193 results about "Linear network coding" patented technology

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing image adaptive identification method and system for territorial space planning, and the method comprises the steps: obtaining a multi-source remote sensing image data set of a research region, feature extraction, cloud detection, quality evaluation and adaptive preprocessing are carried out; carrying out prototype network coding, calculating a category prototype and probability, and supporting fine tuning of a set; carrying out multi-scale cavity convolution and category scale attention fusion; evaluating the adaptability score of the comprehensive fusion feature map set, and carrying out weighted fusion, classification and normalization; change detection is carried out, stable and change regions are segmented, and time sequence context features are extracted and constrained optimization is carried out; entropy is fused, a boundary is decided, uncertainty is estimated, and weighted fusion is carried out according to a change area; conditional random field optimization, confidence level grading and connected domain identification are carried out; the automation level, the adaptive capacity and the recognition reliability of remote sensing monitoring of territorial space planning are improved.

The invention discloses a self-supervised learning negative two-way distillation anomaly segmentation method, which comprises the steps of preprocessing a non-anomaly unsupervised anomaly detection training data set, and constructing a simulated anomaly data set; training a student network encoder by combining knowledge distillation and negative learning thoughts according to the simulated abnormal data set; performing multi-scale feature fusion on an output result of the encoder; inputting a feature fusion result into a student network decoder, and training the decoder through a reverse distillation and self-supervision anomaly reconstruction task; performing feature fusion on output results of corresponding layers of the student network and the teacher network by using feature fusion based on a channel attention mechanism; and taking the fused features as input to train a segmentation network so as to accurately predict an abnormal position. According to the method, a bidirectional distillation network structure is constructed, and a self-supervised reconstruction task is combined with a negative learning idea, so that the decision-making ability of the model is effectively enhanced, and the image segmentation accuracy in anomaly detection is remarkably improved.

Provided are a method and an apparatus for operating an NCR in a wireless communication system. The NCR includes NCR-MT and NCR-Fwd. The NCR receives a control message from a base station through the NCR-MT, and performs a forwarding operation through the NCR-Fwd on the basis of the control message. The control message indicates a beam applied to the forwarding operation of the NCR-Fwd and a forwarding resource in a time domain to which the beam is applied. The NCRFwd is in an OFF state in a time resource other than the forwarding resource. That is, if there is no explicit or implicit instruction from the base station, the NCR-Fwd is always expected to be in an OFF state.

The invention relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

The invention discloses an unmanned aerial vehicle mine resource monitoring system based on multi-modal data fusion, and relates to the field of mine resource monitoring, and the unmanned aerial vehicle mine resource monitoring system comprises a model construction module, an adjustment control module, an atlas construction module, a data fusion module and a monitoring module. A function is introduced for analysis, an unmanned aerial vehicle motion control strategy is obtained; a model is used for entity recognition; a relation triple is obtained through dependency syntactic analysis; coordinate atlas space attributes are fused, a topological relation is constructed, a conflict relation is verified, and implicit relation analysis is completed; the method comprises the following steps: obtaining a mine modular geological map architecture, generating node embedding features by using map attention network coding, associating sensor data with map nodes by using a cross-modal attention mechanism, outputting physical enhancement features, executing monitoring analysis on mine resources, keeping stable unmanned aerial vehicle group control, and ensuring that fused data is accurate.

The invention relates to the field of artificial intelligence, and discloses an E-commerce platform commodity recommendation method based on AI intelligence, and the method comprises the steps: obtaining user environment and behavior data through multi-modal perception, generating a three-dimensional intention vector through space-time convolution network coding, and driving a dynamic strategy selector to configure a recommendation weight; generating an initial recommendation list in combination with the commodity feature library and the user portrait; collecting user interaction feedback in real time, analyzing and outputting a correction vector through a lightweight loop network, dynamically adjusting a strategy, and reordering recommendation results; after a user places an order, a scene complementation generator is started, a complementation commodity sub-list adaptive to a current transaction scene is generated based on knowledge graph multi-hop reasoning, and visual fusion presentation is carried out. According to the technical scheme, accurate intention recognition, millisecond-level strategy correction and dynamic complementary recommendation are achieved, and recommendation real-time performance, adaptability and diversity are improved.

The invention discloses a speech enhancement method and system based on a selective state space model Mama, and the method comprises the steps: carrying out the short-time Fourier transform of a distorted speech, obtaining a time-frequency domain signal, splicing the amplitude, real part and imaginary part of the time-frequency domain signal into a signal of three channels, and enabling the signal to serve as an input signal; an input signal is expanded from three channels to a plurality of channels through a first convolution block of a generator network encoder, different resolution characteristics are extracted through an expanded dense convolution layer of the encoder, a receptive field is increased, and the frequency dimension of the signal is reduced to 1 / 2 of the original frequency dimension by using the last convolution block of the encoder so as to reduce the calculation complexity. The signals expanded by the encoder simulate forward and backward dependency relationships of voice signals under different resolutions through N TF-mamba blocks so as to capture local and global features of long-sequence voice signals, and feature enhancement is realized; and the amplitude and the phase of the enhanced signal are predicted through a complex decoder and a mask decoder to obtain enhanced speech after denoising and reverberation removal.

In a blockchain, transacting a record comprises performing linear network coding on the record or on a data file corresponding to the record, to produce a plurality of coded data parts; and storing at least one of the plurality of coded data parts on the blockchain or storing a proof of knowledge on the blockchain, the proof of knowledge derived from the coded data parts. Linear network coding coefficients might be derived from cryptographic hashes of the plurality of coded data parts. An all-or-nothing transform can use the cryptographic hashes to provide the proof of knowledge.

The invention relates to the technical field of image processing, in particular to a small sample image anomaly detection method based on multi-scale attention and efficient convolution, which comprises the following steps: acquiring small sample image data; the method comprises the following steps: constructing a ResNet network encoder, and constructing decoders of an MSCAM module, an EUCB module and an LGAG module; image features are extracted through an encoder, layer output feature mapping from low levels to high levels is constructed, and different levels of feature information are learned to integrate different levels of semantic information of the image; enhancing feature mapping in the decoder by using a multi-scale convolution attention mechanism; a complex spatial relationship and local attention are integrated by using channel, space and grouping gating attention mechanisms; and output vectors of different levels of the network are spliced. The problem that the accuracy of anomaly detection needs to be further improved due to the fact that features extracted in the early stage of coding are not fully utilized in an existing method is solved.

The invention discloses an image defogging method and device based on deep learning, and belongs to the technical field of image processing. The method comprises the following steps: constructing an image defogging model based on an improved U-Net network, including replacing a down-sampling module in an original U-Net network encoder with a multi-scale enhanced down-sampling module and replacing a jump connection structure in the original U-Net network with a cross attention gating module, and performing iterative training on an image defogging model by using the pre-processed foggy image data set, and defogging the foggy image based on the trained image defogging model to generate a defogged image. By introducing the multi-scale enhanced downsampling module and the cross attention gating module, detail information in a foggy image can be effectively extracted, and the multi-scale feature weight is dynamically adjusted, so that the defogging effect is remarkably improved, and particularly, the defogging efficiency is greatly improved when remote sensing images and common scene images under complex haze conditions are processed. And the method has relatively high robustness and generalization ability.

The invention relates to a knowledge tracking model research method integrating difficulty perception and memory enhancement. According to the method, a graph attention network coding exercise-knowledge point topological relation is constructed, and a difficulty embedding layer and a gating fusion mechanism are combined to realize joint characterization of difficulty features and a topological structure. The difficulty coefficient is innovatively introduced as a dynamic regulation factor of attention weight in memory network updating, the memory intensity of high-difficulty exercises is enhanced, and the discrimination and topological consistency of knowledge representation are improved through a multi-task optimization framework integrating graph structure loss and contrast loss. According to the method, challenges of exercise difficulty perception and knowledge point topological relation modeling are effectively solved, experimental results show that the AUC of the model reaches 0.93 (improved by 7.8% compared with traditional BKT) on an ASSIST2009 data set, the AUC of the model reaches 0.90 (improved by 0.21 compared with KSGAN) on an EdNet data set, objective indexes and teaching scene verification show that the method has remarkable advantages in the aspects of knowledge point correlation modeling and knowledge tracking, and the method is suitable for popularization and application. The method provides an innovative solution for personalized education, and has technical breakthrough and industrial application values.

According to the parameter image generation method and device for shortening the dynamic PET scanning time, the electronic equipment and the storage medium, the 3D image of the time frame of the first 25 minutes of dynamic PET scanning is obtained, feature extraction is conducted on the 3D image, the multi-scale feature map is generated through the network encoder part, the multi-scale feature map is mapped through the network decoder part, and the dynamic PET scanning time is shortened. According to the method, a Ki parameter image for network prediction is generated, the Ki parameter image for network prediction is optimized, the optimized Ki parameter image serves as input of next iteration, the steps are repeated until the set convergence condition is met, and the Ki parameter image meeting the requirement is finally obtained. And the quality of the generated Ki parameter image is beneficial to auxiliary diagnosis, so that the clinical examination efficiency and the comfort of a patient are remarkably improved, motion artifacts which are easy to occur during dynamic PET scanning are reduced as much as possible, and the quality of the generated Ki image and the diagnosis accuracy of a doctor are further ensured.

The invention relates to the technical field of computer vision and target detection, and particularly discloses a lightweight real-time two-wheeled vehicle helmet detection method. According to the method, firstly, a video stream is collected and preprocessed through a traffic monitoring camera, then feature extraction and fusion are carried out through a lightweight backbone network, an encoder and a neck network in sequence, finally, a detection result is output through a decoder, a StripCGLU module is introduced to reduce the calculation complexity, a Pola Former module is adopted to enhance the feature interaction capability, and finally, the detection result is output through a decoder. And a GLBiFPN network is designed to optimize multi-scale feature fusion. According to the method, the calculation complexity and parameter quantity of the model are remarkably reduced, the requirement of edge calculation equipment for efficiency is met while high detection precision is guaranteed, and an effective technical means is provided for safety monitoring in an intelligent traffic system.

The invention discloses a contact network insulator damage detection method and device based on few-sample transfer learning. The method comprises the following steps: inputting a to-be-detected contact network insulator image into a two-stage comparison suggestion coding network; the encoder performs feature encoding and feature extraction on the insulator image, and extracts feature representation of the image; the adaptive feature fusion module obtains an optimal fusion weight through adaptive weight learning; a hierarchical feature extraction module receives output of the adaptive feature fusion module, and extracts local position information of small objects in a shallow network of the encoder, global information of large objects in a deep network and local and global intermediate semantic information in an intermediate network layer; the inter-class and intra-class comparison suggestion coding module receives the output of the hierarchical feature extraction module, calculates the semantic similarity between different classes, and achieves the damage detection of the contact network insulator in a complex environment and a high-speed motion scene. According to the invention, the accuracy and efficiency of contact network insulator damage detection are improved.

The invention discloses a knowledge graph dynamic extension method based on knowledge graph and user behavior fusion. The method is specifically implemented according to the following steps: step 1, data preprocessing; step 2, capturing user behaviors; step 3, coding existing nodes in the map, performing feature initialization on the nodes which are not added into the map, calculating the similarity between a user historical behavior vector and all the nodes, screening out K nodes which accord with user interests, and adding the K nodes into a candidate set; step 4, adding new nodes and edges in the candidate set into the existing atlas, and training and updating parameters of the nodes in the new atlas; and 5, outputting personalized path recommendation conforming to the interest of the user, and generating a final map. According to the method, the user behavior feature vector is generated by using the LSTM network to encode the user historical browsing track and the touch click sequence, the nodes conforming to the user interest are screened out through the vector and added into the knowledge graph, and the space-time navigation path meeting the user interest preference is dynamically generated.

The invention discloses a high-frequency perception semi-supervised electromagnetic shielding light window image segmentation method based on multitask and transformation consistency learning, and the method comprises the steps: 1, constructing an electromagnetic shielding light window OM image data set, carrying out the shooting of an electromagnetic shielding light window, selecting an original image, generating a corresponding same-name label image, and dividing the same-name label image into a training set, a test set, and a verification set; 2, constructing a semi-supervised electromagnetic shielding light window image segmentation network model HAMTC-Net; 3, a network model HAMTC-Net is trained; 4, evaluating the performance of the HAMTC-Net network model by using the verification set and optimizing parameters; and 5, inputting an electromagnetic shielding light window image to be segmented into the trained HAMTC-Net network model, and outputting a segmentation result. According to the method, the global learning ability of a network encoder is enhanced through a multi-task method while the transformation consistency is fully utilized, and the high-frequency characteristics of data are fully utilized to guide training, so that the segmentation precision and generalization ability of the model in a complex electromagnetic shielding light window image are effectively improved.

The invention relates to the technical field of mechanical fault diagnosis, and provides a fault diagnosis and classification method for a bearing of an aluminum alloy impeller die-casting ladling machine, which comprises the following steps: acquiring a vibration signal of the bearing of the ladling machine, carrying out continuous wavelet transform on the vibration signal, generating a time-frequency diagram, and carrying out adaptive grid segmentation on the time-frequency diagram. Grid granularity is adjusted according to the local change rate of the time-frequency graph, multi-scale nodes are generated, edge connection is generated for the multi-scale nodes based on a K-nearest neighbor algorithm, and a multi-scale graph structure is constructed; performing unsupervised feature extraction on the multi-scale image structure, including: performing data enhancement on the multi-scale image structure through edge deletion and feature mask to generate an enhanced view; a graph attention network encoder is used for encoding the enhanced view, graph-level embedding is generated, and graph-level embedding is optimized by comparing a loss function; and based on the optimized graph-level embedding, performing fault classification by using a classifier constructed by a graph attention network and a multi-layer perceptron, and outputting a fault category.

The invention provides a three-dimensional seismic data mixed noise suppression method based on an MSAT-Unet. The method comprises the following specific steps: constructing an MSAT-Unet network comprising a multi-scale expansion convolution residual module, a channel-space attention mechanism and a Transform convolution module; the encoder is improved into multi-scale expansion residual convolution, so that the receptive field is expanded, and the capability of capturing local details and global semantic information is enhanced; a channel-space attention mechanism is integrated behind the decoder, a direction sensitive context is extracted through multi-dimensional adaptive pooling, and details and edge recovery are enhanced; meanwhile, an improved decoder is a Transform convolution module, and the feature reconstruction and complex structure recovery capability is improved; the optimized model carries out training and reasoning on the three-dimensional seismic data, mixed noise can be effectively suppressed, a clear data basis is provided for subsequent interpretation, and the method is high in generalization and robustness and good in performance in the aspect of three-dimensional seismic data denoising.

The invention discloses a blast furnace smelting safety reinforcement learning decision optimization method based on expert strategy constraint. According to the method, an optimization strategy can be learned from an off-line expert track on the premise that operation safety is ensured. Specifically, a conditional generative adversarial mechanism is introduced to realize the alignment of strategy distribution and expert decision, and the exploration ability in a security domain is retained while the expert experience is inherited. And an independent state-action safety evaluation network is designed and a discount factor is introduced, so that the long-term accumulation risk caused by the large hysteresis characteristic of the blast furnace is effectively dealt with. In addition, memory is adopted to enhance network coding historical information, incomplete state observation is supplemented, and information deviation in decision is reduced. According to the method, effective integration of triple guidance mechanisms is realized, knowledge inheritance is realized through distributed alignment, performance improvement is driven through reward optimization, and risk prevention and control are ensured through explicit security constraints.

The invention belongs to the field of cryptography and network security, and provides an unmanned aerial vehicle security cooperative computing method based on a certificateless homomorphic network coding signature, which aims at solving the security problem of data transmission in cooperative computing of an unmanned aerial vehicle group, and mainly comprises the following steps: a KGC initializes an Internet of Things data transmission system by generating private and public parameters; the method comprises the following steps of: running a Setup algorithm to generate a bilinear group, a master key and a hash function by taking a security parameter and a dimension of an augmented vector as input; the unmanned aerial vehicle sends an ID to the KGC for registration, and obtains a part of private keys from the KGC; in order to resist pollution attacks, a user divides data into a plurality of vectors before sending a data packet, and generates signatures for the vectors; and after each unmanned aerial vehicle node receives the data packet transmitted by the channel, performing integrity verification on the transmitted data packet, discarding the damaged data packet, and merging the undamaged data packets. And if the node is an intermediate node, sending a vector and signed data packet to a downstream node.

The invention relates to the technical field of intelligent terminal control, in particular to an intelligent management method and system for an integrated first-aid kit, and the method comprises the steps: setting a step length through a long-short-term memory network, synchronously generating a trend change curve through forward rolling and backward rolling, extracting a trend difference, and marking a periodic fluctuation material type under a sensitivity threshold value; fusion of bidirectional time sequence association and sensitivity screening is realized in trend dynamic capture and change identification, the sensitivity of material shortage prediction is improved, and the sensitivity of material shortage prediction is improved by combining resource weight priority allocation and pointer network coding, setting urgency parameters to cut low-frequency categories and reconstructing a material category sending sequence. And hierarchical scheduling and sequence planning of instruction sending are optimized. Based on remote feedback recording of continuous failure categories and reorganization of inventory synchronous update sending instructions, the actual state of the materials is dynamically incorporated into a cyclic supplement process, the continuous supply guarantee capability of emergency material use is enhanced, and the abnormal response speed and the inventory health management level are improved.

The invention discloses a reverse knowledge distillation-based cross-stage feature fusion method for industrial anomaly detection and positioning, and relates to the technical field of knowledge distillation and transfer learning. The method comprises the following steps: step 1, constructing a teacher network encoder, a student network encoder and a decoder; 2, extracting input image features through a student network encoder, and reconstructing the features through a student decoder; step 3, student network cross-stage feature fusion design; 4, locally sensing a dynamic attention module LDA, and adding a sliding window and convolution to replace original global pooling; and step 5, a self-adaptive multi-scale feature fusion module CAAMS-FF establishes a dependency relationship of a context so as to realize a fine-grained high-quality feature reconstruction effect. And step 6, inputting an anomaly graph obtained by the teacher-student network into the segmentation sub-network to obtain a final anomaly detection and positioning result. While the calculation efficiency is maintained, the spatial positioning precision and the multi-scale adaptability are significantly improved.

The invention discloses an image splicing method and device and a storage medium. The method comprises the steps of fusing paired or grouped images to be spliced into a fused image through a splicing model; the splicing model comprises a splicing network, a correlation analysis network and a fusion network which are connected in sequence; the deep semantic features and the shallow detail features of the to-be-spliced images are extracted through the splicing network, the correlation analysis network can more accurately judge the overlapping relation and the spatial position between the images by mining the context correlation information between the images, and the registration deviation is greatly reduced in combination with the dynamic determination of the size of the minimum bounding rectangle. In the fusion network coding stage, the feature expression capability is enhanced by reducing the size of a feature map, and in the decoding stage, fine fusion of deep and shallow layer features is realized by recovering the size. The three networks cooperate to effectively solve the problems of high calculation complexity, low registration precision, poor splicing quality and the like of a traditional method, and the application requirements of mobile equipment and complex scenes are met.

The invention discloses a ship abnormal behavior detection method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent maritime affair monitoring, and the method comprises the steps: obtaining original ship trajectory data, carrying out the augmentation processing and hybrid standardization processing of the original ship trajectory data, and obtaining a standard trajectory sequence; based on the standard trajectory sequence, pseudo labels are generated through unsupervised clustering, and the pseudo labels are used for representing different normal navigation modes; taking the standard trajectory sequence and the pseudo tag as supervision signals, and training a neural network encoder through comparative learning to obtain a target trajectory encoder; based on a target trajectory encoder, constructing a plurality of normal behavior mode reference models according to the trajectory in each cluster corresponding to the pseudo tag; and inputting a to-be-detected new track into the target track encoder to generate a to-be-detected embedded vector, and performing anomaly judgment according to the distance between the to-be-detected embedded vector and each normal behavior mode reference model. According to the invention, the accuracy of ship abnormal behavior detection is improved.

A method is performed by a network node for precoding of downlink communications predicting downlink channel state. The method receives a compressed-dimensional representation of a channel state that is encoded through an encoder neural network of a UE. The method maps the compressed-dimensional representation through a forward-prediction neural network to generate a compressed-dimensional predicted representation of a forward channel state at least one step forward in time k+Δ, wherein Δ is a number of steps forward in time. The method decodes the compressed-dimensional predicted representation of the forward channel state through a decoding neural network to generate an increased-dimensional predicted representation of the forward channel state, where the increased-dimensional predicted representation is a higher dimensional representation than the compressed-dimensional predicted representation. The method precodes signals for transmission through the downlink channel to the UE based on the increased-dimensional predicted representation of the forward channel state.

According to disclosed embodiments, methods and systems of data transmission are provided. An aspect of the present disclosure is a method comprising receiving an audio stream, parsing the audio stream into packets, encoding each packet using Alphabet Linear Network Coding (ALNC), and transmitting the encoded packets.

The invention provides an encrypted network flow detection method and device based on improved multiple graphs, and a program product. The method comprises the following steps: splitting a to-be-detected encrypted network flow into at least one bidirectional network flow; extracting network flow features in each piece of bidirectional network flow information to construct a multi-graph comprising at least one node pair; each node is constructed through connection information features in the network flow features; each node pair is connected through a first type edge and a second type edge; inputting the multiple graphs into a pre-trained encrypted network flow detection model to obtain a detection result corresponding to the encrypted network flow; the encrypted network flow detection model comprises a relational graph convolutional network encoder and a multi-layer perceptron classifier. The method can solve the problems that an existing encrypted network flow detection method is large in consumption of computing resources and storage resources and poor in practicability. Accuracy and practicability of encrypted network flow detection are enhanced, and possibility is provided for real-time monitoring and quick response to potential security threats.

The invention discloses a knowledge tracking method and system based on group and individual dual-path collaborative evolution, which are used for modeling the knowledge mastering condition of a learner from a problem state, a concept state and an overall knowledge state and comprehensively capturing the dynamic evolution process of the knowledge state. According to the method, the complex interaction relationship among different knowledge dimensions is effectively captured by fusing the LSTM network, the Transform encoder, the multi-head attention mechanism and the MALA linear attention module. Multi-dimensional feature representation is constructed by using question embedding, concept embedding and answer embedding, and fine adjustment of knowledge states is realized through a gating mechanism and a state updating module. The linear attention calculation and rotation position coding technology of amplitude perception is adopted, so that the capturing capability of the model on the long-term dependency relationship is enhanced while the calculation efficiency is ensured. A knowledge forgetting rule is dynamically reflected through a time decay factor and a state updating mechanism, the ability and problem characteristics of a learner are quantified in combination with an IRT model idea, and accurate modeling and performance prediction of the learning process of the student are achieved.

The invention is suitable for the technical field of computer vision, and provides a CNN-Transform-based low-light image multi-task recovery method and system, and the method comprises the steps: constructing a network comprising an encoder and a decoder, extracting local and global features through the encoder, and achieving the low-light enhancement and exposure suppression through the combination of grouping iteration convolution; and the decoder is used for processing the non-uniform motion blur by using a Decoder CNN-Transform module and a self-adaptive deformable cavity convolution block. Shallow layer features and deep layer features are fused between the encoder and the decoder through jump connection. And designing SmoothL1 loss and perception loss joint optimization, improving image brightness, contrast and details, and generating a high-quality result. According to the method, light weight and real-time performance are both considered, multiple degradation tasks can be processed in the same network, a high-quality image basis is provided for tasks such as target detection and semantic segmentation, and the overall quality of the image is effectively improved.

The invention discloses a dynamic emotion recognition method based on unsupervised contrast graph learning electroencephalogram enhancement, which comprises the following steps: carrying out preprocessing and segmentation processing on unmarked electroencephalogram signals, and segmenting the unmarked electroencephalogram signals into overlapped segments with fixed lengths; differential entropy features of each segment of signal are extracted; establishing enhanced graph data by taking electroencephalogram channels as nodes, differential entropy features as node attributes and Euclidean distances between the channels as edge weights; constructing a pre-training model containing graph convolution and long and short term memory network encoders and projectors; executing pre-training: inputting enhanced graph data into an encoder to obtain spatial-temporal characteristics, and mapping the spatial-temporal characteristics through a projector; constructing a dynamically updated feature queue; optimizing the model by comparing loss functions; migrating the pre-trained encoder to a classification network, and performing fine tuning by using label data; and finally realizing electroencephalogram signal emotion classification. According to the method, through comparison learning pre-training, the classifier can learn rich feature representation from the unlabeled data, and dependence on large-scale manual labeling data is effectively reduced.