214 results about "Video fusion" patented technology

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

The invention discloses a personnel positioning method and system based on a 3D Gaussian splash model and video fusion. The method comprises the following steps: firstly, acquiring input data of at least two visual angles through a multi-visual angle video stream acquisition module, and constructing an initial three-dimensional Gaussian model by utilizing a sparse point cloud initialization module; a time sequence dynamic tracking module is combined with an optical flow algorithm to realize cross-frame parameter updating of a Gaussian ellipsoid, and a time sequence consistency optimization module is adopted to suppress parameter drift; identifying a constructor bounding box by using a target detection module, and establishing a corresponding relation between pixels and three-dimensional coordinates through a three-dimensional-two-dimensional space matching module; three-dimensional coordinates are calculated through a multi-view fusion positioning module, and the positioning precision is improved through a multi-sensor fusion optimization module in combination with IMU data. A 3D Gaussian splash model is combined with multi-view geometry and time sequence optimization, high-precision personnel dynamic positioning without marking in a construction scene is realized, and the problems of tracking drift and shielding in a complex environment in a traditional method are effectively solved.

The invention relates to the technical field of computer vision and virtual reality, and discloses a three-dimensional video fusion method based on camera self-calibration and projection texture mapping, and the method comprises the following steps: S1, obtaining video data, and collecting at least one frame of two-dimensional image in a to-be-fused video stream; optionally, the two-dimensional image is preprocessed; and S2, calibrating internal reference of the camera, detecting linear features in the two-dimensional image by using an image processing algorithm, estimating the position of a vanishing point by using a least square method or other optimization algorithms based on the detected linear segment, and calculating an internal reference matrix of the virtual camera according to the optimized vanishing point position. The perspective relation between the video image and the surface of the three-dimensional model is determined through the vanishing point detection technology, accurate fusion of the video image and the three-dimensional model is achieved, the sense of reality of a virtual scene is improved, and the tedious camera calibration process and the complex three-dimensional reconstruction process based on a calibration plate are avoided.

The embodiment of the invention provides a multi-camera video fusion method and system for a robot with a body and a storage medium, and belongs to the field of image processing. The method comprises the following steps: performing time and space alignment on images based on a vision and inertia alignment model to generate a unified aligned image sequence; extracting multi-scale image features based on the uniformly aligned image sequence, and constructing a cross-view residual image for guiding attention fusion operation to generate a fusion image; performing privacy area identification on the fused image, and performing shielding processing on a privacy area to obtain a privacy protection image; and performing semantic compression on the privacy protection image, adjusting an image frame scheduling sequence according to network link state information, and outputting a video signal for controlling a decision. The method is oriented to whole-process optimization of space-time alignment, cross-view fusion and privacy protection processing in the multi-shot image acquisition process of the robot with the body, and video signals for control are output in a self-adaptive mode based on the link state.

The invention discloses a three-dimensional video fusion system, which can realize real-time alignment and registration of a video image and a three-dimensional model by integrating a video stream acquisition module, a sensor data acquisition module and an image processing algorithm, thereby improving the real-time performance and accuracy of the system. Through a depth image processing technology, visual consistency of a video image and a three-dimensional model is optimized, accurate butt joint of the image and the model is ensured, and a visual inconsistency phenomenon is eliminated. According to the method, the video image and the three-dimensional model data are registered by fusing sensor data such as a GPS (Global Positioning System), an IMU (Inertial Measurement Unit) and the like and combining an image processing algorithm, so that the video image and the three-dimensional model data are aligned in the same coordinate system, and spatial synchronization is realized. Accurate integration of sensor data enables the method to maintain high precision and high real-time performance in a dynamic scene.

The invention relates to the field of artificial intelligence cross-modal retrieval, and provides a video text cross-modal retrieval method and system based on spatio-temporal feature fusion. The method comprises the following steps: carrying out key frame sampling and time sequence partitioning on an input video, extracting static visual features through a spatial feature network, and extracting motion features through a time dynamic network; a self-adaptive gating fusion module is adopted to dynamically calculate spatial-temporal feature weights and perform weighted fusion; extracting text semantic features by using a pre-training language model; constructing a double-flow projection network to map video fusion features and text features to a unified measurement space, and optimizing a feature distance by adopting a contrast loss function containing difficult negative sample mining and intra-modal constraint; and outputting a retrieval result according to the cosine similarity sequence. The system comprises four units, wherein the gating fusion module is integrated with an FPGA acceleration circuit. According to the method, mAP (at) 10 is equal to 0.78 in a UCF-101 data set, the time sequence action retrieval accuracy rate is 92.8%, and the single video retrieval delay is 23 milliseconds.

The invention provides an adaptive contrast enhancement video fusion algorithm for a complex logistics scene, and the algorithm comprises the steps: extracting environment features from an initial video data set, analyzing the inter-frame illumination change and object movement speed through employing a convolutional neural network, determining that the current scene is a peak period or a low-illumination environment, and obtaining environment change perception parameters; extracting a key frame from the second video data set, separating a foreground object and a background by adopting an image segmentation technology based on deep learning, and performing detail retention enhancement on the foreground object to obtain a third video data set; real-time verification is carried out on the final video output, if the processing delay exceeds a preset threshold value, the resource distribution proportion is adjusted through a feedback mechanism, mode switching and enhancement processing are executed again, and the updated video output is obtained.

The invention discloses a front-end monitoring method and system based on vibration optical fiber and video fusion, and the method comprises the steps: employing an armored single-mode special optical cable sensitive to an acoustic signal as a sensor, and enabling the armored single-mode special optical cable to be disposed at the middle part and the bottom part of a rolling cage and a fence at two sides of a line or to be buried in the ground, so as to monitor an external vibration signal in real time. The cameras are installed on the vertical rod near the guardrail, the installation height is 3.5 m, and the cameras are arranged along the fence at intervals of 200 m so as to collect videos and images on site in real time. And identifying the vibration optical fiber signals, analyzing the probability that the vibration optical fiber signals are working conditions such as personnel, trains, motor vehicles, wind, rain, vegetation shaking and the like, and generating a vibration optical fiber alarm result. And performing deep learning related algorithm processing on the video and the image, identifying various objects in the scene, and generating a video alarm result. Therefore, by fusing the advantages of the vibration optical fiber and the video image, accurate identification and positioning of the intrusion behavior are realized, and the overall performance of the monitoring system is improved.

The invention discloses a natural resource supervision method based on three-dimensional GIS scene and video fusion, and relates to the technical field of intelligent natural supervision. Comprising the steps of collecting a video stream through a preset observation point, extracting a continuous frame sequence, and calling a space parameter set; performing dynamic region detection on the video frame sequence to generate change feature data; mapping the change characteristic data into a topological structure to be updated based on the projection matrix; calling a pre-constructed three-dimensional GIS scene model, and positioning an update area; according to the type identifier of the change feature data, executing an operation of deleting the associated geometry or constructing and fusing a new geometry; adding a state attribute label to the updated geometry, and storing an update model; the resource change rate is calculated, and when the change rate exceeds a preset industry threshold value, an early warning instruction is generated and pushed to the control terminal. And the space accuracy, the model updating efficiency and the early warning reliability of resource change identification in a complex natural scene are remarkably improved.

The invention discloses an audio and video fusion multi-modal evaluation method for depression detection, and relates to the technical field of intelligent technology and image detection. According to the method for realizing evaluation of the multi-modal depression evaluation framework, deep features in facial expressions and voice data can be effectively extracted, efficient fusion of multi-modal information is realized through an optimized cross-modal interaction mechanism, and the framework provided by the invention not only can capture space-time dynamic changes of facial micro-expressions, but also can realize multi-modal depression evaluation. According to the method and the system, the emotion characteristics in the voice signals can be extracted, so that the accuracy and robustness of depression detection are improved, the weight can be dynamically adjusted according to the importance of different modes through an adaptive multi-score fusion module (AMFM), more accurate depression severity evaluation is realized, and the accuracy and robustness of depression detection are improved. The problem that an existing method is insufficient in modal specificity modeling and information complementarity utilization is effectively solved.

The invention belongs to the technical field of audio and video fusion transmission, and discloses an audio and video signal fusion transmission method and device, equipment and a medium, and the method comprises the steps: extracting 512-dimensional semantic feature vectors of an audio and a video, and generating a joint feature matrix through a cross-modal attention mechanism; calculating a fusion feature flow based on a coefficient alpha output by the scene classification model; a quantum random number generator is used for generating a session master key Kmain, the session master key Kmain is packaged into an encrypted session key Kenc through a Kyber-768 algorithm, and frame-level dynamic encryption is carried out on the fusion feature flow; and selecting a GPMI Type-B wired protocol or wireless transmission based on QUIC according to the transmission environment. According to the invention, the transmission efficiency, the anti-interference capability and the anti-quantum-attack security are improved, and multi-scene requirements are met.

The invention relates to the technical field of data processing, in particular to an auxiliary driving device and system based on video image processing, and the device comprises a frame buffer queue with a multi-channel video collection module aligned with timestamps; a hierarchical calculation task decoupling module identifies task types in the frame buffer queue and marks priority labels, including a first priority label of a radar data analysis and distance superposition task, a second priority label of a vehicle profile generation task and a third priority label of a video fusion task, and outputs a data stream with priority labels; the calculation module generates a task processing result; the genetic algorithm optimization scheduling module generates a resource allocation strategy and controls the calculation module to execute task processing; and the output synchronous control module synthesizes a high-priority task result and performs frame rate synchronous output on a low-priority task. According to the method, the problem of real-time degradation caused by multi-path high-definition video stream processing is solved through task grading and dynamic resource scheduling, and the image output delay of the auxiliary driving scene is reduced.

The invention discloses a multi-source 5G message video cooperative monitoring method and system, particularly relates to the technical field of video monitoring, and is used for solving the problems of multi-camera calibration parameter misalignment and video fusion dislocation caused by thermal deformation asynchronism in the prior art. A temperature sensor is arranged on an industrial camera supporting structure, a thermal deformation three-dimensional displacement compensation vector is calculated, a preferential transmission object is marked according to the displacement gradient of an adjacent camera and a material heat conductivity coefficient threshold value, and the displacement vector is transmitted to an edge calculation node according to the priority through a 5G message. All displacement data are fused at a node end to dynamically correct space calibration parameters, a collaborative calibration matrix is generated, and finally, the matrix is utilized to perform real-time registration on multiple paths of video streams and perform workpiece defect detection, so that the geometric consistency of a multi-camera system in a thermal deformation environment is guaranteed, and the visual detection precision and stability are improved.

The invention provides a traffic data analysis method and device based on multi-channel video fusion, and relates to the technical field of traffic condition analysis, and the method comprises the steps: collecting a multi-channel traffic video stream set; standardization processing and down-sampling processing are carried out, and background identification removal is carried out on the multi-path traffic video frame set; arranging the multi-path traffic foreground image frame set according to time sequence information, carrying out traffic data analysis and abnormal target identification, and determining multi-path traffic operation parameters and abnormal traffic target information; space coding is carried out, and alignment fusion is carried out on the multiple traffic foreground sequence frame sets; and performing parallel tracking processing to obtain a target traffic data analysis result. Through the method and the device, the technical problem of low data processing efficiency caused by lack of an efficient multi-source data fusion mechanism in the prior art can be solved, and the accuracy and comprehensiveness of traffic data analysis are improved through a multi-channel video fusion technology and selection and deployment of monitoring nodes.

The invention discloses a target detection system based on radar and video fusion. The target detection system comprises a monitoring target identification module, a monitoring target tracking module, an image shielding analysis module, an image shielding processing module, an image shielding analysis module, a target speed monitoring module and a target speed analysis module. The method comprises the following steps: identifying a monitoring target in a monitoring area and an action track of the monitoring target, selecting a proper camera to track the monitoring target, judging whether tracking of the camera on the monitoring target is affected by shielding, if so, judging whether other cameras can be used for assisting, and if not, when the monitoring target leaves the shielding range, performing the tracking of the camera on the monitoring target. A monitoring target is recognized through image detection, the monitoring target is tracked through radar detection, and the continuity of the monitoring process of the monitoring target is ensured; according to the invention, when the video monitoring is shielded, the fusion of video detection and radar detection is utilized, so that the accurate monitoring of the target is realized.

The invention discloses an unmanned aerial vehicle inspection system and method for an intelligent street lamp based on multi-source video fusion, and relates to the technical field of unmanned aerial vehicle inspection, and the system comprises an inspection data collection module, an inspection image analysis module, an inspection mode switching module and an inspection task dynamic evaluation module. The inspection data acquisition module, the inspection image analysis module, the inspection mode switching module and the inspection task dynamic evaluation module are in communication connection with one another; wherein the inspection data acquisition module is used for performing flight inspection on street lamps in a target inspection area one by one through an unmanned aerial vehicle with a visible light camera, and setting image calibration array points to match the accuracy of inspection image acquisition; the inspection image analysis module is used for processing and analyzing the acquired image data; according to the invention, the problem that the inspection time consumption and the data processing burden are greatly increased in the existing multi-source image fusion processing mode is solved.

The invention relates to a fusion method of a video stream and a digital twin scene, terminal equipment and a storage medium. The method comprises the following steps: firstly, binding at least one specific view angle and a physical camera corresponding to the specific view angle; selecting the specific view angle in the digital twinning scene, and calling a video stream of the physical camera; then embedding the video stream as a surface texture into the digital twinning scene; and finally, analyzing the video stream decoding frame, extracting the parameter information of the current decoding frame, and dynamically adjusting the texture attribute of the digital twinning scene, thereby effectively improving the overall beauty and reality of the digital twinning scene after video fusion. The problems that after the video and the digital twinborn scene are fused, the whole shows a split feeling, and the relevance is poor are solved, and the user experience effect is improved.

The invention discloses a video fusion method and device, a storage medium and an electronic device, and relates to the technical field of smart home, the video fusion method comprises the steps that N videos collected by N image collection devices are acquired, the N image collection areas are located at different positions of a target area, the image collection angles are different, and N is a positive integer; n is an integer greater than or equal to 2; and carrying out fusion processing on the N videos to obtain a target video. By adopting the technical scheme, the problem that the same event cannot be visually observed through a plurality of cameras is solved.

The invention relates to the technical field of video processing, and discloses a three-dimensional video fusion method and application thereof in a video security system. The method comprises the following steps: processing a static scene and a dynamic scene, constructing a three-dimensional scene model, obtaining an optical flow field corresponding to each layer of image of an image pyramid, obtaining multi-scale optical flow information, and correspondingly matching image feature points of a video frame with image feature points of the three-dimensional scene model to generate a feature point matching result, the corresponding relation between the video frame and the three-dimensional scene model is found according to the feature point matching result, accurate position information is provided for subsequent fusion, the fusion weight is calculated according to the feature point matching result, the fused three-dimensional video is obtained through processing according to the fusion weight, and the fused three-dimensional video can be used in a video security and protection system. More comprehensive and accurate scene information can be provided, the precision and real-time performance of security and protection monitoring can be improved, and the requirement of a video security and protection system for high reliability is met.

The invention discloses a video abnormal event identification method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction of a target video, and obtaining a visual feature, an audio feature and a text feature; performing modal consistency processing on the visual features, the audio features and the text features to obtain a visual consistency feature, an audio consistency feature and a text consistency feature; and calling a preset mature student model to perform multi-scale time sequence processing on the visual consistency feature, the audio consistency feature and the text consistency feature to obtain at least one video fusion time sequence feature and a global weight value thereof, and obtaining an abnormal value according to the video fusion time sequence feature and the global weight value thereof. According to the invention, the comprehensiveness and accuracy of abnormal event recognition are ensured, the adaptability of the model to complex scenes and noise interference is improved, and the accuracy of anomaly detection is improved.

The invention discloses a speaking face generation video detection method and device based on multi-modal global and local collaboration, and the method comprises the steps: carrying out the framing and face region extraction preprocessing of a video, and enabling an input audio to be aligned with an input video frame in a time dimension; a speaking face generation video detection model is constructed and trained, an inconsistent region between video frames is focused on through a preset region attention smooth detection module and a difference capture-time frame aggregation module, and an original audio is input into an audio feature extraction module; carrying out consistency calculation on the input audio and video modal features through a preset audio and video fusion module, fusing the audio and video features so as to discriminate the forged generated video to obtain a video discrimination tag, and then calculating a loss training model; and finally, discriminating a to-be-detected video by using the trained model. According to the method, audio and video features are combined, so that the detection precision and robustness can be effectively improved.

The invention relates to the technical field of video processing, in particular to a video fusion method and system based on a large model. The method comprises the following steps: acquiring a video stream for picture segmentation, identifying a moving target for each segmented picture, predicting a moving target track, matching a minimum pixel region, and determining a key frame; extracting features, and focusing an identification target in the key area; generating a static scene, aligning the recognized target and the scene, obtaining a dynamic shielding relationship, and outputting fused video data; the system comprises a dynamic picture division module, a multi-modal feature extraction module and a dynamic fusion reconstruction module. The method comprises the following steps: identifying a moving target for each segmented image, predicting a moving target track, matching a minimum pixel region, determining a key frame, focusing an identified target in the key region, aligning the identified target and a scene, obtaining a dynamic shielding relationship, and outputting fused video data; trajectory changes of the moving target are processed, and ghosting and dislocation of the fusion result are avoided.

The invention discloses a low-illumination infrared visible light video fusion method based on an LUT and a Transform, and belongs to the field of infrared visible light fusion. Comprising dynamic object shielding extraction; static and dynamic parts are separated; dynamic target enhancement; performing time logic consistency processing; coding and decoding processing; style guidance and final fusion; and a target synthesis step. Through respective reconstruction of the dynamic part and the static part and time sequence consistency processing, the time sequence synchronization problem is effectively solved, and the reconstruction quality is improved. Particularly in multi-source video fusion, the optical flow module is used for accurately shielding the dynamic part of each video sequence, so that the influence of a dynamic background on static scene reconstruction is effectively reduced. And in combination with fine tuning of the large language model, the adaptability and fusion effect of the technology are further enhanced, and especially in a complex dynamic scene, the precision and stability of overall reconstruction are improved.

The present invention discloses a method and system for optimizing the picture quality of panoramic video fusion video, which relates to the technical field of video data processing. The method includes: integrating multiple sets of video image frame data based on the video image frame coding information, and then evaluating the picture quality of the integrated set of video sequence image frame data through a set of video picture quality evaluation indicators to obtain a set of video sequence image frame picture quality evaluation parameter sets; using a dual-channel video picture quality optimization method based on the set of video sequence image frame picture quality evaluation parameter sets to perform mapping processing on the set of video sequence image frame data, and then performing panoramic video enhancement fusion on the processed set of video sequence picture quality optimized image frame data according to the video image frame coding information to obtain a target panoramic optimized fusion video. Through the dual-channel video picture quality optimization, targeted picture quality optimization of the panoramic video is realized, the picture quality clarity and optimization processing efficiency of the panoramic fusion video are improved, and thus the panoramic video monitoring effect is ensured.

The invention belongs to the technical field of civil transportation aviation airport aircraft monitoring, and discloses a panoramic aircraft monitoring system and method based on video stitching and an electronic map. The method comprises the following steps: acquiring a vehicle position, a personnel position, aircraft information and flight dynamic information, and acquiring high-definition camera video stream data by a video gateway; performing video stitching processing on the acquired high-definition camera video stream data; the electronic map GIS server provides an electronic map service; carrying out panoramic video fusion processing on the electronic map and the spliced video, calculating the position information of the aircraft, and completing personnel management, vehicle management, flight dynamic information management and monitoring mode management; and reporting normal monitoring and exception handling conditions to the server through the client. According to the invention, a video fusion and electronic map function complementarity technology is adopted, all-weather panoramic video safety monitoring of the aircraft in different modes is realized, and invading personnel and vehicles can be actively found and alarmed.

The invention relates to the technical field of intelligent conference systems, and discloses a debugging-free audio and video fusion management and control system and method, and the method comprises the steps: carrying out the reverse deduction of a communication protocol of audio and video equipment through analyzing the rising edge features of an electrical signal and a radio frequency carrier modulation mode, and dynamically updating an adaptive rule library; the multi-modal scene perception engine fuses audio stream, video stream and environment stream data, and generates an adaptive scene strategy based on a multi-head attention mechanism; the credible recording enhancement system automatically binds the identity of a spokesman through a voiceprint clustering algorithm, and carries out block chain storage on recording contents according to speaking segments, so that plug and play of multi-brand wired / wireless equipment is realized, and different communication protocols can be automatically adapted without manual configuration; through real-time environment perception and dynamic weight distribution, the accuracy and fluency of conference scene switching are remarkably improved; and meanwhile, voiceprint dynamic registration and block chain evidence storage technologies are adopted, so that the judicial-level recording security is ensured, and meanwhile, the preparation work before meeting is greatly simplified.

The embodiment of the invention provides an audio and video fusion intelligent inspection method and system for a new energy station, and the method comprises the steps: converting the input of different modes into a discrete token sequence in a unified manner, and carrying out the deep interaction and fusion through a cross-mode Transform architecture, thereby effectively aligning features, and capturing the internal correlation between the modes. More importantly, in order to improve the recognition capability of rare faults, a fault prototype library is introduced as priori knowledge, and through an attention mechanism guided by a prototype, the model can actively compare and associate current inspection features with known typical fault modes. According to the design, the model is forced to learn more discriminative context sensing features, sparsity of training data is made up by effectively utilizing condensed fault knowledge, and fault diagnosis generalization ability and reliability of the model in a complex scene are finally improved.

The invention belongs to the technical field of intelligent traffic, and relates to a radar and video fusion dynamic space-time alignment road monitoring method, which comprises the following steps of: firstly, independently retrieving a radar monitoring target and a video monitoring target in a road monitoring area, and adopting a dynamic space alignment and multi-scale time synchronization mode to obtain a radar monitoring target and a video monitoring target; performing space-time alignment processing on the radar monitoring target and the video monitoring target, performing association matching on the radar monitoring target and the video monitoring target after space-time alignment, and judging whether association constraint conditions are met or not based on spatial position consistency, movement track continuity and appearance feature similarity; according to the method and the device, a target association result is adaptively output according to an actual monitoring scene, structured packaging is carried out on the target association result, a fusion target data packet is generated and output for monitoring feedback, and the radar and video fusion perception precision and robustness in a full scene are remarkably improved, so that the accuracy and reliability of road monitoring are effectively guaranteed.

The invention provides a video fusion method and device based on a three-dimensional scene and a storage medium, and belongs to the technical field of video surveillance, and the method comprises the steps: obtaining video frame data, including a video frame image and a corresponding timestamp, collected by a camera installed on sports equipment, obtaining space-time information of the sports equipment, comprising geographic data and corresponding timestamps; extracting image feature points of each video frame image, and constructing a mapping relation between the image feature points and the geographic data; based on the mapping relation, the pose data of the camera is iteratively solved by minimizing a re-projection error, and an objective function corresponding to the re-projection error comprises strong constraint optimization of geographic data; and determining a conversion parameter between a first coordinate system of the video frame image and a second coordinate system of a pre-constructed three-dimensional scene model based on the pose data to obtain a video fusion parameter, so as to fuse the multi-frame video frame data with the three-dimensional scene model, thereby improving the fusion effect of the three-dimensional scene and the video.

The invention relates to the technical field of virtual reality, and discloses a live-action three-dimensional scene-based video fusion method and electronic equipment, and the method comprises the following steps: obtaining the three-dimensional data of a live-action scene through a data collection device, and generating a three-dimensional scene model through a point cloud reconstruction or multi-view geometric method; optimizing the three-dimensional scene model, including grid simplification, texture mapping and light source information initialization, and obtaining the spatial position and radiation intensity distribution of a scene light source. The Passon equation is combined with the variational optimization technology, and precise modeling of illumination distribution of the complex three-dimensional scene is achieved; high-precision modeling and motion trail extraction of a dynamic target are completed through a deep learning segmentation and depth estimation technology; based on Lambert reflection and projection correction, the consistency of a dynamic target light shadow and a scene light field is ensured; in combination with multi-resolution light and shadow optimization and GPU acceleration, the real-time rendering performance is remarkably improved, and the problems of abrupt light and shadow, untrueness and insufficient real-time performance in the prior art are solved.