199 results about "Temporal modeling" patented technology

An AI-enhanced distributed system for neural network-based data compression leverages reinforcement learning optimization and privacy-preserving computation across edge and central computing devices to autonomously optimize efficiency and quality. The system includes a lightweight compression subsystem at edge devices that applies privacy-preserving preprocessing and partially compresses input data before securely transmitting it to central computing devices. A reinforcement learning agent continuously monitors system performance and automatically optimizes compression parameters, model selection, and task allocation based on multi-objective rewards. The central compression subsystem processes data using AI-optimized parameters and temporal modeling components. The system incorporates hardware detection capabilities that automatically select optimal compression models based on available processing resources and implements homomorphic encryption for computation on encrypted data while coordinating federated learning across distributed devices. This AI-enhanced distributed approach improves bandwidth efficiency, energy consumption, and adaptability while ensuring data privacy and security.

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention relates to the technical field of network security, and particularly discloses an edge device network threat detection method and system based on an electric power large model, and the method comprises the steps: capturing a network message sequence in real time, extracting a time sequence randomness feature and a semantic deviation feature from a time dimension and a protocol dimension, and carrying out the fusion to form a comprehensive threat feature vector; performing multi-dimensional feature analysis and time sequence modeling by adopting a lightweight electric power large model to realize millisecond-level threat assessment; establishing a multi-level response mechanism, dynamically triggering a differential protection strategy according to the threat level, and ensuring the reliability and consistency of response actions through digital signature and collaborative verification; according to the method, the complex network attack in the power edge equipment can be effectively identified, the threat detection accuracy and the system defense capability are improved, and the strict requirements of a power system on real-time performance and reliability are met.

The invention relates to the technical field of computer vision, can be applied to the medical field and the financial science and technology field, and discloses a key frame extraction method, device and equipment based on dynamic reinforcement learning and a medium, which are applied to electronic application in a high-frequency transaction abnormal behavior monitoring scene or can be applied to a medical operation key frame extraction scene. The method comprises the steps of obtaining an original video stream and performing preprocessing to generate a standardized video frame; performing feature extraction and feature splicing on the standardized video frame, and performing time sequence modeling on the generated pair frame-level mixed feature vector to generate video-level time sequence representation; generating an enhancement action instruction based on the video-level time sequence representation through the strategy network, and performing enhancement processing on the standardized video frame according to the enhancement action instruction to generate an enhanced video frame; performing optimization processing on the strategy network according to the enhanced video frame to generate an updated strategy network; and performing feature extraction and optimization on the enhanced video frame to generate a target key frame. According to the invention, the key frame extraction precision is improved.

The invention discloses a continuous time dynamics prediction method, system, terminal and medium fusing a diffusion model and a graph frequent differential equation, and relates to the technical field of dynamics prediction.The method comprises the steps that a multi-node time sequence is obtained, network structure inference is conducted on the multi-node time sequence through the diffusion model, and a potential graph structure between nodes is obtained; carrying out continuous time dynamic modeling by adopting a Scheng ordinary differential equation, and predicting a node state at any time point; diffusion reconstruction loss, dynamic prediction errors and structure sparsity constraints are constructed, total loss is established, network structure inference based on a diffusion model and dynamic modeling based on a Shenzheng differential equation are coupled based on the total loss, and collaborative training optimization is achieved. According to the method, the potential graph structure of the system can be stably recovered in a complex noise environment, high-precision and continuous prediction can be carried out on dynamic evolution of the potential graph structure, and the limitation that structure inference and continuous time modeling cannot be considered in the prior art is overcome.

The invention relates to a rolling bearing residual life prediction method based on space-time degradation characteristic decoupling, and the method comprises the steps: obtaining a vibration signal in a full life cycle operation process of a rolling bearing, and obtaining a sample sequence after preprocessing; performing complete ensemble empirical mode decomposition on the sample sequence, and extracting a plurality of IMF signals; on the basis of statistical threshold criterion in combination with energy mutation and self-correlation structure mutation analysis, identifying a degradation starting moment, adding a label to a sample sequence, and dividing a training set and a test set; training the space-time degeneration decoupling network by using the training set to obtain an RUL prediction model; testing the RUL prediction model by using the test set to obtain a prediction result; the space-time degeneration decoupling network combines a degeneration guide feature deconstruction module and a collaborative modeling strategy of a time modeling branch and a space modeling branch, captures time dynamic characteristics and a space hierarchical structure, and improves the accuracy, stability and reliability of RUL prediction.

The invention discloses a global dynamic perception space-time traffic flow prediction method based on graph packet representation learning, and belongs to the technical field of traffic flow prediction, and the method comprises the following steps: S1, traffic data input, S2, traffic graph packet construction, S3, graph packet initial feature extraction, S4, time sequence feature extraction, S5, spatial feature extraction, and S6, traffic flow prediction and output. Through a space-time modeling technology of graph packet representation learning and global dynamic perception, space-time characteristic elements of a traffic road network can be comprehensively covered, traditional traffic indexes such as flow and speed are concerned, elements such as road network topological association and cross-regional multi-hop association are also included, a dynamic dependency relationship between a time sequence and a spatial dimension is deeply mined, and a real-time dynamic perception effect is achieved. Therefore, the prediction result can reflect the real evolution law of the traffic flow more accurately, and a more scientific basis is provided for traffic management and decision making.

The invention provides a traffic flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

The invention provides a multi-modal semantic fusion image and text relevance dynamic analysis method and system, and belongs to the technical field of multi-modal data processing. The method comprises the steps of image and text data preprocessing, feature extraction, fusion semantic vector generation through a bidirectional cross attention mechanism, dynamic relevance score calculation through a time sequence attention long-short-term memory network and combined loss function end-to-end training. According to the method, accurate alignment of image and text features can be realized through a bidirectional cross attention mechanism, the cross-modal matching accuracy is improved, the dynamic correlation analysis capability is enhanced by using time sequence modeling, and the problems of insufficient feature coding suitability and limited time sequence modeling capability in the prior art are solved.

The invention discloses an oil pipeline monitoring method and system based on spatio-temporal modeling and multi-dimensional analysis, and belongs to the technical field of oil and gas pipeline safety monitoring, data processing and intelligent prediction.The method comprises the steps that multi-source spatio-temporal data is collected to construct a probability generation model; performing inversion on the model by using the observation value to reconstruct four-dimensional posterior probability distribution; performing causal inference on the posterior probability distribution to generate a dynamic causal information flow map; and analyzing topological evolution of the atlas to generate a stability monitoring report. According to the method, a technical path of combining probability modeling based on an information field theory and causal dynamics inversion is adopted, and accurate prediction of a systematic risk critical transition precursor can be realized by reconstructing a pipeline holographic state field and analyzing topological evolution of a causal network of the pipeline holographic state field; and the operation safety and the intelligent monitoring level of the long-distance oil pipeline are obviously improved.

The invention provides a monocular vision and sparse IMU rehabilitation action whole body posture estimation method and system, and the method comprises the steps: synchronously collecting video data and inertial data of human body rehabilitation actions through a monocular RGB camera and a plurality of IMUs, and cutting and zooming an image to a preset resolution; extracting a key point thermodynamic diagram from continuous N frames of images by using a sliding window and a residual neural network, and calculating 2D key point pixel coordinates of each frame; splicing the N frames of 2D key point pixel coordinates, the rotation matrix of the IMU and the acceleration signal into an input sequence; cross-modal time sequence modeling is carried out on an input sequence through time Transform, and after high-dimensional features are extracted, weighted average is carried out through a convolutional layer, and 3D relative key point coordinates of the last frame are output through a regression head. According to the method, by fusing monocular vision and sparse IMU cross-modal data, the problem of visual information loss caused by limb self-shielding is effectively solved, and the defect that a traditional pure vision method is insufficient in precision in rehabilitation actions is overcome.

The invention discloses a millimeter wave electrocardiogram reconstruction system and method based on adaptive MODWT and CNN-BiLSTM-CA, and the method comprises the steps: collecting a tiny phase displacement signal of a target thoracic cavity region, and carrying out the multi-band decomposition and key component adaptive screening of an original signal through combining with a multi-scale stationary wavelet decomposition algorithm; and introducing a channel attention mechanism to enhance key information representation, inputting a reconstruction signal into a deep learning model combining a convolutional neural network and a bidirectional long-short term memory network, completing time sequence modeling and nonlinear mapping, and outputting a reconstruction waveform highly consistent with a standard electrocardiogram. According to the method, the signal reduction capacity under the complex interference condition is remarkably improved, high-precision and privacy-friendly remote physiological signal monitoring can be achieved under the condition of not depending on a lead electrode, and the method is superior to a traditional baseline model in the aspects of waveform reduction precision, signal time sequence consistency, model generalization capacity and the like; the method is suitable for various application scenes such as intelligent medical treatment.

The invention discloses a weak supervision video anomaly detection method based on multiple prompts, and the method comprises the steps: 1) obtaining a video with a video-level label, dividing the video into non-overlapping segments, and extracting the initial visual features of the segments through a freezing CLIP image encoder; 2) constructing a Mama-based global-local dependence modeling module, fusing self-attention and a Mama state space model, capturing global and local time dependence of visual features, and outputting time enhanced visual features; 3) constructing an attribute category text prompt module, generating attribute-level and category-level text prompts through LLM, and inputting the attribute-level and category-level text prompts into a frozen CLIP text encoder to obtain text features; 4) constructing a cross-modal alignment module, and constructing a loss function to optimize network parameters; and 5) inputting a test video into the trained model, outputting an abnormal score of the fragment, and judging an abnormal event. According to the method, semantic information is supplemented through multiple text prompts, global and local time modeling and cross-modal alignment are combined, and the accuracy and robustness of weak supervision video anomaly detection are remarkably improved.

The invention discloses a time-space multi-mode video understanding method based on Video LLeMA2. The time-space multi-mode video understanding method comprises the following steps: S1, extracting an image frame sequence and an audio stream sequence of video data; s2, carrying out pretreatment; s3, inputting the image frame sequence into a visual encoder to generate a visual initial feature; s4, inputting the visual initial features into a space-time convolution connector, and generating visual modal features by using three-dimensional convolution and a RegStage module; s5, inputting the audio stream sequence into an audio encoder to obtain audio modal features; s6, aligning the audio modal features with the visual modal features; s7, performing feature fusion; and S8, obtaining a video content understanding result through a language decoder. The video content semantic understanding method is based on the Video LLeMA2 model, integrates multi-modal space-time modeling and language generation technologies, realizes video content semantic understanding, and has the advantages of natural expression and high precision.

The invention discloses a daily runoff prediction method and system based on GWO-VMD feature reconstruction and a mixed deep learning model, a storage medium and electronic equipment. VMD parameters are optimized through a GWO algorithm, an original runoff sequence is decomposed, and multi-scale features are fused; high-precision prediction is realized by combining the local feature extraction capability of a Mamba2 model and the global time sequence modeling advantage of Transform; and the prediction error is further corrected by using the CNN. The method solves the problem that a traditional model is poor in adaptability to non-stable and non-linear data, and has remarkable application value in flood control and disaster reduction and water resource scheduling.

The invention provides a pedestrian intention reasoning method fusing scene interaction features and hierarchical temporal modeling, relates to the technical field of automatic driving, and solves the technical problems of low pedestrian intention prediction accuracy and generalization ability in the prior art. The method comprises the following steps: acquiring man-vehicle distance data, self-vehicle speed data and scene picture data; semantic segmentation is carried out on the scene picture data, and the object attribute of each pixel is identified and identified to obtain a segmented image, bounding box coordinates and pedestrian posture key points; the bounding box coordinate is the position of the pedestrian in the segmented image; extracting scene time sequence features in the segmented image; based on the bounding box coordinates, the pedestrian posture key points, the pedestrian-vehicle distance data and the self-vehicle speed data, pedestrian motion intention features in space-time correlation are extracted; and performing feature fusion on the scene time sequence features and the time-space associated pedestrian motion intention features by adopting a hierarchical temporal strategy, and predicting a pedestrian crossing intention result. The method is used in the pedestrian intention reasoning process.

The invention provides a sensor data recovery method and system based on mask perception space-time modeling, and belongs to the technical field of Internet of Things and intelligent sensing data processing. The method comprises the following steps: modeling a missing position into a trainable potential vector representation through an adaptive missing representation module, and avoiding noise introduced by traditional zero filling or mean filling; a missing perception space-time decoding module is adopted, and a dynamic weight distribution mechanism of mask constraint is combined in the decoding process, so that information is effectively prevented from being excessively smooth; designing a space-time dual-channel feature aggregation unit, and capturing spatial dependence and time sequence dependence between the sensors at the same time; and finally realizing accurate completion of a large-scale space-time sensor matrix through an output recovery unit. According to the method, the accuracy and robustness of sensor data restoration can be remarkably improved, the reliability of subsequent monitoring, prediction and anomaly detection is enhanced, and the method has wide engineering application value.

The invention provides a multi-modal dialogue emotion recognition method based on emotion memory enhancement, and relates to the technical field of emotion recognition, and the method comprises the steps: extracting the multi-modal features of a dialogue; respectively embedding speaker representation embedding vectors into the multi-modal features of the dialogue to obtain multi-modal sequence features; performing time sequence modeling on the multi-modal sequence features by adopting an extended long and short-term memory network to obtain a multi-modal hidden state sequence; inputting the multi-modal hidden state sequence into a preset memory module to obtain a multi-modal memory sequence; performing cross-modal alignment and fusion on the multi-modal memory sequence to obtain a cross-modal fusion feature sequence; and performing time sequence modeling on the cross-modal fusion feature sequence, and mapping an output sequence after secondary time sequence modeling to obtain a final sentiment classification result of the dialogue. The accuracy of emotion recognition in the prior art is effectively improved.

The invention provides a weak supervision video anomaly detection method and system based on prompt learning, and belongs to the technical field of abnormal event detection based on computer vision, and the method comprises the steps: obtaining to-be-processed video data; and processing the acquired to-be-processed video data by using a pre-trained anomaly detection model to obtain a specific classification result of the abnormal events in the video. According to the invention, a video local and global adaptive time modeling module is introduced to capture local and global dependency relationships at the same time, and the relationship between the demand of detailed time modeling and the calculation efficiency is balanced; by utilizing an external knowledge base, the distinguishing capability of the model on different categories is improved; according to the method, a text-video comparison loss function is designed, the similarity of a correctly matched text-video pair is enhanced, the similarity of wrong matching is reduced, and too high similarity of a negative sample is effectively inhibited, so that the distinguishing capability of the model is improved, the matching of the text and the video is more accurate, and the video and text alignment capability of the model is enhanced.

The invention provides a vehicle track prediction method and device and a vehicle. According to the vehicle trajectory prediction method provided by the embodiment of the invention, the point cloud feature and the image feature are fused through the local-global mixed attention mechanism to obtain the multi-modal fusion feature, and the causal time sequence modeling is performed based on the multi-modal fusion feature and the historical trajectory data of the vehicle by using the causal mask and the time convolution network to obtain the time sequence fusion feature. And the time sequence fusion features are used to predict future trajectory data of the vehicle. According to the invention, on-line real-time sequential reasoning of vehicle trajectory prediction can be realized, and the high-real-time requirements of scenes such as automatic driving and the like are met.

The invention discloses a method for generating a personalized recommendation customized desktop of cross-application data of a smart television, which is realized based on an LSTM (Long Short Term Memory) algorithm and comprises the following steps of: intelligently sensing a user identity, capturing user identity characteristics in real time by a smart television terminal through a multi-mode sensing technology, and establishing an independent user session; initializing LSTM network parameters, and preparing a memory unit for time sequence behavior modeling; cross-application time sequence modeling and dynamic recommendation are carried out, a cloud side aggregates a user cross-application behavior sequence, and based on user data reported by the cloud side, an LSTM network extracts features based on a gating mechanism; self-adaptive desktop construction is carried out, desktop topology reconstruction is executed based on a recommendation set, and cross-application data personalized recommendation and user desktop customized service are realized; according to the invention, the cross-application data integration and customized desktop service of the smart television in a real sense are realized.

The invention relates to the technical field of multi-modal artificial intelligence, and discloses a multi-modal video abstraction method and system based on mixed expert dynamic fusion, video features and text features are extracted, the video features comprise time sequence features, the video features, the time sequence features and the text features are fused through mixed experts, and then a video abstraction is generated. According to the method, mixed experts are adopted to fuse features, the mixed experts dynamically select expert combinations according to the fusion features to obtain an optimal fusion strategy of different video-text pairs, features more conforming to a video scene are extracted, so that an abstract more conforming to the video scene is generated, and time sequence features are combined during fusion, so that the fusion efficiency is improved. The key problem of time sequence modeling and modal fusion in the video abstraction task is solved, the accuracy of feature extraction is improved, and the time sequence coherence is also improved.

The invention provides a business resource optimization management method and system based on computing power demand prediction, and the method comprises the steps: collecting multi-source heterogeneous data in real time, so as to construct and continuously update a topological graph which represents the dynamic association relationship among a business entity, a logic service entity and a physical resource entity; a time sequence state of a topological graph is input into a prediction model fusing a graph structure and continuous time modeling, multi-dimensional evolution calculation of a node state is carried out by the prediction model, a dynamic resource value signal is generated by a central scheduler under a hierarchical decision framework, a plurality of service unit intelligent agents are guided to carry out distributed collaborative decision, and a multi-dimensional resource value signal is generated by the central scheduler. Iteratively generating an optimal resource allocation scheme; and before the optimal resource allocation scheme is executed, potential risk assessment and credible automatic execution are carried out. Therefore, in a complex dynamic commercial environment, the computing power resource is converted from global perception, accurate prediction and intelligent optimization to risk-controllable closed-loop management and adaptive configuration.

The invention provides a prospective power technology prediction method based on topic extraction and time modeling. According to the method, power related text data is preprocessed, topic information of a text is extracted, and a power technology topic set is generated based on UMAP algorithm dimensionality reduction and HDBSCAN clustering analysis. Then, keywords are extracted through a TF-IDF method, and a theme and keyword description set is constructed; and in combination with a time label, constructing a time sequence of the power technology theme, eliminating short-term fluctuation and calculating a change rate by utilizing smoothing processing, and further modeling an evolution trend of the power technology theme. And according to a trend modeling result, screening out a prospective power technology theme and an evolution trend thereof, thereby realizing prediction of a future development direction of the power technology. The method can help to predict and identify the technical innovation trend in the power field, and improves the scientificity and foresight of technical decision support.

The invention provides a typhoon path prediction method and system fusing physical constraint and path mutation recognition. The method comprises the steps that a multi-dimensional input feature tensor is obtained through a feature tensor generation module; performing time modeling through a multi-scale modeling module to obtain a deep feature tensor, and performing significance guidance through a significance guidance module to obtain a prediction path sequence; the bending event identification module identifies a bending angle to obtain a plurality of path abnormal scores, the adversarial disturbance analysis module introduces a plurality of preset disturbances to determine the path confidence, and the path prediction output module outputs a path prediction result. According to the technical scheme of the embodiment of the invention, refined modeling can be carried out on the typhoon path, the PINNs physical constraint module is utilized to ensure that the prediction result meets the physical conservation principle, the path mutation detection capability is enhanced through bending event recognition, uncertainty evaluation is realized through disturbance analysis, and the accuracy of the typhoon path detection is improved. And a typhoon path prediction result which is more accurate and credible and has physical consistency is output.

The invention provides an action recognition method based on adaptive skeleton grouping and direction sensitive space-time modeling, and relates to the technical field of skeleton action recognition. Comprising the steps of skeleton action sequence input and feature embedding, adaptive skeleton grouping and direction weighting space-time modeling, trunk modeling and feature refinement, adaptive time down-sampling, multi-stream feature fusion and classification output and action recognition loss evaluation. The method comprises the following steps: acquiring an introduction result through skeleton action sequence input and feature embedding, acquiring an adjustment result through adaptive skeleton grouping and direction weighted space-time modeling, acquiring a processing result through trunk modeling and feature refinement, acquiring a reconstruction result through adaptive time downsampling, performing multi-stream feature fusion and classified output, and finally executing action recognition loss evaluation. According to the method, the skeleton action recognition precision is improved, and meanwhile, the complexity of long sequence modeling calculation is reduced, so that the skeleton action recognition real-time performance and deployment efficiency are improved, and the problem that the skeleton action recognition precision is not high in the prior art is solved.

This application discloses an intelligent method and apparatus for detecting active speech with sentence segmentation based on multi-state temporal modeling. The method includes: receiving audio signals from at least one channel; extracting acoustic feature sequences from the audio signals using a target speech recognition model corresponding to the number of channels; determining the probability distribution of each speech frame corresponding to the acoustic feature sequences belonging to different speech activity states, obtaining a state sequence corresponding to each channel, wherein the speech activity state includes at least one of the following: initial silence state, speech state, intra-turn pause silence state, and inter-turn sentence segmentation silence state; and determining the time of sentence segmentation in the audio signal based on the state sequence. This application solves the technical problem of erroneous sentence segmentation in speech activity detection based on a fixed silence threshold in related technologies.

The invention relates to a three-dimensional semantic scene completion method and device based on geometric and time sequence modeling hierarchical context alignment, and a medium. The method comprises the following steps: obtaining depth features and context features, sensing a cross attention mechanism through depth confidence, supplementing information of a low depth confidence region by using the context features, and generating related features of a current frame; historical frame related features are obtained from the attitude network, cross-frame feature affinity is calculated, the historical frame related features are dynamically optimized, and multi-layer historical frame aggregation features are obtained; and in the unified space, the depth hypothesis of the time feature voxels is adopted as a distance axis, the volume feature voxels are projected to the unified space, global alignment and combination are performed on the geometric feature voxels and the time feature voxels, and final aggregation features are obtained. Compared with the prior art, by introducing a hierarchical context alignment mechanism based on geometry and time sequence modeling, a complex 3D scene can be more accurately understood, and the method is especially suitable for environmental perception in an automatic driving system.

The invention provides a multi-scene driving risk assessment method based on transfer learning. The method is suitable for real-time assessment and short-time early warning of driving risks in tunnel and non-tunnel environments. According to the method, a multi-source data set collected by multiple sensors is constructed based on a natural driving test, static and dynamic characteristics are extracted by using a sliding time window technology, and the driving risk is evaluated through a multi-scene driving risk evaluation model. The scene driving risk assessment model integrates a static information encoder, a variable selection network and an interpretable multi-head self-attention mechanism, has strong feature selection and time sequence modeling capabilities, and improves the adaptability and generalization capability to scarce tunnel data through a transfer learning strategy. Test results show that the method is superior to the existing mainstream model in the aspects of alarm rate, accuracy rate and false alarm rate, and is suitable for a risk assessment and early warning module in an intelligent traffic system.

The invention discloses a multi-scale time sequence prediction method based on an adaptive sparse expert selection strategy and a closed continuous time neural network. The method comprises the following steps: carrying out normalization and low-dimensional feature mapping based on RevIN; performing trend-seasonal structure enhancement processing on the feature sequence after linear mapping; constructing a multi-scale expert model based on the feature sequence after trend-season enhancement; self-adaptive sparse expert selection and load balancing loss calculation are carried out; carrying out weighted aggregation and residual fusion on multi-scale expert output; global modeling of a closed continuous time neural network based on channel weighting is carried out; and finally performing prediction generation and reverse normalization. The multi-scale time series prediction method has the multi-time-scale adaptive modeling capability, the sparse expert efficient selection mechanism and the global continuous time modeling capability, and can be applied to various multivariable time series prediction scenes such as power load prediction, weather prediction, industrial production monitoring, traffic flow prediction and financial price prediction.