636 results about "Signal classification" patented technology

The invention relates to the technical field of sound wave measurement, in particular to an ultrasonic image diagnosis system and method.According to the ultrasonic image diagnosis system and method, matching between reflection characteristics and tissue characteristics is made to have the self-adaptive learning ability through a deep neural network, the targeted recognition effect is improved in the aspect of signal classification, and based on the judgment result of the reflection characteristics, the diagnosis accuracy is improved. When boundary partitioning is carried out on a tissue area, the edge structure is judged by using the combination of three parameters of gray range, gradient direction and texture continuity, contour fuzziness caused by single index judgment is avoided, and the boundary partitioning accuracy is improved by extracting a gray distribution center, an amplitude change track and an edge area continuous change sequence. And the region positioning result is input into a support vector machine, accurate recognition of lesion properties is realized according to boundary classification comparison of morphological structure quantitative features and historical benign and malignant feature data, secondary verification of the structural form is performed after the image is formed, and the diagnosis integrity and the judgment confidence are effectively improved.

The invention provides an EEG (electroencephalogram) classification method based on multi-domain feature fusion. A multi-domain feature extraction network is constructed, the multi-domain feature extraction network mainly comprises a frequency domain feature extraction module and a space-time feature extraction module which are deployed in parallel, a feature fusion module and a classifier module, multiple view features such as a time domain, a frequency domain and a space domain can be separated, and electroencephalogram signal classification is achieved. According to the method, an efficient solution is provided for solving the problem of insufficient multi-domain feature utilization of the electroencephalogram signals, the cross-scene classification precision can be remarkably improved while the model efficiency is kept, and a technical foundation is laid for personalized deployment of brain-computer interfaces.

The invention relates to the technical field of bidirectional PCS converters, and discloses a VSG control method and device for a DSP numerical control high-frequency bidirectional PCS converter, and the method comprises the steps: carrying out the multi-signal classification of an output voltage, an output current, a DC bus voltage and a PWM modulation waveform of the PCS converter through a DSP chip, and obtaining a frequency spectrum feature vector; calculating a VSG parameter group based on the spectrum feature vector, inputting the VSG parameter group into a depth deterministic strategy gradient network for parallel training, and generating a VSG real-time control strategy; applying phase-locked loop compensation to the VSG real-time control strategy to obtain a PWM modulation signal after synchronous compensation; and carrying out real-time fault early warning on the PWM modulation signal after synchronous compensation, and carrying out seamless switching on a grid-connected and off-grid mode by adopting voltage feed-forward decoupling control to obtain a smooth transition control sequence. According to the method, the control period and the calculation load of the PCS converter under the high-frequency switching condition are reduced, and the control precision under the complex working condition is ensured.

The invention discloses a radio interference identification method based on electromagnetic spectrum monitoring, and the method comprises the following steps: S1, collecting spectrum data in an electromagnetic environment through broadband spectrum monitoring equipment, and carrying out the digital sampling processing; s2, adopting a diffusion probability model to automatically remove environmental noise and non-interference signals; s3, automatically generating an expansion data set of edge and small sample interference signals by adopting an attention-enhanced data synthesis expansion technology; s4, extracting multi-dimensional time-frequency domain dynamic correlation characteristics of the spectrum interference signal based on a Transform structure; s5, adopting an attention enhancement self-supervision algorithm to generate a self-supervision soft label of an unknown interference type; s6, dynamic back diffusion iteration is carried out through the diffusion probability model, and an interference classification result is obtained; and S7, constructing a closed-loop dynamic feedback mechanism by using an interference classification result. According to the invention, accurate identification of radio interference signals is realized, and the accuracy, dynamic adaptability and automation degree of interference signal classification are improved.

The invention provides a motor imagery classification neural network method based on multi-scale spatial-temporal feature fusion. According to the method, the EEG signal decoding precision in a motor imagery task is effectively improved by designing a multi-scale spatial-temporal feature fusion network. The method comprises the following steps: firstly, processing an original EEG signal by adopting standardized preprocessing; secondly, extracting features of the signals under different time scales through a multi-scale time sequence attention module, and enhancing a long-term time sequence dependency relationship in combination with a self-attention mechanism; thirdly, a self-adaptive channel weighting module is introduced, the weight of each electrode signal is dynamically adjusted, and representation of spatial features is optimized; and finally, fusing the spatio-temporal characteristics through a Transform module, capturing complex interaction of the spatio-temporal dependency relationship, and finally outputting a decoding result through a classifier. The method can effectively overcome the defects of an existing method in the aspects of spatial-temporal feature fusion and dependent modeling, improves the accuracy of EEG signal classification and the system robustness, and has a wide application prospect.

The invention relates to the technical field of intelligent diagnosis of power equipment, and provides a multi-mode fusion extra-high voltage converter transformer fault diagnosis method and system, and the method comprises the steps: collecting a multi-mode signal from a sensor of an extra-high voltage converter transformer, and classifying the signal into a plurality of modes; performing intra-modal feature reinforcement learning on the multi-modal data by adopting a SimCLR framework to obtain a discriminative representation feature vector zm of each modal; inputting the zm into a Transform branch encoder of a corresponding mode, and obtaining context feature vector enhancement representation hm of each mode; mapping the hm of different modalities to the same dimension in a unified manner, and performing weighted attention fusion to obtain feature vectors zf of all modalities after fusion; and inputting a multi-layer perceptron classifier (MLP) to obtain a fault category prediction result of the extra-high voltage converter transformer. According to the method, the multi-mode signals are processed and fused in parallel, and the fault recognition capability of the model on the extra-high voltage converter transformer in the complex operation state is improved.

A method for the classification and detection of faults in a microgrid having a distance relay, includes measuring the first plurality of voltage signals and the first plurality of current signals of the microgrid, calculating a plurality of fault-loop impedance signals, comparing the plurality of fault-loop impedance signals and a plurality of reference impedance values, inputting the plurality of difference values to a convolutional neural network (CNN)-gated recurrent unit (GRU) hybrid model, generating a reference tripping signal, defining a deep reinforcement learning (DRL) agent for a deep reinforcement learning (DRL) model. The method further includes processing a second plurality of voltage signals and current signals of microgrid and classifying the one or more fault signals into one or more fault types.

The invention relates to a motor imagery electroencephalogram signal classification method based on multi-scale time sequence fusion. The method comprises the following steps: S1, standardizing electroencephalogram signals before inputting a network model; s2, inputting the processed data into a multi-scale channel attention convolution module, wherein the multi-scale channel attention convolution module focuses on capturing low-level spatial-temporal characteristics with discrimination from original motor imagery electroencephalogram signals (MI-EEG); s3, segmenting the electroencephalogram signal into a plurality of local time sequence segments by sliding the window; s4, inputting the data output by the sliding window into the time fusion residual network in parallel so as to further extract advanced time features from the time sequence; and S5, fusing the high-order time sequence characteristics of all windows through a full connection layer, and outputting probability prediction of a motor imagery task through a Softmax function. The method can effectively solve the problems that convolution scale division of the motor imagery network is limited, and features of all channels and advanced time features are ignored, and the average classification accuracy of the motor imagery electroencephalogram signals is improved.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm control method and system based on motor imagery and eye movement tracking. (2) constructing an electroencephalogram signal classification model and performing training; (3) calibrating an eye movement tracking module and a mechanical arm control module; (4) inputting data collected in real time into an electroencephalogram signal classification model and an eye movement tracking module, and generating a user intention and a to-be-grabbed target label; and (5) an instruction decision is made according to the intention of the user, the to-be-grabbed target label is input into a mechanical arm control module, a mechanical arm control instruction is generated, the mechanical arm control instruction is driven to execute the grabbing action, and the control process of the mechanical arm is achieved. According to the scheme, more efficient man-machine interaction is achieved, through combination of motor imagery electroencephalogram decoding and eye movement tracking, mechanical arm selection target grabbing operation can be conducted through brain control and eye movement without manual operation, and the real-time performance and stability of a control system are improved through optimization of the decoding process.

In order to solve the problem of inaccurate classification caused by fixed time-frequency resolution and lack of physical rationality in sample enhancement in a traditional classification method, the invention relates to a partial discharge signal classification method based on a physical constraint generative adversarial network, and the method comprises the following steps: extracting a partial discharge signal and a historical partial discharge signal, and carrying out the double-window time-frequency coding of the partial discharge signal and the historical partial discharge signal; correspondingly obtaining a fused time-frequency graph and a historical fused time-frequency graph; determining a corresponding discharge type label based on the historical partial discharge signal, establishing a physical constraint generative adversarial network, and training the physical constraint generative adversarial network in the historical fusion time-frequency graph based on the discharge type label; and inputting the fusion time-frequency graph into the trained physical constraint generative adversarial network to obtain a fusion feature graph corresponding to the fusion time-frequency graph, performing time sequence modeling on the fusion feature graph, mapping the fusion feature graph into a time sequence feature vector, and performing classification on the corresponding partial discharge signals based on the time sequence feature vector.

The invention discloses a power distribution network fault knowledge graph construction method and system based on multi-modal data, equipment and a medium, and belongs to the technical field of intelligent power grids, and the method comprises the steps: carrying out the change matching of spatial position signals and meteorological parameters based on regional monitoring space-time records, meteorological environment parameters and equipment operation state data, dividing time periods, and obtaining a power distribution network fault knowledge graph; and analyzing according to spatial position association, and screening data overlapping to obtain a multi-modal data association distribution table. According to the invention, through matching and screening of space-time records, meteorological parameters and equipment state data, multi-modal data integration and abnormal feature extraction are realized, and in combination with cross analysis of signal fluctuation and meteorological conditions, abnormal signal classification and path association are optimized, and a fault propagation rule is accurately analyzed. The overall optimization significantly improves the efficiency of abnormal recognition, fault location and propagation analysis of the power distribution network, enhances the operation reliability and fault processing capability, reduces data redundancy and errors, and improves the safety and stability of the power grid.

The invention discloses a motor imagery electroencephalogram signal decoding method and system based on a Sinc filter, and belongs to the field of brain-computer interfaces, and the method comprises the following steps: carrying out convolution calculation on a motor imagery electroencephalogram signal and the Sinc filter to obtain a frequency characteristic signal of the motor imagery electroencephalogram signal; performing channel connection on the characteristic signals of the motor imagery electroencephalogram signals of different frequency bands to generate high-dimensional electroencephalogram characteristic signals; obtaining a parallel multi-branch space-time convolutional network, performing time domain feature extraction, spatial feature extraction and average pooling dimension reduction processing on the high-dimensional electroencephalogram feature signal to obtain a channel weight, and performing weighted calculation on the target feature map based on the channel weight to obtain a space-time feature map; and obtaining class labels of the motor imagery electroencephalogram signals after mapping processing and activation processing, and obtaining a decoding result. According to the method, the Sinc filter and the space-time convolutional neural network are combined, and the accuracy and robustness of motor imagery electroencephalogram signal classification are improved.

The invention discloses an electroencephalogram signal classification method based on a dynamic gating hybrid expert model, which focuses on efficient detection of electroencephalogram signals and comprises the following steps: 1, acquiring electroencephalogram signal data and preprocessing the electroencephalogram signal data to generate a standardized electroencephalogram signal sample; 2, constructing a dynamic gating hybrid expert model composed of a shared feature layer, a hybrid expert module and a dynamic gating network; 3, designing a mixed loss function containing classification loss and load balancing loss; 4, training a dynamic gating hybrid expert model based on the hybrid loss function; and 5, realizing electroencephalogram signal classification by utilizing the trained dynamic gating hybrid expert model. By means of a dynamic expert selection mechanism, expert combination is optimized in real time through a gating network, and the electroencephalogram signal classification sensitivity is effectively improved; meanwhile, a hardware sensing architecture is introduced, multiplication operation is greatly reduced, energy consumption optimization and the classification accuracy of the electroencephalogram signals are remarkably improved, and the application value of the electroencephalogram signals in the medical field is enhanced.

The invention discloses a rapid identification method applied to defibrillation requirements of an AED device, and belongs to the technical field of medical equipment. According to the method, multiple links including acquisition, preprocessing, feature extraction, mode classification, real-time classification and defibrillation triggering work cooperatively, and advanced signal classification and mode recognition technologies are introduced, so that efficient and accurate electrocardiosignal analysis is realized in the AED device, the response speed and accuracy in the emergency treatment process are remarkably improved, and the emergency treatment efficiency is improved. Multi-dimensional features of electrocardiosignals are extracted and input into a pattern classification model, automatic recognition of arrhythmia is achieved, whether defibrillation operation needs to be carried out or not can be accurately judged, the possibility of misjudgment is reduced, self-optimization can be carried out according to new electrocardiosignal data through a dynamic learning and retraining mechanism, and the accuracy of defibrillation is improved. The adaptive capacity to a complex heart rhythm mode is improved, the first-aid response time is effectively shortened, the safety of a patient is guaranteed, and the first-aid efficiency and the treatment effect are improved.

The invention relates to a dynamic weighted electroencephalogram signal classification method capable of resisting individual differences. The method comprises the following steps: step 1, acquiring motor imagery electroencephalogram signals of multiple subjects by using electroencephalogram acquisition equipment; 2, preprocessing the collected electroencephalogram signals, wherein the preprocessing comprises filtering, denoising and segmenting; step 3, constructing a hybrid expert model based on CNN-Transform; step 4, training the hybrid expert model; and 5, performing classification prediction on the electroencephalogram signals of the target subject. According to the method, the advantages of CNN and Transform are combined, and a hybrid expert mechanism of dynamic routing is introduced, so that the classification performance and generalization ability of the model in a cross-subject scene are remarkably improved.

Disclosed are system and techniques for classifying events such as emergencies. A system can include a computer system to perform operations including: receiving sensor signals from a group of devices at a location, determining whether one or more of the sensor signals exceed expected threshold levels, in response to determining that the one or more of the sensor signals exceed the expected threshold levels, correlating the sensor signals, classifying the correlated sensor signals into an emergency event based on applying an artificial intelligence (AI) model to the correlated sensor signals, the AI model having been trained to classify the correlated sensor signals into a type of emergency, determine a spread of the emergency event, and determine a severity level of the emergency event, generating, based on information associated with the classified emergency event as output from the AI model, emergency response information, and returning the emergency response information.

The invention belongs to the technical field of electromyographic signal classification detection, discloses an electromyographic signal classification detection method combining SNN and HDC, provides an electromyographic signal classification detection framework combining SNN and HDC, and aims to realize ultra-low power consumption operation. In the framework, the SNN executes event-driven feature extraction by using a random untrained weight, so that the calculation overhead is reduced to the greatest extent; and the HDC realizes anti-noise classification through high-dimensional representation. The integration of the two not only realizes real-time detection of energy conservation, but also is particularly suitable for being applied to resource-limited scenes such as wearable equipment and the like. The method provided by the invention realizes 95% of average accuracy (the peak accuracy is 96.44%) in three types of fatigue recognition tasks, and the training speed is 5.7 times faster than that of a one-dimensional convolutional neural network (1D-CNN) and 45 times faster than that of a five-dimensional long-short-term memory network (5D-LSTM). Even under the condition that only 20% of training data is used, the method can still keep the accuracy of 90% or above, and the high efficiency and robustness of the method in actual deployment are fully proved.

The invention discloses a seismic signal classification model training method and device and a medium. The method comprises the steps of converting target seismic signal data into a two-dimensional grey-scale map; performing visual coding on the two-dimensional grey-scale map under the initial model weight of the visual coding model; performing feature coding on the target seismic signal data; and inputting the visual coding vector and the feature coding vector obtained by coding into a contrast learning model to optimize an initial model weight to obtain a target model weight. Constructing a seismic signal classification model by taking the target model weight as a model initial parameter; and carrying out iterative training on the seismic signal classification model through the training data set. Therefore, self-supervised learning is carried out on the visual coding vector and the feature coding vector through the contrast learning model, potential values in the data are fully mined, and iterative training is carried out by taking the target model weight as the initial parameter of the initial seismic signal classification model, so that the model training efficiency is improved while the model precision is ensured.

The invention discloses a driver cognitive load electroencephalogram signal classification method based on a dynamic graph attention network. The method comprises the following steps: constructing a dynamic graph attention neural network; electroencephalogram data of a driver in the driving process are obtained in real time and divided according to time windows, data of each time window is preprocessed and then input into the trained dynamic graph attention neural network, and a classification result of the electroencephalogram data corresponding to the time window is obtained; wherein the dynamic graph attention neural network comprises an electroencephalogram signal acquisition unit, a feature extraction unit, a graph attention unit and a classification unit; wherein the electroencephalogram signal acquisition unit is used for acquiring electroencephalogram data in real time and preprocessing the electroencephalogram data through a construction map structure; a feature extraction unit constructs a feature matrix of nodes in the graph structure; the graph attention unit comprises a plurality of graph attention layers based on a multi-head graph attention mechanism, and updates the feature matrix based on the adjacent matrix; and the classification unit performs classification based on the final feature matrix and outputs a classification result.

The invention discloses a state monitoring method for a bridge structure, and belongs to the technical field of bridge measurement and monitoring, and the method comprises the steps: obtaining an original data set of the inclination angle of a bridge main tower and the tension of an inhaul cable, analyzing a signal time feature representation factor, and carrying out the linear interpolation adjustment information judgment; and based on the original data set, analyzing an inhaul cable tension update value, then analyzing an inhaul cable tension residual error and an interpolation adjustment effect label, and carrying out signal reconstruction effect judgment. According to the method, the bridge structure can be continuously monitored, linear interpolation adjustment information is carried out by analyzing signal time characteristic characterization factors, whether adjustment is carried out or not is judged, calculation power waste is avoided, and the inhaul cable tension is aligned with a main tower inclination timestamp by analyzing inhaul cable tension update values through linear interpolation adjustment. A stay cable tension update value is analyzed to eliminate a cross-sensor clock asynchronous error, signals are classified by analyzing a frequency coherence factor, mutual interference of different frequency components in broadband signals is avoided through classification processing, and the structural health monitoring precision is remarkably improved.

The invention relates to the field of bionic hidden underwater acoustic communication, in particular to an underwater acoustic communication hidden performance evaluation method, device and equipment based on a neural network, and the method comprises the steps: carrying out the feature extraction of a to-be-evaluated underwater acoustic signal, and obtaining a feature sequence of the to-be-evaluated underwater acoustic signal; converting the feature sequence into a two-dimensional matrix with a preset size; the two-dimensional matrix is predicted according to a pre-trained underwater acoustic signal classification neural network model, a probability matrix is obtained, the probability matrix comprises a first probability and a second probability, the first probability represents the probability that the to-be-evaluated underwater acoustic signal belongs to the bionic modulation signal, and the second probability represents the probability that the to-be-evaluated underwater acoustic signal belongs to the bionic modulation signal; the second probability represents the probability that the underwater acoustic signal to be evaluated belongs to the real marine organism signal, and the underwater acoustic signal classification neural network model comprises an input layer, a first convolutional layer, a first pooling layer, a second convolutional layer, a second pooling layer, a global pooling layer and a full-connection output layer which are connected in sequence. The dependence on artificial feature design in a traditional recognition method is reduced, and the recognition stability in a complex underwater environment is improved.

The invention discloses an electromyographic signal data generation method based on deep learning, and belongs to the field of deep learning. The method comprises the following steps: firstly, building a confrontation model framework comprising a depth angle sequence encoder, a context encoder, an electromyographic signal generator and a discriminator, and placing the confrontation model framework on a hand joint of a user to generate electromyographic signal data; wherein the depth angle sequence encoder is used for encoding a hand joint angle sequence control signal, inputting the hand joint angle sequence control signal into the context encoder to generate potential information of an electromyographic signal, and generating a corresponding electromyographic signal by using the electromyographic signal generator; the electromyographic signal discriminator judges whether each time step accords with a real electromyographic signal mode or not; and then, training an electromyographic signal classification network based on a convolutional neural network by using the electromyographic signal data, and deploying the electromyographic signal classification network to an actual embedded device or a mobile terminal to perform real-time gesture recognition. According to the method, an existing electromyographic signal data set is effectively expanded, meanwhile, the sequence information is used as a control signal, and the data generation quality is effectively improved.

The invention provides a few-sample motor imagery electroencephalogram signal classification method. In a source domain pre-training stage, a source domain data set, a feature mapping module and a classification module are constructed, the feature mapping module and an auxiliary classifier are pre-trained, and the pre-trained auxiliary classifier is discarded. In the meta learning stage, a source domain data set is divided into a meta training task set, based on a cross-data-set motor imagery decoding framework, task parameters are optimized on a support set through internal circulation gradient descent, loss is calculated in a query set to update meta parameters, a feature mapping module and a classification module are jointly trained, and the adaptability and generalization ability of the model are enhanced. In the target domain verification and classification stage, a small number of electroencephalogram signals of a target domain user are collected to serve as an adaptation set, and the motor imagery electroencephalogram signal classification efficiency and accuracy are improved through an internal circulation gradient descent fine tuning model based on parameter initialization in the meta-learning stage; source domain pre-training lays a foundation, meta-learning enhances adaptability, and target domain fine tuning realizes accurate classification.

The invention discloses an underwater acoustic signal classification method based on comparative learning and feature fusion, and the method comprises the steps: collecting an original underwater acoustic signal, and carrying out the enhancement preprocessing of the original underwater acoustic signal, and obtaining a preprocessed underwater acoustic signal; outputting the preprocessed underwater acoustic signals to a classification neural network model to obtain an underwater acoustic signal classification result; wherein the classification neural network model is obtained according to the following steps: constructing a multi-modal heterogeneous feature fusion network; performing self-supervised comparison pre-training on the constructed multi-modal heterogeneous feature fusion network to obtain a pre-trained multi-modal heterogeneous feature fusion network; a classification module is built, and the classification module takes fusion features output by the multi-mode heterogeneous feature fusion network as input and takes probability distribution of each category as output; and performing progressive supervision fine tuning on the pre-trained multi-modal heterogeneous feature fusion network and the classification module to obtain a classification neural network model.

The invention relates to an electroencephalogram signal classification method based on multi-scale causal convolution and KAN attention. The electroencephalogram signal classification method comprises the steps of 1, EEG data collection and preprocessing; 2, EEG data enhancement; and 3, motor imagery task classification based on EEG signals. According to the method, the spatial-temporal characteristics of the EEG signals under different frequencies can be effectively extracted, the multi-scale nonlinear characteristics are efficiently fused and weighted, and the problem that nonlinear fitting of the EEG signals in a motor imagery task is insufficient is solved, so that more accurate and reliable motor imagery classification is realized.

The invention provides a superconducting quench detection system and method, and the system comprises an OFDR signal collection module which is used for obtaining an optical frequency domain reflection signal and transmitting the optical frequency domain reflection signal to a data fusion and preprocessing module; the voltage signal acquisition module is used for acquiring and transmitting a voltage signal; the data fusion and preprocessing module is used for fusing and preprocessing the received optical frequency domain reflection signal and the voltage signal and outputting a processing result; and the CNN model training and recognition module is used for constructing and training a CNN model for quenching signal classification and recognition based on the processing result of the data fusion and preprocessing module, and outputting a detection result. According to the superconducting quench detection system based on optical frequency domain reflection, the convolutional neural network and voltage signal fusion, the advantage of high-precision distributed measurement of the OFDR technology, the powerful feature extraction and classification capability of the CNN and the characteristic that the voltage signal directly reflects the state change of the superconductor are comprehensively utilized, and faster and more accurate detection of superconducting quench is achieved.

The invention discloses a lightweight surface electromyogram signal classification method and system. The method comprises the steps that original surface electromyogram signals are acquired; maintaining a dynamic reference vector capable of tracking the change of the physiological baseline of the signal; standardizing the original signal by using the dynamic reference vector, and generating a channel hybrid matrix according to the vector; extracting a local time-frequency characteristic pattern from the standardized signal; transforming the local time-frequency feature map by using a channel hybrid matrix so as to output a collaborative adaptive feature map capable of adapting to muscle collaborative mode change; and through a parallel sequence modeling module fusing a state space model and a multi-head self-attention mechanism, processing the collaborative adaptive feature map to obtain a final action category. The method improves the robustness of the model to individual difference and muscle fatigue, has the advantages of light weight and low delay while maintaining high classification precision, and is suitable for mobile or wearable equipment.

The invention provides an intrusive electroencephalogram signal classification system based on SHAP interpretable feature selection and Transform, and relates to the technical field of brain-computer interfaces and intelligent neural information processing. The system comprises a candidate feature extraction module, an SHAP feature optimization module, a space-time Transform classification model and an interpretability and visualization module. The candidate feature extraction module is used for extracting time domain, frequency domain and nonlinear candidate features from the preprocessed interventional electroencephalogram signals; the SHAP feature optimization module performs importance scoring and recursive screening on the candidate features based on a random forest and a Shapley value method to generate an optimal feature subset; according to the space-time Transform classification model, a space-time feature matrix is constructed through feature embedding and a time position coding mechanism, and high-precision classification is achieved through a multi-head self-attention structure; and the interpretability and visualization module is combined with the SHAP heat map and the attention weight map to provide physiological interpretation of a model discrimination basis. The system is suitable for various interventional brain-computer interface scenes such as neural rehabilitation and motion intention recognition.

The invention discloses a method for predicting the charging load of an electric vehicle, and the method comprises the steps: collecting the historical charging load data of the electric vehicle, and carrying out the one-time decomposition of the time series data of the charging load through employing an ICEEMDAN model, and obtaining 10 components; performing sample entropy calculation on components obtained by primary decomposition, performing signal classification by adopting K-medoids clustering according to a sample entropy result to obtain a high-frequency component, an intermediate-frequency component and a low-frequency component respectively, optimizing parameters in an MTS-Mixers model, a Crossform model and a DeepESN model by using an improved AO optimization algorithm, and performing signal classification by adopting K-medoids clustering to obtain a high-frequency component, an intermediate-frequency component and a low-frequency component; and predicting the high-frequency component, the intermediate-frequency component and the low-frequency component by using an MTS-Mixers model, a Crossform model and a DeepESN model, and finally reconstructing prediction results of the three models to obtain a prediction result of the charging load.

The invention belongs to the field of electroencephalogram signal classification detection, and particularly relates to a neuropsychiatric disease electroencephalogram diagnosis method based on iteration polar coordinate attention, which comprises the following steps: acquiring a multi-channel EEG (electroencephalogram) signal and preprocessing the multi-channel EEG signal; inputting the EEG preprocessing signal into a pre-trained improved LaBraM model to obtain a plurality of node features; calculating a Pearson's correlation coefficient matrix and a cosine similarity matrix according to the EEG preprocessing signal, and then constructing a brain function fusion connection matrix as an adjacent matrix through an iteration polar coordinate attention mechanism; according to the method, the large-scale pre-trained EEG model and polar coordinate attention are used for brain graph structure construction for the first time, and collaborative optimization of time feature generalization and space structure modeling capacity is achieved.