664results about How to "Improve capture ability" patented technology

The invention relates to the technical field of multi-axis linkage numerical control machining, and discloses a multi-axis linkage numerical control machining state monitoring method and system.The method comprises the steps that vibration, acoustic emission and cutting force signals in the multi-axis linkage numerical control machining process are collected and preprocessed, and first multi-source sensor data are obtained; performing feature extraction and compressed sensing to obtain a low-dimensional feature subset; performing fusion judgment to obtain an abnormal state detection result; training a deep learning model comprising a backbone network and a plurality of branch networks to obtain a state evaluation model; and inputting second multi-source sensor data collected in real time into the state evaluation model for state evaluation to obtain a processing state classification result and a parameter estimation value. According to the method, cooperative state evaluation and prediction simulation of multiple machine tools are realized.

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention discloses an industrial and commercial park electrical load power prediction method, and relates to the technical field of electrical load prediction, and the method comprises the steps: obtaining park electrical equipment power data, and obtaining a standardized time sequence power matrix through normalization preprocessing; performing wavelet denoising and empirical mode decomposition on the power matrix, and separating a power trend component from a fluctuation component; constructing a trend prediction sub-model by using a long-short term memory network, and constructing a fluctuation prediction sub-model by using a seasonal ARIMA model; carrying out weighted combination on the two sub-models through an integrated learning method, and constructing a comprehensive prediction model; and finally, based on the historical prediction error, setting an adaptive threshold value for constraint processing, and outputting a final prediction result. According to the method, power data features are adaptively separated through a multi-level decomposition strategy, a dual-model collaborative prediction mechanism is adopted, environmental factors are introduced to improve prediction precision, a self-adaptive correction mechanism is designed to ensure result reliability, and accuracy and stability of park power load prediction are remarkably improved.

The invention provides a riverbed evolution prediction method based on multi-source data fusion, and belongs to the technical field of hydrological resource monitoring. The method comprises the following steps: acquiring and fusing multi-source data by using different devices, performing data preprocessing to form unified three-dimensional grid data, and then generating a multi-scale feature map based on a designed feature extraction network; based on the designed multi-channel convolutional neural network, learning and extracting features on multiple channels at the same time, and performing hierarchical expression and fusion on different information sources; and finally, based on the designed time sequence regression network, extracting a complex dependency relationship in the time sequence through a double-layer LSTM structure, learning residual error information through an adaptive residual error correction module to improve prediction precision, and obtaining a final prediction result. The method shows relatively high applicability and robustness in a complex river channel environment, and can cope with various river bed form changes caused by natural factors and human activities through multi-source data fusion and an innovative feature extraction and regression method.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention relates to the technical field of cold-chain logistics monitoring, and discloses a temperature prediction method and system for a multi-modal AIGC cold-chain logistics monitoring platform, and the method comprises the steps: carrying out the multi-modal data collection and data fusion of a cold-chain logistics transportation process based on a cold-chain logistics monitoring platform, and obtaining a multi-modal fusion data matrix; inputting an AIGC model to carry out feature extraction and inter-modal feature weight calculation to obtain a multi-modal feature importance vector; performing cross-modal weighted fusion calculation and associated feature map construction to obtain a temperature state feature sequence; inputting a multi-modal deep belief network to carry out probability distribution modeling to obtain a temperature prediction probability distribution sequence; performing time sequence characteristic analysis to obtain a temperature prediction interval sequence; and performing dynamic threshold judgment to obtain a multi-stage temperature early warning trigger signal. According to the invention, the temperature prediction accuracy of the cold-chain logistics monitoring platform is improved.

The invention discloses an electric power product operation fault detection system based on big data, and relates to the field of electric power fault detection systems, and the system comprises a data collection module which collects the multi-parameter operation data of various electric power products, including electrical and equipment states and environmental parameters, can dynamically adjust the collection frequency, and caches and encrypts the transmission data; the data preprocessing module is used for carrying out abnormal value correction, noise removal, normalization and integrity check; the feature extraction module is used for extracting key and time-frequency domain features by using multiple algorithms, selecting sensitive features according to a fault mode and identifying newly added features; and the fault detection model construction module constructs a model by combining machine learning and deep learning algorithms with ensemble learning and the like, and can update training. Through multi-module cooperation, features are effectively extracted to construct the model, accurate fault diagnosis and prediction are realized, visual display and knowledge support are provided, the intelligent level and efficiency of power product fault detection are improved, and stable operation of a power system is guaranteed.

The invention discloses an unmanned vehicle formation cooperative hunting method, device and equipment based on pilot following, and a medium, and relates to the technical field of multi-agent control. The method comprises the following steps: determining role division of a leading vehicle and a following vehicle, planning a path by the leading vehicle through an improved fast random exploration tree algorithm, sending the path and a control strategy to the following vehicle in stages, dynamically adjusting the position and speed by the following vehicle according to an instruction, ensuring formation consistency and task execution, and realizing information sharing between unmanned vehicles through a communication network. The pilot vehicle broadcasts a path and a task instruction in real time, and the following vehicles receive and analyze the path and the task instruction and then dynamically adjust the motion trail, thereby ensuring the cooperative consistency of the formation and the efficient stability of information transmission; each vehicle executes a hunting task according to a task instruction and adjusts a motion track and speed in real time, the following vehicle keeps a formation according to a path instruction, the pilot vehicle dynamically adjusts a global path according to environment change and a target position, the instruction is updated in real time through a communication network, and finally effective hunting and capturing of a target are realized. The method is suitable for a target hunting task in a complex environment, and reliable technical support is provided for a multi-target complex scene.

The invention discloses an event analysis method, system and device based on task generation and multiple modes, which are used for quickly and accurately mastering the development trend of an event. The method comprises the steps that structured event information is generated through multi-modal data, and an event relation is obtained; constructing an event graph; extracting a multi-modal feature, and obtaining a multi-modal emotion relationship vector by using a cross-modal attention mechanism; based on a preset graph neural network, combining the event graph to obtain an emotion feature vector; calculating a comparative learning loss value, and obtaining a target emotion feature based on the comparative learning loss value; generating a target event atlas by using a cross-modal attention mechanism; extracting relation characteristics, and combining the attention weight and a preset time sequence model to obtain an event prediction map; extracting a causal chain and reasoning, and obtaining a trigger condition and an emotion factor after reasoning; and combining the event prediction map and the target event map with the node feature sequence, the trigger condition and the emotion factor to obtain an event analysis result.

The invention discloses a risk early warning method and device constructed based on dynamic rules, equipment and a medium, and relates to the technical field of risk early warning, and the method comprises the steps: extracting a user static attribute and a dynamic behavior sequence, constructing an original data set, carrying out time sequence analysis, generating a behavior time sequence response feature, carrying out cross-modal feature fusion, and constructing a space-time perception behavior vector; constructing a dynamic rule graph structure according to the risk rule and the corresponding standard space-time perception behavior vector, iteratively updating a risk rule weight based on a graph convolutional network, constructing a wavelet domain risk quantification model, and generating a multi-scale risk value; and inputting the newly generated space-time perception behavior vector into the wavelet domain risk quantification model, generating a to-be-judged multi-scale risk value, and performing risk early warning when a threshold condition is not met. Through time sequence analysis and a multi-scale risk quantification model, the accuracy of risk identification is improved, and multi-dimensional and multi-scale risk analysis is supported, so that the accuracy and real-time performance of risk management are effectively improved.

The invention discloses a deep learning-based deep forged compressed face image identification method, which belongs to the field of artificial intelligence image identification, and comprises the following steps of: 1, carrying out frequency domain modal extraction on a deep forged compressed face image by adopting dynamic adaptive discrete cosine transform; 2, a multi-head self-attention mechanism and a multi-dimensional entropy state load mutual feedback mechanism are adopted to extract frequency domain high-dimensional load features; step 3, extracting spatial domain features based on the pre-trained deep residual network; 4, performing feature fusion on the frequency domain high-dimensional load features and the spatial domain features based on an attention mechanism; and 5, discriminating and classifying the fusion features by using a classifier comprising a full connection layer. According to the method, high-accuracy identification can be carried out on the compressed face image manufactured by adopting a deep counterfeiting means.

The invention discloses an electroencephalogram signal feature extraction method and system based on self-adaptive time-frequency feature collaboration, relates to the field of electroencephalogram signal processing, and solves the problems that in existing electroencephalogram signal feature extraction, fusion is carried out in a simple splicing or averaging mode, complementarity and correlation between time domain and frequency domain features are not fully considered, and time domain and frequency domain features are not fully considered. In order to solve the problems of insufficient overall characterization capability and classification performance of signals and the like, the method comprises the following steps: acquiring electroencephalogram signal data, preprocessing the electroencephalogram signal data, constructing a dynamic residual network in combination with dynamic convolution, and training a model based on the preprocessed electroencephalogram signal data; performing a contrast test on the dynamic residual network and other deep learning network models, and designing an adaptive time-frequency collaborative network model to fuse time-domain features and frequency-domain features in the model; and electroencephalogram signal feature extraction based on adaptive time-frequency feature cooperation is completed. The method is also suitable for the field of electroencephalogram signal feature extraction and fusion.

The invention relates to the technical field of pressure sensing, and discloses a pressure distribution data acquisition method and device, and the method comprises the steps: carrying out the calibration and signal preprocessing of a pressure sensor array, and obtaining an initial state matrix, a preprocessing parameter set, and a response curve database; calculating a pressure change rate to obtain a pressure sampling dynamic trigger threshold value and a pressure sampling control matrix; performing partition control on the pressure data acquisition process to obtain a pressure acquisition partition control model; collecting target pressure data and inputting the target pressure data into the time weighted processing network model for feature extraction to obtain multi-modal pressure distribution feature data; the multi-modal pressure distribution characteristic data is input into a closed-loop control system, and the pressure sampling parameters and the pressure processing parameters are updated to obtain a pressure acquisition control parameter set, so that high-precision pressure distribution acquisition is realized, and the limitation of a traditional method in processing non-linear and time-varying pressure signals is solved.

The invention relates to the technical field of audio testing, and discloses an intelligent audio testing system frequency response analysis method, device and equipment, and the method comprises the steps: carrying out the audio parameter initialization of an intelligent audio testing system, and obtaining a testing parameter matrix and an excitation signal; performing dual-channel acquisition and time-frequency feature extraction to obtain a time-frequency feature data set; executing double-branch processing of time domain noise reduction and frequency domain enhancement to obtain a purified feature data set and a feature weighting matrix; performing segmented recursive calculation and feature aggregation to obtain a frequency response feature vector; decomposing the frequency response feature vector according to a test frequency band, and performing environment compensation and nonlinear calibration to obtain a standard frequency response data set; and inputting the standard frequency response data set into the deep neural network for feature recognition and mode classification to obtain a frequency response curve and a phase characteristic curve, thereby improving the test precision, the anti-interference capability, the automation degree and the adaptability of the intelligent audio test system.

The invention relates to the technical field of artificial intelligence, and discloses an anti-fraud model training method based on deep learning, which comprises the following steps: step 1, preprocessing and constructing multi-modal data, carrying out abnormal value processing, missing value filling and feature standardization on original data, representing processed transaction data and geographic information through a feature extraction module, and carrying out feature extraction; the equipment information and the social relation graph are embedded through an embedding layer and a graph neural network; step 2, based on data enhancement of self-supervised learning, pre-training unlabeled data by designing a behavior prediction task and a mask filling task so as to initialize a main model by using generated model parameters, and generating a synthetic sample extension training data set; by designing a behavior prediction task and a mask filling task, automatic learning and enhancement of features are realized by using label-free data, the dependence on labeled data is reduced, the detection capability of the model on fraudulent behaviors is remarkably improved, and the diversity of training data is enriched.

The invention discloses a lightweight eye fundus blood vessel image segmentation model based on Mama. The model comprises the following steps: an encoder and a decoder comprise at least four layers of SlimPVM Blocks; gradually downsampling the input feature map through maximum pooling operation; the CCA Bottleneck module is used for calculating the self-attention of the cross-channel in the height dimension and the width dimension of the feature map; the encoder and the decoder carry out feature fusion through selective jump connection, and the selective jump connection carries out weighted selection on features through a channel attention mechanism; the decoder uses SlimPVM Block to refine the features after up-sampling, the model recovers lost spatial dimensions through convolution transpose operation, and coding features from selective jump connection are combined; and the decoder finally outputs a final segmentation mask generated through a 1 * 1 convolution layer, and the segmentation mask can accurately describe the boundary of the target area in the medical image. The method is specially used for retinal vessel segmentation tasks, model parameters are reduced, calculation overhead is reduced, and meanwhile segmentation performance is kept and even improved.

The invention relates to a time sequence optimization method and system for a time-sharing light source controller. The method comprises the following steps: acquiring a high-speed optocoupler trigger signal, an eight-channel output voltage signal and an inter-channel delay signal of a time-sharing line scanning light source controller, and generating a multi-channel time sequence characteristic data matrix; performing dynamic subspace decomposition and bidirectional residual operation to generate an anti-interference enhanced feature vector; performing long-range dependency relationship analysis between channels, and generating channel cooperative control characteristics; constructing a hierarchical predictive control model, and generating an optimal trigger time sequence; issuing the optimal trigger time sequence to each output channel, collecting channel output trigger delay data in real time, and calculating a compensation trigger time sequence; and generating an optimization control parameter group and updating the optimization control parameter group to the hierarchical predictive control model. According to the invention, the time sequence jitter problem of multi-channel output is effectively solved, and the synchronism and stability of light source triggering are ensured.

The invention provides a close-range radio frequency electromagnetic field immunity test method and system, and relates to the technical field of communication detection, and the method comprises the steps: collecting near-field electromagnetic field distribution data and near-field frequency spectrum power distribution data formed by a radio frequency interference source on the surface of a tested device; constructing a near-field electromagnetic field distribution model; according to the frequency spectrum overlapping degree between the near-field frequency spectrum power distribution data and the working frequency band of the tested equipment, calculating the interference intensity of a radio frequency interference source on the tested equipment, and calculating the power of a target interference source according to the interference intensity through a dynamic frequency spectrum adaptation algorithm; regulating and controlling a radio frequency interference source to generate a radio frequency interference source signal with target interference source power; and monitoring an electromagnetic coupling coefficient of the tested equipment for the radio frequency interference source signal based on the electromagnetic field intensity distribution model so as to generate an immunity test result. Therefore, an actual interference scene of modern wireless equipment in a near-field environment can be simulated more accurately, and comprehensive evaluation of the equipment immunity under high-frequency and near-field conditions is realized.

The invention discloses an electric power system state estimation method based on graph contrast learning. The method comprises the following steps: constructing graph sequence data of electric power system state estimation; constructing an electric power system state estimation model based on graph contrast learning, and putting the graph sequence data into the electric power system state estimation model based on graph contrast learning; inputting the node feature sequence and the masked edge features into a hybrid encoder of a dynamic edge convolutional neural network combining a self-attention mechanism and a residual block and a graph convolutional neural network with a dynamic propagation strategy of a gating mechanism to comprehensively process the features of nodes and edges; comparing an updating mechanism and a sampling symmetry training strategy of model parameters of the learning framework through a graph; and calculating a node voltage phase angle by using the branch phase angle difference, and outputting a state estimation result. According to the method, the internal structure and mode of the power system data can be effectively learned by combining graph contrast learning and a deep neural network technology, and the capability of identifying and filtering abnormal data is improved.

The invention provides a multivariable multi-step runoff prediction method based on dynamic variational mode decomposition, and belongs to the technical field of drainage basin runoff prediction.The method comprises the steps that various hydrometeorological data of a research drainage basin are collected; decomposing an original runoff sequence by adopting a dynamic variational mode decomposition strategy VMD, performing sliding window slicing processing on drainage basin underlying surface and meteorological data and upstream hydrologic station runoff data, and combining target hydrologic station runoff data subsequences obtained by decomposition into a first characteristic variable; meteorological factor screening is carried out on the meteorological station data, a screening result is used as a second characteristic variable, and the first characteristic variable and the second characteristic variable are combined together to form an input characteristic variable; a sparrow search algorithm SSA is adopted to optimize parameters of a long short-term memory network LSTM to construct the long short-term memory network LSTM, and future runoff is predicted. According to the method, the defect that future forecast information is introduced by binding decomposition is effectively overcome, and the applicability of the decomposition-integration forecast model in actual engineering is enhanced.

The invention discloses an alternating current charging pile ordered charging power regulation and control method, and relates to the technical field of alternating current charging pile charging power regulation and control, and the method specifically comprises the following steps: obtaining the operation state information of each alternating current charging pile in real time, and generating a voltage fluctuation influence index and a load dynamic response index of each alternating current charging pile; constructing a power influence evaluation model for the generated voltage fluctuation influence index and the load dynamic response index of each alternating current charging pile, generating a power influence evaluation coefficient of each alternating current charging pile, performing analysis after generation, and evaluating the influence degree of the three-phase voltage phase difference fluctuation on the charging power of each alternating current charging pile. And the alternating current charging piles are divided into low-influence charging piles, medium-influence charging piles and high-influence charging piles according to the evaluation result. According to the method, the problem that the charging power is influenced by three-phase voltage phase difference fluctuation is solved, and the efficient and stable effects of dynamic evaluation and accurate regulation and control of the power of the charging pile are achieved.

The invention relates to the technical field of subgrade settlement prediction, in particular to a subgrade settlement prediction method based on deep learning, electronic equipment and a medium, the subgrade settlement prediction method provided by the invention adopts a subgrade settlement prediction model for prediction, and the model adopts the combination of a convolutional neural network and a bidirectional long-short term memory network. Spatio-temporal features are effectively captured, and the prediction precision and stability of the model are improved; by introducing a channel attention enhancement mechanism module, dynamic weighting is performed on different feature channels, the selection ability of the model to key features is improved, and the sensitivity of the model to subtle changes in data and the capture ability of the model to global features are enhanced; the model is trained through the genetic algorithm, the efficiency is improved, and the genetic algorithm can automatically find an optimal combination in a complex hyper-parameter space, so that the model can show good generalization ability on different roadbed settlement data sets, and the prediction precision of the model is improved.

The invention discloses a speech enhancement method and system based on a time-frequency graph convolutional network in the technical field of speech signal processing, and the method comprises the steps: obtaining to-be-enhanced noisy speech data, inputting the to-be-enhanced noisy speech data into an improved metric generative adversarial network (CMGAN), and outputting an enhanced speech time domain signal; the improvement method of the metric generative adversarial network CMGAN comprises the following steps: replacing an encoder module of the metric generative adversarial network CMGAN with a time-frequency graph convolution encoder module; in the time-frequency graph convolution encoder module, a time convolution network and a graph convolution network are used for respectively extracting a long-time dependency relationship and global frequency characteristic information of noise-containing voice data to be enhanced, and dense residual error connection is introduced to fuse multi-scale characteristic information at the same time. The speech enhancement performance is improved under the condition of less model parameter quantity and calculation quantity.

The invention discloses a high-resolution remote sensing image double-branch segmentation method, which comprises the following steps: acquiring a high-resolution remote sensing image data set, preprocessing the high-resolution remote sensing image data set, and dividing the high-resolution remote sensing image data set into a training set, a verification set and a test set; constructing a semantic segmentation network model of the high-resolution remote sensing image, wherein the semantic segmentation network model comprises a detail branch, a semantic branch and an up-sampling part; inputting the training set into a high-resolution remote sensing image semantic segmentation network model for training, calculating a loss function, performing back propagation, updating network model parameters, obtaining an optimal parameter network model, and verifying the optimal parameter network model by using the verification set; and inputting the test set into the trained optimal parameter network model, and outputting an accurate segmentation image of the high-resolution remote sensing image. According to the method, feature extraction and fusion are carried out in a multi-level and multi-scale manner by capturing target information of different types and scales in the high-resolution remote sensing image, so that the segmentation precision of the high-resolution remote sensing image is improved.

The invention discloses a multi-modal sentiment analysis method and device, and relates to the technical field of sentiment analysis, and the method comprises the steps: carrying out the feature extraction and modal alignment of to-be-detected multi-modal sentiment data, and determining an aligned text feature, an aligned video feature and an aligned audio feature; performing feature enhancement on the aligned video features and the aligned audio features based on the aligned text features to generate enhanced video features and enhanced audio features; performing cross-modal cross attention fusion by adopting the aligned text features, the enhanced video features and the enhanced audio features, and constructing interactive text features, interactive video features and interactive audio features; and performing later fusion on the interactive text features, the interactive video features and the interactive audio features, performing emotion prediction, and outputting an emotion analysis result. The feature quality of audio and video modals is improved by adopting an audio and video feature enhancement mechanism, and cross-modal feature interaction and multi-modal late fusion are effectively combined, so that the reliability of multi-modal sentiment analysis is improved on the whole.

The invention relates to the technical field of computer vision and image processing, in particular to a robust image matching system and method based on multi-scale attention feature fusion, and the system comprises a feature extraction module which extracts a multi-resolution feature map containing image features from a to-be-matched image; the position coding module performs spatial position coding on the image features to improve the spatial perception capability of the model; the multi-scale attention module performs multi-scale enhancement processing on the image features, so that the model is more effectively focused on the significant part of the image; the attention module establishes a dependency relationship between image features; the coarse-level matching layer module generates a coarse-level matching result and screens out a matching pair with relatively high confidence coefficient; and the coarse-to-fine module carries out fine processing on the coarse matching result, so that more accurate feature matching is realized. According to the method, the effectiveness and reliability of the method are verified by testing the image matching performance of the to-be-tested picture under the conditions that the to-be-tested picture contains different illumination changes, extreme visual angle changes, repeated textures and the like.

The invention provides a myocardial infarction positioning method based on cross-modal knowledge fusion. The method comprises the following steps: acquiring a target 1D-VCG signal and constructing a 2D-VCG image data set; extracting spatial features, time sequence features and pseudo tags from the 2D-VCG signals and the 1D-VCG signals through a teacher model and a student model; aligning and transforming the spatial features and the time sequence features through a cross-modal generator; and cross-modal knowledge fusion and MI positioning are realized through a cross-modal generator. According to the method, through knowledge enhancement and student enhancement strategies, the accuracy of myocardial infarction positioning is improved, and the overall framework of cross-modal learning is optimized through the adaptive weight method; the method is realized in a knowledge distillation form, spatial features of 2D-VCG are successfully fused into 1D-VCG time sequence analysis, and the positioning accuracy of myocardial infarction is remarkably improved by combining cooperative work of a teacher model, a student model and a cross-modal generator.

The invention discloses an emotion recognition method based on a multi-domain attention fusion network, and the method comprises the steps: firstly obtaining an EEG signal, carrying out the preprocessing of the EEG signal, carrying out the channel feature extraction through a space-frequency domain mixed channel attention module, and capturing a weighted attention feature; secondly, weighting attention features, and extracting space and frequency domain features of EEG signals through a CNN module; and combining the improved multi-dimensional self-attention with the BiSRU, and capturing comprehensive features of space and frequency domain features. And finally, classifying the comprehensive features through a Softmax function, and outputting an emotion recognition result. According to the method, the capability of capturing the long-term dependency relationship of the electroencephalogram signals is enhanced, and the emotion recognition performance and efficiency are effectively improved.

The invention relates to the technical field of data processing, and discloses a radio frequency fingerprint identification method based on a deep optimization adaptive attention mechanism, and the method comprises the steps: receiving a target equipment radio frequency signal, and carrying out the down-conversion, filtering and digital sampling of the target equipment radio frequency signal, and obtaining an original IQ data sample; preprocessing the original IQ sample to obtain a training data set; inputting the training data set into a convolutional neural network for pre-training to obtain a pre-training model; performing fine tuning on the pre-training model to obtain a downstream task model; and using the downstream task model to classify and identify unknown equipment data. Through time window segmentation and feature enhancement, the definition and consistency of radio frequency signals are improved, and meanwhile, the influence of noise and interference is reduced; and the effect of improving the capturing capability of the model on multi-scale and key features in a complex environment is achieved through the differentiated attention mechanism module.