1253 results about "Vector generation" patented technology

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

A method and related system for generating document embeddings within an embedding space based on a set of structured documents by determining (i) a first vector based on a first segment of a first document and (ii) a second vector based on a second segment of the first document and updating association vectors indicating the second segment based on a distance between the first and second vectors. The method also includes generating a document embedding based on the association vectors, generating a candidate vector based on a candidate document, and determining a result indicating that a second distance between the candidate vector and a first document embedding satisfies a document embedding distance threshold. The method may also include generating a new document by providing, to a text generation model, a portion of the candidate document and a portion of the second segment of the first document.

The invention belongs to the technical field of computer vision and deep learning, and particularly relates to a PCB defect detection method based on combination of a visual converter and conditional diffusion. Comprising the following steps: constructing an unlabeled PCB image data set and carrying out data preprocessing and enhancement to obtain a preprocessed image; executing a self-supervised pre-training task on the preprocessed image to obtain a feature extraction network; based on a conditional diffusion model, generating a synthetic defect PCB image and a label thereof by using the features output by the feature extraction network and the defect type control vector; mixing the synthetic defect image with a small number of real defect images to construct a training set; performing training adjustment on the defect detection model by adopting the training set to obtain a trained defect detection model; performing PCB defect detection by using the trained defect detection model; according to the method, the robustness and the cross-domain generalization ability are remarkably improved, the missed detection risk is reduced, and the rapid and stable quality control requirement of the production line is met.

The invention relates to the technical field of government and enterprise services, and discloses an intelligent adaptation system and method for government and enterprise services, and the method comprises the following steps: carrying out the structural analysis of an original policy text through a deep learning model, including word segmentation, word vector generation, dominant condition extraction and implicit condition reasoning, and finally constructing a policy knowledge graph; the multi-source heterogeneous data is integrated, dynamic enterprise feature vectors are generated through spatial-temporal feature construction, industrial association diagram modeling and spatial-temporal diagram convolution calculation, the enterprise state change gradient is detected, and the abnormal fluctuation risk is recognized; and mapping the policy condition vector and the enterprise feature to a hyperbolic space, calculating a hyperbolic distance and converting the hyperbolic distance into a matching degree, superposing timeliness attenuation, data freshness punishment and state mutation filtering, and outputting a final matching result. According to the invention, a closed-loop system of policy analysis, accurate matching, resource allocation and feedback optimization is finally constructed, the financial resource waste rate is compressed to a low level, and the government affair service efficiency is improved.

The invention relates to the technical field of machine vision, in particular to an intelligent machining positioning method and system based on machine vision, and the method comprises the steps: collecting and preprocessing original visual image data, and extracting geometric structure features and surface morphological features from the preprocessed visual image data, thereby comprehensively mining the characteristics of a workpiece; when the machining positioning offset vector is determined, the geometric structure features are matched with the preset machining reference template, and the workpiece machining position deviation can be preliminarily determined; on the basis of surface morphological characteristics, compensation adjustment is carried out on a machining positioning offset vector to generate an optimized positioning vector, the complex condition of the workpiece surface is considered, positioning deviation possibly generated according to a geometric structure purely is corrected, and positioning is more accurate; a machining path adjusting instruction is generated according to the optimized positioning vector and transmitted to a machining control unit, machining equipment can be effectively guided to adjust the machining path according to the actual state of the workpiece, it is ensured that the machining process is accurate and efficient, and the workpiece machining quality and production efficiency are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a generation strategy optimization method, device and equipment based on a dynamic environment and a medium. Correcting the generated action vector by combining a domain constraint strategy to obtain a compliant action vector, constructing a multi-dimensional reward vector according to feedback after execution, scaling the reward vector into a reward signal, and finally updating the pre-training generative model by adopting a self-adaptive strategy optimization module based on the reward signal and an interaction track to obtain a reward result. And collaborative evolution of strategy generation and environmental response is realized. According to the method, by introducing dynamic environment information and domain constraints, compliance correction and optimization updating of the generative strategy are realized, and the stability and practicability of the model in a complex environment are improved.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention discloses an unmanned ship cluster cooperative communication method and system, particularly relates to the technical field of unmanned ship cluster communication, and is used for solving the problem of poor unmanned ship cluster cooperative communication. According to the method, a cooperative communication mechanism with real-time sensing and self-adaptive capabilities is constructed, and effective links meeting communication distance and link weight conditions are screened out by collecting spatial position, speed and course information of the unmanned surface vehicle, constructing a standardized state vector and generating and dynamically maintaining a communication topological graph; monitoring a link load in real time, calculating a node congestion sensing factor, triggering path optimization, selecting an optimal path based on a distance and the load, and adjusting a forwarding rate; a scheduling factor is constructed by combining a task time limit, an action node range and a delay sensitivity level, a priority score is generated, a communication task is scheduled to an optimal link and an optimal time period according to a task priority, and space-time collaborative scheduling optimization of an unmanned ship cluster according to a task level is realized.

The invention discloses an AI search recommendation method, system and device combined with commodity semantic understanding and a medium, and belongs to the technical field of commodity recommendation. The method comprises the steps that semantic vectors of commodity titles and descriptions are generated; generating a feature vector of the commodity image; fusing the semantic vector of the commodity title and description with the feature vector of the commodity image to generate a comprehensive semantic vector of the commodity; generating a user interest vector; updating the user interest vector by using a recurrent neural network based on the user interest vector; and carrying out joint modeling on the comprehensive semantic vector of the commodity and the updated user interest vector to generate a recommendation result. According to the method, a content semantic driving modeling mode is adopted, recommendation judgment can still be conducted through the deep matching relation between commodity image-text semantics and user behavior preferences even under the condition that user historical behaviors are limited or new commodities are online, good cold start adaptability is achieved, and the method is suitable for popularization and application. And meanwhile, high recommendation difference and content diversity are shown for different user groups.

The invention relates to the technical field of image analysis, in particular to a dispensing track optimization control system based on visual template conversion, which comprises a visual template analysis module, a contour point position correction module, a track vector generation module, a multi-parameter linkage feedback module and an optimal path planning module. According to the method, a boundary is extracted through clustering pixel superposition brightness and channel weight, a line segment frequency screening track is counted, feature stability and precision are enhanced through datum line construction, a deviation value is calculated through gradient direction segmentation boundary, a matching degree is improved through tangent point interpolation correction contour, and a weight factor is generated through included angle change and tool parameter normalization. A reference point is optimized through superposition increment, a path fusion tool attribute dynamic adaptation parameter is adopted, a multi-dimensional data fusion visual offset and floating weight real-time adjustment track is acquired, an execution point and an adjustment vector are superposed to calculate a distance matrix, an optimal path is screened through minimum distance and energy consumption, efficiency and consumption are both considered, and the system robustness and execution economical efficiency are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a task processing method and device based on visual attention enhancement, equipment and a medium. Visual hierarchical features are extracted, a double fovea attention module processes and fuses high-level visual features, a side suppression network obtains enhanced visual features, and a cross-modal fusion module generates fusion features by taking the enhanced visual features as query vectors and taking language components and action components as key and value vectors; and fusing the feature input decision network to generate target category and position information, generating feedback information based on actual label difference, and updating module parameters to complete a target task. According to the invention, through combination of a bionic vision mechanism and multi-modal attention fusion, the visual feature extraction and background suppression capability is improved, and the target capture efficiency and recognition precision in a complex scene can be improved.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice text bidirectional conversion method, device, equipment and medium, and the method comprises the steps: respectively executing voice recognition or voice synthesis operation according to the type of input information; for the voice information, noise suppression parameters are generated in combination with the lip movement video data, noise reduction processing is executed, and the recognition accuracy is improved; for text information, a pre-generated speaker style vector is obtained, the vector is cited in the speech synthesis process to generate natural personalized speech, and lip movement information and tactile feedback which are synchronous with speech output are generated. According to the method, complex noise is suppressed by fusing lip movement data, personalized voice is generated by using the style vector, and lip movement and touch information is output, so that bidirectional real-time conversion of voice and text in a complex environment is realized, and recognition accuracy, voice naturalness and interaction synchronism are effectively improved.

The invention provides an industrial defect image generation system and method based on deep learning, and relates to the technical field of artificial intelligence. A defect form adaptive module, a physical attribute modulation module and a multi-mechanism fusion module are integrated in a defect image generation module; dynamically selecting a feature extraction unit according to the defect type label based on a pre-constructed generative network model, and generating a defect feature map; generating an affine transformation parameter based on the physical attribute vector, and performing channel-by-channel linear modulation on a defect feature map of a middle layer of the generative network model, so that a multi-scale defect feature map finally generated by the generative network model contains specified defect type features and physical attribute features; and fusing the multi-scale defect feature image and the defect-free background image to obtain a fused defect image. The problems that in the prior art, industrial defect image generation is insufficient in sense of reality, poor in controllability and poor in fusion effect are solved, the method can be used for data enhancement of industrial visual inspection, and downstream model performance is improved.

The embodiment of the invention discloses a spinal metastatic tumor treatment scheme generation system. According to one specific embodiment, the system comprises a data processing server, an information fusion server and a scheme generation server which are in communication connection with one another, and the data processing server is used for preprocessing multi-source patient data to obtain standard multi-source patient data; the information fusion server is used for executing the following steps: performing feature code fusion on standard multi-source patient data to obtain a multi-source patient feature vector; performing feature enhancement on the multi-source patient feature vector to obtain a joint patient characterization vector; generating an initial therapeutic schedule result based on the joint patient characterization vector; and the scheme generation server is used for performing feature decision processing on the initial treatment scheme result to obtain a final treatment report. According to the embodiment, waste of computing resources can be reduced, and system response time can be shortened.

The invention relates to the technical field of security auditing, in particular to a security auditing method and system for an industrial control system, and the method comprises the following steps: aiming at a key control task, a communication task and a security task of a real-time operating system, collecting a period, a starting timestamp, a finishing timestamp, a central processing unit occupied time slice and peak memory usage amount data. According to the method, the period, timestamp, central processing unit occupation and memory usage data of a key task of a real-time operating system are collected, a task execution time boundary and a resource consumption envelope are set, and then an expected relation rule set of a task time sequence and resource consumption is constructed by applying historical data statistics and logic rule deduction; and meanwhile, the key length of symmetric and asymmetric encryption, initialization vector generation, hash algorithm selection, key derivation parameters and encryption operation context are stipulated, so that a comprehensive and specific ICS behavior specification baseline is established.

A robot control method based on tactile prediction pre-training comprises the following steps: acquiring and generating a human playing data set consisting of three-channel image tensors in an offline stage, and training a constructed conditional diffusion model comprising a tactile encoder, a tactile decoder and an action and visual encoder; in the online stage, the trained conditional diffusion model is integrated into a standard imitation learning strategy network, and an action instruction of the robot is generated according to the state of the robot, the current visual features and the tactile feature vectors extracted by the imitation learning strategy network. According to the method, a specific agent task is completed by training a deep neural network model, that is, a future tactile signal sequence is predicted according to historical information and future action intentions; the model is enabled to characterize generic haptic features contacting physical dynamic laws for further migration into downstream robot control tasks.

The invention provides a recommendation method and system fusing big language model reasoning and multi-source trajectory information, and the method comprises the steps: firstly obtaining user historical learning behaviors and static attribute information after receiving a user recommendation request, and constructing a static interest vector; in combination with the initial feature vector of the learned knowledge point and the map enhancement vector of the first-order neighbor node of the knowledge map, generating explicit and map extension interest vectors, and fusing to obtain a user interest vector; screening N unlearned knowledge points to form a candidate set through similarity analysis of user interest vectors and unlearned knowledge point vectors and / or reasoning of a large language model on user association information; and screening the target knowledge points through mastery degree verification of the pre-modified knowledge points, and outputting a recommendation result after sorting. According to the method, recommendation accuracy and suitability are improved, and personalized learning requirements are met.

A method and related system for generating a document using different portions of an embedding space includes obtaining a related document based on a first text, generating first vectors in an embedding space based on the first text and second vectors in the embedding space based on the related document, and determining a first region in the embedding space based on the first vectors and a second region in the embedding space based on the second vectors. The method further includes generating a first portion of a structured document based on the first vectors and third vectors in a third region within the first region but not within the second region. The method further includes generating a second portion of the structured document based on the first and second vectors and the first portion of the structured document.

The invention discloses an online video content intelligent pushing method combined with a learning interest model. The method comprises the following steps: constructing a dynamic interest vector based on multi-source user behavior data, generating a user interest portrait vector set, and performing interest dimension clustering and weight distribution; generating a video content feature vector set according to a clustering result of the user interest portrait vector set; establishing a multi-dimensional association relationship between the user interest portrait vector set and the video content feature vector set, and outputting a user-video matching confidence matrix; converting the user-video matching confidence coefficient matrix into a push sequence based on a multi-objective optimization strategy and issuing the push sequence; and feedback behaviors of the user on the pushed video are collected in real time to realize closed-loop optimization. The method has the following advantages and effects: accurate perception and deep semantic matching of the dynamic learning interest of the user can be realized, and the accuracy, timeliness and user satisfaction of content distribution are remarkably improved, so that the learning efficiency and experience are optimized.

The invention, which relates to the technical field of artificial intelligence, discloses an artificial intelligence-based education evaluation and feedback system comprising a data acquisition module, a vector generation module, a prediction module, an error region positioning module and a feedback module. The system constructs a unified high-dimensional cognitive state vector by collecting answering behaviors, eye movement tracks, facial micro-expressions, voices and intonations and electroencephalogram signals of students; generating a learning evolution path map based on a dynamic Bayesian network and a causal reasoning mechanism, and predicting future learning bottleneck nodes; an error region is recognized through semantic deconstruction and graph matching, and context-associated personalized feedback content is generated in combination with a generative language model; according to the system, an evaluation feedback closed loop of cognitive state modeling, accurate identification of an erroneous region and intelligent feedback pushing is realized, and the accuracy of education evaluation and the effectiveness of intervention are improved.

The invention relates to the technical field of risk management and control and data processing, and discloses a data management and control system for post-loan risk monitoring, and the system comprises a multi-source data fusion module which is used for obtaining post-loan financial information of a borrower and external association information, and generating a multi-dimensional feature vector; the post-loan risk assessment module is used for constructing and training an integrated learning model comprising a feature decoupling layer, a base model layer and a meta-model layer, obtaining a post-loan risk assessment model, inputting a multi-dimensional feature vector, and generating a post-loan risk score, a risk contribution degree weight and a post-loan risk type; and the post-loan risk management and control module is used for matching disposal strategies according to post-loan risk types when the post-loan risk score is greater than a dynamic threshold value, performing priority ranking on the disposal strategies based on the risk contribution degree weights, generating a risk disposal scheme and performing real-time adjustment according to the disposal feedback of the risk disposal scheme. According to the invention, accurate evaluation and efficient management and control of post-loan risks can be realized, and the risk management capability of financial institutions is effectively improved.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention discloses an adaptive evaluation method for the health degree of an electrolytic cell, and the method comprises the following steps: collecting the multi-dimensional operation parameters of the electrolytic cell in real time, and carrying out the preprocessing of the collected time series data, so as to construct a training sample with a time window; extracting a multi-scale time sequence feature from the training sample to form a feature vector; inputting the feature vector into a weight adjustment network, and outputting a dynamic weight vector; weighting the feature vector by using the dynamic weight vector to generate a weighted feature vector; inputting the weighted feature vector into a performance prediction model, and outputting a short-term performance prediction value of the electrolytic cell at a future moment; after the corresponding real performance value is obtained, calculating a prediction error of the short-term performance prediction value, and constructing a reinforcement learning reward signal based on the prediction error; updating a strategy of the weight adjustment network through a reinforcement learning algorithm by utilizing a reward signal, thereby optimizing dynamic weight vector generation at a subsequent moment; based on the dynamic weight vector and the feature vector at the current moment, a comprehensive health degree index of the electrolytic bath is obtained through calculation; according to the method, main factors influencing the equipment health degree in different stages are intuitively revealed, and a basis is provided for operation and maintenance decision making.

The invention discloses a network security attack path prediction system based on a graph neural network, and relates to the technical field of network security, and the system comprises a data collection module which collects network full-link time sequence security data, and outputs standardized time sequence data through time sequence alignment and abnormal noise reduction processing; the time sequence diagram construction module is used for constructing a dynamic attack graph containing nodes and time sequence edges; the feature learning module introduces a time sequence attention mechanism, calculates a time-space fusion attention coefficient based on a graph attention network framework, and outputs a node embedding vector; the reasoning and pruning module is used for generating attack paths based on node embedding vectors and outputting a high-value attack path set; and the analysis decision module is used for carrying out importance sorting on all nodes on the high-value attack path, determining a path core risk point and generating a key node decision basis of the attack path. According to the method, the problem that the traditional technology cannot accurately capture the attack behavior time sequence dependence is solved, and the high-precision prediction of the attack path is realized.

One embodiment sets forth a method for providing relevant search results for search queries. According to some embodiments, the method can be implemented by a client computing device, and includes the steps of (1) receiving a query, wherein the query is associated with a user account, and the user account is associated with a user account vector, (2) generating a query vector based at least in part on the query, (3) generating an output vector based at least in part on the query vector and the user account vector, (4) obtaining, based at least in part on the query, a plurality of digital asset vectors, wherein each digital asset vector of the plurality of digital asset vectors corresponds to a respective digital asset, (5) comparing the output vector to the plurality of digital asset vectors to generate respective similarity scores for the plurality of digital asset vectors, (6) filtering the plurality of digital asset vectors in accordance with the similarity scores to establish a filtered plurality of digital asset vectors, and (7) displaying, in accordance with the filtered plurality of digital asset vectors, respective affordances for the respective digital assets that correspond to the filtered plurality of digital asset vectors.

The invention discloses a multi-source unmanned aerial vehicle track fusion method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring unmanned aerial vehicle monitoring data; performing analysis and feature extraction on the unmanned aerial vehicle monitoring data, and forming a standardized data feature vector; generating a confidence score of each sensor at each moment based on the data feature vectors; dynamically adjusting parameters of a UKF or PF algorithm according to the confidence score, and performing trajectory fusion based on the adjusted parameters to obtain an initial trajectory fusion result; and performing optimization based on the initial trajectory fusion result, and dynamically adjusting computing resources according to a system load and a confidence score to obtain a final trajectory fusion result. By implementing the method provided by the invention, the fusion precision, the system robustness and the real-time calculation efficiency in a complex environment can be remarkably improved, and intelligent multi-platform data processing without manual intervention is realized.

The invention discloses a bridge inclination and settlement monitoring method and system based on multi-sensor fusion, and belongs to the field of bridge structure monitoring, and the method comprises the steps: obtaining the data of a multi-source heterogeneous sensor; the sensor data is converted into space-time diagram data, and the space-time diagram data comprises the steps that each sensor is mapped into nodes in a diagram, edges between the nodes are defined according to the physical connection relation of the bridge structure, and multi-source heterogeneous sensor data are unified into dynamic feature vectors with the same dimension on the nodes through learnable feature mapping; inputting the time-space diagram data into a preset neural network model, performing spatial feature aggregation on the dynamic feature vector through a diagram attention mechanism, performing time feature extraction through a time convolutional network, and generating a hidden state vector fused with time-space information; and generating a monitoring state value of the bridge based on the hidden state vector. According to the invention, depth feature fusion of spatial perception is realized, and the sensitivity of anomaly recognition is improved.

The invention provides a multi-class anomaly detection method and system based on a pre-training visual language model in a training data scarcity scene, and relates to the technical field of anomaly detection. A first pre-training visual language model is used for obtaining feature representation of a small number of normal sample images in a text space; using a second pre-training visual language model to obtain global features and block features of a small number of normal sample images, constructing and training an adaptive prompt vector generator, in the training process, updating parameters of the adaptive prompt vector generator to obtain a trained adaptive prompt vector generator, and obtaining a training result of the adaptive prompt vector generator; according to the method, the ability of a pre-training visual language model is effectively combined, a training prompt vector generator is guided, a self-adaptive prompt vector for an anomaly detection task is generated, a one-to-many visual memory warehouse and a one-to-many prompt vector warehouse are constructed, a one-to-many training normal form is adapted, and the detection performance of anomaly detection in a training data scarcity scene is improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a landslide susceptibility prediction method based on knowledge map and spatial-temporal feature fusion, and the method comprises the steps: extracting an inference feature vector from a geological knowledge map through a map neural network; extracting a spatial feature vector from the multi-source spatial data by using a convolutional neural network; extracting a time sequence feature vector from the rainfall time sequence data by using a Transform model; generating an attention weight based on the reasoning feature vector, and performing adaptive weighted fusion on the space and time sequence feature vectors by using the weight to obtain a fused feature vector; and inputting the fusion feature vector into the prediction model, and outputting the landslide occurrence probability. The geological priori knowledge in the knowledge graph is introduced to guide the fusion process of the spatial-temporal characteristics, so that the model can focus on the key disaster-inducing factor combination, the accuracy and reliability of prediction are remarkably improved, and the interpretability of the model is enhanced at the same time.

The invention discloses a traffic flow prediction method and device based on multi-level space-time and perception fusion, and the method comprises the steps: dynamic multi-level feature embedding, space-time and local mutation perception modeling and super-domain interaction fusion: firstly constructing a dynamic multi-level feature embedding module, and fusing original flow data, periodic labels and adaptive feature vectors; generating high-dimensional feature representation; then, a space-time and local mutation perception attention module is constructed, time dependence features, space dependence features and local mutation perception features are extracted through parallel time, space and local mutation perception attention mechanisms, finally, a super-domain interaction fusion module is constructed, and multi-source features are integrated through a cross attention and gating mechanism; uniform space-time representation is generated, and prediction robustness is improved. According to the method, an end-to-end framework for traffic flow prediction is formed, joint modeling and efficient prediction can be carried out on space-time dependence and non-stationary sudden change in a complex traffic scene, and butt joint with a traffic management system is facilitated.