902 results about "Sequence Feature" patented technology

The invention discloses an adaptive teaching real-time feedback method based on multi-modal fusion, and the method comprises the following steps: synchronously collecting text modal information, voice modal information and image modal information generated by students in a teaching process, forming multi-modal original data information, and extracting historical student interaction behavior data; preprocessing the multi-modal original data information, and respectively generating corresponding text, voice and image sequence features; a visual feature encoder and a sequence feature encoder are adopted to encode each modal sequence feature to obtain a high-dimensional feature; inputting the modal high-dimensional features into a cross-modal fusion network for deep fusion; parameters of the feedback model are optimized through a model-independent element learning feedback regulation and control algorithm, and a personalized feedback strategy is generated; generating comprehensive feature representation according to the fusion features, and outputting personalized teaching feedback; and the interaction information is updated based on the feedback behavior data to realize closed-loop optimization.

The invention discloses a drug target affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

The invention discloses a health risk assessment method and early warning system based on multi-source data analysis. The health risk assessment method comprises the following steps: S1, collecting and preprocessing multi-source health data through medical detection equipment; s2, extracting key health indexes, sequence features and statistical features based on the health data set; s3, adopting a recurrent neural network model to construct a health risk assessment model; s4, optimizing structural parameters of the health risk assessment model by adopting an improved dragonfly algorithm; s5, performing performance evaluation on the health risk evaluation model by using the optimal parameter set; s6, deploying the final health risk assessment model in a health risk assessment and early warning system; and S7, when the health risk assessment result exceeds a preset risk threshold, automatically generating early warning information. According to the method, the recurrent neural network model, the improved dragonfly algorithm and the multi-source health data fusion optimization technology are combined, and health risk assessment and early warning based on multi-source data analysis are realized.

The invention provides a polar region sea ice thickness inversion method based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the feature extraction of SAR data, MODIS data, AMSR2 data and MIRAS data in a target region, constructing an image and sequence feature data set, carrying out the fusion of the image and sequence feature data set through employing a designed multi-modal data fusion network, and obtaining a polar region sea ice thickness inversion result. A new sequence feature data set is obtained, multiple machine learning regression models are used for training the sequence feature data set, the machine learning regression model with the optimal index is selected for sea ice thickness inversion, optical data are deleted, and a sea ice thickness inversion model is obtained through re-training; and dividing the target area into a clouded area and a cloudless area by using the cloud mask data, and applying corresponding inversion models to the clouded area and the cloudless area respectively. According to the method, the advantages of various remote sensing data in sea ice thickness inversion are fully fused, continuous large-area ice thickness information extraction of sea ice can be realized, and the method has better universality.

The invention discloses a fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention provides a small target detection method and system based on an improved YOLOv8 structure, and the method comprises the following steps: carrying out the preprocessing of a to-be-detected image, so as to obtain an input standardized image; inputting the standardized image into a YOLOv8 backbone network, and extracting feature maps of different scales; the feature map is input to a YOLOv8 neck network for feature fusion, the neck network introduces an ASF mechanism, a scale sequence feature fusion module and a three-feature encoder module are integrated, cross-scale enhancement and integration are performed on features of different resolutions, and a multi-scale fusion feature map is generated; constructing P2, P3, P4 and P5 detection layers based on the fused feature map; the P2-P5 detection layer predicts candidate target frames and category confidence of the candidate target frames respectively; soft non-maximum suppression is adopted to process the candidate target frame, a linear or Gaussian attenuation function is utilized to adjust confidence, a redundant frame is suppressed, and a final detection result is output. According to the method, the recognition capability of small targets in dense and complex scenes is remarkably improved.

The invention relates to the technical field of network security of a power monitoring system, and discloses an attack event prediction method and system based on low-intensity abnormal mode data, and the method comprises the steps: intercepting abnormal data fragments from historical multi-source heterogeneous data based on the low-intensity abnormal mode data selected from the historical multi-source heterogeneous data; analyzing to obtain a target abnormal data fragment; training the gating loop network model based on target key time sequence abnormal features obtained by performing feature extraction on the target abnormal data fragments to obtain a future time sequence feature prediction model; performing structured processing on the low-intensity abnormal event to obtain a spatial correlation feature topological structure; obtaining future time sequence features based on the future time sequence feature prediction model; and obtaining an attack event prediction result based on an abnormal event evolution graph constructed by fusing the future time sequence feature and the spatial correlation feature topological structure. According to the method provided by the invention, accurate identification can be carried out in the preparation stage of the attack event.

The invention discloses a text abstract generation method and system based on sparse attention acceleration, and the method comprises the steps: reading long text data, carrying out word segmentation and embedded coding processing, extracting a sequence feature vector, and mapping the sequence feature vector into a query matrix Q, a key matrix K and a value matrix V; constructing an abstract generation network, wherein the abstract generation network comprises a sparse attention calculation module, a feedforward calculation module, a prediction head module and a key value cache module; inputting the query matrix Q, the key matrix K and the value matrix V into an abstract generation network, passing through a backbone network formed by stacking a sparse attention calculation module and a feedforward calculation module for multiple times, processing by a prediction header module, and caching a historical decoding state in real time through a key value caching module to obtain an initial text feature vector; and based on the initial text feature vector, executing an autoregressive decoding process through an abstract generation network, and outputting a final abstract result. According to the method, the decoding process can be accelerated, and the semantic integrity and coherence of the generated abstract are ensured.

The invention discloses an antibacterial peptide recognition method and system based on a sequence-structure dual-channel neural network, and the method comprises the steps: splicing amino acid features and amino acid-level manual features extracted by ProtT5 to obtain peptide embedding, and transmitting the peptide embedding to a sequence channel composed of a plurality of Transform blocks to extract the sequence features of the peptide; predicting a three-dimensional structure of the peptide by using ESM-Fold to construct an adjacency graph, taking amino acid features obtained by ESM-2 as node features of the adjacency graph, and performing layer-by-layer extraction and enhancement by fusing structural channels of multi-head graph attention, a residual network, layer normalization and a feedforward neural network; and carrying out maximum pooling and splicing on the sequence features and the structural features, and then, carrying out antibacterial peptide prediction. According to the method, a multi-feature fusion strategy is adopted, meanwhile, the three-dimensional structure information of the antibacterial peptide is introduced, and the sequence and the structural features are fused through a two-channel architecture, so that the recognition accuracy of the antibacterial peptide is effectively improved.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

The invention provides a multi-modal characterization molecular property prediction method based on hierarchical bidirectional cross attention, and relates to the technical field of machine learning assisted organic chemistry, and the method comprises the following steps: S10, generating same-molecule multiple sequences for data enhancement; s20, coding the sequence features through a pre-trained molecular language model MolBERT; s30, performing multi-modal feature fusion through a layered bidirectional cross attention mechanism; s40, establishing a prediction head; s50, in the reasoning stage, only the feature extraction and fusion steps are executed, and a molecular property prediction result is output through the trained prediction head. According to the method, the molecular sequence, the topological graph structure and the fingerprint features are effectively integrated, so that the prediction precision of the model on a plurality of MoleculeNet (molecular network benchmark) public data sets is superior to that of an existing method.

The invention provides a distribution network traveling wave fault point intelligent positioning method and system based on deep time sequence feature learning, and belongs to the technical field of intelligent power distribution detection based on deep learning. Firstly, a high-speed traveling wave sensor is arranged on a distribution line, multi-dimensional three-phase voltage and current signals are collected, and a data set of fault types, phases, grades and positions is constructed; then, fault state features are extracted through topology perception normalization, symmetric component mapping and two-channel time sequence modeling, and accurate recognition of fault types, related phases and grades is achieved through an attention mechanism; furthermore, a fault intelligent positioning model based on a topological graph is constructed, a tower sensing weight and a line information bearing weight are introduced, and space-time embedding is extracted through a graph attention network and an echo state network, so that line fault classification and accurate positioning are realized. Finally, through combination of off-line model training and on-line system deployment, real-time identification and positioning of power distribution network faults are realized, and accuracy, robustness and response speed of fault diagnosis are effectively improved.

The invention provides a drug target binding affinity prediction method based on multi-modal data fusion. The method comprises the following steps: firstly, extracting sequence feature information of drug SMILES and target FASTA, then constructing an affinity graph, modeling drug molecules and target protein molecules into an undirected graph, and extracting molecular-level features of atoms, bonds, residues and contact. And fusing the hierarchical graph structure information of the affinity graph and the molecular graph to obtain the graph structure feature representation of the drug-target spot. The sequence feature information and the graph structure feature representation are further fused by using intramolecular and intermolecular attention fusion mechanisms. And finally, performing affinity prediction by using the fused features, and outputting a drug-target binding affinity score. According to the method, sequence and structural information are effectively fused, the accuracy of drug target affinity prediction is improved, and the problems of insufficient information fusion and insufficient structural information utilization in an existing method are solved.

The invention relates to the technical field of data processing, and discloses a child autism behavior recognition method and system based on data analysis and a medium. The method comprises the steps of collecting and preprocessing child multi-modal behavior data; extracting nonlinear features to obtain a time sequence feature matrix; identifying a repeated behavior mode through a three-dimensional convolutional network; analyzing behavior time sequence change by using a time convolutional network; fusing a plurality of feature representations to obtain a comprehensive feature vector; a multi-classifier system is applied to identify autism behavior types and evaluate severity. By extracting the nonlinear time sequence features, constructing the deep spatial-temporal feature extraction network and designing a feature fusion mechanism and a multi-classifier integration system, the method can overcome the limitations of a single mode, a linear feature and a single algorithm in the prior art, and improves the accuracy and interpretability of autism behavior recognition.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention discloses a drug target prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing dynamic gating fusion on graph structure features and sequence features of a target drug to obtain drug features; performing dynamic feature enhancement on the digitized sequence of the target protein, and further obtaining protein features through context sensing optimization; obtaining a target affinity score of the target drug and the target protein by using a prediction model based on the drug characteristics and the protein characteristics; wherein the prediction model is obtained through feature representation training marked with actual affinity scores on the basis of a neural network. According to the method, the prediction precision of drug target interaction is improved through deep fusion of drug multi-modal features and protein sequence context perception optimization. The method can realize high-precision prediction of the drug target, and can be widely applied to the technical field of drug target prediction.

The invention discloses a multi-modal data fused shield tunneling machine cutter state fault prediction system, which comprises a state acquisition module for acquiring multi-modal state data, decomposing the multi-modal state data into time and space related characteristics, and obtaining optimal state data through low-rank matrix decomposition and compression; the anomaly simulation module is used for collecting fault state data, constructing an improved lightweight weight migration network to perform cross-domain feature extraction to obtain a cross-domain common feature vector, and obtaining common features through a feature alignment algorithm based on adversarial training on the basis of the cross-domain common feature vector and the fault state data; and the fault prediction module is used for performing fault prediction through a space-time cross attention dynamic migration network on the basis of the obtained optimal state data and common characteristics, so that long sequence characteristics can be captured when the cutter is abnormal, the calculation amount can be reduced when the cutter is normal, and the real-time performance and the accuracy of fault prediction are improved.

The invention relates to a method for classifying antihypertensive peptides by fusing sequence and structure multi-modal features and combining contrast-generative combined optimization. The method comprises the following steps: extracting sequence feature representation and structure feature representation of peptide fragments; performing multi-modal feature enhancement of comparison-generative joint optimization on the sequence feature representation and the structural feature representation; and performing peptide classification on the enhanced feature representation by using a preset Kan-Conv structure and tag smooth joint optimization classification model. According to the method, high-precision recognition of the functional activity of the antihypertensive peptide is achieved, information in the three aspects of the sequence, the structure and the generative potential space is creatively and comprehensively utilized, the blank that the structure-sequence synergistic effect is ignored in the existing peptide function prediction field is filled, and the screening efficiency and prediction reliability of the antihypertensive peptide are remarkably improved.

The invention discloses a diopter detection data fusion processing system based on deep learning, and relates to the technical field of medical detection. The system comprises a multi-modal data acquisition module, a data preprocessing and aligning module, a dynamic sequence analysis module, a deep learning regression core module, a personalized calibration module and a result output and visualization module which are connected in sequence. The system obtains objective detection data through a multi-modal data acquisition module, and multi-modal data is formed after preprocessing and space-time alignment; the dynamic sequence analysis module extracts adjustment response time sequence features; the deep learning regression core module calculates an initial diopter parameter; the personalized calibration module performs parameter optimization in combination with the biological characteristics of the user; and finally, a visual quality simulation result is generated through a result output and visualization module. According to the method, the multi-modal data fusion and deep learning technology is utilized, rapid and accurate diopter automatic detection is realized, and the detection efficiency and the individuation level are remarkably improved.

The invention discloses a gas recognition method based on multi-source information fusion and environmental perception, and the method comprises the steps: constructing a deep feature learning framework of multi-source fusion through combining the spatial response features, time sequence features and external environmental information of gas; the method comprises the following specific steps: preprocessing collected gas data, and respectively extracting features of an image mode, a sequence mode and an environment mode; fusing the image features and the sequence features through a cross attention fusion module, and capturing the space-time correlation of the data; a cross-modal attention compensation module is introduced, so that main-modal gas data adaptively gathers key information in an auxiliary-modal environment, and effective compensation of environmental factors on gas recognition performance is realized; and finally, gas category prediction is performed through a classification decision head. According to the method, the problems that the detection is easily interfered by environmental factors, the stability is poor or the qualification is inaccurate due to the fact that modeling depends on single modal data in the existing gas identification technology are solved.

The invention belongs to the field of industrial thermal engineering and intelligent modeling, and discloses a heat exchanger heat exchange coefficient prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring multi-dimensional operation data through a signal acquisition system, cleaning abnormal and blank values, standardizing, and segmenting into time sequence samples by adopting a sliding window method; a double-layer physical information long-short-term memory network is constructed, and a time sequence feature and a physical equation residual error are combined to generate a space-time fusion feature matrix. And a composite loss function including data loss, physical equation loss and physical consistency loss is designed, physical and data driving influences are balanced through hyper-parameter tuning, and accurate prediction of the heat exchange coefficient is achieved based on a gradient descent optimization model. The method combines field physical laws and data features, improves the reliability and physical interpretability of prediction, and is suitable for operation optimization of the heat exchanger of the desulfurization wastewater treatment system of the thermal power plant.

The invention discloses a shaft operation state intelligent prediction method based on a hybrid deep learning framework, and belongs to the field of petroleum engineering oil-gas field development engineering, and the method comprises the following steps: building a geological sequestration shaft transient temperature-pressure coupling mathematical model, and carrying out the numerical solution; constructing a double sequence feature database covering CO2 injection and leakage working conditions; adopting a parallel architecture to process the double sequence features to construct an improved double attention network; the method comprises the following steps: establishing a hybrid deep learning framework DT-DANet by coupling an improved double attention network and a time fusion Transform; and carrying out collaborative optimization training on the hybrid deep learning framework by adopting a composite loss function, wherein the output of the framework is a wellbore pressure profile, a temperature profile and phase state distribution under different time steps. According to the method, high-precision prediction of shaft multi-parameter dynamic evolution in the CO2 geological storage process is realized.

The invention discloses a large language model battery evaluation method and device based on multi-modal fusion, and relates to the technical field of battery intelligent management. The method comprises the steps that according to time sequence operation data, an improved multi-scale gating time sequence model is used for time sequence feature extraction, and operation time sequence features are obtained; according to the log text data and the monitoring image data, performing semantic feature extraction by using a multi-modal large language model to obtain fused semantic features; performing medium-term fusion reasoning by using an improved large language model according to the operation time sequence features and the fusion semantic features to obtain a battery evaluation result; generating a battery risk detection result and a corresponding explanatory text according to a preset alarm threshold value and a battery evaluation result based on the post-masking time sequence attention flow and the post-masking semantic attention flow; and carrying out risk visualization according to the battery risk detection result and the corresponding explanatory text. The invention relates to a high-reliability battery evaluation method based on a multi-modal large language model.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention belongs to the technical field of intelligent traffic, and discloses a multi-scale space-time fusion image feature extraction method based on traffic flow, which comprises the following steps of: 1, constructing a dynamic image generation module; a self-adaptive adjacency matrix is generated in combination with historical traffic data, spatial embedding and time embedding, the matrix is used for spatial-temporal feature extraction of a GCN layer, and the generated adjacency matrix can flexibly capture static and dynamic relationships; 2, constructing a learnable weighting module; according to the module, the weights of different features are adaptively adjusted, so that various feature information is effectively fused; 3, constructing a sequence feature mapper; through a recurrent neural network (RNN) and a sequence compression-expansion mechanism, dynamic features of time sequence signals are effectively extracted and enhanced. According to the method, adaptive adjustment of an adjacent matrix is realized through a dynamic graph generation module, the multi-feature fusion capability is improved in combination with a learnable weighting mechanism, and the long sequence modeling capability of an RNN layer is optimized by adopting a sequence compression and expansion strategy.

The invention discloses a millimeter-wave radar fusion system and method for electrocardiograph monitoring false alarm recognition, and the method comprises the steps: carrying out the multi-scale time-frequency feature extraction of a millimeter-wave radar signal and an electrocardiograph signal, obtaining a radar feature sequence and an electrocardiograph feature sequence, carrying out the time alignment of the radar feature sequence and the electrocardiograph feature sequence, and inputting a multi-modal fusion architecture; outputting cross-modal joint embedding features based on the multi-modal fusion architecture; a cross-modal consistency judgment model obtained based on self-supervised contrast learning optimization training is constructed, whether a cross-modal feature inconsistent state exists in the joint embedded features or not is judged, and if yes, it is judged that a potential false alarm event exists; and based on the potential false alarm event, extracting sequence feature fragments before and after alarm triggering in the joint embedded feature, inputting the sequence feature fragments into an anomaly classification network, outputting a final judgment result about whether false alarm is formed, and when the final judgment result is false alarm, generating an alarm adjustment instruction and sending the alarm adjustment instruction to a monitoring terminal.

The invention discloses a network attack detection method fusing momentum contrast learning and Transform, and the method comprises the steps: collecting a large amount of untagged data of an industrial system network, carrying out the unsupervised learning through employing the momentum contrast learning and fusing the Transform, extracting the high-dimensional and long-sequence features in the data, constructing a feature embedding space with high discrimination, and carrying out the recognition of a network attack through employing the momentum contrast learning and the Transform. The method comprises the following steps of: performing dimension reduction on high-dimensional features by using a principal component analysis method, taking the features subjected to dimension reduction as input of a BDSCAN density clustering algorithm, performing clustering classification, optimizing parameters in the DBSCAN algorithm through a grey wolf optimization algorithm, outputting optimal algorithm parameters through iteration, and ending a program until a detection performance requirement is met. The method can be better suitable for a scene with high-dimensional and complex data, is suitable for a condition that the data has no label, improves the overall network detection accuracy and robustness, and enhances the network detection practicability and adaptability.

The invention discloses a behavior recognition method and device based on dynamic context, equipment and a medium, and the method comprises the steps: obtaining a dynamic context graph through cross-modal modeling among multi-modal data, fusing long / short-term events in a monitoring video through dynamic upper and lower graphs, carrying out the cross-modal semantic connection, carrying out the multi-modal feature fusion, and carrying out the multi-modal feature fusion. Determining a gating strategy vector and a gating weight matrix according to a splicing result; and performing multi-modal fusion according to the gating strategy vector, the gating weight matrix and the multi-modal sequence feature to obtain a multi-modal fusion feature, and performing abnormal behavior identification on the monitoring video based on the multi-modal fusion feature. Richer information support is provided for decision making through feature fusion, so that the decision making accuracy in a complex scene is improved. The method can be applied to security and protection monitoring scenes in the financial field or the medical field, so that the abnormal behavior recognition accuracy in the security and protection monitoring scenes is improved.

The invention relates to a logistics order processing efficiency analysis method based on cloud computing, and the method comprises the steps: constructing a multi-dimensional and multi-node behavior log and environment variable synchronous collection system, and obtaining standardized structured data through timestamp calibration, abnormal value elimination, semantic normalization and desensitization processing; and based on node features, sequence features and environment embedding, generating a multi-node multi-scene feature vector, and inputting the multi-node multi-scene feature vector to a multi-order causal nested graph neural network for dynamic causal modeling among nodes, events and constraints. Through combination of anti-fact inference and multi-source evidence fusion, hidden bottleneck automatic identification, attribution report output and optimization suggestion generation are realized; through expert and user feedback mechanisms and working condition and rule change adaptive network structure adjustment, long-term accurate optimization and dynamic closed-loop updating of the bottleneck diagnosis model are realized. According to the invention, the intelligence and adaptive level of order flow bottleneck identification can be improved, and a high-quality data basis and intelligent decision support are provided for flow optimization in a complex logistics scene.