478 results about "Scene analysis" patented technology

The embodiment of the invention provides a scene space three-dimensional model dynamic modeling method based on multi-modal data, and the method comprises the steps: fusing the feature information of a video sequence, point cloud data and IMU data, and building a space-time corresponding relation through employing a particle filtering algorithm. Scene semantic understanding is realized based on a Transform multi-modal scene analysis network, scene scores are calculated in combination with spatial density, depth occlusion and shape change features, and an optimal modeling algorithm is selected through a strategy network of deep reinforcement learning training. Meanwhile, scene dynamic features are analyzed by using a deep neural network and a graph neural network, adaptive grid subdivision and geometric constraint reconstruction of a dynamic region are realized, and a reconstruction threshold is dynamically adjusted according to a scene semantic type. According to the method, the three-dimensional reconstruction quality and efficiency of a large-scale complex scene are effectively improved.

The embodiment of the invention provides a scene space model adaptive modeling method based on multi-source data fusion. Complete scene information is constructed by fusing a video sequence, point cloud data and IMU (Inertial Measurement Unit) data. A deep learning network is adopted to carry out scene analysis, a scene complexity score is calculated based on a scale coefficient, a shielding degree and a geometric deformation coefficient, and intelligent matching of a modeling strategy is realized. Meanwhile, a scene dynamic area is predicted by using an optical flow algorithm, a geometric deformation field and a camera pose change, the modeling quality is monitored through reconstruction errors, and self-adaptive switching of a modeling algorithm is realized. The method effectively solves the problems of unstable precision and poor adaptability of a traditional modeling scheme in a complex dynamic scene, and improves the quality and efficiency of three-dimensional scene reconstruction.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention relates to the technical field of data processing, and discloses a BIM-assisted steel structure construction safety management early warning method, device and equipment. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition and space-time alignment processing on a steel structure construction site to obtain an engineering data stream; carrying out dynamic noise reduction and feature extraction on the engineering data flow to obtain a standardized multi-modal feature vector, and carrying out time sequence correlation analysis and mode mining to obtain a potential risk index set; performing multi-level threshold calculation and context sensitivity evaluation on the potential risk index set to obtain an adaptive early warning trigger matrix; performing multi-dimensional cross validation and causal chain analysis on the self-adaptive early warning trigger matrix to obtain a graded and classified risk assessment report; and carrying out scene analysis and response scheme generation on the graded and classified risk assessment report to obtain an executable multi-step emergency plan. According to the invention, the efficiency and accuracy of BIM-assisted steel structure construction safety management early warning are improved.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention provides a self-adaptive scene analysis and target generation method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: extracting the depth information of a scene image sequence, constructing three-dimensional point cloud data, calculating the distance and direction matrix between objects, and generating a scene space feature vector. Meanwhile, a semantic label graph is obtained through semantic segmentation, a semantic incidence matrix is constructed, and a scene semantic vector is calculated. Inputting the two into a variational auto-encoder to obtain a target feature vector, calculating an attention score based on the target feature vector, and generating a target description vector with a weight; and finally, target contour features are generated through a probability graph reasoning network, and a final target result is generated in combination with the regional attention graph. According to the method, the accuracy and efficiency of target generation are improved.

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

The invention discloses an intelligent cabin interaction method and system combined with digital twinning, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting the multi-dimensional perception data of a cabin, and constructing a layered cabin digital twinning model; performing scene analysis on the cockpit digital twin model, extracting driver state features, predicting driver potential interaction demands, generating a multi-modal interaction strategy set in combination with the driver potential interaction demands and a current driving scene, and performing simulation evaluation on the multi-modal interaction strategy set to obtain an interaction evaluation index set; performing multi-objective decision optimization under the driving safety constraint based on the interaction evaluation index set, and determining and adaptively executing an optimal interaction strategy; and acquiring actual execution data and driver feedback data of the optimal interaction strategy, and incrementally updating the hierarchical cabin digital twin model. According to the invention, the interaction adaptability and the safety reliability of the intelligent cabin system in a complex driving scene are obviously improved, and an effective technical support is provided for realizing intelligent man-machine cooperative driving.

The invention relates to the field of computer vision and the field of image processing, in particular to a blind guiding scene recognition method based on a multi-modal visual large model. Comprising the following steps: data acquisition: acquiring image or video data of a typical blind guiding scene, marking, constructing a data set, introducing an image prompting mechanism, and dividing a training set and a test set; constructing a multi-modal visual large model which comprises an image coding module, a semantic prompt coding module, a visual language fusion module and a scene analysis semantic decoding module; the model is tested and optimized, and a multi-task loss function is adopted for optimization; a blind guiding auxiliary system is constructed, image acquisition, visual understanding and voice broadcasting functions are integrated, and a closed-loop process is realized. According to the method, the problems of strong target dependence, poor universality and weak semantic comprehension ability of the existing blind guiding identification technology are solved, accurate identification and semantic feedback of complex scenes are supported, and the intelligent level and the environment adaptability of a blind guiding system are improved.

The invention relates to the technical field of live broadcast, and discloses a live broadcast scene perception recommendation system, which is characterized in that a data acquisition module is used for acquiring multi-source data in a live broadcast process in real time; the scene analysis module is connected with the data acquisition module and is used for receiving and integrating the acquired data and analyzing the live broadcast scene; the user portrait construction module is used for tracking a behavior track of a user on a live broadcast platform in real time, and constructing a dynamic user portrait in combination with historical watching data, a collection record, a consumption record and an interactive behavior in current live broadcast; a recommendation strategy generation module generates a personalized recommendation strategy according to the result of the scene analysis module and the user portrait constructed by the user portrait construction module; the recommendation content pushing module pushes the recommendation content generated by the recommendation strategy generation module to the user; according to the method, personalized and precise recommendation is realized, and the watching experience and participation degree of the user are effectively improved.

The invention discloses a big data platform asset intelligent sensing method based on LLM and a customizable MCP, and the method comprises the steps: defining a standard resource library of a model context protocol, constructing a model context protocol server, integrating a plurality of scanning tools, and carrying out the automatic adaption; receiving a natural language demand input by a user, and calling a pre-trained LLM model; the scene analysis module converts a natural language demand into a standardized scene description object; the intelligent scanning module is used for describing an object according to a standardized scene, generating an optimal tool execution chain, executing a complete scanning task, obtaining a task scanning result and unifying formats of heterogeneous data in the task scanning result, and the conflict resolution module is used for removing conflicts; the multi-source data association module fuses network topology resources and flow data captured by a probe to construct a dynamic asset atlas including asset attributes, service dependence and vulnerability information, and the report generation module generates a structured report. The method can provide a global risk perspective and decision support.

The invention relates to the technical field of artificial intelligence, in particular to a spatio-temporal context extraction method and system for real world model training, and the method comprises the steps: extracting all image information and text semantic information through content feature vectors; structural information such as spatial layout of a scene and relative positions of people and objects in the collected image is extracted through the spatial structure feature vector, and feature information such as appearance, posture and action of a target object is extracted through the entity feature vector; obtaining a time sequence feature vector according to the content feature vectors, the spatial structure feature vectors and the entity feature vectors in a plurality of preset time periods, capturing multi-dimensional dynamic changes of the target physical place in a period of time, and analyzing the extracted vectors to obtain scene analysis feature data; all feature information is integrated to obtain the spatio-temporal context, the representation richness and comprehensiveness of the target physical place are improved, and the representation degree and adaptability of the real world model obtained through subsequent training to the target physical place are achieved.

The invention relates to the technical field of computer vision, and provides an image generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a description text of a target image; based on a large language model, performing scene analysis on the description text to obtain a scene graph corresponding to the description text; and based on a multi-modal image generation model, generating the target image by applying the description text and the scene graph. According to the image generation method and device, the electronic equipment and the storage medium provided by the invention, scene analysis is performed on the description text by virtue of the powerful natural language processing capability of the large language model, the scene graph corresponding to the description text is obtained, and the target image is generated in combination with the description text and the scene graph. And meanwhile, by referring to the text information for describing the text and the space and semantic information represented by the scene graph, the quality and semantic consistency of the generated image can be improved, so that a more accurate and reasonable image can be generated.

The invention relates to a source load time sequence matching typical scene identification and construction method. The method comprises the following steps: step 1, a multi-time scale wind-light-load scene generation method of source load matching time sequence characteristics; 2, constructing a typical scene set and an extreme scene set oriented to supply and consumption; step 3, typical example analysis, including wind and light load characteristic analysis, typical scene set construction, extreme scene set construction and guarantee supply and consumption oriented scene analysis; the method has the advantages that multi-time-scale modeling is achieved, dynamic changes are better reflected, supply and consumption demands are effectively balanced, and stability and economical efficiency are improved.

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

The invention discloses a video monitoring system for medical health big data privacy management, and relates to the technical field of video monitoring, the system comprises the following components: an image acquisition module, an environment and privacy management module, a data monitoring module, a face recognition module and a privacy display control module; through combination of a high-resolution and low-illumination camera of the image acquisition module and a scene analysis technology, automatic adaptation to different medical environments and illumination conditions is realized, and meanwhile, the environment and privacy management module automatically sets and adjusts monitoring strategies and camera parameters according to privacy levels of different areas, so that the safety of the environment and privacy management module is improved. The definition and availability of a monitoring picture are ensured, the labor cost is effectively reduced, in addition, the system is excellent in the aspect of protecting the privacy of the patient, acquisition of the privacy of the patient by unauthorized personnel is strictly limited through the technical means of reducing the monitoring sensitivity of a ward and other privacy areas, fuzzy processing and the like, and the safety of the patient is improved. And the privacy protection level of the medical place is greatly improved.

The invention discloses a user question answering method and device, equipment and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining the input information of a user; performing application scene analysis on the input information and semantic intention understanding on the input information by using a multi-level associated scene analysis model, and determining an actual application scene of the input information; calling a core element template preset for the actual business scene, and extracting core element information in the input information according to the core element template; and acquiring scene data, inputting the reference knowledge, the cue word and the scene data into the trained large language model as input data, and obtaining a reasoning result output by the large language model. According to the method, the data correlation problem that knowledge quoted by an existing LLM is inaccurate and incomplete is solved, association matching between the problem and business scene data is perfected, and therefore the reply effect of the LLM on scene related problems of all industries and fields is improved.

The invention relates to the technical field of video source switching, and discloses an intelligent video source switching system and method for a director switching station. The method comprises the following steps: acquiring an input video stream of a current video source through a director switching station, and performing N-gram feature vector calculation and inter-frame difference analysis to obtain video frame segmentation data; performing scene analysis on the video frame segmentation data to obtain a scene feature signature; performing spatial-temporal feature extraction and fusion on each video frame in the input video stream according to the scene feature signature to obtain a scene spatial-temporal fusion feature set; inputting the scene space-time fusion feature set into an LSTM-based lightweight time sequence convolutional network model to carry out video switching behavior prediction so as to obtain video switching behavior prediction data; and inputting the video switching behavior prediction data into a double-depth Q learning model based on a Markov decision process to carry out video source switching decision to obtain a video source switching decision result, thereby improving the intelligent video source switching accuracy of the director switching station.

The invention provides a function adaptive test method and system for an electro-tricycle controller, and relates to the technical field of tricycle function test, and the method comprises the steps: carrying out the scene analysis according to a road surface impact factor and an environment impact factor, and constructing a plurality of scene parameter groups; performing simulation modeling according to the vehicle attribute information and the plurality of scene parameter groups to generate a plurality of driving twin models; executing vehicle load simulation by using the plurality of driving twinborn models to meet a predetermined performance index and a predetermined risk threshold as a constraint, and determining a plurality of maximum adaptive loads; and constructing a scene-load matching library to execute a subsequent freight task. According to the invention, the technical problem that the maximum load of the tricycle cannot be accurately analyzed in combination with an application scene in an existing freight tricycle function test method, so that the adaptation degree of the maximum load and the scene is low, and the freight safety of the tricycle is influenced can be solved, and the purposes of ensuring the operation stability and safety of the tricycle and goods and improving the freight safety are achieved. And the service life of the vehicle is prolonged.

The embodiment of the invention relates to the field of voice control, and discloses an equipment control method and device, equipment and a storage medium, and the method comprises the steps: determining identity information and a target execution task from obtained control voice information sent by at least two users; performing scene analysis on each target execution task to determine target scene information; analyzing whether task conflicts exist among the target execution tasks or not by utilizing the identity information and the target scene information; when the task conflicts exist, screening the target execution tasks with the task conflicts as conflicting execution tasks, and determining the priority ranking of the conflicting execution tasks; and determining the working state of the corresponding target equipment according to the priority ranking of each conflict execution task. According to the equipment control method disclosed by the invention, the problem that multi-user voice control conflicts and cannot be coordinated is solved, multi-user voice cooperative control can be carried out in combination with the user identity information and the actual scene, and the user experience is improved.

The invention discloses a self-adaptive sound effect management method and system with a scene analysis module, and relates to the field of self-adaptive sound effect management, and the method comprises the following steps: S1, collecting environmental acoustic characteristics and user behavior data in real time through the scene analysis module; s2, dynamically extracting user hearing preference parameters and scene classification labels; s3, constructing a virtual audition sweet zone matched with the physical space; and S4, dynamically adjusting the phase delay and the energy distribution proportion of each frequency band, and generating an optimal loudness equalization curve meeting the multi-scene constraint. According to the method, adaptive matching of sound field characteristics is realized in a complex dynamic scene through a multi-level perception-optimization closed-loop architecture, acoustic feature extraction and physical space modeling are deeply fused, the technical boundary of traditional pure signal processing is broken through, and the problem of multi-target conflict is solved based on cross-modal collaborative learning and a global optimization algorithm.

The invention relates to the technical field of computer simulation, in particular to a simulation scene construction method and device based on an intelligent agent and a simulation physical engine, and the method comprises the steps that a target intelligent agent determines the type of the simulation physical engine according to scene demand information of a user, and calls a target simulation physical engine corresponding to the target intelligent agent; setting target scene parameters through the target agent, generating simulation scene data based on the target simulation physical engine and the target scene parameters, and performing scene analysis and rendering operation according to the simulation scene data through the target agent so as to generate a target simulation scene in the simulation sandbox; and extracting the key data of the simulation scene data, and converting the key data into the target format scene description data, so that a user performs real-time interaction with the simulation scene according to the target format scene description data, thereby improving the intelligent level and interaction experience of the simulation scene, greatly reducing the use threshold of the user, and improving the user experience. And the method can be well suitable for non-professional user groups in various industry fields.

The invention discloses a building facade three-dimensional surveying and mapping system and method based on an unmanned aerial vehicle and AI, and relates to the technical field of building surveying and mapping, and the system comprises a scene analysis module which is used for executing the information analysis of calibration scene information, and marking each surveying and mapping sub-region as a to-be-collected node; the acquisition planning module is used for calculating the node transition probability of each node to be acquired; the surveying and mapping search module is used for configuring a virtual unmanned aerial vehicle and performing selective iteration of surveying and mapping sub-regions by using pheromone concentration and heuristic factors; the clustering sharing module is used for carrying out three-dimensional surveying and mapping on the exterior facade of the building and establishing a shared information pool in a clustering manner; and the surveying and mapping optimization module is used for carrying out collaborative decision optimization on the local path and updating the acquisition path according to a collaborative decision optimization result. The technical problems of low surveying and mapping efficiency and large measurement error caused by difficulty in meeting large-scale and high-precision surveying and mapping requirements of modern buildings in the prior art are solved, and the technical effect of improving the surveying and mapping efficiency and precision of the buildings is achieved.

The invention discloses an intelligent shooting range training system based on real-time data acquisition, relates to the technical field of intelligent shooting range training based on real-time data acquisition, and is used for solving the problem that an existing intelligent shooting range training system based on real-time data acquisition only pays attention to a single aspect of a training effect. The system is difficult to provide personalized training suggestions according to specific conditions of trainees; and a traditional training method may lack scientific verification. Comprising a real-time information acquisition module, an information analysis module, a scene analysis module and an optimization processing module. The real-time information is acquired through the real-time information acquisition module, and the behavior and physiological data are analyzed by the information analysis module to obtain comprehensive and deep analysis, so that the pertinence and effectiveness of training are improved; the method comprises the following steps: identifying different scene mode factors and setting corresponding evaluation weights; personalized improvement suggestions are provided for trainees, the best physiological state can be kept, the training effect is comprehensively improved, and the training experience is optimized.

The invention particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention relates to the technical field of power grid identification, and discloses a county power distribution network multi-dimensional operation mode identification method, system and device based on typical scene analysis, and a medium, and the method comprises the steps: obtaining the historical operation data of a county power distribution network, carrying out the extraction of multi-dimensional features based on the preprocessed historical operation data, and obtaining the multi-dimensional operation mode of the county power distribution network; carrying out clustering analysis on the operation data of the power distribution network through an improved time sequence clustering algorithm, and determining a typical scene of each category; and constructing a county power distribution network operation mode recognition model, classifying the operation states of the power distribution network, constructing a mapping relationship between the operation mode and a planning decision, and generating an optimal planning scheme. According to the invention, precise recognition of the operation mode of the county power distribution network is realized, the recognition accuracy of the operation mode of the power distribution network is improved, and precise planning is supported; the power distribution network planning cost is reduced and the resource utilization efficiency is improved.

The invention discloses an unmanned aerial vehicle low-altitude photography image enhancement method and system. The method comprises the steps of data preparation, motion joint deblurring optimization, multi-source data registration, scene adaptive enhancement and image comprehensive enhancement. The invention relates to the technical field of unmanned aerial vehicle image enhancement, and realizes high-precision deblurring, precise registration and adaptive visual enhancement of an unmanned aerial vehicle image by fusing inertial measurement unit guided blurring kernel prediction, cross-modal graph structure matching and elastic transformation optimization and an enhancement strategy dynamic adjustment mechanism based on scene analysis. The image definition, the structure consistency and the scene readability are remarkably improved, and the method is suitable for image processing tasks in a complex low-altitude flight environment.

The invention discloses a first visual angle scene analysis method based on text supervision, and belongs to the field of image processing, in particular to the field of first visual angle visual scene analysis. According to the invention, a cross-modal prompt learning module is provided, visual and text learnable prompt vectors are introduced, and a visual language model pre-trained on third-person data is finely adjusted, so that the visual language model can be applied to a first visual angle image with a complex target relationship; in addition, the invention also provides a representation knowledge migration module, the feature level knowledge of the visual language pre-training model is distilled to the first visual angle encoder after fine tuning, the cross-modal association capability of the first visual angle model is improved, and thus the target in the first visual angle scene is accurately segmented based on the category text. The invention innovatively provides a first view scene analysis method based on text supervision. The corresponding segmentation pseudo mask is generated by using the category text of the first view image to relieve the problem of pixel-level annotation scarcity.

The invention discloses a violation snapshot method based on image recognition, which relates to the related technical field of traffic video monitoring and comprises the steps of environment perception, scene analysis, scene feature library establishment, multi-modal data acquisition, server model aggregation, model issuing and fine adjustment and violation behavior recognition. The invention further discloses a violation snapshot system based on image recognition. The violation snapshot system comprises an environment sensing module, a data acquisition module, a data processing center, a violation behavior recognition module and a snapshot and recording module. Different from a fixed snapshot strategy of a traditional method, the system can dynamically adjust the snapshot strategy according to real-time understanding of a traffic scene, the system can automatically improve the snapshot frame rate when detecting a high-risk scene in which a traffic accident is about to occur, and the snapshot frame rate can be automatically increased in a road section in which the traffic flow is small and the scene is simple. Snapshot resource consumption can be properly reduced; therefore, a complex and changeable traffic environment can be better dealt with, and the snapshot effectiveness and the resource utilization efficiency are improved.

The invention relates to the technical field of data processing, in particular to a digital twin scene efficient rendering method based on dynamic and static combination and a vertex shader, which comprises the steps of early-stage preparation and scene analysis, three-dimensional modeling and material mapping, vertex shader design and optimization, dynamic and static combination rendering process design, post-processing and optimization, and deployment and integration. The method has the beneficial effects that the static scene is quickly rendered by using the pre-rendered cache data, the dynamic element is dynamically rendered by combining the real-time data source and the vertex shader, and the dynamic element and the real-time data source are seamlessly combined by using the image fusion algorithm, so that the rendering speed of the digital twinning scene is remarkably improved, and the rendering efficiency of the digital twinning scene is improved. The real-time performance and interactivity of dynamic elements are ensured, natural fusion of static and dynamic scenes is realized through fine regulation and control of weight factors, a high-quality and vivid digital twinning rendering image is finally presented, and the user experience and the application effect are greatly enhanced.