58 results about "Linear embedding" patented technology

The invention belongs to the technical field of power system load prediction, and relates to a long-range multivariable load prediction method and system based on time-frequency domain collaboration, and the system carries out the normalization and stabilization of a multivariate load time sequence through a data preprocessing module; the feature embedding module performs linear embedding on the block sequence to construct high-dimensional feature representation; the state space coding module extracts long-range dependency features and generates depth time sequence representation; the decoding prediction module maps the coding features into a preliminary prediction sequence; the time sequence alignment module identifies a leading-lagging relation among multiple variables and aligns a time sequence; the frequency domain optimization module realizes frequency domain component fusion based on adaptive filtering; and the model training optimization module is used for performing training and optimization through a signal attenuation loss function. The method can effectively improve the precision and robustness of long-range multivariable load prediction, and especially has obvious advantages in the aspects of processing complex dependency relationships and dynamic time delay.

The invention belongs to the technical field of multi-body dynamics analysis, and discloses a wind driven generator transmission chain rigid-flexible coupling multi-body dynamics analysis method based on dynamic mode decomposition, and the method comprises the steps: firstly, enabling multi-degree-of-freedom time series data to be non-linearly embedded into a high-dimensional feature space through an encoder neural network; extracting a dominant mode by utilizing intrinsic orthogonal decomposition (POD), and constructing a low-dimensional feature space; parameterized dynamic mode decomposition and radial basis function regression are adopted, a mapping relation between system parameters and Koopman operators is established, and accurate prediction of dynamic characteristics under variable working conditions is achieved; and finally, reconstructing a physical response through a decoder, and optimizing model parameters in combination with an error driving mechanism. The problems that a traditional method is low in calculation efficiency, poor in nonlinear adaptability and difficult in multi-parameter coupling prediction are effectively solved, the efficiency and precision of transmission chain dynamic analysis are remarkably improved, and reliable technical support is provided for state monitoring and service life prediction of the wind turbine generator.

The invention relates to a high-altitude parabolic detection method based on a Swinin-Transform and YOLOv8 fusion detection algorithm, and the method comprises the steps: decoding a video stream into an RGB image sequence, preprocessing the RGB image sequence, segmenting the preprocessed image into image blocks, and converting the image blocks into an initial feature sequence through linear embedding; the method comprises the following steps: firstly, extracting features in a layered manner by using a Transform Block of a Swin-Transform, firstly, capturing local features of a bottom layer by window self-attention, performing down-sampling after fusion, and then capturing cross-window features by moving window attention to obtain advanced features; inputting the advanced features into a YOLOv8 detection network, and obtaining a target frame and confidence through multi-scale feature fusion, anchor frame matching and prediction decoding; and finally, optimizing a detection result through non-maximum suppression and outputting the detection result. Through fusion of Swinin-Transform and YOLOv8, global context and local detail features are considered, the recognition precision of a multi-scale parabolic target is improved, the problems of missing detection, false detection and positioning deviation of a traditional method are solved, and the method can adapt to complex scenes and realize real-time detection.

The invention discloses an infrared and visible light image fusion method based on text supervised contrast learning. Comprising the steps that an infrared image, a visible light image and text description of the infrared image and the visible light image of the same scene are acquired, and multi-modal image features are extracted through linear embedding and cross attention Mama modules by adopting a preprocessing and image block division strategy; a pre-training text encoder is used for obtaining text semantic features, dimension alignment and weighted fusion of images and text features are achieved through an image-text feature alignment fusion module, and a fused image is generated. End-to-end training is carried out through image-text contrast learning loss in combination with pixel reconstruction and infrared intensity retention loss, and the semantic consistency and the thermal target retention capability of the fused image are effectively improved. The fusion process further comprises image-text matching weight calculation, feature weighting adjustment and fusion convolution operation. The method has the advantages of being high in semantic guiding capacity, excellent in fusion effect, accurate in hot target expression and the like, and is suitable for a multi-modal image processing task.

The invention discloses a transformer abnormity identification method based on voiceprint feature analysis, and belongs to the field of power equipment state monitoring and intelligent diagnosis. The method comprises the following steps: firstly, analyzing an iron core acoustic mechanism based on a magnetostrictive effect, and establishing a three-dimensional model through finite element simulation to obtain vibration and sound field characteristics; in a complex substation environment, a hybrid noise reduction method combining density peak clustering and a CEEMDAN-wavelet threshold is provided, and the signal-to-noise ratio is effectively improved. Then extracting Mel-frequency cepstrum coefficients (MFCC) and spectrum features, and performing local linear embedding (LLE) dimension reduction to form a compact feature set; in the recognition stage, a convolutional neural network framework is designed, specifically, a spectrogram and an energy spectrum are modeled through a two-dimensional CNN, an MFCC tensor obtained after dimensionality reduction is modeled through a three-dimensional CNN, and accurate diagnosis of mechanical faults such as core looseness is achieved. The method has the advantages of being non-contact, anti-noise and high in recognition precision, and real-time diagnosis and early warning of mechanical abnormity of the transformer can be achieved under complex working conditions.

The invention discloses a wheat imperfect grain identification method based on a double-branch network and feature fusion, and the method comprises the following steps: S1, inputting a wheat imperfect grain image, and generating initial features through image segmentation and linear embedding; s2, performing multi-stage feature extraction by adopting a double-branch network architecture, specifically, improving a Swin Transform and a multi-scale dynamic convolution branch; s3, after each level of feature extraction, performing interactive fusion on the double-branch features through a cross attention fusion module; s4, aggregating multi-scale features through a four-stage feature fusion module; and S5, finally outputting an identification result through a pooling linear layer. According to the method, the accuracy rate in the wheat imperfect grain multi-classification task is remarkably improved and reaches 98.82%, a high-precision and low-cost automatic solution is provided for grain quality detection, and the problems that an existing method is insufficient in recognition precision and limited in feature expression ability are solved.

A method and an apparatus for forecasting a future time target variate, a computer device, and a storage medium are disclosed. Specifically, a method for forecasting a future time target variate is disclosed. The method includes: dividing a history target variate and covariates of target variates into a plurality of patches channel-wise, and performing linear embedding, wherein the covariates of target variates comprise a covariate of the history target variate and a covariate of the future time target variate; performing position embedding on a plurality of tokens and a learnable forecasting token; wherein the plurality of tokens are linear embedded from the plurality of patches; inputting the plurality of tokens and the learnable forecasting token into an encoder; and forecasting and outputting the future time target variate based on the encoder and the learnable forecasting token. According to the foregoing manner, in a case of zero-shot training, the future time target variate can be accurately forecast through limited history target variates and covariate information of the target variates, thereby having a large inspiration effect on the field of basic models in time series forecasting.

The invention discloses an LPBF aluminum alloy performance prediction method and system based on a self-attention mechanism, and belongs to the technical field of metal material science. The method comprises the following steps: acquiring components, thermodynamic parameters and corresponding target performance values of a plurality of aluminum alloys as data set samples; performing word embedding coding on elements of the aluminum alloy in the sample to obtain element vectors; performing dual-channel coding of main element linear embedding and trace doping element logarithmic embedding on the mass fraction of the aluminum alloy element in the sample to obtain a mass fraction vector; inputting the encoded samples into a prediction model to train the prediction model; the prediction model comprises a self-attention module and a prediction module; and inputting the components and thermodynamic parameters of the to-be-predicted aluminum alloy into the trained prediction model, and predicting the target performance of the to-be-predicted aluminum alloy. According to the method, the implicit mapping relation among components, indexes and performance is deeply mined, a dynamic weight distribution model of element interaction in the multi-element alloy system is established, and the dimension limitation of a traditional linear regression method is broken through.

A method of motion correction image reconstruction for photon-counting CT images includes scanning a subject via a photon-counting CT scanner device to obtain measured projection data; performing, via motion correction circuitry of the motion correction system, a LLE motion correction algorithm on the measured projection data to obtain motion correction data; generating, via reconstruction circuitry of the motion correction system, reconstructed image data from the motion correction data; and outputting corrected image data based, at least in part, on the reconstructed image data. A binary bad pixel mask is applied to exclude contributions from the bad pixels to the measured projection data. An unreliable volume mask is applied to exclude contributions from X-ray beams that passed through unreliable portions of the reconstructed image data. A virtual static object removal algorithm is performed to remove a static object from the measured projection data.

The invention discloses an automatic driving vehicle dynamics modeling method based on a time-varying parameter neural network, and belongs to the field of vehicle dynamics. Comprising the steps of obtaining operation data of a vehicle under different working conditions, performing preprocessing, and constructing a sample set according to a time sequence slicing mode; constructing a neural network model based on the physical constraints and the time-varying parameters; training and verifying the neural network model by adopting the sample set to obtain a neural network model after parameter optimization for state prediction; the neural network model comprises an input layer, a GRU network, a physical constraint layer and an output layer; input data enters the neural network model through the input layer, and dimension transformation and time sequence packaging are carried out through the linear embedding layer; then time sequence features are extracted through the GRU network, bounded mapping is carried out on output vectors of the GRU network through a physical constraint layer, a time-varying parameter estimation value is obtained, the time-varying parameter estimation value is substituted into a vehicle kinetic equation for calculation, and a physical prediction state is obtained.

The invention discloses a steel rail surface defect detection method. A steel rail image is divided into image blocks with fixed sizes through a patch partitioning module, and the image blocks are mapped to a low-dimensional feature space through a linear embedding layer. Then, the low-dimensional feature vectors are sequentially input into a Multi-Swin Transformer Block (a shift window multi-head self-attention block) and a patch merging module, and semantic information is extracted layer by layer; and then, a multi-scale up-sampling decoder receives feature maps of different sizes, multi-head self-attention is utilized to enhance the spatial relationship and semantic understanding ability, the resolution is gradually recovered through a patch expansion module, and a feature fusion effect is integrated and optimized through a channel. And the integrated feature map is recovered to the original input size through a patch expansion module with a one-time multiplying power of 4, and a segmentation mask is generated through a convolutional layer. The method can accurately sense the defect degree, realizes efficient and accurate steel rail surface defect detection and positioning, is suitable for railway maintenance and safety monitoring, and ensures stable operation of facilities.

The invention discloses a time series prediction method based on a multi-modal enhanced large language model, which comprises the following steps: acquiring historical time series data, constructing a standardized input matrix and setting core task parameters; dividing data blocks through a sliding window mechanism, and converting the data blocks into uniform dimension features through linear embedding; a semantic prototype is generated based on a large language model vocabulary, and time sequence features and semantic features are fused through multi-head cross-attention; alternately splicing the data blocks and the corresponding semantic information, and constructing a self-multi-modal input sequence; designing a three-level structured prompt including context, task target and modal guidance, and fusing the three-level structured prompt with a multi-modal sequence; and training the lightweight model by adopting a frozen training strategy, and outputting a prediction result of a specified time step in the future. According to the method, through single-source data enhancement and prompt guidance, the time sequence reasoning capability of a small-parameter large language model is activated, high-precision prediction is guaranteed, efficient deployment is achieved, and the method adapts to long-term and short-term prediction tasks in the fields of electric power, traffic, meteorology and the like.

The invention discloses a steel pipe weld defect detection method based on twin VMama bimodal feature fusion, which comprises the following steps: constructing an image data set and a steel pipe weld defect detection model, the model comprising twin coding, feature fusion, decoding and a classifier; the twin coding is composed of a parameter-shared double-branch coding part 1 and a coding part 2, and each coding part is composed of a block division and linear embedding module and a structure unit formed by stacking a plurality of scales layer by layer; the feature fusion part is composed of a cross VMamba feature fusion module; the decoding part is composed of a channel sensing VSS module and a block extension; the training set trains the model, and the test set monitors convergence to obtain a trained model; inputting the verification set into the trained model to obtain a defect detection result; the problems that unobvious defects and pseudo defects are difficult to detect in a single ultrasonic image or an X-ray image, the characteristics of slender and micro crack defects are difficult to detect in a CNN (Convolutional Neural Network)-based method during image processing, and the calculation complexity of a Transform-based method is high are solved.

The invention discloses a high-speed train traction system fault detection method based on local linear embedding, and belongs to the technical field of fault diagnosis. The method comprises the steps that firstly, traction system sensor data are collected and preprocessed; secondly, mapping high-dimensional data to a low-dimensional space by utilizing a local linear embedding algorithm, and reserving a local geometric structure of the data; thirdly, reconstructing original data based on the data matrix of the low-dimensional space, calculating residual signals, and extracting fault features; and finally, judging whether the high-speed train traction system breaks down or not by comparing the evaluation function with a threshold value. According to the method, the limitation of processing high-dimensional and nonlinear data by a traditional method is effectively solved, the accuracy of fault detection is improved, fault early warning of the high-speed train traction system can be realized, and the method has a relatively high engineering application value.

The invention discloses a modulation identification and code element rate estimation method and device, a storage medium and equipment, and belongs to the technical field of communication. The method comprises the following steps: converting a radio frequency signal transmitted by a wireless channel, separating a real part and an imaginary part of a baseband modulation signal, and splicing the real part and the imaginary part into a two-dimensional matrix; the multi-task model comprises a convolution module, a pooling module, a DRA module, an SST module and a full connection module, and the DRA module is composed of a plurality of DA units and a jump connection structure with an ECA unit; the SST module is composed of a Patch Partition unit, a linear embedding unit and a Swin TransformerBlock unit, and the Swin TransformerBlock unit is composed of a Patch Partition unit, a linear embedding unit and a Swin TransformerBlock unit; and processing the two-dimensional matrix by using a multi-task model to obtain a modulation mode and a code element rate. According to the method, modulation identification and code rate estimation can be synchronously carried out, the performance is good, the complexity and parameter quantity are low, the overhead is small, the deduction speed is high, and the generalization ability and robustness are high.

The invention relates to the field of electric power big data, in particular to a distribution network single-phase earth fault detection method based on a time sequence model. The method comprises the following steps: firstly, constructing a fault identification model based on Transform, wherein the fault identification model comprises a linear embedding layer, a Transform main body, a feature fusion module and a prediction block; and then a real acquisition data set and a simulation data set comprising a large amount of sample data are obtained through a data direct acquisition mode and a software simulation mode respectively. Carrying out two-stage training on the constructed fault recognition model by adopting the two types of data sets; wherein only a material simulation data set is adopted in the first round of training, and a mixed data set is adopted in the second stage. And finally, monitoring the single-phase earth fault in the power distribution network in real time by using the trained network model. According to the technical scheme provided by the invention, the problems of high positioning difficulty and low positioning precision of the single-phase earth fault in the existing large-scale power distribution network can be solved.

This invention discloses a hierarchical image classification method and system based on CA-SwinT, belonging to the field of artificial intelligence technology. The method includes: S1, acquiring the original image and preprocessing it; S2, extracting features from the image using a SwinT module; S3, fully fusing the extracted image features and classification label information using a double cross-attention mechanism to construct a fine mapping between the image and hierarchical labels; S4, generating corresponding labels based on the fine mapping in S3 using a label generation module and updating the classification label information; S2 includes: slice segmentation processing, linear embedding processing, two-level SwinT module processing, block merging processing, two-level SwinT processing, and linear embedding processing. This invention can effectively extract rich feature information from images in different dimensional spaces, and more efficiently identify multi-level labels in images.

The invention relates to a river flow prediction method, which comprises the following steps of: 1, dividing a daily runoff data set, and performing multi-stage decomposition on a training set by using Haar wavelets to obtain de-noising parameters; 2, multiplexing the verification set and the test set to obtain data; step 3, carrying out first-order differential transformation, and zooming to an interval of [0, 1] by using MinMaxScaler to obtain a normalized value of a differential sequence; 4, mapping the data to a high-dimensional space through a linear embedding layer, and then carrying out position coding to obtain data; step 5, inputting the data obtained in the step 4 into a DTransformer encoder, so that a catastrophe point obtains a higher attention weight so as to strengthen difference characteristics of adjacent time steps and obtain encoder output; 6, inputting the data obtained in the step 5 into an LSTM decoder, and performing feature recombination to obtain encoder output; 7, mapping the data obtained in the step 6 to 7-dimensional output through a full connection layer; and 8, performing inverse normalization and inverse difference transformation on the data obtained in the step 7 to obtain final output.

The application belongs to the technical field of image processing, and discloses a chest and abdominal cavity bleeding automatic segmentation method and device based on deep learning, which comprises the following steps: obtaining and normalizing chest cavity or abdominal cavity CT volume data, and then performing linear embedding to generate initial features; inputting the initial features into an encoder for multi-stage encoding, in which the encoding features of different levels are respectively enhanced in the frequency domain according to different semantic levels to generate level-aware frequency domain enhanced features; the input features are modeled by a deformable mixed window multi-head self-attention mechanism in at least one stage of the encoder, and the attention calculation results are weighted and fused by using the frequency domain guide weight generated based on the frequency domain enhancement results; the encoding features are input into a decoder for multi-stage decoding and fusion with the corresponding level features of the encoder to gradually restore the spatial resolution; and the bleeding area segmentation results corresponding to the CT volume data are generated according to the final output features of the decoder. The application can efficiently and accurately segment the bleeding area and assist clinical decision-making.

The present invention relates to the field of 3D modeling technology, and discloses an intelligent evaluation method for the body postures of teenagers based on 3D modeling, including: First step, spatially model the body postures of teenagers by using topology methods, represent the body postures of teenagers as a high-dimensional manifold space, and capture the topological relationships between various postures in the posture space through homology group analysis of the high-dimensional manifold space; Second step, after obtaining the topological structure of the body postures of teenagers, apply manifold learning technology to reduce the dimension of the high-dimensional posture data, and use the locally linear embedding method to optimize the expression of the data. By combining topology methods and homology group analysis, the modeling of the body postures of teenagers in the high-dimensional manifold space is realized, the topological relationships between various postures in the posture space can be effectively captured, a 3D posture model is obtained, and the problem that complex posture changes cannot be accurately described in the prior art is solved.

The invention discloses a dual-path Transform-based sky and ground wave identification method in a Rowland system. The method comprises the following steps: performing learnable discrete wavelet decomposition on a preprocessed signal received by a Rowland receiver to obtain a low-frequency component and a high-frequency component; projecting the components to a potential embedding space through independent linear embedding layers, and obtaining an embedding vector with position information in combination with position coding; inputting the embedded vector into a double-path Transform encoder, and respectively extracting low-frequency and high-frequency characteristics; the encoded features are reconstructed and fused through learnable inverse wavelet transform, and unified global representation is obtained; inputting the global representation into two layers of stacked Encoder for further optimization to obtain deep features; and finally, inputting the deep features into a multi-layer perceptron to realize sky and ground wave recognition. According to the invention, by introducing learnable wavelet decomposition and reconstruction, a dual-path encoder structure and a sparse attention mechanism, the sky and ground wave signals are effectively separated and fused, so that the success rate of ground wave recognition is remarkably improved.

The present disclosure provides a training method of an image extraction model and an image extraction method, which can be applied to the fields of image processing and pattern recognition. The image extraction model comprises a linear embedding network, an encoding network and a decoding network. The training method comprises: processing a sample image obtained by using the linear embedding network to obtain an embedding feature map; processing the embedding feature map by using the encoding network to obtain i encoding feature maps; processing the i encoding feature maps by using the decoding network to obtain a weight fusion feature map, wherein the decoding sub-network is constructed based on a local-global attention layer and a weight feature fusion layer; performing segmentation head mapping processing on the weight fusion feature map to obtain an image segmentation result, wherein the image segmentation result represents a geological change attribute of a target geographical environment region; and training the image extraction model according to the image segmentation result and label data corresponding to the image segmentation result to obtain a trained image extraction model.

The present invention relates to the technical field of artificial intelligence, and disclosed are a CA-SwinT-based hierarchical image classification method and system. The method comprises: S1, acquiring an original image, and preprocessing the original image; S2, performing feature extraction on the image by means of a SwinT module; S3, using a dual cross-attention mechanism to fully fuse extracted image features and classification label information so as to construct fine mapping between the image and a hierarchical label; and S4, on the basis of the fine mapping in S3, generating a corresponding label by means of a label generation module, and updating the classification label information. S2 comprises: patch-based processing, linear embedding processing, two-stage Swin module processing, patch merging processing, two-stage Swin processing, and linear embedding processing. In the present invention, rich feature information of an image can be effectively extracted from spaces of different dimensions, thereby identifying a multi-level label of the image more efficiently.

The invention provides a chromosome classification method based on sequence cross-correlation. The method comprises the following steps: acquiring all chromosome images in a whole chromosome cell map; the image is filled to a multiple of 8 and then is cut into 8 * 8 image expansion data blocks, and position information is generated; splicing image extended data blocks into a sequence, and inputting the sequence into a feature encoder containing global and local self-attention modules after linear embedding and position information embedding; output features are pooled and then classified through a classification head, 24 types of probability distributions are obtained through softmax, and the highest probability index is taken as the category. According to the method, chromosome size information is reserved, the overall relation between single chromosome details and whole-graph chromosomes is considered, and generalization is better under the scenes of chromosome crossing, adhesion, slide production difference and the like.

The invention discloses a ship Z-shaped navigation method guidance and control method based on line-of-sight guidance, and belongs to the technical field of water surface ship transportation under severe sea conditions, and the ship Z-shaped navigation method guidance and control method comprises the following steps: building a ship motion mathematical model; wind wave interference factors are added into the ship motion mathematical model, and a ship motion model under the severe sea condition is obtained; designing a preliminary controller by adopting a third-order closed-loop gain shaping algorithm; introducing the nonlinear composite function into the preliminary controller to realize ship course keeping control, and optimizing the preliminary controller in combination with nonlinear feedback and nonlinear embedding methods to obtain a final controller; a Z-shaped navigation guidance strategy is designed based on a sight guidance algorithm, so that the ship sails according to a Z-shaped route under severe sea conditions; and adjusting the waypoint by using the new sailing direction distance to enable the ship to quickly return to the planned route when the severe sea condition is recovered to the conventional sea condition. The method has an influence on improvement of the development of safety, reliability and intelligence of the ship.

The present application relates to an image feature selection method, product, computer device and storage medium. The present application unifies adaptive graph learning, nonlinear spectral embedding, clustering structure mining items based on orthogonal non-negative decomposition, and linear feature space learning into a framework, in which linear embedding highlights the extraction of discriminant information, and nonlinear embedding emphasizes the retention of the intrinsic structure of the data, while being unified under the consistency of data partitioning. Adaptive graph learning can simultaneously consider the local essential structure and global clustering structure of manifold data. By introducing relaxation items based on orthogonal non-negative decomposition, low-dimensional embedding focuses on retaining the intrinsic structure of the data, while linear feature space learning emphasizes the extraction of data discriminant information. At the same time, the two have the same clustering structure to obtain accurate clustering results.

The invention discloses a three-dimensional human body posture estimation method and system based on space-time semantic adaptive learning, computer equipment and a storage medium, and the method comprises the steps: S1, inputting a video frame sequence, obtaining two-dimensional coordinates of human body key points in each frame of a 2D video through a two-dimensional posture extractor, and generating a 2D skeleton; s2, performing linear embedding, spatial embedding and time embedding on the extracted 2D attitude through an LST module; s3, through a double attention mechanism embedded in the DAS-TF module, further capturing a spatial dependency relationship and a time dynamic change in the 2D attitude data, obtaining an enhanced spatial-temporal feature representation, and outputting three-dimensional coordinates of key points of a human body; and S4, after the first round of parameter updating is completed, performing a second round of parameter optimization through a BV-Loss module, and outputting an optimized three-dimensional human body posture estimation result. According to the method, the precision and robustness of three-dimensional human body posture estimation are improved, and the method belongs to the field of vision and the field of deep learning.

The present application discloses a neural network model quantization method, system, device and computer medium for quantizing a neural network model, including obtaining the weight values, hyperparameters and position serial numbers of each network layer in the target neural network model to be quantized; performing linear embedding on the weight values, hyperparameters and position serial numbers to generate a target embedding matrix; processing the target embedding matrix based on a pre-trained Transformer model to obtain the quantization bit numbers of each network layer in the target neural network model; and quantizing the target neural network model based on the quantization bit numbers to obtain a target quantized neural network model. In the present application, by processing the target embedding matrix with a Transformer model to obtain the quantization bit numbers of each layer in the target neural network model, the model size and memory occupancy can be reduced, while the precision loss of the original network is small. In addition, the amount of computation can be greatly reduced and the limitation is low.

A maskless Transform framework-based intelligent identification method for operation events of a small nuclear power system comprises the following steps of: 1, acquiring multi-source heterogeneous time sequence data related to an operation state by using a sensor system or a simulation platform carried by the small nuclear power system to form an original data set; 2, preprocessing the original data set; 3, creating a linear embedded layer network, and mapping a multi-dimensional parameter vector in the sample sequence to a high-dimensional feature space by using the network to form an event data embedded vector; 4, performing position coding on the event data embedding vector, and adding time sequence information in the sample sequence into the event data embedding vector; (5) a maskless Transform encoder network formed by stacking N identical encoder layers is built, and N is a positive integer greater than or equal to 1, and N is a positive integer greater than or equal to 1; 6, the event data embedding vector is input into a maskless Transform encoder network, and a feature sequence is output; and 7, calculating the probability that the sample sequence belongs to each operation event category through a Softmax function, and judging the category with the maximum probability as a final recognition result. The method is high in recognition accuracy and strong in time sequence characteristic capturing capability.