module, an action evaluation module and a user feedback module. The data acquisition module acquires real-time video data of a moving target and extracts skeleton key point data; the data preprocessing module performs optimization processing on the skeleton key point data; the action modeling module is used for modeling and predicting the action state of the moving target by utilizing a target constraint dynamic optimization model based on the optimized skeleton data; the action evaluation module generates action scores and optimization suggestions through local action decomposition and action matching analysis; and the user feedback module provides real-time feedback information and generates a training summary report according to the action score. The method can achieve the efficient analysis and precise evaluation of the motion of the moving target in a complex scene, provides personalized optimization guidance, and is widely suitable for physical training and teaching scenes.

The invention relates to the technical field of video behavior recognition, and discloses a behavior recognition and early warning system for a real-time video stream. The system comprises a spatio-temporal feature modeling module, a behavior fragment extraction module, an anomaly propagation modeling module, a risk area positioning module and an early warning strategy generation module. The spatial-temporal feature modeling module builds a dynamic model based on historical data, captures a skeleton key point three-dimensional coordinate sequence, a motion optical flow vector field and a micro-expression intensity spectrum, and outputs a theoretical behavior mode vector; the behavior fragment extraction module generates a multi-modal difference feature tensor through cross-modal difference analysis; the exception propagation modeling module generates an exception propagation path risk probability distribution cloud picture in combination with spatial constraint and trajectory information; the risk area positioning module identifies a high-risk area and marks a boundary; and the early warning strategy generation module dynamically configures monitoring parameters, starts high-frame-rate micro-expression capture for a high-risk area, and performs a track disturbance test on an adjacent area.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention discloses a video multi-target intelligent detection method and system based on mercuric chloride hardware. A hardware decoding module decodes an input video stream in real time, generates video frames and stores the video frames in a shared memory queue. And inputting the video frame into the YOLOv5 model converted by the mercuric chloride OMG tool, pre-loading a plurality of model instances into a memory by using an ACL interface of the mercuric chloride NPU, calling different model instances through a polling scheduling mechanism, and outputting structured data comprising a multi-target detection frame, a category label and confidence. And carrying out non-maximum suppression processing on the reasoning result, and judging whether the target is in an alarm monitoring area or not by adopting a central point detection method. According to the invention, real-time multi-target intelligent detection of the input video stream can be realized. And the decoded video frames are stored in a shared memory queue, so that efficient data transmission and processing are realized. The multi-model parallel reasoning improves the detection precision, and is suitable for the application scene of real-time video multi-target intelligent detection.

The invention discloses a video analysis-based multi-scene operator violation behavior identification method and system, and belongs to the technical field of intelligent operation safety monitoring and artificial intelligence identification, and the method comprises the steps: collecting a real-time video stream of a multi-scene operation site; recognizing a continuous action time sequence in the real-time video stream by using an action recognition depth model; constructing the continuous action time sequence into an action behavior sequence; the action behavior sequence is constructed into a directed behavior graph with time, space and action labels, the directed behavior graph is compared with a directed behavior graph corresponding to the standard action behavior sequence, and illegal behaviors are recognized; and carrying out multi-mode early warning on the identified illegal behaviors. According to the method, the bottleneck that the traditional image recognition technology is weak in action sequence semantic understanding and poor in environmental adaptability is broken through, and accurate recognition and real-time early warning of illegal behaviors in multi-scene operation are achieved.

The invention provides a real-time video translation and audio and picture synchronization method and system based on a multi-modal large model, and relates to the technical field of video translations, and the method comprises the steps: obtaining a source video; extracting the source video based on the multi-modal large model to obtain a multi-modal feature; fusing the multi-modal features through a cross-modal attention mechanism to generate a context semantic vector; translating into a target language text in real time based on the context semantic vector, and processing the translated language text based on the multi-modal features to obtain a translated language sound source; and performing mouth shape adjustment on the source video based on the translation language sound source, and merging the translation language sound source and the mouth shape animation video to obtain a real-time translation video with synchronous sound and picture. According to the method, the limitation of traditional single-modal translation is broken through, and the semantic accuracy of translation is remarkably improved by dynamically aligning the context information through the multi-modal features in combination with a cross-modal attention mechanism.

The invention relates to the field of data processing, and provides a 5G-based video call information processing method and system, and the method comprises the steps: continuously obtaining a real-time video frame sequence and 5G network environment perception data in a video call scene, carrying out the multi-dimensional state mapping processing of the 5G network environment perception data, constructing a network transmission adaption model, and carrying out the real-time video frame sequence and 5G network environment perception data. Generating a video coding control instruction based on the network transmission adaptation model, performing content-aware coding conversion on the real-time video frame sequence, and outputting a coding optimization stream; in the transmission process of the coding optimization stream, link state fluctuation information is obtained through a 5G network feedback channel, transmission strategy dynamic calibration is performed on the coding optimization stream according to the link state fluctuation information, and a calibration transmission stream is obtained; and carrying out decoding time sequence alignment processing on the calibration transport stream, generating a visual call output sequence which is synchronous with the time of the original video stream unit, and pushing the visual call output sequence to a receiving end presentation device.

The invention discloses a construction site safety monitoring method and system based on a large model and RAG, and the method comprises the steps: carrying out the intelligent analysis of real-time video data of a construction region based on a visual large model, and recognizing the behavior safety, violation operation, dangerous facilities and environment risks of construction personnel; adding a time label to the analyzed security event text to form a construction site security event log; reasoning and analyzing the security event log, determining whether security early warning is generated or not, and pushing early warning information; carrying out on-site intelligent voice broadcasting according to the early warning information; through a multi-RAG architecture, a construction site safety log database, a public construction safety management database and an enterprise internal safety management database are constructed, and construction safety information is stored by using a vector database and a graph database. By adopting the technical scheme of the invention, the problems of insufficient real-time monitoring capability, limited intelligent analysis capability and lack of effective knowledge base support in the existing construction site safety supervision are solved.

The invention relates to the technical field of transformer substation intelligent patrol, and provides a transformer substation three-dimensional fusion patrol method and system based on digital twinborn and autonomous identification. According to the method, a fused three-dimensional model is constructed through multi-source data acquisition and a three-dimensional Gaussian splash algorithm, and in combination with deep learning-based point cloud semantic segmentation and clustering, an equipment-patrol means coverage relationship is generated. Creating a virtual inspection proxy object based on a three-dimensional virtual environment, and controlling terminals such as an unmanned aerial vehicle to collect real-time video image data; the system carries out automatic identification on pictures, automatically completes equipment level alignment and standard point location identification, generates fine control of camera zooming, horizontal rotation, pitching and the like, and realizes standardized view finding and acquisition. By combining an enhanced recognition algorithm, traditional image processing and a deep learning model are fused, model self-evolution is realized through incremental learning, flexible expansion and collaboration of various patrol terminals are supported through a unified interface, and refined, real-time and intelligent patrol operation and maintenance requirements of an intelligent substation are met.

The invention discloses a multi-view child motion coordination ability evaluation system and method based on deep learning, belongs to the technical field of motion evaluation, and solves the problems that child motion evaluation in the prior art mainly depends on manual observation and simple physical testing, and multiple angles and key details of child actions are difficult to synchronously track. The method comprises the following steps: training to obtain a skeleton point detection model and an athletic ability evaluation model, acquiring real-time video streams of personnel entering a field based on an acquisition camera, identifying skeleton key points in a preprocessing data set by the skeleton point detection model, performing multi-person detection on a personnel matching result based on an athletic area division method, and evaluating the athletic ability of the personnel. The exercise ability evaluation model carries out quantitative analysis on multi-person detection results; according to the invention, visual identification and motion state detection technologies are combined, a front view angle and side view angle dual-camera layout is adopted, and a deep learning algorithm is matched, so that automatic children dynamic motion evaluation is realized. The action process can be completely captured, and the detection accuracy and efficiency are improved.

The invention discloses an AR (Augmented Reality) game camera system based on combination of live-action and 3D (Three-Dimensional) game elements, which comprises a live-action acquisition module for acquiring RGB (Red, Green, Blue) images and depth information, and processing the RGB images to form a real-time video stream; the semantic segmentation module is used for carrying out semantic segmentation on the real-time video stream, identifying an interactive surface and establishing a dynamic interactive region topology database; the virtual element generation module is used for constructing a 3D model library based on a game engine and dynamically matching the 3D model library with the live-action collection information through an adapter interface to generate virtual elements; the virtual-real fusion engine adopts a multi-layer rendering pipeline technology to realize seamless fusion of virtual elements and a real environment; the interactive response module is used for realizing interactive response between the player and the virtual object; and the adaptive calibration module is used for adjusting parameters of the SLAM algorithm according to the semantic segmentation result. According to the method, accurate fusion and natural interaction of the virtual elements in a complex real scene are realized, more immersive user experience is provided, and high precision and stability can be kept in different environments.

The invention provides a method and a device for identifying and alarming a safety helmet under a mine and computing equipment, the method comprises the following steps: acquiring a frame image of a real-time video stream from a camera device, the real-time video stream being used for fixedly monitoring a working place under the mine; performing human body detection identification on the frame image to obtain a human body identification area; performing head area positioning on the human body recognition area to obtain a head recognition area; carrying out safety helmet wearing identification detection on the head identification area to obtain a safety helmet wearing identification result; and rechecking the safety helmet wearing identification result based on a semantic segmentation model, and determining whether to output a safety helmet non-wearing alarm signal or not according to a rechecking result. According to the technical scheme of the invention, accurate safety helmet wearing identification can be realized for mines with complex environments and under-mine environments.

The invention discloses a video stream adaptive low-delay real-time transmission method and system based on edge calculation. The method comprises the following steps: receiving a real-time video stream from a network camera, creating a pipeline queue, adding timestamp information for each video frame, and setting a queue protection mechanism; the coded video frames are taken out from the input queue, and the frames in the video are processed through hardware acceleration decoding; the resource use condition of the system is monitored in real time; executing a self-adaptive frame skipping decision according to a performance monitoring result; timestamp generation: dynamically calculating a timestamp interval according to an actual processing frame rate; receiving the decoded original video frame and the corresponding timestamp information, accelerating decoding by using hardware, and executing a video coding operation; and packaging and transmitting the coded video data, and providing a standard protocol interface to be connected with a client for playing. According to the scheme, stable low delay and relatively low resource occupation can be kept, and meanwhile, the video quality is remarkably improved.

The invention relates to the technical field of image processing, in particular to an unmanned aerial vehicle positioning method and system based on machine vision, and the method comprises the steps: obtaining a current frame image and a reference image in a real-time video stream of an unmanned aerial vehicle, generating an initial matching pair set, and calculating the structural consistency of each matching pair, the method comprises the steps of adaptively determining a screening threshold value of a current frame image based on structural consistency, determining a screening matching pair set by utilizing the screening threshold value, evaluating a positioning contribution weight of each matching pair of the screening matching pair set, executing weighted pose calculation based on the positioning contribution weights, and obtaining an instantaneous pose of an unmanned aerial vehicle in the current frame image. And inputting the instantaneous pose as an observation value into a time sequence filtering model, performing time sequence fusion in combination with a motion model of the unmanned aerial vehicle, and outputting the final pose estimation of the unmanned aerial vehicle in the current frame image so as to complete the accurate positioning of the unmanned aerial vehicle. The method improves the accuracy of unmanned aerial vehicle positioning.

The invention discloses a reasoning resource allocation optimization method and system for multi-camera real-time video monitoring, and relates to the field of multi-camera real-time monitoring. According to the invention, through reasonable reasoning of resource allocation and combination of a video frame rate dynamic adjustment strategy, optimization processing of video streams of a plurality of cameras is realized. Particularly, in the method, different reasoning resources are allocated according to the priorities of the detection tasks of different cameras, so that the cameras with abnormal event detection requirements obtain more computing resources, and the cameras without abnormal events use fewer resources, thereby achieving the balance between the optimal performance and the computing resources.

The invention provides a safety production behavior monitoring method and system based on AI video analysis. The method comprises the steps of collecting a real-time video data stream of a production area; inputting each frame of video image in the real-time video data stream into a target detection model for target detection to obtain a personnel target output by the target detection model and a target position coordinate of the personnel target in each frame of video image; cutting out a local image area of the personnel target in each frame of video image based on the target position coordinate, and performing feature recognition based on the local image area to obtain personnel features; performing comparison on the basis of the personnel characteristics and the personnel standard behavior characteristics to obtain personnel behavior states, and performing track association on the basis of the personnel behavior states corresponding to the continuous multi-frame video images to obtain personnel behavior tracks; and performing safety production behavior monitoring based on the personnel behavior state and the personnel behavior track, and generating an abnormal behavior early warning signal. According to the method and the device, the real-time performance and the accuracy of safety monitoring in a production scene are improved.

The invention relates to the technical field of photovoltaic power station operation and maintenance, in particular to a photovoltaic power station intelligent inspection system based on AI vision, which comprises a video acquisition module, a geometric reference construction module, a tremor offset resolving module, a coordinate inverse correction module and a defect fine calibration module, the video acquisition module is used for acquiring a real-time video stream of an unmanned aerial vehicle polling photovoltaic array, and performing time-space synchronization calibration on the video stream to generate an original image sequence. According to the invention, the computer vision technology is utilized to calculate a current frame blanking point in a video picture as a tiny offset of a reference object relative to a reference position in real time, the displacement is deducted from a GPS coordinate, and a coordinate inverse correction module is utilized to inversely calculate a visual axis offset into a ground projection error. The shake amount and the shake direction of the camera at each moment can be accurately calculated, and then the GPS coordinates are corrected in turn, so that the geographic accuracy of defect positioning is improved, and the operation and maintenance personnel can accurately find a fault component.

The invention provides a real-time video image compression method based on deep learning, and relates to the technical field of video image compression, and the method comprises the steps: carrying out the key feature recognition through employing an attention mechanism; performing convolution training optimization on the video image sample data set by using a deep learning network structure; a self-encoder structure is designed to carry out feature map encoding compression; a video image compression adaptive network is generated through series fusion; a real-time video image frame is collected for preprocessing, and feature compression processing is performed on a standard video image frame based on a video image compression adaptive network. According to the method and the device, the technical problem that the video compression quality is reduced due to the fact that the generalization ability is insufficient in the face of various scenes and the video compression strategy is difficult to adaptively adjust according to different scenes in the prior art can be solved, the adaptive network is constructed through the combination of deep learning and the auto-encoder, and the video compression quality is improved. And the video compression strategy is dynamically adjusted according to the contents of different video images, so that the video compression quality is improved.

The invention discloses a ship target detection and tracking method and system based on video image processing, and the method comprises the steps: 1), collecting a channel panoramic video stream through a high-definition camera, and extracting a continuous image sequence; 2) constructing a dynamic channel background model by using a Gaussian mixture model (GMM), and updating the background in real time to eliminate interference of water surface fluctuation and illumination variation; (3) enhancing the data obtained in the step (1) by adopting a Mosaic enhancement method, and establishing a data set; 4) optimizing a Yov7 model, and introducing a lightweight CBAM attention mechanism to carry out model training; (5) the trained weight model is experimented on the verification set, and target detection of the ship is achieved; and 6) adopting a kernel correlation filtering algorithm to realize real-time tracking of the moving ship. According to the method, the technology in the field of computer vision is utilized, the real-time video image number of the large-flow channel is effectively utilized, multi-target recognition and real-time tracking are achieved, and the method has high accuracy and robustness.

A computer-vision system or engine that (a) generates from a pixel stream a digital representation of a person and (b) determines attributes or characteristics of the person from that digital representation and (c) based on those attributes or characteristics, outputs data to a cloud-based analytics system that enables that analytics system to identify and also to authenticate the person. The attributes or characteristics of the person include their pose, and the system or engine analyses that pose to extract a facial image from a video stream that is the best facial image for use by the cloud-based analytics system to identify and authenticate the person. The computer-vision system or engine outputs the facial image to the cloud-based analytics system, but does not output the full-frame real-time video to the cloud-based analytics system.

The invention belongs to the technical field of electronic information, and discloses an unmanned aerial vehicle control method for non-cooperative target tracking, which comprises the following steps of: 1, acquiring a real-time video stream of a target by a pan-tilt camera, and extracting a target feature vector; step 2, defining and calculating a normalized visual error vector, and adjusting the attitude of the holder based on the target feature vector obtained in the step 1, so that the target is kept in the center of the image; outputting a pan-tilt yaw angle, a pan-tilt pitch angle and a normalized visual error vector; 3, designing a tracking quality factor and a pitch angle scale factor according to a result output in the step 2, and dynamically adjusting the maximum speed of the unmanned aerial vehicle to calculate an expected speed vector; and obtaining the speed and position of the unmanned aerial vehicle at the next moment. According to the invention, decoupling control is carried out on the motion of the unmanned aerial vehicle and the rotation of the holder, and the motion trajectory of the unmanned aerial vehicle is controlled on the premise of ensuring that the target is always in the view field range of the unmanned aerial vehicle, thereby realizing accurate and stable tracking of the non-cooperative target.

The invention discloses a fire detection and early warning method and system based on improved YOLOv8, and belongs to the technical field of image recognition, and the method comprises the steps: building a fire data set, marking the fire data set, and dividing the fire data set into a training set, a verification set and a test set; the YOLOv8 network is improved; the original YOLOv8 network and the improved YOLOv8 network are trained, evaluated, adjusted and tested, and an optimal model is determined; deploying the optimal model to an edge AI development board of a ground station; a real-time video stream of the unmanned aerial vehicle is obtained through the unmanned aerial vehicle camera and is transmitted to the edge AI development board for fire detection; a fire signal is detected by using the edge AI development board, and early warning is carried out through the fire alarm module; the system comprises a fire data set establishment and processing module, a YOLOv8 network improvement module, a YOLOv8 network training module, an optimal model deployment module, a fire detection module and a fire early warning module. The method has the technical advantages of high precision and light weight.

The invention relates to the technical field of parcel sorting, in particular to a parcel detecting and sorting method and system based on machine vision. According to the method, historical parcel motion trajectory data and real-time video information are fused, a category and weight differentiation trajectory model is constructed, sudden offset is captured in combination with computer vision, the trajectory is accurately predicted, and the attitude offset is obtained. The initial fixed parameter of the actuator is dynamically adjusted to be a first action parameter based on the offset, a compensation coefficient is generated through trajectory tracking error verification, and a second action parameter is obtained; meanwhile, friction coefficient changes are calculated through the real-time track and pressure data, a dynamic response coefficient is determined, and finally a multi-sorting-port cooperation instruction is generated to achieve path dynamic distribution. According to the process, through historical and real-time data fusion, dynamic parameter adjustment, closed-loop feedback and multi-system cooperation, the problems of traditional sorting static presetting, parameter blind adjustment, resource allocation imbalance and the like are solved, and the sorting precision, adaptability and efficiency are improved.

The invention discloses a real-time video processing system based on an ampere-path FPGA. The real-time video processing system comprises an SD card, an ampere-path FPGA board card and an HDMI displayer. The SD card is used for storing original video data in a Bayer format; the A-path FPGA board card comprises an FPGA end and an ARM end, the FPGA end comprises an SD card reading control module, an image cache FIFO module, a Bayer-to-RGB module, a seven-in-one image processing module, an SDRAM read-write control module and an HDMI display control module, the first four modules are connected in sequence, and the SDRAM read-write control module is connected with the Bayer-to-RGB module, the seven-in-one image processing module and the HDMI display control module; wherein the ARM end comprises a Cortex-M0 processor core, an AHBLiite-Interconnect data bus and all peripheral interfaces, and all the modules are connected in sequence; the ARM end comprises a Cortex-M0 processor core, an AHBLiite-Interconnect data bus and all the peripheral interfaces; the ampere-path FPGA board card is used for completing all video processing functions; and the HDMI display outputs the processed video. The method is good in video processing effect, low in resource consumption, high in flexibility, high in stability and low in hardware cost, and the hardware is 100% localized.

The invention relates to the technical field of violation monitoring, and provides an unattended system based on an AI model and video analysis, the unattended system comprises an AI language large model, a video monitoring device, an image analysis node and a linkage alarm device, the video monitoring device is configured with a sound-light alarm assembly, the image analysis node comprises a plurality of AI edge calculation boxes, and the AI edge calculation boxes are connected with the AI language large model. The linkage alarm device comprises a plurality of protocol conversion middleware. The video monitoring equipment is used for collecting a real-time video stream of a current monitoring area and inputting the real-time video stream into the image analysis node; the AI language large model is used for analyzing target image features of the image analysis nodes, determining violation behaviors of violation events and generating natural language alarm information; wherein the violation behavior is determined by an AI edge calculation box; protocol conversion configuration is performed on the linkage alarm device, and a sound-light alarm signal based on natural language alarm information can be generated on the video monitoring device in the current monitoring area through the linkage alarm device.

The invention discloses a match data processing method and system based on multi-terminal collaboration, relates to the technical field of data processing, can be applied to the field of match live broadcast, and mainly aims at solving the problems that live broadcast data processing of existing match data processing highly depends on manual broadcast director and is low in timeliness. The method mainly comprises the following steps: inputting penalty information through a penalty input interface of a penalty terminal, forwarding the penalty information to a timing and scoring system, calling at least one target triggering pendant matched with a penalty object and a penalty type, generating real-time match condition data according to the target triggering pendant and a real-time video stream, and sending the real-time match condition data to the penalty terminal. The real-time match condition data is displayed in a live broadcast mode through a live broadcast interface of the live broadcast terminal; intercepting a playback video stream according to the marking timestamp and the penalty type of the video point marking instruction; and in response to data updating of the timing and scoring system, generating match condition playback data according to the timing and scoring updating pendant and the playback video stream, and displaying the match condition playback data in a live broadcast manner through a live broadcast interface. The method is mainly used for event data processing.

The invention provides a fish school density distribution similarity measurement-based bait casting decision-making method, which comprises the following steps of: acquiring static pictures, dynamic videos and real-time video streams of a water area containing fishes, and preprocessing to obtain an input data set; an Ifeed model is constructed, and the Ifeed model comprises a multivariate input preprocessing and image foreground target extraction module, a fish school feeding behavior analysis and density distribution estimation module, a target key point extraction and full connection distance calculation module and a fish school gathering trend visualization and bait casting decision support module; inputting the input data set into the Ifeed model, and training the Ifeed model through the input data set; static pictures, dynamic videos or real-time video streams of a water area to be fed are collected and input into the trained Ifeed model, and gathering degree visual data and feeding strategies of fish schools in the water area to be fed are obtained. Feed waste can be reduced, water quality pollution is reduced, cost is reduced, efficiency is improved, and the purpose of accurate feeding is achieved.

The invention belongs to the technical field of computer vision and deep learning, and relates to a cultured fish intelligent feeding system based on multi-feature fusion and a control method thereof, and the system comprises an image acquisition module, a space-motion feature extraction module, a motion track feature extraction module, a feeding state recognition module, a feeding control module and a feedback adjustment module. Extracting a depth optical flow image reflecting fish school feeding state characteristics through a fish school real-time video stream; classifying the fish school feeding state image samples to form a space-motion optical flow feature map data set; meanwhile, fish swimming trails are extracted, and a swimming trail feature map data set is formed; and training is carried out to obtain an optimal model weight, real-time identification is realized, an identification result is transmitted to the feeding control module, the feeding state of a fish school after feeding is completed is evaluated, the feeding amount is adjusted, or a feeding decision is optimized. According to the invention, real-time, accurate and efficient identification and decision control of the feeding state of the fish school can be realized, and intelligent feeding is realized on a low-cost edge end computing platform.

The invention relates to the technical field of wireless streaming media, in particular to a real-time video transmission method and system based on 5G, and the method comprises the following steps: based on mobile terminal equipment, analyzing the signal strength and service bearing change of a 5G base station, screening an optimal access sequence, comparing channel capability with video partition characteristics, and adjusting coding parameter distribution. And analyzing link capability and packet loss performance, and optimizing uplink and downlink compression parameters to obtain a compression change trend. According to the invention, by dynamically identifying the signal association and network load change between the mobile terminal and the 5G base station, the multi-dimensional access priority sequence is generated in combination with the real-time bandwidth demand, the coding and allocation parameters are flexibly adjusted according to the video partition content characteristics and the wireless channel adaptation condition, and the access priority sequence is optimized in combination with the uplink and downlink performance and the frame type change. Dynamic regulation and control and synchronous updating of compression parameters are automatically completed, continuous transmission of video streams in a complex network environment is achieved, and the end-to-end consistency of video content and the integrity of picture data are improved.