307 results about "Information embedding" patented technology

The invention discloses a Transform-based traffic flow prediction method and device capable of sensing a local-global time-space relationship, and the method comprises the steps: firstly obtaining the historical traffic data of all to-be-predicted road sections, the historical data comprising the traffic flow feature data of the predicted road sections, the static adjacent matrix data between road network nodes, and the sampling timestamp data of the historical data; secondly, a spatio-temporal information embedding layer is constructed to provide multiple types of embedding input for a model trunk, the learning ability of the model is enhanced, and three different types of embedding are respectively historical data information embedding, time information embedding and space node self-adaptive embedding; then, constructing a local-global time dependence extraction module, respectively learning short-time and long-time time dependence relationships in the data by using a multi-scale TCN and a self-attention mechanism in a time dimension, and meanwhile, introducing a double-path self-adaptive information gating fusion technology to realize effective fusion of time features of different hierarchies; then constructing a local-global spatial dependency extraction module, respectively learning local and global spatial dependency relationships in the data by using a dynamic-static graph convolutional network and a self-attention mechanism in spatial dimension, and realizing effective fusion of spatial features of different levels based on a two-way adaptive information gating fusion technology; and finally, mapping the potential spatial-temporal feature representation into a prediction result through a full connection layer network.

The invention provides a Mama-based multi-modal dialogue emotion recognition method and system, and belongs to the field of dialogue emotion recognition. The problems that an existing multi-mode emotion recognition method is limited in memory ability, information is not fully utilized, and noise accumulation exists in long sequence fusion are solved. Comprising the following steps: processing dialogue data in different modes by adopting different preprocessing modes to obtain corresponding feature codes; performing convolution and information embedding on different feature codes; external attention is adopted to capture semantic information in each modal; interaction among different modes is realized by using a cross fusion mechanism; filtering noise in each mode by adopting Kalman filtering and establishing a relation between the modes; fusing the semantic information of different modals by using grouping pooling and cross attention, and then fusing the fused semantic information with a Kalman filtering result to obtain a fusion result; mapping the fusion result into a predefined emotion category; the method and the device are applied to multi-mode dialogue emotion recognition.

The invention discloses a 3D human body posture estimation method based on a diffusion model. According to the method, firstly, a continuous human body dynamic video is split into RGB images, two-dimensional coordinate information, embedded time steps and positions of all joint points of a human body are extracted from the RGB images through a 2D posture detector, then predicted 3D postures are output through a diffusion model, and low-frequency key information is reserved and high-frequency noise is filtered in combination with discrete cosine transform (DCT), so that the 3D postures are obtained. And the calculation complexity is greatly reduced. And based on a multi-hypothesis aggregation strategy of confidence and consistency evaluation, weighted fusion is performed on the predicted 3D postures, so that the precision and robustness of posture estimation are improved. The performance of the model in a dynamic scene is further enhanced by a time step embedding mechanism and time consistency scoring, so that the model adapts to a complex motion environment and a shielding condition. The method is low in computing resource demand, is suitable for being deployed on resource-constrained equipment in the fields of human-computer interaction, virtual reality, motion analysis, medical rehabilitation and the like, and has a wide application prospect.

The invention provides an interest point recommendation method based on a graph-enhanced user context information network. The interest point recommendation method comprises the following steps: acquiring historical track data of a user and preprocessing the historical track data; utilizing an interest point supplementing module to fill the interest point between two adjacent interest points of which the road network distance and the sign-in time difference are greater than a threshold value in the historical trajectory data to obtain enhanced trajectory data; inputting the enhanced track data into a user context information embedding module to extract a context information embedding vector of the user; according to the enhanced trajectory data of all users in the network, constructing a global trajectory graph by taking the interest points as nodes and taking a sign-in sequence of the users to the adjacent interest points as edges; inputting the global trajectory graph into a general travel module, respectively extracting POI embedding and user embedding by utilizing a GCN network and an MCL network to obtain a POI embedding matrix and a user embedding matrix, and fusing the POI embedding matrix and the user embedding matrix to obtain a POI-user embedding vector of the user; and inputting the context information embedded vector of the user, the POI-user embedded vector of the user and the global trajectory graph into a prediction module for prediction to obtain an interest point recommendation result of the user. According to the method, the core problem in the prior art is effectively solved, the accuracy and scene adaptability of recommendation of the next interest point are remarkably improved, and the method has important application value and wide development prospects in the fields of intelligent transportation, personalized position service and the like.

The invention belongs to the technical field of graph classification, discloses a graph classification method based on subgraph integration and position sensing, and relates to a graph classification method SIPA combining substructure embedding and node position sensing. The SIPA firstly extracts sub-graphs through two different strategies so as to capture multi-sample sub-structures in the graphs, the structural features of the sub-structures are coded by adopting a graph convolutional network, and information of different sub-graphs is effectively fused through an attention mechanism. Then, anchor nodes are introduced to calculate relative position information of nodes, and the information is embedded into node representation to better capture global position features. And finally, a graph information bottleneck mechanism is used for optimizing node representation and removing redundant information irrelevant to a classification task. The method not only effectively learns the local structure information, but also enhances the perception capability of the relative position information of the nodes. Experimental results show that SIPA is superior to an existing baseline model in five data sets, and the superiority of SIPA in a graph classification task is verified.

The invention discloses a policy service recommendation method and system based on a large language model, and relates to the field of policy information, and the method comprises the steps: analyzing a policy file, extracting policy terms with a numbering structure, building a unique identifier, and forming a term node set; identifying explicit or implicit reference behaviors in terms, constructing a reference path, and generating a reference directed graph; constructing a clause mapping tree based on the target clauses; mapping tree nodes are extracted to form structured prompt information, and the structured prompt information is embedded into large language model input; generating a policy report paragraph containing clause reference content; and outputting the structured policy text with clause reference chain closure and recommending the structured policy text to the user. According to the method, the clear reference context combined with the user information can be provided for the large language model, the large language model is guided to generate a text with a reasonable structure and complete terms for recommendation, and the problem of inaccurate generation when an existing model processes a complex reference structure is solved.

The invention discloses a tourist attraction recommendation system adopting a virtual reality technology, belongs to the field of image data processing, and is used for solving the problems of how to improve defects in image information processing and improve scene fidelity and how to increase a virtual reality scene interaction function and improve scene interactivity. The system comprises seven main modules including a data acquisition module, a virtual reality scene construction module, a user information acquisition module, a recommendation algorithm module, a virtual reality experience module, a feedback collection module and a recommendation adjustment module. According to the technical scheme, through integration of the above modules, in the image modeling unit, a finer three-dimensional modeling technology is combined with accurate geographic information fusion and rich text information embedding operation, so that real scenic spots in a virtual scene can be restored as much as possible in a one-grass-one-wood and one-tile manner, highly-vivid and immersive experience is provided for a user, and the user experience is improved. And the user can feel the real atmosphere and detail characteristics of the scenic spot like being personally on the scene.

The invention belongs to the technical field of image analysis, and particularly relates to an electronic component defect detection method and system based on machine vision, and the method comprises the following steps: S1, obtaining visible light, X-ray and three-dimensional structured light images of an electronic component to be detected, and generating respective multi-scale feature pyramids through independent feature extraction networks; s2, generating a multi-modal fusion feature pyramid; s3, applying a candidate region generation network to propose a candidate defect region; s4, processing the spatial topological relation graph through a graph neural network, and embedding spatial context information into candidate defect region features; and S5, if the minimum distance between the candidate region features and all the pre-stored prototypes is greater than a set discrimination threshold, determining that the candidate region is a potential defect of an unknown type. According to the method, the detection range is widened, the robustness of feature expression is improved, the limitation of insufficient information of a single data source is overcome, and the accuracy, comprehensiveness and perspectiveness of detection are improved.

The invention provides an active-disturbance-rejection control method for an electromagnetic bearing two-level digital switching power amplifier embedded with model information. The method comprises the steps that an electromagnetic bearing two-level digital switching power amplifier mathematical model suitable for controller design is established based on the Fourier series theory and coil current characteristics; a direct current component in the mathematical model is defined as a modeling disturbance term, and feedforward compensation is directly carried out in the control law; parameter perturbation and an unmodeled part in the mathematical model are defined as unknown perturbation items, and real-time estimation is carried out through an extended state observer; a current item and a control input item in the mathematical model are used as known model information to be embedded into the design of the extended state observer; designing a feedback control law to carry out real-time compensation on the current item, the modeling disturbance item and the unknown disturbance item; a zero-order retainer and a current observer are adopted to carry out discretization processing on the controller, and the discretization processing is realized on a digital control platform based on a DSP (Digital Signal Processor). The method is high in current response speed and high in control precision.

The invention discloses a reversible information hiding method and system for enhancing image smoothness, and relates to the technical field of information security, and the system comprises a preprocessing module, an information embedding module and an optimization recovery module. According to the method, the Sobel operator is utilized to calculate the image gradient map, the region is divided based on the threshold T, the smooth region and the texture region can be accurately distinguished, an accurate basis is provided for differential processing of different regions, region characteristics are fitted, large-size blocks and sub-blocks of the smooth region are divided to facilitate subsequent refinement processing based on the gradient mean value, and the processing efficiency is improved. The small-size blocks of the texture area are beneficial to operation aiming at texture features, the rationality and effectiveness of overall processing are improved, meanwhile, the pixel mean value in the large-size blocks of the smooth area is calculated, the pixel and mean value difference value is recorded, reversible low-pass filtering is carried out, and the reversibility of the image in the whole information hiding and recovering process is ensured.

The embodiment of the invention provides an address data matching method and related equipment, and belongs to the technical field of geographic information services. The method comprises the following steps: constructing an address annotation corpus according to input address information data and a preset address database; the method comprises the following steps: generating a geographic information embedding vector according to a preset geographic information knowledge graph, performing address element analysis in combination with an address annotation corpus to obtain an address element sequence so as to construct a dictionary tree, and performing similarity screening through a spatial hierarchical matching algorithm to obtain a similar address set; generating an address embedding vector matrix through a preset word embedding vector model, and performing feature extraction through a preset semantic feature extraction model to obtain semantic-level similar features; according to input address information data, multi-dimensional character similarity matching is carried out to obtain character-level similar features, then weighted fusion is carried out in combination with semantic-level similar features, and target matching address data is determined according to a weighted fusion result. According to the embodiment of the invention, the address data matching accuracy and efficiency can be improved.

The invention discloses a generative image steganography method and device based on Stable Diffusion and discrete wavelet transform, and the method comprises the following steps: S1, sampling a potential representation of a to-be-generated image through employing a Stable Diffusion diffusion model, embedding secret information into a potential space of the diffusion model, processing the potential representation through employing discrete wavelet transform to carry out frequency domain information modulation, and carrying out the frequency domain information modulation; obtaining the potential representation after the secret information is embedded; s2, inputting the potential representation embedded with the secret information into a diffusion model, and generating a visual natural steganographic image through a diffusion inversion process; s3, inputting the received steganographic image into a diffusion model, and decrypting the received steganographic image without original model weight modification and empty prompt conditions to obtain embedded secret information; according to the method, the frequency domain embedding technology is combined with the potential diffusion model, so that high-fidelity, high-capacity and high-robustness image steganography is realized.

The invention provides a hyperspectral target detection method based on generative self-supervised learning and wavelet transform. The hyperspectral target detection method comprises the following implementation steps: acquiring a pre-training / testing sample set and a fine tuning sample set; constructing a spatial-spectral reconstruction model based on wavelet transformation and performing generative self-supervised pre-training on the spatial-spectral reconstruction model; constructing a priori constrained hyperspectral target detection network model and carrying out fine tuning training on the model; and obtaining a hyperspectral target prediction result and a detection result. In the pre-training process, the multi-scale spatial and spectral features can be effectively extracted and fused by combining a dual-branch information embedding module of wavelet transform, so that an encoder can learn feature representation from multi-scale spatial-spectral information; and the hyperspectral target detection network model is finely adjusted by using a priori constrained cross entropy loss function, and nonlinear transformation is performed on a prediction result, so that background information can be suppressed by fully utilizing a priori target spectrum, and the hyperspectral target detection precision is improved.

The invention discloses a heterogeneous network embedding method based on dynamic heterogeneous network decomposition and a hierarchical space-time attention mechanism. The method comprises the following steps: dynamic network decomposition: decomposing a heterogeneous network sequence into independent subgraph sets according to edge types; multi-order node attention aggregation: for each sub-graph, fusing information of nodes and different edge types; semantic level attention is embedded, wherein global semantic embedding is generated in combination with global relation type weight and meta-path long-short-term memory network coding; time dynamic modeling: respectively calculating a time attention weight and a Monte Carlo sampling approximate Horkes intensity, and then aggregating and splicing results of the time attention weight and the Monte Carlo sampling approximate Horkes intensity to obtain a final embedding result. According to the method, through cross-granularity semantic modeling and efficient time sequence dependence learning, an innovative solution is provided for node representation of the dynamic heterogeneous network.

The invention discloses a mapping knowledge domain embedding method fusing meta-information and logic rules, and relates to the technical field of computers. The method comprises the following steps: inputting a to-be-predicted knowledge graph into a knowledge graph embedding module; in the multi-granularity meta-information embedding module, for each entity to be predicted in the knowledge graph to be predicted, obtaining ontology information embedding and multi-hop neighbor relation embedding of the entity to be predicted; carrying out average pooling processing on each of the multi-hop neighbor relation embedding, and processing the neighbor relation embedding after the average pooling processing through a multi-layer perceptron and learnable parameters to obtain a hierarchical attention weight of each neighbor relation embedding; each multi-hop neighbor relation embedding comprises a plurality of neighbors; and performing weighted summation on the ontology information embedding and the multi-hop neighbor relation embedding based on the hierarchical attention weight to obtain meta-information embedding of a to-be-predicted entity in the to-be-predicted knowledge graph. According to the method, the determination precision of the meta-information embedding of the entity can be improved.

The invention discloses a rule engine quality inspection method and device, a storage medium and a computer program product, and relates to the technical field of software testing, the method comprises the following steps: receiving an expected target description input by a user, and determining an executable script code corresponding to a rule engine to be subjected to quality inspection based on the expected target description; extracting parameter information corresponding to the rule definition text of the rule engine to be subjected to quality inspection; embedding the expected target description, the executable script code and the parameter information into a preset cue word template; and calling a quality inspection model to perform quality inspection on the cue word template to obtain a rule engine quality inspection result. The rule engine is converted into the script code based on the mainstream computer programming language from the proprietary grammar, so that the quality inspection efficiency and the quality inspection accuracy of the quality inspection model on the rule engine are improved.

The present application belongs to the technical field of image analysis, and particularly relates to a kind of electronic component defect detection method and system based on machine vision, comprising the following steps: S1, the visible light, X-ray and three-dimensional structure light image of the electronic component to be measured are acquired, and respective multi-scale feature pyramids are generated through independent feature extraction network respectively;S2, multi-modal fusion feature pyramid is generated;S3, candidate region generation network is applied to propose candidate defect region;S4, spatial topological relation graph is processed through graph neural network, and spatial context information is embedded into candidate defect region feature;S5, if the minimum distance of candidate region feature and all pre-stored prototypes is greater than the set discrimination threshold, then it is determined that the candidate region is unknown type potential defect.The present application widens the detection range and improves the robustness of feature expression, overcomes the limitation of insufficient information of single data source, and improves the accuracy, comprehensiveness and foresight of detection.

The invention discloses a composite circuit substrate material parameter pixel gray scale coding method and device and a storage medium, and relates to the technical field of composite substrate material physical property modeling simulation. According to the method, the composite circuit substrate unit image is pixelated, the pixel gray value is used as a matrix material parameter coding space, and then matrix material parameter information is embedded into the substrate unit image, so that fusion coding of wiring pattern features and material mechanical parameters is realized when two or more materials exist in the same layer of substrate; and the accuracy of predicting the equivalent mechanical parameters of the substrate unit through the wiring image information of the substrate unit and the deep convolutional neural network is improved.

The invention relates to the field of quality management optimization, and discloses a vaccine clinical test quality management system optimization method and system, and the method comprises the steps: obtaining vaccine clinical test data of a target test group, building an interaction information network according to the extracted case sharing test information, carrying out the node graph convolution propagation of the interaction information network, and obtaining a target test group; a structural information embedding vector is obtained, a structural outlier center of vaccine clinical test data is identified, abnormal type hierarchical analysis is performed on the structural outlier center to obtain a quality deviation classification result, and a local structural abnormal cluster is identified according to an acquired abnormal propagation path. And generating a local quality control strategy according to the quality deviation classification result and the local structural anomaly cluster, and optimizing the vaccine clinical test quality management system. According to the method, the structural outlier center in the vaccine clinical test data can be effectively captured and the test data interaction diagram structure can be constructed in the vaccine clinical test data lacking a natural interaction network.

The embodiment of the invention provides an end-to-end image compression method and system based on window local attention and generalized chessboard space channel context, and belongs to the technical field of image processing. The method comprises the following steps: constructing a transformation network based on an attention module and a stacked residual block; the transformation network based on the attention module and the stacked residual block is used for executing adaptive transformation of contents through dynamic representation and neighborhood information embedding to obtain potential features; establishing a generalized chessboard space channel context model; the generalized chessboard space channel context model is used for carrying out entropy coding on the potential features; and obtaining image compression data according to the transformation network based on the attention module and the stacked residual block and the generalized chessboard space channel context model. According to the method, redundancy can be eliminated to the maximum extent, excellent rate distortion performance is achieved, and meanwhile high-throughput parallel computing efficiency is ensured.

The invention provides a coding method and system of a Transform class model for channel state information prediction. The coding method comprises the following steps: S1, acquiring and processing channel state time sequence data; s2, inputting the processed data into an encoder to extract features; s3, CFR data needing to be predicted are embedded, and position codes of the model are added; decoding the relevant parameters using a decoder; s4, processing the output of the decoder, and predicting channel state information; and S5, taking the CFR data and the CSI data obtained in the actual scene as the input of the model, and predicting channel frequency response information. According to the method, the time sequence characteristics of the channel state information are calculated and coded, and the time information is brought into the model, so that the relative stability time of the channel at the future moment is considered, the problem of weak time sequence information embedding capability in a Transform model is solved, and the accuracy of channel state prediction is improved.

The present invention discloses an H.265 / HEVC video adaptive steganography method based on improved RDO. The embedding of the secret information includes three parts: extracting the carrier, calculating the distortion cost value, and STC adaptive embedding. According to the characteristics of the block size and the PU partitioning mode, the appropriate PU partitioning mode is classified and extracted from all coding units with a size less than 64×64 in P frames as the carrier, and the distortion cost value of the carrier during simulated embedding is calculated. According to the distortion cost value, the STC algorithm is used for the carrier to achieve adaptive secret information embedding, realizing a data hiding method with the minimum total distortion cost; since the method of the present invention comprehensively considers factors such as video bitrate increase, video picture quality, and inter-frame distortion transfer when calculating the distortion cost value, the method of the present invention can effectively reduce the bitrate increase caused by steganography. The stego video after steganography has better picture quality, lower bitrate, and higher security.

The invention discloses an image watermarking method for a stable diffusion model, and the method comprises the steps: selecting one of all potential features in a reverse diffusion process of the stable diffusion model as a target potential feature, and constructing an image watermark generation recognition model which comprises a watermark information embedding module and a watermark extraction module; the watermark information embedding module is used for embedding user watermark information into the target potential features, and inputting the obtained watermark target potential features instead of the original target potential features into the remaining sub-models of the stable diffusion model to generate a watermark image; and the watermark information extraction module is used for extracting user watermark information from the watermark potential features obtained by carrying out DDIM inversion on the watermark image, and the user generating the watermark image can be identified according to the extracted user watermark information. According to the method, the user watermark information is embedded into the potential features of the stable diffusion model, so that the image traceability with high robustness and high concealment is realized on the premise of not damaging the quality of the generated image.

The invention discloses a device upgrading method, system and device and a computer readable storage medium, and relates to the technical field of embedded device upgrade.The device upgrading method comprises the steps that after an upgrading mirror image file uploaded by a user is received, the upgrading mirror image file is analyzed to automatically extract a to-be-upgraded firmware identifier and a to-be-upgraded component identifier; according to the method, the matched reference upgrading process of the to-be-upgraded component identifier in the pre-stored corresponding relation and the link information of the reference component are utilized, after firmware identifier consistency verification is carried out, the link information of the reference component is embedded into the reference upgrading process and then is automatically executed, operation and maintenance personnel do not need to manually screen scripts and configure communication parameters, and the efficiency is improved. And the risk of script calling errors and link parameter input errors is avoided. The technical problem of upgrade errors caused by model misjudgment and script calling errors in the manual upgrade process is solved, and the technical effects of non-inductive upgrade of the control unit of the reference component and improvement of the upgrade efficiency are achieved.

A preprocessor for use with a machine learning system for control of computerized avatars provides for an embedding of avatar control information in a speech response file machine learning system for improved perception of emotional intelligence.

The invention discloses an academic community discovery and analysis method driven by a citation network, and the method comprises the steps: constructing the citation network, constructing an adjacent matrix A and a node attribute matrix X according to the citation network, and extracting a core sub-network information matrix S based on a k-core algorithm; constructing a dual-channel sparse graph attention auto-encoder, respectively taking (X, A) and (S, A) as input of the two channels to learn low-dimensional embedding of the two channels, and obtaining joint embedding Z through a dynamic weighted fusion strategy; reconstructing adjacent matrix information by adopting an inner product decoder, reconstructing node attribute characteristics and core sub-network information, and calculating reconstruction loss; and inputting the joint embedded Z into a self-supervised clustering module, performing joint optimization on the information embedding and reconstruction module and the self-supervised clustering module, and finally outputting a group division result of the papers / authors. According to the method, complementary fusion of attributes and a core structure is realized, robustness in noise and sparse scenes is enhanced, and discriminability and stability of group division are improved.

The invention provides an electronic bill image reversible information hiding method and device based on stream encryption, and belongs to the technical field of information hiding. In the method, an image owner encrypts an original carrier image by using encryption keys K1 and K2, and transmits the encrypted image to a data hider, and the data hider completes embedding of hidden information by using an encryption key K3. After an image user receives an encrypted image containing hidden information, if the image user obtains decryption keys K1 and K2 from an image owner, the image can be decrypted through the decryption keys K1 and K2; if the decryption key K3 is obtained from the data hiding person, hidden information in the image can be extracted based on the decryption key K3; and if the decryption keys K1, K2 and K3 exist at the same time, the original carrier image can be recovered in a relatively lossless manner. Meanwhile, the method also improves a generative adversarial network structure, and the improved network is utilized to realize accurate restoration of the image. According to the method, the image restoration quality is improved while the capacity of the embedded information is ensured.

The invention provides an edge deletion network community detection method based on dual-channel deep embedding clustering, and the method comprises the steps: firstly proposing three edge deletion strategies which are used for simulating an edge deletion condition possibly occurring in an actual network; then, an edge enhancement algorithm based on random walk is put forward to recover missing edge information under different edge deletion strategies; a clustering model based on double-channel depth embedding is introduced and comprises an information embedding core module and a self-supervised clustering core module. According to the method, the missing edge condition in a real network is simulated by providing three different edge deletion strategies, and the missing edge is effectively recovered in combination with an edge enhancement algorithm based on random walk, so that the robustness and accuracy of community detection in an edge missing scene are greatly improved. Network core node information and high-order neighbor information are respectively subjected to embedded representation, node features with higher distinction degree are obtained through a dynamic weighted fusion strategy, and more sufficient information support is provided for subsequent community division.

The invention discloses an emotion recognition method and system based on multi-modal data. The method comprises the steps that the multi-modal data in the task execution process of a target object is acquired; performing time dimension consistency processing on the audio data and the video data, and performing original feature extraction on the audio data and the video data which are consistent in time dimension to obtain multi-modal high-dimension original features; performing time sequence feature extraction on the multi-modal high-dimensional original features to obtain low-dimensional key time sequence features; performing cross-modal similarity calculation and adaptive fusion on the voice modal features and the visual modal features in sequence to obtain modal fusion features; performing dynamic gating adjustment and residual connection fusion on the modal fusion feature and the statistical information embedding feature of the target object in sequence to obtain a target fusion feature; and inputting the target fusion feature into an emotion recognition network for classification processing to obtain an abnormal emotion recognition result of the target object. According to the method, the accuracy of emotion recognition is improved.

A TEC prediction method based on a Transform model embedded by spatio-temporal information comprises the following steps: firstly, extracting TEC historical data and spatio-temporal characteristic elements, calculating a mathematical expression related to the TEC historical data and the spatio-temporal characteristic elements, calculating indexes representing a solar activity intensity index and a geomagnetic activity intensity index as characteristic vectors of a TEC prediction model, and generating a corresponding query matrix, a key matrix and a value matrix; dividing the preprocessed data set into a training set and a test set according to a certain proportion, generating a multi-head self-attention score matrix for the training set by adopting a multi-head self-attention mechanism, calculating multi-head self-attention scores of the multi-head self-attention score matrix, merging multiple groups of output vectors to obtain an output vector, and using the test set for model performance evaluation. The trained prediction model can predict the TEC of the subsequent 24 hours based on the feature information of the first 120 hours. Compared with a traditional method, the method has the advantages that parallel computing can be achieved, efficiency is improved, meanwhile, the long-distance dependency capturing capacity of the model is further improved, and the prediction effect of an original model is effectively improved.