64 results about "Sequence segmentation" patented technology

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 provides a bidirectional recurrent neural network acoustic logging curve reconstruction method. The reconstruction method comprises the steps of S1, acquiring data and performing sequence segmentation; s2, carrying out normalization processing on the logging curve; s3, carrying out superposition networking and training on the bidirectional recurrent neural network structure block; s4, model testing and parameter storage; and S5, reading the model parameter file to reload the model, importing the to-be-reconstructed logging data, generating a prediction curve, and storing and exporting a result. A bidirectional recurrent neural network algorithm of artificial intelligence deep learning is adopted, bidirectional depth sequence information contained in a logging curve is effectively captured, the curve reconstruction accuracy is improved, unmeasured, missing and low-quality curves are reconstructed, multi-curve information is fused, and the sensitivity of a reconstructed acoustic curve is improved. The system has the advantages of being low in cost, high in efficiency and high in precision.

The invention discloses a data transmission processing system for an industrial internet, and the system carries out the progressive sequence segmentation and local time sequence extraction of a time queue of a pushed data size through employing an AI-based data analysis and coding mode. And intelligently judging whether to start a data compression module to perform data compression on the push data according to the multi-gating time sequence propagation representation of the local time sequence mode characteristics of each push data volume. Therefore, when to perform data compression can be intelligently determined, the problem of resource waste or excessive compression possibly caused by a fixed compression strategy is avoided, the data compression quality is improved, the data transmission efficiency is improved, and meanwhile, the data quality and real-time performance are ensured.

The invention discloses a power grid intelligent inspection method and system combined with abnormal positioning, and relates to the technical field of intelligent inspection. According to the method, periodic sequence segmentation analysis is performed on the inspection data, and the rhythm stack difference sequence is constructed, so that burst fluctuation fragments in each period can be captured, and potential abnormal characteristics with repeated offset trends can be mined; through generation of a local offset structure coding sequence, a spatial offset mode between rhythm nodes is accurately described, and quantitative modeling of implicit structure changes in inspection data is realized. In addition, by comparing structural differences in the current period and historical data, abnormal clues with a complementary relationship on the spatial path are explored, on the basis, the judgment capability of an abnormal initial region is enhanced, and powerful support is provided for subsequent path recombination and risk early warning.

The invention relates to the technical field of data storage management, in particular to a data management method and system for an automobile transaction platform. The method comprises the following steps: obtaining a to-be-stored data sequence corresponding to an automobile transaction platform, and carrying out index pair classification extraction and storage space limit quantification calculation to obtain a storage release memory space limit degree corresponding to each type of index field; performing sequence storage division on the corresponding to-be-stored data sequence to obtain a storage data sequence segment corresponding to each type of index field; obtaining a query access rate and a storage interval approach access degree of the corresponding sequence segment, and carrying out storage activity evaluation analysis and index storage priority measurement analysis to obtain an index storage priority measurement of each type of index fields for each sequence segment; and carrying out priority storage management on the corresponding storage data sequence segments. According to the invention, mass data in the automobile transaction platform can be managed efficiently and accurately, so that the storage process of the data is optimized.

The invention provides a self-adaptive control method for improving new energy power generation grid-connected stability of a micro-grid, and the method comprises the steps: carrying out the description according to a corrected phase change trend, fusing the real-time feedback data of voltage deviation, generating a reactive compensation instruction sequence segmentation adjustment scheme, and determining a preliminary compensation capacity dynamic distribution strategy; the microgrid system is regulated and controlled in real time through the optimized control instruction sequence in combination with the power grid stability constraint condition, the actual execution effect of the compensation action is obtained, and whether the system voltage deviation meets the stability requirement or not is judged; according to the stability requirement judgment result of the system voltage deviation, the execution effect is fed back to a micro-grid control layer, a closed-loop mechanism is adopted to carry out iterative updating on the instruction sequence segmentation adjustment scheme, and a final reactive compensation regulation and control scheme is determined.

The invention relates to the technical field of risk portrait generation based on a knowledge graph, and discloses a risk portrait generation method based on the knowledge graph. According to the scheme, the document is segmented through the sentence end symbols, semantic coherence is ensured, and the accuracy and stability of candidate word extraction are improved. Continuous Chinese character sequence segmentation is adopted, dependence of a word segmentation tool is avoided, the occurrence frequency and position of candidate words are recorded, and a detailed statistical basis is provided for risk factor screening. A minimum occurrence number constant is set, noise interference and information coverage are balanced, the screening flexibility is improved, and candidate word screening is more representative. And counting the co-occurrence relationship of candidate words in the sentence, quantifying the co-occurrence frequency through an indicator function, enhancing word association expression, and providing key data for initial graph construction. Finally, the scheme constructs a knowledge graph based on the candidate words and the co-occurrence relation thereof, ensures clear expression of risk information, and improves the accuracy of the system in risk identification and portrait generation.

The invention relates to an industry electricity consumption prediction method based on retrieval enhancement generation. The method comprises the following steps: collecting industry historical electricity consumption data and preprocessing the data to obtain a historical electricity consumption sequence; constructing a historical electricity utilization sequence segmentation model, and segmenting the historical electricity utilization sequence; extracting a multi-dimensional feature vector of each historical electronic sequence, extracting a low-dimensional feature vector of each historical electronic sequence, constructing a vector database, and storing all the low-dimensional feature vectors; constructing a retrieval enhancement generation model, and identifying a power consumption time sequence mode corresponding to the low-dimensional feature vector of each historical power consumption electronic sequence in the vector database; the method comprises the following steps: collecting industry real-time power consumption data, constructing a real-time power consumption sequence, extracting a query vector, querying in a vector database based on the query vector, and fusing query results to obtain a fused feature vector; and constructing an industry electricity consumption prediction model, and predicting future industry electricity consumption based on the fusion feature vector as a covariable and the real-time electricity consumption sequence.

The invention discloses a focus segmentation method and device based on association information between images, a medium and a product, and relates to the field of image processing, and the method comprises the steps: obtaining a tumor slice sequence; the tumor slice sequence is input into the trained Mesh-SSM UNet sequence segmentation model, and a focus region segmentation result sequence is obtained; an improved UNet network model is adopted as the Mesh-SSM UNet sequence segmentation model; in an improved UNet network model, a visual feature extraction unit of each coding layer of a coder adopts a 2D selective scanning module and a mesh propagation selectable scanning module group which are connected in series, and a visual feature extraction unit of each decoding layer of a decoder adopts a 2D selective scanning module; the mesh propagation selectable scanning module group is used for extracting correlation characteristics between image sequences from the input image sequences. Based on the lesion segmentation method, the segmentation precision of the lesion region can be improved.

The invention discloses a large model reasoning method and device based on multi-level cache, a storage medium and equipment, and the method comprises the steps: receiving a reasoning task, and carrying out fixed-length lexical sequence segmentation processing on the reasoning task to determine at least one lexical sequence corresponding to the reasoning task, the lengths of other lexical element sequences except the last lexical element sequence in the lexical element sequences are fixed lengths, and the length of the last lexical element sequence is smaller than or equal to the fixed length; aiming at the lexical element sequence with the fixed length, a target data block corresponding to the lexical element sequence is inquired in a video memory, if the lexical element sequence does not exist in the video memory, the target data block corresponding to the lexical element sequence is inquired in a memory, and when the data blocks in the video memory are evicted out of the video memory, the data blocks are copied into the memory; if the tail lexical element sequence is smaller than the fixed length, the reasoning task is executed according to the target data block and the tail lexical element sequence through the large model; if the tail lexical element sequence is equal to the fixed length, the reasoning task is executed according to the target data block through the large model.

The invention belongs to the technical field of medical image processing, and discloses a medical image sequence semi-supervised segmentation model construction method and application, and the method comprises the steps: constructing a medical image sequence semi-supervised segmentation network which comprises a first segmentation network, a feature projection module, a second segmentation network, a space-time memory module and a channel and space attention module; utilizing a space-time memory module to obtain enhanced features containing semantic information; using double cross attention perception to obtain fusion features, and inputting the enhanced features and the fusion features into a second segmentation network with time perception to generate high-quality sequence segmentation; and training the medical image sequence semi-supervised segmentation network by using the consistency loss of the segmentation labels # imgabs0 # and # imgabs1 # output by the two segmentation networks, the labeled parts in the segmentation probability graphs # imgabs2 # and # imgabs3 # and the supervised loss of the corresponding manual annotation, and obtaining a trained medical image sequence semi-supervised segmentation model. According to the invention, the segmentation performance of the medical image sequence can be improved.

The application discloses a kind of multi-group one-dimensional frequency energy density spectrum ratio measurement precision evaluation method and system, belong to marine observation technical field, comprising: the training data sample of establishing including M group synchronous observation sea wave one-dimensional frequency energy density spectrum sequence;21 similarity evaluation value sequences are constructed;21 similarity evaluation value sequences are sorted according to the order of evaluation value from big to small, and at least two dimensions in sequence segmented average, overall average, fluctuation amplitude, fluctuation rate, distribution concentration and dispersion degree are analyzed, to determine the comprehensive weight coefficient of 21 similarity evaluation values;The one-dimensional frequency energy density spectrum sequence of two synchronous observations to be evaluated is obtained, and the corresponding 21 similarity evaluation values thereof are calculated;The final similarity evaluation value NRD is calculated, to evaluate the ratio measurement precision of two to be evaluated sequences.The application constructs a multidimensional evaluation system, can comprehensively reflect the similarity of two sea wave spectrum sequences.

The application discloses a kind of communication power supply operating state intelligent monitoring methods based on machine learning, comprising the following steps: S1, obtains multiple-source monitoring data and pre-processes;S2, sequence segmentation is carried out using sliding time window and statistical feature vector is extracted, forms state variable set;S3, each state variable is encoded to construct state node and establish weighted directed connection, and construct operating state evolution diagram;S4, state transition matrix is constructed, main transition feature is extracted and low rank is approximately reconstructed, sequence learning is carried out to construct state update function;S5, to state node, multiple-step recursion is carried out, and the Euclidean distance between recursive state and risk boundary is calculated, to determine state monitoring result;S6, error is calculated and state update function parameter is iteratively updated.The application can realize the multiple-step recursion prediction and risk trend identification of communication power supply operating state, improve the accuracy and stability of communication power supply operating state monitoring.

The invention discloses a bit sequence preprocessing method and an OOK symbol generation method and device, and belongs to the technical field of Internet of Things. The method comprises the following steps: acquiring a first bit sequence of which the length is a first number L; under the condition that the first number L is not the integral multiple of the second number M, processing the first bit sequence into a second bit sequence of which the length is a third number L '; wherein the second bit sequence is used for being divided into at least one sequence segment, the length of which is a second number M, for OOK modulation, and M OOK symbols corresponding to each sequence segment are obtained.

This invention discloses a multi-source electromagnetic noise suppression method based on noise classification and deep learning, belonging to the field of geophysical electromagnetic exploration technology. The method includes: segmenting and preprocessing the original electromagnetic observation sequence; using an improved U-Net network with an encoder embedded in a Mamba time-series modeling module for low-frequency noise suppression; identifying strong noise types in the data segments using a ROCKET classifier; based on the classification results, calling a second improved U-Net network trained for the corresponding noise type for class-based targeted denoising; and finally, splicing the data segments to obtain complete, high-quality data. This invention, through a phased processing framework of "low-frequency pre-suppression—noise classification—class-based denoising," combined with the strong time-series modeling capabilities of the Mamba module and the efficient classification performance of ROCKET, significantly improves the suppression accuracy and signal fidelity for complex, multi-source electromagnetic noise, and is particularly suitable for processing ground and airborne electromagnetic exploration data.

The present application provides a sequence segmentation method, device, and computer-readable storage medium; the method includes: obtaining a sequence to be segmented including n segmentation units, wherein n is an integer greater than 1; performing sequence segmentation on the n segmentation units to obtain a candidate segmentation sequence score set corresponding to the sequence to be segmented; wherein the candidate segmentation sequence score set is a set consisting of candidate segmentation sequence scores corresponding to each candidate segmentation sequence, each slice in each candidate segmentation sequence constitutes the sequence to be segmented, and the candidate segmentation sequence score is a combination of the scores of each slice corresponding to each slice; obtaining the highest candidate segmentation sequence score in the candidate segmentation sequence score set; and determining the candidate segmentation sequence corresponding to the highest candidate segmentation sequence score as the sequence segmentation result of the sequence to be segmented. Through the present application, the effectiveness of sequence segmentation in natural language processing can be improved.

The invention relates to a parameter reduction meta learning load prediction method based on sequence segmentation and a bidirectional GRU network. According to the method, knowledge migration from source load data to a target prediction task is realized based on meta-learning, and the prediction effect under the conditions of small samples and distribution drift is improved. Firstly, a task data division method based on decomposition characteristic time sequence distribution similarity quantization DP is provided, it is ensured that each task has unique time sequence characteristics, and the meta-learning generalization ability is improved. Secondly, feature gradient parameter reduction is introduced into inner layer circulation of meta-learning, and the memory overfitting problem is relieved; besides, a bidirectional GRU network is introduced as a basic model, the long-term and short-term dependency capture capability of the sequence is enhanced, the network structure is kept simple, model parameters can quickly adapt to a target task under limited training data, and good prediction performance is achieved under the conditions of small samples and distribution drift.

This application relates to the field of generator fault identification technology, and discloses a method and apparatus for early fault identification of steam turbine generators based on vibration. The method includes: acquiring training samples; extracting multiple continuous vibration signal segments from each vibration signal sequence sample, constructing grayscale images based on each continuous vibration signal segment, and generating a grayscale image set; training an initial convolutional neural network classifier using an unsupervised sequence segmentation method on each grayscale image set to obtain a target convolutional neural network classifier. The unsupervised sequence segmentation method generates pseudo-labels by automatically determining the optimal early fault occurrence time for each grayscale image set, and uses the pseudo-labels to train the initial convolutional neural network classifier; acquiring the vibration signal to be identified in real time, dynamically constructing the vibration signal segment to be identified based on the acquired vibration signal, constructing the grayscale image to be identified based on it, and inputting the grayscale image to be identified into the target convolutional neural network classifier to determine whether a fault exists.

The present invention discloses a weather data prediction method based on sequence segmentation and frequency domain attention, including: S1: constructing a weather data set; S2: constructing a weather prediction model based on an informer model; importing weather data into the PatchTST local feature extraction module in the encoder to obtain the segmented local features; S3: importing the local features in step S2 into the DCT frequency domain attention module to obtain enhanced frequency domain features; importing the local features in step S2 into the multi-head sparse attention module at the same time to obtain sparse attention features; S4: the encoder feedforward layer splices the enhanced frequency domain features and sparse attention features in step S3 through learning factors, and obtains the first feedforward layer output through dimensional conversion; S5: importing the first feedforward layer output into the decoder to obtain the predicted weather data; S6: constructing a loss function, minimizing the loss function to optimize the model. The present invention can accurately capture the short-term fluctuations and long-term trends of weather data.

This invention belongs to the field of acoustic sensing demodulation, specifically a demodulation method for a distributed fiber optic acoustic wave sensing system based on FPGA time-sequence segmentation. This invention uses an FPGA as the processing module for signal demodulation. On the FPGA, three signals with a 120-degree phase difference output from the distributed fiber optic system are acquired. Then, time-sequence segmentation is achieved by FIFO buffering of the spatial point data, ensuring data alignment at each spatial point during signal demodulation. Finally, moving average and other signal demodulation processes are performed on the data at each spatial point in the time domain. This invention requires only a small amount of data buffering to achieve demodulation of the entire system, reaching the theoretical maximum distance for signal demodulation. Ultimately, it achieves signal demodulation of a long-distance distributed fiber optic acoustic wave sensing system with high repetition rate under low resource consumption, providing a new approach and method for hardware-based signal demodulation of distributed fiber optic sensing systems.

The invention discloses a multi-scale component grasping posture estimation method and system, which includes acquiring scene images and annotating them to obtain a data set; building a posture estimation neural network model; inputting the data set into an image block encoding module to output a coding feature sequence; inputting the coding feature sequence into a sliding window attention retention feature encoding module to perform sequence segmentation with perception windows of different sizes and respectively apply the attention retention mechanism to map to high-dimensional feature output; inputting the high-dimensional feature into a multi-scale space pyramid perception module and optimizing and outputting the optimized feature sequence; a window attention retention feature decoding module receives the optimized feature sequence and outputs the grasping posture; setting a loss function and updating the network parameters according to the gradient situation to obtain a trained posture estimation neural network model, completing the grasping posture estimation of the industrial real grasping operation scene image, and completing the grasping operation task. The grasping efficiency and accuracy are improved, and the hardware cost is reduced.

The invention relates to an all-sky aurora video unsupervised event segmentation method based on bidirectional perception, which comprises the following steps of: 1, inputting an aurora image sequence, randomly selecting a target frame, forming two subsequences which take the target frame as a center by using the front frame and the rear frame of the target frame as positive samples, and enabling the features of the target frame to have context information; step 2, carrying out comparative learning on the extracted target frame features to obtain a higher-level representation, masking the target frame, then carrying out bidirectional feature reconstruction, reconstructing the target frame, and training a reconstruction model; 3, testing the trained reconstruction model, performing reconstruction and similarity calculation on the aurora image sequence in the test set frame by frame to obtain an error curve, regarding the error curve as a boundary probability curve, performing smoothing processing by using a filter, setting a boundary probability threshold, and regarding a frame which is greater than the boundary probability threshold and is a peak value as a boundary frame; the method has the characteristic of high aurora sequence segmentation accuracy.

The invention relates to the technical field of generator fault identification, and discloses a vibration-based turbonator early fault identification method and device, and the method comprises the steps: obtaining a training sample; intercepting a plurality of continuous vibration signal segments from each vibration signal sequence sample, and constructing a grayscale image according to each continuous vibration signal segment to generate a grayscale image set; and training the initial convolutional neural network classifier by using each grayscale image set and adopting an unsupervised sequence segmentation method to obtain a target convolutional neural network classifier, and generating a pseudo-tag by automatically determining the optimal early fault occurrence time for each grayscale image set according to the unsupervised sequence segmentation method. Training an initial convolutional neural network classifier by using the pseudo labels; the method comprises the steps of obtaining a to-be-recognized vibration signal in real time, dynamically constructing a to-be-recognized vibration signal segment based on the obtained vibration signal, constructing a to-be-recognized grayscale image according to the to-be-recognized vibration signal segment, inputting the to-be-recognized grayscale image into a target convolutional neural network classifier, and determining whether a fault exists or not.

The invention relates to the technical field of ground surface settlement monitoring, and discloses a ground surface real-time monitoring system based on a shield construction tunnel. A data acquisition unit of the system obtains a displacement measurement value from an earth surface sensor and generates time sequence data; the sequence segmentation unit analyzes mutation points in the sequence and segments the sequence into a plurality of data segments according to the mutation points; an exception marking unit performs statistical test on each data segment to identify an exception data segment set; the trend evaluation unit performs trend line fitting on the abnormal set and evaluates the fluctuation degree of fitting deviation; the matrix conversion unit organizes the time sequence data into a specific matrix structure and performs matrix decomposition; a feature derivation unit derives feature parameters from the decomposition result; and the monitoring control unit adaptively adjusts a monitoring strategy according to the characteristic parameters and generates a settlement report. According to the system, accurate and self-adaptive monitoring and early warning of ground surface settlement are realized through multi-stage data processing and intelligent analysis.

The invention provides a neural network action recognition data processing method, and solves the problems that feature extraction in a traditional action recognition system depends on manual design, the model training efficiency is low, cross-platform deployment is difficult and the like. According to the method, by constructing an end-to-end deep learning model, direct mapping from original sensor data to gesture categories or sentences is achieved, meanwhile, multi-device deployment is supported by using cross-platform characteristics of TensorFlow, and an efficient and stable processing scheme is provided for wearable action recognition devices. The system specifically comprises a data preprocessing module, a 1D CNN model construction module, a model training module and a model deployment module. The data preprocessing module supports loading of an open source triboelectric sensor action data set from multiple formats, and signal standardization, data enhancement and sequence segmentation are achieved. The 1D CNN model construction module adopts a multi-layer 1D convolution structure, and introduces a time sequence attention mechanism and multi-scale feature fusion, thereby improving the recognition precision. The model training module uses an Adam optimizer and a learning rate attenuation strategy to realize early stop and check point storage. And the model deployment module supports model export, model compression and cross-platform deployment. Experimental results show that the recognition accuracy of the 1D CNN model on a test set reaches 91.3%, which is superior to that of a traditional method, and the 1D CNN model shows good performance in a continuous action sentence recognition task.

This invention relates to the field of fault location technology, and discloses a method and system for rapid fault location in operation and maintenance. The method includes: acquiring operational data and denoising it to obtain a denoised data sequence; segmenting the sequence and identifying anomalies, determining the anomaly range through trend offset, and obtaining anomaly markers; if the waveform distortion metric of the anomaly marker exceeds a threshold, obtaining the anomaly location through time series analysis; extracting periodic sequences, grouping similar behaviors among devices to obtain associated device clusters; constructing a causal graph among devices, obtaining a list of potential anomalous devices through propagation path backtracking and node dependency sorting; assessing the impact degree of each device and identifying the faulty device; and obtaining the final fault location based on the topology information of the faulty device through cross-cluster association decomposition, causal edge weight adjustment, and graph structure simplification. This method can accurately and quickly locate the source of a fault in complex network environments with severe noise interference and tight device coupling.

The application discloses a kind of multi-source data's reliable alignment method, device, equipment, storage medium and product.Identify the discontinuous fragment of the measurement data sequence of each sensing device and associate sensing device identification, obtain measurement data variation section set;Determine the data channel of each associated sensing device based on sensing device identification and determine overlapping path section through network diagnosis, generate path network atlas;The target measurement data sequence that flows through each overlapping path section is segmented and processed to obtain target data segment, and different target data segments that flow through each overlapping path section at the same time are subjected to data flow consistency analysis to determine an interference channel list;Each target data segment in the target measurement data sequence via the interference channel is subjected to validity determination, and the effective data segment and the measurement data sequence of the non-interference channel are aligned and combined according to time to obtain multi-source reliable alignment data.It ensures data quality, avoids distortion information mixed into fusion results, and improves the accuracy and reliability of downstream decision-making.

This application relates to the field of image processing technology, specifically to a high-voltage power supply DNA sequencing visual inspection method and system. The method includes: acquiring a four-channel DNA electrophoresis image using Sanger sequencing technology; acquiring contours in the DNA electrophoresis grayscale image; constructing a DNA local density confidence coefficient and local DNA density parameters for each pixel based on the grayscale values ​​of each pixel within a channel and its neighboring pixels; acquiring the density run matrix for each pixel; constructing a DNA density distribution heterogeneity coefficient and channel tailing effect evaluation parameters for each pixel, and based on these, constructing a sequencing segmentation probability coefficient for each pixel; acquiring a decision threshold for each pixel; using the center of each contour as a seed point, and acquiring each DNA fragment based on an adaptive threshold using a region growing method, thereby obtaining the DNA sequence. This application can improve the accuracy of detecting the base sequence of a DNA sequence.

The present invention discloses a precipitation prediction method and system based on artificial intelligence algorithm and knowledge graph, which belongs to the field of weather forecast technology, and includes the following steps: constructing a multimodal data container, inputting different types of meteorological data into the multimodal data container according to their own structural characteristics to obtain multimodal data, performing spatiotemporal alignment, data cleaning and preprocessing, and data sequence segmentation on the multimodal data; constructing a knowledge graph about precipitation prediction; creating a multimodal precipitation prediction model based on artificial intelligence algorithm, and intelligently correcting the predicted precipitation data. The present invention not only makes full use of different types of multimodal meteorological data, improves the accuracy of precipitation prediction through a multimodal precipitation prediction method, but also constructs a precipitation prediction knowledge graph, which can intelligently correct the results of model prediction to reduce the uncertainty of the artificial intelligence algorithm and improve the accuracy and reliability of model prediction.

The application discloses a cross-sequence multi-behavior sequence recommendation method based on a Transformer. The method is as follows: firstly, a user historical interaction sequence is segmented, and then cross-sequence heterogeneous relationship propagation is performed; then, short-term interaction mode learning is performed; next, global representation aggregation is performed; finally, a recommendation content list is generated by combining user recent preferences and item embedding, and meanwhile, content priority is considered; and model parameters are adjusted by using user historical interaction for label data enhancement. The application proposes a user sequence segmentation method, guarantees that a subsequence maintains relatively concentrated and stable preferences of a user, and enables the model to more accurately capture dynamic changing preferences of the user, proposes a similar subsequence determination mechanism and a method for performing cross-sequence information propagation between similar subsequences, and preserves time coding information in the information propagation process, thereby improving the diversity and accuracy of recommended contents.