449 results about "Quantized neural networks" patented technology

The invention discloses an intelligent duck shed monitoring method and system based on image recognition, and the method comprises the steps: obtaining environment monitoring data from an environment monitoring device, obtaining original image data of a breeding region from an image collection device, and processing the original image data through an image enhancement algorithm, thereby obtaining a preprocessed image data set; according to weight parameters output by the duck group behavior and environment correlation model, performing weighted fusion on the environment monitoring data and the preprocessed image data set to generate a multi-modal feature data set; if the group behavior index in the multi-modal feature data set deviates from a preset threshold range, extracting a spatial-temporal feature map through a convolutional neural network, and generating individual behavior trajectory data in combination with a multi-target tracking algorithm; and inputting the individual behavior trajectory data into the lightweight neural network model, and outputting a health state classification result. According to the intelligent duck breeding system, accurate monitoring and intelligent adjustment of the duck breeding environment are achieved, and the breeding efficiency and the duck group health level are effectively improved.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention discloses an intelligent security and protection monitoring system and method fusing multi-modal data analysis, and relates to the field of intelligent monitoring, and the method comprises the following steps: collecting a video stream, infrared thermal imaging data and an environment audio signal in real time through a heterogeneous sensor array; operating a lightweight neural network at an edge computing node to carry out space-time alignment and feature level fusion on the multi-source data, and generating an enhanced environment sensing matrix; a dual-channel anomaly detection mechanism is adopted, a first channel identifies short-term sudden abnormal events through a dynamic threshold self-adaptive model, and a second channel early warns potential risk behaviors through a time sequence prediction model; according to the system and the method, a dynamic privacy protection mechanism is adopted, behavior analysis is completed on the premise of ensuring biological feature safety, and transformation from passive monitoring to active early warning is realized in combination with a space-time prediction model; the problem of multi-view target tracking is solved through a distributed consensus algorithm, and the monitoring reliability in a complex scene is remarkably improved.

The invention relates to the technical field of data processing of organic display panels, in particular to a dynamic compensation method for driving signals of an organic display panel, which comprises the following steps of: acquiring brightness data in parallel by a photoelectric sensor array in different regions, and synchronously acquiring threshold voltage offset and environment temperature parameters of a thin film transistor; and after noise filtering, inputting into an aging feature extraction module and storing in a historical database. In the feature extraction stage, convolution kernel aggregation threshold voltage data is utilized to generate a regional aging coefficient matrix, the weight of a convolution kernel is dynamically adjusted based on a lightweight neural network, and an aging feature model is optimized in combination with real-time temperature parameters. The real-time problem of pixel-level compensation in a high-resolution scene is solved, and the calculation load of an embedded system is reduced.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention provides a distributed collaboration-based computing network intelligent agent system and a resource dynamic scheduling method thereof, and belongs to the technical field of computing network integration. The system comprises an edge agent used for sensing local computing power, network bandwidth and task load in real time, predicting task demand fluctuation by using a lightweight neural network, adjusting resource allocation weight in real time in combination with network topology change, and executing a preliminary task scheduling decision; the regional collaborative agent is used for aggregating multiple edge node states based on federated learning, generating a cross-node resource scheduling strategy, verifying the credibility of a computing power transaction smart contract and determining a cross-domain resource allocation scheme; and the cloud management agent is used for constructing a global resource portrait model according to the information provided by the edge agent and the regional collaborative agent, performing long-term strategy optimization, issuing global strategy information, and constructing and updating a computing power transaction smart contract based on a preset computing power transaction smart contract template. According to the invention, multi-level refined scheduling of computing network resources is realized.

The invention relates to the technical field of neural network model construction, in particular to a lightweight neural network model construction method, which comprises the following steps: based on a target task data set, in a lightweight basic operator library comprising a depth separable convolution, an inverted residual structure and an attention mechanism module, constructing a lightweight neural network model; and searching and jointly optimizing network structure parameters and weight parameters through the differentiable neural architecture to obtain an initial lightweight network model. And deploying the initial model in a target hardware simulation environment, and generating a Pareto optimal model cluster through structural re-parameterization and hardware-aware progressive channel pruning iterative optimization by taking model precision, reasoning delay and memory occupancy as collaborative optimization targets. And selecting a reference student model from the clusters according to deployment constraints, constructing a distillation framework taking the initial model as a teacher model, and performing fine adjustment by adopting a mixed strategy fusing multi-dimensional distillation loss to obtain a final model. The model gives consideration to precision and efficiency, and the detection efficiency and the quality control level are improved.

The invention belongs to the technical field of computer vision, and relates to a lightweight image feature point detection and matching method and related equipment thereof. The method comprises the following steps: performing feature extraction on a target image through a lightweight SuperPoint neural network model to generate feature information; performing super-pixel segmentation processing on the image to generate a super-pixel segmentation result; performing initial matching on the feature information through a lightweight SuperGlue neural network model to generate an initial matching pair; performing outlier filtering on the initial matching pair based on a superpixel segmentation result, and determining an optimized target matching pair; performing geometric consistency optimization on the optimized target matching pair by adopting an adaptive exponential moving average algorithm to generate a final matching result; the lightweight SuperPoint neural network model is generated by carrying out hierarchical progressive pruning processing on an original SuperPoint neural network model; the lightweight SuperGlue neural network model is generated by carrying out attention head pruning processing and graph network layer pruning processing on an original SuperGlue neural network model.

The invention discloses a layered energy management method of a fuel cell hybrid tramcar with self-adaptive working condition perception. In the recognition layer, a sliding window mechanism is adopted to extract time domain and frequency domain features of load conditions, feature data are clustered based on a spectral clustering algorithm driven by a deep auto-encoder, a data set with category labels is obtained, and a deep dynamic learning vector quantization neural network classifier is trained; in the strategy layer, a double-delay depth deterministic strategy gradient reinforcement learning algorithm is adopted, a reward function is constructed, and lithium battery SOC fluctuation penalty term limit parameters in the reward function are adaptively adjusted according to the real-time load working condition category output by the recognition layer; training the reinforcement learning agent to obtain an optimal power distribution scheme between the multi-stack fuel cell power generation system and the lithium battery; and according to the performance degradation degrees of different fuel cell stacks, a distributed cooperative control strategy considering performance difference is adopted to distribute the output power of each stack, so that the coordinated control of the running state of the multi-stack fuel cell power generation system is realized.

The invention discloses a circuit breaker image edge detection method fusing spatial constraint fuzzy clustering and lightweight network optimization, and the method carries out the local adaptive threshold calculation through combining spatial constraint FCM and Otsu algorithms, and optimizes the edge detection process of a Canny operator. According to the method, fuzzy classification is carried out on a circuit breaker image by adopting spatial constraint FCM to obtain a strong marginal probability graph; the circuit breaker image is subjected to block processing through local adaptive threshold calculation, a global threshold is generated through integration, and then the global threshold is input into a Canny operator for accurate edge extraction. In order to further improve the detection effect, a lightweight neural network PiDiNet is used to correct a Canny output image. According to the method, the edge detection precision of the circuit breaker image can be effectively improved, and the method is suitable for edge extraction tasks in high-noise and complex background environments.

The embodiment of the invention provides a neural network quantification method and system, electronic equipment and a storage medium, and belongs to the technical field of computers. According to the method, activation output distribution of a target network layer of a neural network model is acquired, the activation output distribution is analyzed to determine data distribution characteristics, and a corresponding quantification strategy is determined according to the data distribution characteristics. Performing quantization processing of a first expected quantization type on the activation output value of the target network layer according to a quantization strategy to obtain an expected quantization result, and then determining a network quantization error according to the expected quantization result; and if yes, determining the quantization strategy and the first expected quantization type as quantization parameters of the target network layer so as to quantize an activation output value of the target network layer of the neural network subsequently according to the quantization parameters. On the premise of considering the reasoning precision of the neural network model, the appropriate quantization strategy is determined according to the data distribution characteristics of network activation, and the quantization efficiency of the neural network is improved.

The invention relates to the technical field of industrial Internet of Things and wireless sensor networks, in particular to a large-scale industrial fault diagnosis system and method based on Bluetooth MESH, and efficient monitoring and fault early warning of industrial equipment are realized through low-power-consumption sensor nodes, edge computing, a Bluetooth Mesh network and a clustered Mesh tree architecture. The system comprises a client, a server, a gateway, a cluster head and a cluster node, the cluster node comprises a sensor node and a relay node, a lightweight neural network model is built in the sensor node, and edge reasoning and fault detection can be realized locally. Stable communication and real-time cooperation of large-scale nodes are ensured through a decentralized architecture of Bluetooth Mesh, establishment of a dynamic cluster head chain and optimization of an RSSI threshold value. And a low-power-consumption mechanism combining event driving and fixed polling is adopted, so that the energy consumption of the system is remarkably reduced. In-cluster communication loads are reduced through a clustering architecture, and the communication efficiency and the anti-interference capability of the system are improved in combination with channel separation of Mesh and BLE protocols.

The invention relates to the technical field of intelligent robots, and discloses a face recognition method of an intelligent robot, which comprises the following steps: step 1, lightweight face detection and feature extraction: performing face detection and feature extraction through a lightweight neural network structure, and combining model compression and edge calculation to reduce calculation complexity; and step 2, illumination and attitude adaptability is realized through multi-modal fusion, and face processing under different illumination and attitude conditions is carried out based on the extracted face features by utilizing fusion of an RGB image and an infrared image, adaptive illumination enhancement and 3D attitude correction. Through a multi-modal fusion technology, 3D attitude correction and self-adaptive illumination enhancement, different illumination conditions and attitude changes can be adapted, then the recognition stability of the system in a complex environment is remarkably improved, it is ensured that face recognition has high accuracy and robustness under the conditions of insufficient illumination, strong light or multi-angle offset, and the face recognition accuracy and robustness are improved. And the application requirements of the intelligent robot in multiple scenes are met.

The invention provides a gas extraction multi-parameter monitoring method and system based on edge calculation, and relates to the technical field of coal mine gas extraction. The method comprises the following steps: arranging multi-parameter sensor nodes in drill holes, pipelines and gas gathering stations, and accessing an edge calculation unit; performing time synchronization, zero drift correction, outlier elimination and normalization on the data of the gas concentration, the negative pressure, the flow, the temperature, the humidity and the hydrogen sulfide concentration to generate feature vectors; calling a lightweight neural network model for reasoning, and outputting an extraction efficiency score and a leakage risk degree; when the risk degree exceeds the limit, an acousto-optic alarm is triggered and a speed reduction instruction is issued; when the efficiency is insufficient, the negative pressure is adjusted; compressing and encrypting the result, and reporting the result to a cloud platform through a wireless network at regular time; and the cloud performs model incremental training based on historical data and feedback, and issues an update model through hot replacement. According to the invention, local intelligent analysis, rapid early warning and dynamic adaptive optimization are realized.

The invention discloses a millimeter-wave radar sparse point cloud-based human body posture recognition method, which belongs to the technical field of human body posture recognition, and comprises the following steps of: acquiring three-dimensional point cloud data of human body actions through a millimeter-wave radar, and preprocessing the three-dimensional point cloud data; and training a lightweight neural network model by using the preprocessed point cloud data, wherein the model comprises an edge convolution module and a grouping sparse Transform encoder module. The edge convolution module extracts spatial geometric features and detects dynamics, static postures are directly classified, dynamic postures capture a time sequence dependency relationship through a grouping sparse Transform module, attention calculation is only carried out on frames with feature changes exceeding a threshold value, and mean pooling aggregation is carried out on other frames. And finally, classifying the human body postures based on the spatial geometric features and the time sequence dependency relationship to obtain a classification result. The device is simple in structure, accurate in recognition and suitable for efficient deployment of edge equipment.

The invention relates to the technical field of DNA fragment data processing, in particular to a DNA fragmentation gene detection data processing method based on artificial intelligence. Segmenting the DNA fragmentation detection data into a plurality of local data blocks, and executing continuous homology analysis on each local data block; dynamic weights are distributed to annular and branch structures in the multi-scale topological feature matrix by using an attention mechanism; extracting a base correlation mode of a base sequence in the DNA fragmentation detection data through one-dimensional convolution to obtain sequence features; performing tensor splicing on the weighted topological features and the sequence features in a hidden space; performing coarse-grained classification on the fused high-order feature vectors through a multi-layer perceptron, and outputting a preliminary classification result; and constructing a lightweight neural network sub-module based on the preliminary classification result to obtain a final classification result, thereby effectively improving the accuracy and interpretability of the classification result.

Embodiments described herein provide techniques to facilitate hierarchical scaling when quantizing neural network data to a reduced-bit representation. The techniques includes operations to load a hierarchical scaling map for a tensor associated with a neural network, partition the tensor into a plurality of regions that respectively include one or more subregions based on the hierarchical scaling map, hierarchically scale numerical values of the tensor based on a first scale factor and second scale factor via the matrix accelerator circuitry, the first scale factor based on a statistical measure of a subregion of numerical values of within a region of the plurality of regions and the second scale factor based on a statistical measure of the region that includes the subregion, and generate a quantized representation of the tensor via quantization of hierarchically scaled numerical values.

The invention discloses a locomotive wheel polygon damage detection method based on a lightweight neural network, and the method comprises the steps: obtaining axle box vertical vibration acceleration response signals of a heavy-load locomotive under the combination of different operation speeds and different wheel polygon abrasion degrees, and carrying out the preprocessing of the signals, the method comprises the following steps: acquiring frequency domain characteristics of a signal through fast Fourier transform, constructing a sample data set based on the frequency domain characteristics, constructing a wheel polygon damage detection network model, training the model by using the sample data set, and identifying a wheel polygon abrasion amplitude by using the trained polygon damage detection network model. And completing damage detection of the polygon of the wheel. The method can realize accurate and quantitative detection of the polygon abrasion degree of the heavy-load locomotive wheel, has the characteristics of accuracy, high efficiency and strong robustness, and also has relatively good interpretability.

The invention provides an equipment abnormal voiceprint detection system. The equipment abnormal voiceprint detection system comprises a voiceprint collection module used for collecting original voiceprint signals in real time and preprocessing the original voiceprint signals; the edge end detection module is deployed on edge computing equipment and is used for carrying out real-time anomaly detection on the voiceprint features by utilizing a one-dimensional lightweight neural network model; the abnormity credibility evaluation module is used for converting a real-time abnormity detection result into a probabilistic abnormity credibility score; the incremental data screening module is used for screening high-value samples from the real-time voiceprint data based on a dynamic density trend sensing algorithm and caching the high-value samples in the edge; the cloud model evolution module is used for carrying out evolution training on the detection model by utilizing a continuous learning mechanism; the generation and playback module is used for jointly generating pseudo samples through a variational auto-encoder VAE and a generative adversarial network GAN; and the model updating module is used for compressing the evolved cloud model and then issuing and replacing the original model in the edge end detection module so as to form a cloud-edge collaborative sustainable evolution closed loop.

The invention discloses a lightweight network architecture for medical image segmentation, and the specific implementation comprises the steps: (1) obtaining original medical image data and a segmentation label of a target region, and constructing a training data set suitable for a segmentation task; (2) a lightweight neural network architecture is designed, the segmentation performance is improved through multi-scale feature interaction and cross-layer information fusion, and meanwhile features are effectively extracted and key areas are enhanced; (3) introducing a dynamic feature enhancement mechanism, and adaptively optimizing detail information and semantic expression according to different segmentation requirements; and (4) performing efficient segmentation of a target area on a test image through a model trained by matching a preprocessing technology and a post-processing technology. The invention provides an end-to-end medical image segmentation scheme, which has the characteristics of high efficiency, accuracy, strong robustness and support of real-time processing, is suitable for various medical image segmentation scenes, and provides powerful support for disease auxiliary diagnosis.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle detection tracking device, method and equipment, which comprises a data sensing unit, a calculation unit, a mechanical and power supply unit, a communication and storage unit and a countering execution unit, and is characterized in that the data sensing unit is used for acquiring image data and servo turntable rotation angle data; the calculation unit is used for deploying a lightweight neural network model and a multi-algorithm fusion process, processing sensor data and realizing unmanned aerial vehicle target detection and tracking logic control; the mechanical and power supply unit is used for providing physical support, servo motion capability and energy supply for the device; the communication and storage unit is used for real-time data transmission and storage and encryption archiving of historical data and image videos; and the countering execution unit is used for implementing signal interference or physical interception on the detected and tracked target. Therefore, the problems of easy target loss, low small target identification precision, poor complex environment adaptability, insufficient countering means and the like of a traditional device are solved.

The invention relates to the technical field of intelligent gateways, and discloses a multi-mode industrial Internet of Things intelligent gateway based on edge computing and an implementation method thereof, and the method comprises the steps: obtaining sensor data streams of a door magnetic sensor, a human body sensor, a temperature and humidity sensor and a smoke detector in an intelligent region; constructing a space-time fusion data matrix based on the sensor data stream; inputting the space-time fusion data matrix into an edge layer quantization neural network, a fog layer recurrent neural network and a cloud layer large model for distributed reasoning to obtain a reasoning result set; and performing priority queue scheduling and zero-copy transmission on emergency events, important events and conventional events in combination with the reasoning result set to generate a control instruction sequence, so that parallel operation of data acquisition and processing is realized, and the intelligent level, the response performance and the operation reliability of an intelligent regional Internet of Things system are improved.

The invention discloses a power equipment state monitoring method based on Beidou high-precision time service and related equipment, and relates to the technical field of Beidou satellites.The power equipment state monitoring method based on Beidou high-precision time service comprises the steps that Beidou satellite signals are obtained, and real-time ionosphere data are collected; based on the Beidou satellite signal and the ionosphere real-time data, ionosphere delay compensation is carried out according to a preset dynamic compensation model, a high-precision Beidou time service signal is determined, and accurate synchronization of the multi-source data of the power equipment is realized according to the high-precision Beidou time service signal; the method comprises the steps of extracting feature information of real-time operation parameters of power equipment, obtaining global model knowledge of preset edge nodes in combination with a federal learning mechanism, analyzing and judging the state of the power equipment in real time according to a pre-trained lightweight neural network model, and obtaining real-time monitoring information of the health degree of the power equipment, the lightweight neural network model is obtained by performing parameter compression training through channel pruning and quantitative perception training.

The invention relates to the field of emerging software and information technology service, in particular to an artificial intelligence-based application system integrated management method, which comprises the following steps of: analyzing an API (Application Program Interface) log, structured data, an unstructured document, business work order data and business image data by pre-training a large model, and generating a unified semantic representation in combination with multi-modal contrast learning; a knowledge distillation technology is adopted to migrate the large model capability to a lightweight neural network, and a domain knowledge base is generated based on fine tuning of business work order data; a user multi-mode instruction is analyzed, a DAG service agent template library is matched, and an execution chain is dynamically combined based on a reinforcement learning strategy, so that text processing, multi-system collaborative reasoning and RPA service process automation are realized; and collecting an execution log, and updating lightweight network parameters and an intelligent agent strategy library through an online distillation algorithm to form a closed-loop optimization mechanism. According to the method, the service response efficiency can be improved, and the problems of enterprise multi-service system data splitting and process stiffness are solved.

The invention provides a plasmodium real-time detection method based on a lightweight neural network model. The plasmodium real-time detection method comprises the steps of obtaining a blood smear image data set and performing preprocessing; establishing a lightweight plasmodium detection model based on a deep learning algorithm; inputting the preprocessed blood smear image data set into a plasmodium detection model for iterative training; performing plasmodium detection on a to-be-detected blood smear image by using the trained plasmodium detection model; according to the method, a lightweight detection model is constructed based on a deep learning algorithm, so that high-precision plasmodium detection is realized, and the real-time performance of detection is effectively improved; meanwhile, the lightweight model design can adapt to a low-resource environment, so that the model can efficiently run on low-power-consumption equipment, and automatic real-time malaria detection requirements in remote areas and low-resource areas are met; in addition, various forms and life cycle stages of the plasmodium can be accurately identified, and the detection comprehensiveness is improved.

The invention discloses an intelligent interaction control method for AI glasses, and relates to the technical field of AI glasses, and the method comprises the following steps: synchronously collecting user input signals and environmental parameters through a multi-source sensor group built in the glasses, the sensor group at least comprising an IMU, a binocular camera, a microphone array and a physiological sensor; carrying out real-time fusion processing on the multi-modal input data by adopting a lightweight neural network to generate an interaction intention feature vector; based on real-time detection results of environmental noise intensity and illumination conditions, dynamically distributing weight coefficients of all interaction modes; generating a hierarchical response instruction according to the weight coefficient and a confidence threshold; and continuously optimizing the personalized interaction strategy of the user through a federal learning framework. According to the AI glasses intelligent interaction control method, through multi-modal weighted fusion, edge AI acceleration and federated learning optimization, the interaction success rate in a complex environment is improved to 93.6%; the end-to-end delay is controlled within 28ms; and a user-defined interaction strategy is supported.

The invention relates to the technical field of gas detection, in particular to a gas detection system and method based on an AI technology, and the system comprises a differential sensing module, a drift compensation processor, a dynamic risk assessment unit and a cloud-edge collaborative learning framework. Compared with the prior art which adopts a single-sensor independent compensation scheme and has the defects that the false alarm rate is high and long-term drift is difficult to correct under environmental interference, the scheme innovatively introduces a dual-mode differential sensing architecture and an adaptive drift compensation algorithm, a reference sensor is packaged in an inert gas environment, and the reference sensor and a main sensor are subjected to real-time collaborative analysis; the pre-trained lightweight neural network is combined to dynamically eliminate temperature and humidity cross interference, the detection precision and stability under complex working conditions are remarkably improved, and the aging distortion problem of the semiconductor sensor is effectively solved.

The invention relates to a lightweight neural network model implementation method and system for small target detection in a complex aerial photography scene, and belongs to the technical field of computer vision and unmanned aerial vehicle target detection. In order to solve the problem of low detection precision caused by small target size, complex background, easy feature submerging and the like in an aerial image of an existing unmanned aerial vehicle, the method comprises the following steps: constructing a multi-scale adaptive hierarchical feature enhancement module MSAHFE, and combining multi-scale pooling and differential edge enhancement to improve feature sensitivity; constructing a lightweight feature selection module LAFS based on an attention mechanism, and screening high-correlation features by using a space and frequency double-domain attention mechanism; a lightweight detection head LWDeect is constructed, and shared packet convolution and self-calibration convolution are utilized to reduce the calculation complexity. The method has the advantages of being high in detection precision, small in parameter quantity, high in reasoning speed and the like, and is suitable for complex aerial photography and other application scenes needing real-time small target detection.

The invention relates to an instant denoising method for electroencephalogram signals of students in a classroom scene. The method comprises the following steps: based on electroencephalogram acquisition equipment and a Daisy expansion board, acquiring electroencephalogram signal data of a student through a dry electrode device; and carrying out sliding mean filtering on the electroencephalogram signal data to remove baseline drift, eliminating noise by adopting a band-pass filter to obtain preprocessed electroencephalogram signal data, and inputting the data into a lightweight neural network model to carry out channel-by-channel denoising processing to obtain a denoised electroencephalogram signal. The model comprises a multi-scale feature extraction layer, a time sequence modeling module and a residual connection structure. According to the method, through lightweight neural network design such as multi-scale feature extraction, time sequence modeling and a residual connection structure, the denoising effect is improved, the real-time performance and high efficiency of electroencephalogram denoising processing are guaranteed, and an electroencephalogram data basis with a high signal-to-noise ratio is provided for subsequent teaching quality evaluation.