51 results about "Affinity matrix" patented technology

The invention provides a video anomaly detection method and system based on semantic and amplitude depth collaboration, and the method comprises the steps: extracting a visual feature sequence of a video frame; enhancing the feature vector of the corresponding time step in the visual feature sequence by using the amplitude and energy of each time step to obtain an enhanced feature sequence; calculating a semantic similarity matrix of the enhanced feature sequence; calculating a sparse affinity matrix according to the amplitude of each time step in the visual feature sequence; fusion is carried out to obtain a final attention weight; and performing weighted aggregation on the value vector of the enhanced feature sequence by using the final attention weight, and inputting the value vector into a classifier to obtain a frame-level anomaly score so as to realize video anomaly detection. According to the method, through one-time parameter-free feature engineering, deep collaboration of semantics and amplitude information is forcibly realized on a model architecture level, and the problem that an existing weak supervision video anomaly detection method is insensitive to feature amplitudes is solved.

The invention discloses an equipment manufacturing energy consumption and state visual monitoring method based on an industrial internet of things, solves the problem of passive fixation of an industrial monitoring visualization technology in the prior art, and realizes autonomous and intelligent generation of an optimal visual view matched with a real-time working condition. The method comprises the following steps: collecting industrial field multi-source heterogeneous data in real time, abstracting a monitoring object as an entity node, and constructing a feature vector containing static and dynamic attributes; constructing and updating a dynamic knowledge graph based on nodes and features, wherein relation edges of the dynamic knowledge graph comprise dynamic edges generated by static connection and real-time analysis; inputting the atlas into a pre-trained multi-attention graph neural network to obtain a display priority score and a visual affinity matrix of each node; calculating coordinates of all nodes on an interface through a layout optimization algorithm in combination with the priority and the affinity matrix; and generating a visual drawing instruction according to the coordinates and the node attributes, and driving a display device to render a monitoring interface.

The invention provides a text travel scene adaptive illumination dynamic regulation and control method and system based on personal perception, and relates to the technical field of scene perception, and the method comprises the steps: capturing visitor multi-dimensional behavior data and scene environment state data in real time through a distributed sensor network; constructing a body perception model fusing the visitor spatial position, the moving speed, the staying duration and the physiological wake-up degree, performing space-time correlation coding on the multi-dimensional behavior data, and generating a body perception feature vector; establishing a dynamic affinity matrix based on the feature vector and scene semantic information, and mapping the dynamic affinity matrix to a spatial topological structure of lighting equipment to form a regional lighting coupling map; according to the map node weight distribution and visitor perception state clustering result, generating a partition cooperative illumination regulation and control instruction through a multi-objective optimization algorithm; and carrying out online evolution on the model by utilizing the regulated and controlled visitor behavior track and physiological index change. According to the invention, accurate matching of the illumination environment and the visitor immersion state can be realized, and the experience quality of a text travel scene is improved.

The application discloses a kind of city area photovoltaic cluster dynamic grid division methods based on space-time coupling tensor, comprising: synchronously collecting the active power time series and geographic coordinates of all photovoltaic nodes in city area;Build the heterogeneous space adjacency matrix considering electrical topology constraint, calculate the static space correlation degree between any two photovoltaic nodes;Build the time-shift cross-correlation matrix that captures the moving characteristics of weather system, quantify the dynamic time correlation degree between nodes;Perform space-time feature tensor fusion, obtain global space-time affinity matrix;Photovoltaic nodes are divided using improved spectral clustering algorithm, and the optimal grid number is adaptively determined by contour coefficient, finally generate photovoltaic cluster grid division scheme at this moment;According to grid drift threshold, trigger grid reconstruction.The application can effectively consider physical topology constraint and meteorological time delay characteristics, realize the accurate aggregation of photovoltaic cluster, significantly improve the accommodation capacity and regulation flexibility of distribution network to distributed energy.

The invention provides a task scheduling method and device, equipment and a medium, and the method comprises the steps: building an inter-task communication matrix based on the task communication traffic between every two sub-tasks in a plurality of to-be-scheduled sub-tasks; constructing a network affinity matrix based on the bandwidth and the congestion index between every two task processing nodes in the plurality of task processing nodes to be scheduled; performing clustering processing on the plurality of subtasks based on the inter-task communication matrix to obtain a task cluster set, and performing clustering processing on the plurality of task processing nodes based on the network affinity matrix to obtain a node cluster set; and according to the resource demand information of each sub-task and the resource supply information of each task processing node, mapping the task cluster set and the node cluster set, and determining a mapping result between the plurality of sub-tasks and the plurality of task processing nodes. Therefore, the task cluster with dense communication can be mapped to the node cluster with high network affinity, the communication overhead is reduced, and the end-to-end time delay of the task is reduced.

The invention discloses a power distribution network dynamic partitioning method based on a composite electrical distance. The method comprises the steps that the composite electrical distance between each pair of buses in a power distribution network is calculated; constructing an affinity matrix according to the composite electrical distance, and constructing an undirected weighted graph based on the affinity matrix; calculating a Laplacian matrix for the undirected weighted graph to obtain a preliminary partition of the power distribution network; a mixed integer linear programming method is adopted to optimize the preliminary partitioning result, it is ensured that each sub-region meets set constraints, and optimized partitions are output; topological change of a power distribution network and output fluctuation of a distributed power supply are monitored in real time, and when the change exceeds a preset threshold value, a dynamic adjustment mechanism is triggered, and a partition adjustment strategy is executed. According to the method, on the basis of comprehensively describing the electrical similarity of the bus, the engineering feasibility and the running state adaptability of the partitioning result can be realized by combining constraint optimization and a dynamic adjustment mechanism.

The invention relates to the technical field of power system automation, and particularly discloses a power network fault early warning method based on a large model, and the method comprises the steps: directly mapping the data of a high-frequency power sensor into a time sequence soft prompt vector which can be understood by the large model through a lightweight encoder and a linear projection technology; deep alignment of continuous signals and discrete semantics is realized, and information loss and efficiency bottleneck caused by numerical textualization are avoided. And then, directionally retrieving an operation and maintenance knowledge base by using the soft prompt vector, introducing a cross-modal affinity matrix to construct a semantic gating mechanism, dynamically screening and enhancing retrieved key knowledge fragments according to real-time waveform characteristics, and automatically inhibiting redundant text noise irrelevant to the current working condition. And finally, deep attention interaction and reasoning are performed on a composite sequence formed by an instruction, enhanced knowledge and soft prompt through a large model, a control signal containing fault classification and disposal suggestions is output, and real-time accurate early warning considering high-frequency signal sensitivity and expert knowledge logicality is realized.

The invention discloses a multi-view image fusion representation learning method, device and equipment based on double-layer optimization, and relates to the technical field of machine learning. The method comprises the following steps: splicing feature matrixes of a multi-view data set to obtain a multi-view data matrix; calculating an initial affinity matrix of each view, and initializing a fusion affinity matrix, a view weight set, potential feature representation and a shared mapping matrix; a bilayer optimization model is constructed to learn potential feature representations. The model comprises an objective function of an upper-layer optimization task for updating potential feature representation and sharing a mapping matrix under the constraint of a fixed fusion affinity matrix, and an objective function of a lower-layer optimization task for updating the fusion affinity matrix and a view weight set under the constraint of the fixed potential feature representation. And executing alternative optimization iteration: executing upper-layer optimization task update and first, and then executing lower-layer optimization task update and until ending. And outputting the final potential feature representation for executing the task.

The invention relates to the technical field of image processing, in particular to an unsupervised PolSAR classification method based on comparative learning, and the method comprises the steps: obtaining original PolSAR image data to form multi-view data; performing superpixel segmentation on the image to generate multi-view primitive samples in one-to-one correspondence with superpixels; obtaining potential feature representation of each view, and generating a self-representation coefficient matrix of each view through a forward self-representation module; applying cross-view relation consistency constraint by comparing loss, and optimizing a self-representation coefficient matrix of each view; fusing the self-representation coefficient matrixes of all the optimized views to generate a uniform affinity matrix, and executing spectral clustering to obtain clustering labels of element samples; according to the mapping relation between the element sample and the pixel established by the superpixel segmentation, the clustering label is mapped back to the pixel level, the image classification result is obtained, and the classification accuracy and robustness are remarkably improved.

The application provides a deep multi-view clustering method and system based on collaborative training, relates to the fields of data mining and machine learning, and specifically includes the following steps: obtaining multi-views to be clustered, and pre-training a deep self-encoding model for each view; calculating an affinity matrix of each view, setting an initial weight of each view and an initial dynamic learning factor between the views; performing iterative formal training on the deep self-encoding model of each view, updating the weight of each view and the dynamic learning factor between the views until a preset iteration stopping condition is met, and outputting final hidden layer features and clustering centers; and obtaining a clustering result based on the final hidden layer features and the clustering centers; the application designs a deep multi-view weighted graph embedding clustering algorithm based on dynamic collaborative training, adopts a dynamic collaborative training idea for multi-view data, deeply mines complementary information between the multi-views, and improves the clustering effect for multi-view data sets.

The invention discloses a pathological image segmentation method and system based on coevolution generation type difficult sample mining, and the method comprises the steps: constructing a mask synthesis engine guided by biological information, and generating a cell nucleus mask through introducing a cell affinity matrix and structure prior based on a graph theory; establishing a segmentation-oriented adversarial renderer, and aligning the generated image with a real image in a feature space by using a multi-layer feature discriminator; implementing a closed-loop co-evolution strategy, dynamically identifying vulnerability categories by using performance feedback of the segmentation model, and guiding a generator to carry out adaptive difficult sample mining; and obtaining a cell nucleus segmentation result through alternate mutual promotion of the generator and the segmentation model and regression fine tuning of real data. According to the method, the problems that in existing small sample learning, generated data lacks biological rationality and visual fidelity cannot be converted into segmentation performance are solved, and the segmentation precision and generalization ability of the model are remarkably improved under the condition of extremely few labeled data.

The embodiment of the invention provides an incremental multi-view data clustering method and system based on a cross-time consensus graph, and belongs to the technical field of artificial intelligence. The method comprises the following steps: integrating historical knowledge with a consensus affinity matrix of current view information and learning time based on kernel induction expression, and constructing a dynamic consensus graph; spectrum embedding and discrete label learning are carried out; and alternately optimizing the consensus affinity matrix, the orthogonal rotation matrix, the consensus spectrum embedding matrix and the discrete clustering label matrix of the moments in the dynamic consensus graph by adopting staged updating variables. The method can efficiently adapt to incremental environment application, and is better in clustering precision, higher in calculation efficiency, higher in time sequence stability and better in robustness.

The invention discloses a multi-view agricultural image clustering method based on enhanced multi-order similarity learning, and relates to the technical field of image clustering, and the method comprises the following steps: preprocessing image data, constructing an initial affinity matrix which is used for representing the affinity between data points, the method comprises the following steps: capturing a local structure and a neighborhood relationship of data points through first-order similarity and second-order similarity, stacking and rotating all affinity matrixes into a third-order tensor, constraining by using a weighted tensor Schatten-p norm, and optimizing all modules in a unified optimization framework. According to the method, through parallel mining of multi-order similarity, weighted tensor Schatten-p norm optimization and unified spectral clustering fusion, multi-view data complementary features are fully utilized, clustering precision and robustness are improved, and reliable technical support can be provided for precise agricultural application such as crop health monitoring and pest and disease damage detection.

The invention discloses an insulator defect feature extraction method and device based on multi-centroid transfer learning, and the method comprises the steps: collecting insulator defect image data sets of various environments based on the environment influence factors of an insulator; dividing the insulator defect image data set in combination with the defect type of the insulator to obtain a source domain defect image set and a target domain defect image set in different environments; obtaining a source domain affinity matrix and a target domain affinity matrix through similarity analysis, calculating a corresponding Laplacian matrix, and obtaining sample clustering results corresponding to the source domain and the target domain; according to the sample clustering results corresponding to the source domain and the target domain, performing spectral clustering on the samples of each insulator defect type to obtain a source domain class mass center and a target domain class mass center; and carrying out transfer learning between the source domain and the target domain through the source domain centroid and the target domain centroid to obtain a feature projection matrix so as to realize defect feature extraction which is not influenced by scene change.

The invention discloses a malignant tumor image fusion analysis method and system based on graph structure consensus synergy, and the method comprises the steps: receiving multi-modal medical image data, extracting an initial high-dimensional feature vector, and projecting the initial high-dimensional feature vector to a plurality of independent potential semantic subspaces; a feature influence value and a dimension discrimination score are calculated through a dual significance evaluation mechanism, node features are weighted, edge weights are modulated, and a sample specificity initial multi-path diagram is constructed; after node features are linearly expanded, an affinity matrix is generated and sparsified, and a robust optimization graph is output through graph structure enhancement and contrast learning; coding each plane feature by adopting a graph attention network, and generating a fusion feature through high-discrimination feature splicing and weighted summation of other features; and constructing a global aggregation graph by taking the fusion features as nodes, and aggregating global information to output a diagnosis result. The system correspondingly comprises a multi-modal feature extraction unit, a multi-modal feature analysis unit and the like, multi-modal image semantic synergy and structure optimization are achieved, and malignant tumor diagnosis accuracy is improved.

The invention belongs to the technical field of target detection, and relates to a target detection method and device based on spatial semantic similarity knowledge distillation and a medium. Respectively inputting the image samples in the training set into a teacher model and a student model for feature extraction, and outputting a teacher feature map and a student feature map; obtaining a semantic similarity matrix and a spatial distance similarity matrix of each feature map; fusing the semantic similarity matrix and the spatial distance similarity matrix of each feature map to obtain a global affinity matrix of each feature map; generating a local neighborhood mask for the global affinity matrix of each feature map, and obtaining a spatial semantic similarity matrix of each feature map; according to the method, target detection identification is carried out based on the teacher feature map and the spatial semantic similarity matrix thereof and the student feature map and the spatial semantic similarity matrix thereof, target detection distillation loss is constructed, a student model is trained, target detection is carried out by using the trained student model, and the target detection precision is improved.

The invention discloses a city area photovoltaic cluster dynamic grid division method based on a space-time coupling tensor. The method comprises the steps that active power time sequences and geographic coordinates of all photovoltaic nodes in a city area are synchronously collected; constructing a heterogeneous space adjacency matrix considering the electrical topology constraint, and calculating the static space correlation degree between any two photovoltaic nodes; constructing a time-shifting cross-correlation matrix for capturing the moving characteristics of the weather system, and quantifying the dynamic time correlation degree between nodes; executing spatio-temporal feature tensor fusion to obtain a global spatio-temporal affinity matrix; dividing photovoltaic nodes by using an improved spectral clustering algorithm, adaptively determining an optimal grid number through a contour coefficient, and finally generating a photovoltaic cluster grid division scheme at the moment; and triggering grid reconstruction according to the grid drift threshold. According to the method, physical topology constraints and meteorological time delay characteristics can be effectively considered, accurate aggregation of photovoltaic clusters is realized, and the acceptance capability and regulation and control flexibility of a power distribution network to distributed energy sources are remarkably improved.

The invention discloses a data exchange task scheduling method and system based on resource prediction. A resource prediction model is constructed by acquiring a resource state change vector of a computing node in a preset time period, and resource availability in a future scheduling window is predicted; obtaining a to-be-scheduled data exchange task queue, and determining task characteristics of each task; based on the task characteristics and the resource availability prediction result, calculating the dominant demand degree of the task to the resource and the implicit attraction of the resource to the task, and carrying out task and resource matching to obtain a task-resource affinity matrix; and generating a scheduling strategy according to the affinity matrix to realize data exchange task scheduling. By introducing a resource availability prediction mechanism, the resource utilization rate is improved, scheduling conflicts and resource bottlenecks are avoided, and the overall performance of a data exchange platform is enhanced.

The application discloses a network (SGM-LGAT) for constructing a multi-level graph structure guided by superpixels for HSI classification. The main steps include the following: we create different stages of the adjacency connection graph of hyperspectral data from the superpixel representation by using a hierarchical superpixel segmentation algorithm to effectively utilize the spatial topology, and obtain the adjacency matrix and the affinity matrix of different stages. Then, we also design a lite GAT module (LGAT) to prune the graph by spectral sparsification to remove some edges in the graph that are not important for the task, and use the adjacency matrix to assign a unique attention coefficient to each remaining edge. In addition, the LGAT is used to concatenate with the spectral branch to update and fuse the features. The SGM-LGAT has small calculation amount and high efficiency, can mine the features of the HSI from the perspective of multi-scale hierarchy, and proves through a large number of comparative experiments on three benchmark datasets that the performance is competitive with other deep learning-based methods.

The invention discloses an incomplete multi-view clustering method for missed view recovery and subspace representation alignment. The method comprises the steps that S1, a model objective function is established, wherein the model objective function comprises a collaborative view recovery module CVR and a tensor subspace learning module TSL; s2, unifying a framework objective function: fusing a CVR module and a TSL module, introducing a hyper-parameter control HSIC item weight, and obtaining an ARSI-IMVC final objective function; s3, optimizing an objective function: introducing an auxiliary variable sum, constructing an augmented Lagrange function, and alternately updating each variable through an ADMM algorithm until a convergence condition is met; s4, generating a clustering result: carrying out iterative updating on all initial variables until a stop condition is met; after the subspace representation is obtained, an affinity matrix is constructed for spectral clustering. The method shows excellent clustering performance and stability under various data sets and different missing rates, and can be widely applied to unsupervised learning scenes depending on multi-view data, such as image recognition, text classification, biological information and the like.

The invention provides a two-channel different matching graph contrast learning method and system based on structural feature adaptive fusion enhancement, and the method specifically comprises the steps: calculating the structural similarity between nodes of an original input graph G, calculating the feature similarity between the nodes through employing a cosine similarity algorithm, and carrying out the integration based on the structure and feature information of the nodes, thereby obtaining a two-channel different matching graph. The fusion similarity between the nodes is obtained; distributing a homogeneity probability and a heterogeneity probability for each edge based on the fusion similarity, carrying out edge microsampling by utilizing a re-parameterization technique, and generating a homomorphic enhanced view and a heteromorphic enhanced view; performing dual-channel coding by using a low-pass encoder and a high-pass encoder to obtain node representations of the homomorphic enhanced view and the heteromorphic enhanced view; and designing a comparison loss function based on an affinity matrix and a marginal loss function based on clustering guidance, and training the model. According to the method, node representation with good learning performance can be obtained on homomorphic graph data and heteromorphic graph data.

This invention discloses a method and system for image comparison and retrieval of power equipment based on semantic object relationships. The method first acquires images of the power equipment and uses cosine similarity to obtain normal images for comparison from a standard library. An instance segmentation model is used to obtain an object mask set. This mask set is then mapped to a two-dimensional space to obtain an attention affinity matrix and calculate a semantic alignment score. The mask set is processed to construct an attribute graph, and a rotation-invariant spatial consistency score is calculated. Visual features and text embedding features are acquired, projected, added, and input into a Transformer architecture to obtain fused features. Text attributes are obtained through an encoder, and attribute similarity is calculated. Finally, a comparison score is calculated for intelligent fault diagnosis. This invention improves robustness to geometric transformations and semantic changes, making it suitable for scenarios such as intelligent operation and maintenance of power equipment, and significantly improving the accuracy and robustness of retrieval.

The invention discloses a knowledge-enhanced Chinese multi-mode hatred speech detection method, and belongs to the technical field of natural language processing. The method comprises the following steps: acquiring an image-text pair; extracting visual, scene text, text and hatred knowledge features in parallel through a feature representation module; inputting the text and the prompt template into a large language model to generate a background knowledge text, and splicing the background knowledge text with the text features and hatred knowledge features after BERT coding to form final text features; by means of a knowledge-guided cross-modal attention mechanism, an image-text affinity matrix generated by the CLIP is introduced into an attention map, multi-modal feature fusion is achieved, and the hatred category is predicted. According to the method, large language model background knowledge and a Chinese hatred vocabulary are introduced, the problems of Chinese context understanding, modal difference and hatred word recognition are remarkably relieved, the higher accuracy rate, precision rate, recall rate and F1 score are obtained on a CMMHS data set, and the method is suitable for Chinese multi-modal hatred content auditing on a social platform.

The present disclosure relates to a photoswitchable azobiaryl compound, a photoswitchable affinity ligand, a photoswitchable affinity matrix, the use of a photoswitchable compound, a photoswitchable affinity ligand, or a photoswitchable affinity matrix for isolating and / or purifying a target molecule, a method of isolating and / or purifying a target molecule, and a process for the preparation of a photoswitchable azobiaryl compound.

The invention relates to the technical field of mode recognition, in particular to a multi-view multi-target association and tracking task framework under a real world dynamic platform. The framework comprises the following modules which are executed in sequence: a data input and initial feature extraction module, a cross-view shape and attitude estimation module, a similarity and affinity matrix calculation module, a real-time multi-view feature synchronization module, an association and tracking execution module and a model joint optimization module. The framework can solve the technical problems of low association precision and unstable tracking in a real dynamic scene.

The invention belongs to the technical field of data analysis, and particularly relates to a tracing and analyzing method and system for adhesive tape production process data, and the method comprises the following steps: S1, obtaining multidimensional process parameter data containing timestamps and multisource heterogeneous batch identification data in the adhesive tape production process; aiming at the multi-dimensional process parameter data and the batch identification data, respectively calculating a time sequence kernel matrix based on dynamic time bending and a classification data kernel matrix based on semantics, and carrying out multi-kernel fusion to obtain a uniform affinity matrix; and S2, taking each data point as a vertex, defining a vertex subset of which the affinity between any two vertexes in the affinity matrix is higher than a first preset threshold and the scale is N as a hyperedge, and obtaining a hypergraph representing the high-order relevance of the production process. The method has the beneficial effects that when a fault occurs, reverse reasoning is carried out on a state transition path by utilizing a belief propagation algorithm, and tracing from a fault phenomenon to key influence factors is realized.

The invention discloses a heterogeneous computing power dynamic scheduling method and device based on set communication, equipment and a medium, and relates to the technical field of cloud computing, and the method comprises the steps: building a communication link between each node in a heterogeneous computing power cluster and a controller through a communication connector, so as to enable the controller to send a resource detection instruction to each node, the resource information of the computing power unit in each node is obtained; sending a communication test instruction to the computing power unit by using a set communication algorithm, and determining a communication index matrix of the computing power unit based on a communication operation type of the communication test instruction, so as to construct a topological graph based on the resource information and the communication index matrix; determining a candidate computing power unit set of the target task from the topological graph, and constructing an affinity matrix about the candidate computing power unit set; and determining a target computing power unit set by using the affinity matrix, executing the target task by using the target computing power unit set, and dynamically updating the topological graph based on the target computing power unit set. According to the invention, communication compatibility and dynamic scheduling of heterogeneous computing power can be realized.

This invention discloses a multimodal information collaborative enhancement method and system for target parsing. The method first dynamically extracts local features from the original image from multiple perspectives through radial bias sampling, thereby constructing a rich and complementary set of visual features. Next, it calculates the affinity matrix between implicit text features and multi-view visual features to obtain the region probability distribution and calculate its information entropy. Then, a heatmap generated based on the information entropy dynamically highlights key regions. The framework generates candidate boxes based on anchor point features and combines the heatmap to optimize the final localization result, completing a closed loop from feature alignment to target localization. The entire model performs backpropagation and iterative optimization by accumulating the contrast alignment loss and the detection box ranking loss, continuously enhancing the model's localization performance until training converges. This end-to-end process ensures deep collaboration between visual and text signals at multiple levels and in multiple steps, ultimately achieving highly robust weakly supervised target localization.

The invention provides a dynamic priority adjustment method for industrial data migration of a productivity platform, which belongs to the technical field of industrial data migration, and comprises the following steps of: performing logic fragmentation according to a topology level and a business importance degree, and calculating a data access affinity matrix to solve an initial migration sequence; outputting predicted migration time consumption and resource occupancy by using a migration time prediction model, constructing a multi-dimensional state space input bandwidth allocation strategy generator to output a dynamic bandwidth quota, monitoring the migration progress in real time, triggering a priority re-evaluation process when a rate deviation exceeds a threshold value, adjusting a migration queue according to an emergency index, and determining the migration time. Cross-data-center migration is optimized by adopting a BBR transmission protocol and a multi-path transmission technology, global state refreshing is periodically executed to dynamically adjust the position of each logic fragment in a migration queue, and the problem of key service data migration delay caused by the fact that task priorities cannot be dynamically adjusted according to real-time states in the industrial data migration process is solved.

The application provides a video anomaly detection method and system based on semantic and amplitude depth cooperation, which comprises the following steps: extracting a visual feature sequence of a video frame; enhancing a feature vector at a corresponding time step in the visual feature sequence by using an amplitude and energy of each time step to obtain an enhanced feature sequence; calculating a semantic similarity matrix of the enhanced feature sequence; calculating a sparse affinity matrix according to the amplitude of each time step in the visual feature sequence; fusing to obtain a final attention weight; weighting and aggregating a value vector of the enhanced feature sequence by using the final attention weight, and then inputting the value vector into a classifier to obtain a frame-level anomaly score, so that the video anomaly detection is realized. The application forcibly realizes the deep cooperation of semantic and amplitude information at the model architecture level through one-parameter feature engineering, and solves the problem that the existing weakly supervised video anomaly detection method is not sensitive to feature amplitude.