670 results about "Code module" patented technology

The invention discloses an intelligent geometric reasoning and semantic understanding method based on a three-dimensional large language model, which comprises the following steps of: acquiring point cloud data of a building component through three-dimensional scanning equipment, associating text information, and constructing a multi-modal three-dimensional large language model comprising a geometric perception coding module, a context semantic understanding module and a parameter efficient fine tuning module; a cross-modal contrast loss and task instruction fine tuning strategy is adopted in model training, and finally semantic recognition, attribute completion and historical background analysis results of the building components are output. The method is suitable for building heritage digital protection, intelligent building process monitoring and three-dimensional digital archive management, component function recognition precision and cultural semantic mining capability in a complex scene can be improved, and real-time semantic updating and interactive response of a dynamic construction environment are supported.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The invention discloses a sentiment analysis method based on prototype guide mode fusion and prompt enhancement, and constructs a multi-mode sentiment analysis network which comprises a multi-mode coding module, a prototype guide mode fusion module, a dynamic mode weight adjustment mechanism and a context prompt generation module. The method comprises the following steps: firstly, extracting semantic features of each mode by using a multi-mode encoder, and constructing a prototype feature library based on a labeled sample to describe typical representations of different modes under each category; and then, dynamically evaluating modal contribution through prototype similarity to realize modal adaptive fusion. Furthermore, a context prompt is generated according to a similarity retrieval result of the input sample and the prototype library, and the pre-training language model is guided to complete sentiment classification. According to the method, the problems of modal inconsistency, information redundancy, weak small sample generalization and the like can be effectively relieved, and the accuracy and robustness of sentiment analysis are improved.

The invention discloses an integrated sensor multi-modal data edge computing system optimized by a large model. The integrated sensor multi-modal data edge computing system comprises a data preprocessing module which obtains pre-training parameters of each modal data through the large model; the modal specific coding module optimizes attention weight through an encoder branch to determine key information of data of each modal, and advanced feature representation of each modal is obtained; the fusion strategy selection and execution module is used for calculating the correlation between the rest modes and the final core mode, and inputting the advanced feature representation of the mode with the strongest correlation in the rest modes and the advanced feature representation of the final core mode into a cross-mode encoder to generate the fusion feature representation of the fused modes; continuing to perform modal fusion operation until all modalities are fused, and generating final fusion feature representation; and the cross-modal coding and task processing module finally generates a multi-modal fusion model prediction result through cross-modal feature representation. According to the invention, the performance and adaptability of multi-modal data processing and the data processing efficiency are improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention provides a Chinese quintessence art gene decoding method and system based on cross-modal AI, and belongs to the crossing field of artificial intelligence and digital media art, and the method comprises the steps: S1, constructing a Chinese painting-music-text multi-modal data set; s2, inputting the traditional Chinese painting image into a visual encoder improved based on CLIP-ViT, and outputting a 512-dimensional visual Token sequence through a normalization module, a position encoding module and a Transform encoder; s3, inputting the visual Token sequence and the emotion label into a cross-modal adapter, and directly mapping the visual Token to a music hidden space by adopting a self-attention mechanism to obtain a music embedding vector; and S4, inputting the user parameters into the improved high-frequency fidelity generative adversarial network to generate Chinese traditional music audio conforming to five sound orders. According to the method, intelligent semantic communication between visual art and auditory art is realized.

The invention belongs to the field of star flash mouse communication, and discloses a star flash mouse low-power-consumption communication system and method based on Polar code and SLE collaborative optimization, the system adopts a layered architecture design, collaborative optimization of energy efficiency and performance is realized through deep coupling of a physical layer and a link layer, and the performance of the system is improved. The core of the system is composed of three functional units including a Polar code coding module, an SLE control engine and a cross-layer optimization interface, and a complete closed-loop control system is formed. According to the invention, through deep cooperation of the Polar code and the SLE mechanism, the overall power consumption is reduced by 42.7% compared with the traditional scheme; according to the Polar code dynamic construction method based on track prediction, an ultra-low bit error rate of 10 <-5 > magnitude can still be kept in a 2.4 GHz complex interference environment, and in combination with signal-to-noise ratio self-adaptive coding parameter adjustment, it is ensured that transmission delay within 0.5 ms is always kept from a low-speed office scene to a high-speed electronic sports scene; in the aspect of engineering implementation, the method has the capability of quickly adapting to hardware platforms of different manufacturers, and a complete solution with high performance and low cost is provided for large-scale commercial use of the satellite flash technology in the consumer electronics field.

The invention discloses a code generation method and device based on a natural language and a medium. The method comprises the following steps: obtaining candidate functions provided by a user based on a target system, and determining function demand information described by a natural language; determining target information from the feature information according to the function demand information; wherein the feature information is determined in advance according to candidate functions; and generating a cue word according to the function demand information and the target information, inputting the cue word into the large language model to generate a code module for executing the function demand information, and integrating the code module into the target system. According to the method, the feature metadata corresponding to the candidate functions of the target system is combined with the natural language information of the user to guide and generate the code module for executing the user-defined function requirements, so that the user is allowed to generate the code module which can be integrated into the target system through the natural language on the scene site; and the processing efficiency of the user function requirements is improved.

The invention discloses a reference video object segmentation method and system based on motion modeling and multi-modal interaction, and the method comprises the steps: taking a video sequence and natural language description as input, and generating a preliminary segmentation mask through a text coding and mask decoder; a Kalman filtering motion modeling module is introduced to predict the motion trail of the target object, and time sequence consistency optimization is carried out on the preliminary segmentation mask; fusing the historical track of the object and the action semantics in the semantic features on the basis of a key action semantic coding module to realize action semantic alignment and mask dynamic correction; the segmentation quality of the current frame is subjected to multi-dimensional scoring based on a representative frame screening mechanism, the representative frame is screened out to update a memory bank, and the long-term tracking stability is improved. According to the method, the problems of target drift, insufficient semantic alignment and memory pollution in a complex dynamic scene in the prior art are effectively solved, and the segmentation precision, robustness and semantic consistency are remarkably improved while the light weight of the model is kept.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention relates to the field of twitter recognition, in particular to a twitter classification method based on harmonic enhancement and time-frequency semantic joint modeling, which comprises the following steps: collecting twitter samples and carrying out noise reduction and standardized preprocessing, carrying out multi-scale convolution operation on Mel spectrograms by utilizing a layered acoustic encoder, extracting time-frequency features in combination with a channel attention mechanism, and classifying twitter classification results. The method comprises the following steps of: generating adaptive position codes through a dynamic time-frequency joint coding module, carrying out time-frequency mode modeling by combining a global-local interaction mechanism, introducing a semantic fusion module which comprises a frequency band pyramid unit, a harmonic enhancement unit and a time-frequency gating unit, realizing dynamic weighted fusion of multi-layer features, and carrying out time-frequency mode modeling through a global-local interaction mechanism. And inputting the fusion features into a classification layer, training a network by adopting a cross entropy loss function and a gradient descent algorithm, and outputting bird categories through a full connection layer, thereby solving the key problems of insufficient description of a non-stationary time-frequency mode, insufficient modeling of a harmonic structure, reduction of recognition performance in a complex noise environment and the like in the prior art.

The invention relates to the technical field of information, and discloses a digital right near-field real-time cancel-after-verification system based on edge computing, which comprises a space-time coding module used for generating a unique cancel-after-verification identifier according to the geographic position and time of a user, and transmitting the identifier to a dynamic weight module; a dynamic weight module which is connected with the space-time coding module, generates a right and interest distribution strategy based on a federated learning model and an optimization algorithm, and outputs the optimized strategy to a block chain fragmentation module; the block chain fragment module is connected with the dynamic weight module, dynamically divides a consensus network according to the real-time cancel-after-verification density, executes distributed verification in fragments and generates a cancel-after-verification authorization instruction; and the cancel-after-verification execution module is used for distributing cancel-after-verification tasks according to the authorization instruction and synchronously updating the multi-node inventory state. By adopting the technical scheme of gridding coordinate mapping, time window division and nonlinear hash of the space-time coding module, the technical effect that the verification identifier is unpredictable and uniquely bound is achieved.

The invention discloses a transformer substation scene three-dimensional semantic segmentation method fusing local geometry and global context, and the method comprises the steps: obtaining the three-dimensional point cloud data of a transformer substation scene, and constructing a semantic segmentation data set; a three-dimensional semantic segmentation model is constructed, and fusion features integrating local geometric information, global context information and neighborhood information are extracted through a down-sampling module, a position coding module, a local-global fusion module and a neighborhood propagation unit in sequence; integrating the multi-scale fusion features through an up-sampling propagation module, and outputting a semantic category prediction result by using a semantic segmentation head; designing a loss function, and training the model; and loading the trained three-dimensional semantic segmentation model to realize refined segmentation of the substation scene. According to the method, the understanding capability of the complex three-dimensional structure of the transformer substation is effectively enhanced, the segmentation precision of refined power equipment parts is remarkably improved, and reliable technical support is provided for intelligent operation and maintenance of the transformer substation.

The invention relates to the technical field of numerically-controlled machine tool diagnosis, and discloses a numerically-controlled machine tool fault diagnosis system based on machine learning. The system comprises a multi-source sensing data acquisition module for acquiring multi-dimensional sensing data such as vibration spectrum, spindle current waveform, temperature distribution, servo motor encoder feedback and the like; the operation feature coding module receives the multi-dimensional sensing data, extracts time domain statistical features and frequency domain energy distribution features, and generates a multi-source feature coding result; the incremental learning analysis module dynamically updates the feature weight through an incremental learning algorithm, and constructs an incremental training data set; the genetic optimization module optimizes the network structure and hyper-parameter configuration of the fault diagnosis model according to the incremental training data set, and generates optimized network structure parameters; and the integrated diagnosis decision module receives the current operation state data and the optimized network structure parameters, fuses diagnosis results of a plurality of base classifiers through an integrated learning algorithm, and outputs fault type classification signals.

The invention belongs to the technical field of smart tourism, and discloses a destination global tourism big data management platform based on data processing, and the platform comprises a sensing qualified point gathering module which collects global tourism data, constructs a geographic grid point network, sets a spatial grid point reconstruction rule, and carries out the automatic refining or merging of grid points; a track triggering mechanism is introduced, and when it is detected that the behavior frequency in the preset target area reaches a preset behavior frequency threshold value, fine-grained modeling is started to generate a geographic sensing structure; the behavior deconstruction coding module is used for analyzing continuous behaviors of tourists in a geographical perception structure and constructing a multi-dimensional situation coding vector; reconstructing a complete behavior chain of the tourist through a behavior jigsaw type deconstruction method and an implicit behavior compensation method; and a solid support is provided for deep analysis and intelligent service of global tourism data.

The invention relates to a power load spatio-temporal dynamic knowledge graph construction and load prediction method, which comprises the following steps of: constructing a text and digital sequence hybrid vector coding module, providing a hierarchical entity relationship joint extraction framework oriented to power system load data, constructing a Multi-Encoder-Bi-GRU-CRF power load entity recognition model, and constructing a power load entity model. Constructing a power load spatio-temporal dynamic knowledge graph in combination with a predefined relation rule base; meanwhile, time-space sub-graphs are divided, a space-time coupling self-adaptive adjacency matrix is constructed, and the space-time dependency relationship between nodes is quantified; and finally, combining the knowledge graph node embedded vector and the adjacency relation embedded vector, and jointly extracting the spatial feature and the time feature of the power load by adopting a space-time diagram convolutional neural network. Therefore, the load prediction algorithm provided by the invention not only can give full play to the advantages of multi-modal semantic integration and space-time modeling capability of the knowledge graph, but also can improve the load prediction precision, assist in realizing refined energy management of the power system and assist in making an optimal scheduling strategy, and has a good engineering application prospect.

The invention belongs to the technical field of education informatization and artificial intelligence, and discloses an education scene intelligent identification system based on multi-modal perception. The system is composed of a multi-source education scene data acquisition module, a multi-modal data preprocessing and semantic unified coding module, a high-dimensional time sequence feature extraction module, a multi-modal feature fusion module, a time sequence action intelligent identification module, a real-time misoperation detection and personalized feedback module, and an intelligent teaching plan dynamic generation and process evaluation module. The system is composed of a multi-level security data management and domestic reasoning card adaptation module and a teaching data visualization and intelligent decision support module. Through multi-modal data fusion, time sequence action recognition, edge end real-time reasoning and domestic AI accelerator card adaptation, a whole-process dynamic perception and intelligent feedback system is constructed, experiment teaching, process monitoring, intelligent scoring, teaching plan generation and data visualization closed loop are achieved, the cloud dependence and data safety bottleneck is broken through, and three-level platform linkage of cities, districts and schools is supported.

The invention relates to a small sample target detection system and method adaptive to an airport complex scene. The system comprises the steps that an image trunk feature extraction module extracts a multi-scale semantic feature map of an input image; the text embedding and coding module is used for coding and extracting an input text representing a category to obtain a semantic embedding vector; a category perception convolution kernel construction module extracts local visual features from the mesoscale feature map, and weights the local visual features to generate a category perception convolution kernel; a sliding convolution region matching module calculates the response intensity of each position and category perception convolution kernel in the multi-scale semantic feature map, and determines a center point based on the response intensity; the precise positioning module extracts a plurality of low-confidence threshold candidate frames to construct a candidate frame set, and selects the candidate frame closest to the center point from the candidate frame set as a target detection result; small target detection can be carried out by considering the detection speed, the positioning precision and the semantic generalization ability under the scene that samples are scarce and category features are easy to confuse.

The invention discloses a multi-modal image registration method and system based on deformation adaptation and computer equipment, and the method comprises the steps: collecting a plurality of groups of multi-modal images, carrying out the gray standardization, and constructing a diversified registration data set; building a registration network model comprising a pyramid coding module, a deformation adaptive module, a cross-modal interaction module and a registration parameter estimation module; inputting an image pair into the modules in sequence, respectively extracting basic feature mapping, deformation feature mapping and interaction enhancement feature mapping, and finally outputting an estimation conversion parameter matrix; a training process is supervised through a preset loss function, optimal network parameters are selected, and a trained registration model is obtained; in practical application, an image pair to be registered is input into the trained model, a conversion parameter matrix is obtained, and image registration is completed. The multi-modal image registration performance can be effectively improved, and the method still has good robustness and adaptability especially under the condition that serious geometric distortion and significant modal difference exist.

The invention relates to the technical field of artificial intelligence, in particular to an interaction system and method with an emotion dynamic evolution memory function, a medium and a processor. The interactive system comprises a sensing layer, a processing layer and a decision-making layer, the perception layer comprises a semantic text coding module and a fusion processing module; the semantic text coding module outputs the acquired audio as semantic features and high-dimensional features, and performs linear space conversion processing; the fusion processing module and the data output by the semantic text encoding module are linearly spliced to form multi-modal feature vectors, and the multi-modal feature vectors are classified into the current emotion state of the intelligent agent and the emotion state of the user; the processing layer is used for correcting the data output by the sensing layer; and the decision-making layer is used for carrying out information decision-making and storage on the data output by the processing layer. The technical problem that in the prior art, the number of labels is limited, undefined interaction modes are difficult to process, and consequently the character of a robot is limited is solved.

The invention relates to an automatic driving trajectory prediction method based on multi-source information fusion, which is characterized in that a deep learning technology is applied to construct a complete trajectory prediction method comprising a feature extraction module, a coding module, an information fusion module and a multi-modal trajectory decoding module. Wherein the feature extraction module is used for extracting vectorization representation of lane information and agent information in a traffic scene; the coding module generates various agent codes and lane codes based on the vectorization representation; the information fusion module integrates various codes to generate scene fusion codes; and finally, a multi-modal trajectory decoding module decodes the scene fusion code based on the MLP to generate a multi-modal prediction trajectory, and a final prediction trajectory is obtained according to the confidence of each trajectory. Compared with the prior art, the method can accurately model the complex interaction relationship between the intelligent agents in the traffic scene, thereby effectively improving the accuracy of automatic driving track prediction.

The invention discloses a test method and device, electronic equipment and a storage medium, and relates to the technical field of firmware updating, a change code module of to-be-tested firmware is analyzed, an associated influence code module and an influence factor are tracked, a change wave range is determined objectively and quantitatively, and manual subjective judgment is replaced; a test case set is generated according to the influence factor and a preset rule, it is ensured that the test focuses on an affected area, and the pertinence and precision of the test are improved; the test case set is executed in the pre-configured test environment, and test environment deployment can be automated. According to the method and the device, the manual intervention requirement of testers is reduced, a large amount of energy is not required to be spent on manually screening the use cases, the technical problems of low precision and lack of automation caused by manual selection of the test use cases are solved, and the technical effect that the test process is more efficient and more accurate is achieved.

The invention belongs to the field of robot control, and particularly relates to a mobile robot path optimization system and method supporting track mode switching, and the system integrates the operation scene and the real-time load state of a mobile robot through a coding module, and generates a navigation task seed containing a path cost vector, a navigation strategy identifier and a scene conversion point parameter; and the central scheduler dynamically selects and calls a corresponding track navigation strategy sub-model or a flat ground navigation strategy sub-model according to the navigation task seed, and generates an executable track control instruction by combining with a space-time reservation table for realizing multi-vehicle conflict avoidance and resource scheduling based on a continuous value reservation weight. According to the method, load-adaptive path planning and multi-vehicle efficient cooperation are realized, smooth and seamless navigation switching between a track and a flat ground scene is guaranteed through the switching prediction sub-model, and the working efficiency and the system reliability in a mixed complex environment are remarkably improved.

Methods and systems for improving modernization of legacy software using an intelligent discovery platform are described herein. A client-based agent may generate metadata regarding the received legacy software. The code metadata may be analyzed by a code classifier module, which computes a plurality of score factors from the metrics from the legacy software metadata using a knowledge base from a modernization platform. The classified code metadata may be used by a project-specific model to derive a plurality of sub-scores based on the plurality of score factors associated with the legacy software. An analytics engine may then identify a code module from the legacy software having a greatest derived vulnerability score factor. A graphical interface including reconstructed code, corresponding modern code, and an explanation of vulnerabilities may then be generated by the analytics and reporting component for the identified code module.

The invention relates to a vision-semantic collaborative perception method and system for an automatic driving vehicle. Comprising a visual-semantic feature coding module and a visual-semantic feature fusion module. The visual-semantic feature encoding module comprises a text semantic encoder and a visual space-time encoder; a visual space-time encoder extracts single-frame semantic and structural features based on a 2D visual encoding model, and a 3D visual encoding model is adopted for modeling to form 3D frame-level fusion visual perception features; and the visual-semantic feature fusion module comprises a time converter, a cross converter and a context enrichment converter, and is used for adaptively adjusting a fusion weight based on a dynamic weight fusion strategy to generate required visual-semantic feature fusion information. According to the invention, the multi-modal weight is automatically adjusted, the environment perception error and decision delay are reduced, the adaptability and response precision of the automatic driving vehicle to the trunk line logistics complex traffic scene are improved, and the safe and stable operation of the intelligent driving system in the complex road environment is ensured.

The invention provides a text retrieval-oriented adaptive length embedding method and system, and the method comprises the steps: encoding an original document into a high-dimensional embedding vector by using a trained embedding model, and obtaining an original document embedding matrix X belonging to Rn * d; carrying out matrix learning transformation on the embedded vector through a transformation matrix fitting module to obtain a transformed embedded vector; inputting the converted embedded vector into a hybrid coding module for hybrid coding, dividing the converted embedded vector of each document into a fixed-length dense part and a variable-length sparse part, dynamically adjusting the length of the sparse part according to the semantic complexity of the document, and then performing similarity calculation by combining the dense part and the sparse part to obtain a similarity value; and thus, self-adaptive text retrieval is realized. According to the method, the resource utilization efficiency of the system is remarkably improved, and the retrieval accuracy and robustness are also ensured. And the method is particularly suitable for a large-scale retrieval system and an application environment with strict requirements on storage and computing resources.

Methods and systems for improving modernization of legacy software using an intelligent discovery platform are described herein. A client-based agent may generate metadata regarding the received legacy software. The code metadata may be analyzed by a code classifier module, which computes a plurality of score factors from the metrics from the legacy software metadata using a knowledge base from a modernization platform. The classified code metadata may be used by a project-specific model to derive a plurality of sub-scores based on the plurality of score factors associated with the legacy software. An analytics engine may then identify a code module from the legacy software having a greatest derived vulnerability score factor. A graphical interface including reconstructed code, corresponding modern code, and an explanation of vulnerabilities may then be generated by the analytics and reporting component for the identified code module.

The invention relates to an emotion recognition method and device based on multi-modal consensus and diversity decoupling. The method comprises the following steps: firstly, collecting multi-modal input data including language, vision and audio signals and carrying out corresponding preprocessing; then, constructing a multi-modal consensus and diversity decoupling emotion recognition model which comprises a multi-modal decoupling coding module, a prototype-Gram unification module, a feature enhancement module, a diversity classification module and an emotion prediction head; then, inputting the preprocessed multi-modal input data into the multi-modal consensus and diversity decoupling emotion recognition model, and performing model training optimization based on a total loss function formed by emotion prediction task loss, decoupling loss, unified target loss and diversity loss; and finally, inputting the multi-modal data to be recognized into the trained multi-modal consensus and diversity decoupling emotion recognition model, and outputting an emotion recognition result. And the accuracy, robustness and interpretability of the multi-modal emotion recognition system are improved.

The invention discloses an equipment invariance enhanced multi-modal deep learning model and application thereof, and the model comprises an input module, a coding module, a modal fusion module, a classification module, an equipment adversarial branch module, an output module and a final total loss function. Extracting features through a coding module to obtain audio features and text features; after the audio features and the text features are processed by the modal fusion module to obtain final joint representation, the classification module processes the final joint representation to obtain classification results and corresponding probabilities, and the classification results and the corresponding probabilities are output by the output module; the equipment confrontation branch module enables an audio encoder to confront an equipment classification head in a training stage; and finally, introducing the total loss function in a training stage to optimize the model. The model provided by the invention has the capability of realizing high-accuracy identification on various respiratory system diseases on the premise of not depending on acquisition equipment of a specific brand, and shows excellent generalization performance and robustness in multi-equipment and multi-center data.

The invention discloses a cloud-edge collaborative video content intelligent analysis and understanding system, which belongs to the technical field of video intelligent analysis, and comprises an edge intelligent sensing module, a semantic feature coding module, a cloud deep understanding module and a collaborative decision module, the coding module adopts Riemannian manifold mapping and quantum heuristic coding to realize efficient compression, the cloud module realizes deep semantic understanding through a graph neural network, the collaborative decision module constructs a closed-loop feedback mechanism to dynamically optimize parameters of each module, and the four core modules are deeply coupled to form a collaborative system. According to the method, cloud edge capability complementation, resource optimization configuration and continuous improvement of system performance are realized, the defects of a traditional scheme in the aspects of cloud edge cooperation capability, semantic understanding depth and adaptive optimization are effectively overcome, and an efficient, real-time and accurate technical scheme is provided for intelligent video analysis.