616 results about "Spiking neural network" patented technology

The invention discloses an event and RGB image fused SNN target tracking method and system, and the method comprises the steps: converting an event flow from an event camera into an event frame image through a representation method based on time stacking, and enabling the event frame image to be aligned with an RGB frame image from an RGB camera in time and space; performing target detection on the event frame image # imgabs0 # and the RGB frame image # imgabs1 # after alignment by using a spiking neural network SNN, wherein the spiking neural network SNN comprises a feature extraction module, a feature fusion module and a detection head; and learning the similarity of the targets by using the anchor frame extracted by the spiking neural network SNN and the fusion feature map # imgabs2 # to perform multi-target tracking. The invention aims to realize fusion of the fusion event and the RGB image so as to ensure high precision and high efficiency of multi-target tracking, and keep stable detection performance and tracking performance in static and high-motion scenes.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on a pulse neural network. The method comprises the following steps: constructing a fault diagnosis network, wherein the fault diagnosis network comprises an improved gating attention coding module and a pulse residual network; the method comprises the following steps: replacing a common convolution layer of a gating attention coding module with a one-dimensional sparse convolution layer, and replacing a two-dimensional batch normalization layer with a one-dimensional batch normalization layer to obtain an improved gating attention coding module; setting a pulse neuron in the fault diagnosis network as a PSN neuron, and embedding a time step shrinkage layer in the pulse residual network; and inputting the mechanical vibration signal of the current sampling period into a fault diagnosis network, and outputting a predicted fault category label of the current sampling period. According to the invention, high-efficiency and high-precision mechanical fault intelligent diagnosis is realized.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

The invention relates to a neuromorphic computing system and computing method based on a spiking neural network, and the system comprises a neuromorphic processor which is used for constructing a hardware-based spiking neural network through employing a plurality of physical neurons, and carrying out the pulse coding and target recognition of a real-time input image; the central processing unit is realized on the basis of an RISC-V instruction architecture and is used for executing a control instruction required for constructing the pulse neural network; the neuromorphic processor is also used for carrying out online learning on the pulse neural network under the control of the control instruction; and the memory is used for storing program instructions required by the operation of the central processing unit and data transmitted between the central processing unit and the neuromorphic processor. The invention further provides a neuromorphic calculation method. According to the technical scheme of neural morphology calculation, high balance among precision, calculation speed and energy efficiency is achieved, and dynamic and complex task data can be processed accurately and efficiently with low power consumption.

The invention provides a power grid dispatching strategy optimization method and system, and the method comprises the steps: deploying a device based on a quantum entanglement technology between transformer substations, and collecting the state information of a power grid, and encrypting and transmitting the state information to a control center through a quantum network; a pulse neural network processor is used in a control center to extract spatial-temporal characteristics, and a quantum game theory model is used to generate an optimized scheduling strategy. Multi-modal verification data is collected, including visual deformation, abnormal sound monitoring, and operational resistance data. A genetic algorithm and deep reinforcement learning are utilized to optimize a decision model, and a dynamic weight distribution mechanism is included. And through a photon-quantum hybrid computing architecture execution model, a scheduling strategy is collaboratively optimized, and a result is fed back to a physical power grid. According to the method, the quantum technology and the spiking neural network are combined, new energy fluctuation is captured in real time, the multi-target weight is dynamically adjusted through the quantum game theory model, and the scheduling strategy robustness is improved. And a photon-quantum hybrid computing architecture is adopted, so that the feature extraction speed and the optimization efficiency are improved.

A method is provided for processing data in a neural network system. The method includes receiving input data; processing the input data through a first set of neural network layers configured to perform data processing using MatMul-free techniques to produce intermediate data; further processing the intermediate data through a second set of neural network layers configured to simulate spiking neural network (SNN) functionalities using MatMul-free techniques; and outputting a result based on the processed data from the second set of neural network layers.

The invention relates to the technical field of construction management, and discloses a railway temporary facility construction progress optimization method and system based on artificial intelligence. According to the invention, through heterogeneous data fusion and conflict graph modeling, accurate quantification of construction contradictions is realized; a multi-agent auction mechanism is combined with a virtual credit system, so that the fairness and efficiency of resource allocation are improved; the self-healing mechanism of the spiking neural network and quantum genetic optimization break through the limitation of the traditional algorithm from local and global levels respectively. In the system level, an edge computing node and a distributed consensus engine guarantee real-time decision reliability, a dynamic digital sand table realizes visual prediction of risks, and a self-organizing control tower guarantees standard compliance. Practical verification shows that the number of times of resource conflicts is reduced, the early warning accuracy of major risks is higher during emergency response, and the construction efficiency and safety are remarkably improved.

The invention relates to the technical field of image processing, in particular to a super-resolution reconstruction system and method based on a pulse neural network, and the system comprises an image pulse encoder, a pulse feature enhancer and a differentiable pulse decoder. An image pulse encoder simulates a receptive field of a retina by using DoG response, performs adaptive pulse distribution on an input low-resolution image in combination with an image gradient, and converts the low-resolution image into a space-time pulse sequence reflecting a high-frequency region and a low-frequency region in the low-resolution image; a pulse feature intensifier performs coarse-grained and fine-grained structure reconstruction of a low-resolution image on the space-time pulse sequence by using a pulse time sequence dependent plasticity mechanism to obtain a granularity feature pulse; and the differentiable pulse decoder converts the granularity characteristic pulse to obtain a corresponding high-resolution image. According to the method, efficient and low-consumption image super-resolution reconstruction is realized through a bionic retina coding mechanism and a pulse time sequence optimization strategy.

The technical scheme of the invention discloses an epilepsy diagnosis system based on electroencephalogram data channel selection and a KAN convolutional spiking neural network, which is characterized by comprising a data processing module, a channel feature extraction module, a self-adaptive channel selection module and a feature classification module. According to the method, an effective solution is provided by introducing KAN convolution, channel selection and a spiking neural network. The introduced attention mechanism allows the model to dynamically pay attention to the most relevant EEG channel and signal characteristics, so that personalized channel selection is realized. Through the mechanism, the model can distribute different weights for different individuals, only key signals most related to epileptic seizure are selected, the calculation burden of redundant channels is avoided, and meanwhile the classification precision is improved. Finally, the artificial neural network is converted into the pulse neural network, an event-driven calculation mode is adopted, energy consumption is effectively reduced, and the method is particularly suitable for being applied to portable equipment.

The invention discloses an impurity sorting and removing system based on machine vision, and the system comprises a data collection and preprocessing layer which is responsible for obtaining multi-dimensional physical characteristics of tea leaves and impurities, and constructing three-dimensional scene representation; the feature analysis and decision-making layer is used for extracting light field fusion features by utilizing a ResNeSt-50 backbone network, directly outputting a preliminary classification confidence coefficient through a full connection layer, forming a first layer of decision-making candidates, simulating branches by combining rigid body dynamics to predict a blade movement track, performing Bayesian correction on the preliminary classification confidence coefficient after a shielding probability graph is sampled and calculated through Monte Carlo Dropout, and forming a second layer of decision-making candidates; meanwhile, a Cook-Torrent BRDF model is integrated to carry out material reflection correction, a feature map after material correction is output, and a correction value is converted into decision threshold offset; and the knowledge storage and update layer is used for constructing a bimodal memory pool composed of a gradient direction matrix and an LRU elastic cache, and generating a pseudo sample supplement long-tail category in combination with the four-layer full-connection pulse neural network.

The invention discloses an unmanned aerial vehicle target detection method based on a pulse neural network, and the method reduces the delay through a directly trained pulse neural network, enhances the extraction of spatial-temporal features through a pulse multi-scale attention mechanism, and improves the accuracy of target detection. The method comprises the steps of obtaining a to-be-detected image of an unmanned aerial vehicle, and performing pulse coding on the to-be-detected image; inputting the pulse signal into a pulse residual backbone network formed by pulse residual basic blocks for feature extraction, and generating a multi-scale and multi-level feature map; inputting the feature map into a pulse multi-scale attention mechanism for processing; and inputting the processed feature map into a detection head, carrying out target detection, and outputting a result. According to the method, richer image feature information can be extracted, the method has the advantages of efficient calculation performance and low energy consumption, and low delay is realized through a relatively small time step length.

The invention is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

The invention discloses a brain-like chip-oriented spiking neural network compiling method, which comprises the following steps of: expanding a spiking neural network Relay operator in a TVM, accessing and binding to a hardware operator library, automatically realizing intermediate representation conversion and optimization from a spiking neural network model to brain-like chip adaptation, and realizing compilation of a brain-like chip-oriented spiking neural network. In combination with optimization methods such as pulse neural network model-oriented operator fusion optimization and brain-like chip-oriented data layout conversion, the method is realized by fully utilizing high performance of a hardware operator library; and finally, generating a deployment file package based on a TVM infrastructure and an LLVM compiler, and efficiently completing compiling and deployment of the spiking neural network model on a brain-like chip. According to the invention, the technical problems of high learning cost and low development efficiency of the existing neural network compiling method based on DSL and the neural network compiling method based on hardware intra function or operator API can be solved.

A hybrid neural network architecture is disclosed that integrates matrix multiplication-free (MatMul-free) transformation layers with spiking neural network (SNN) layers for efficient, low-power computation. The system includes an interface module configured to convert intermediate continuous-valued data from MatMul-free layers into a spike-compatible format using encoding techniques such as rate coding, phase coding, or threshold-based conversion. The SNN layers process the spike-encoded data in an event-driven manner, enabling sparse, temporal inference. Training is supported by a hybrid optimization strategy combining backpropagation in MatMul-free components with surrogate gradient descent or spike-timing-dependent plasticity (STDP) in SNN layers. The architecture reduces computational complexity, supports real-time adaptability, and enables deployment in energy-constrained environments such as edge devices and neuromorphic platforms. The system may be implemented in hardware, software, or a co-designed pipeline optimized for dynamic sensor data, control signals, or continuous inference tasks.

The invention relates to an edge end spiking neural network compression and deployment method and system, and belongs to the technical field of machine learning, the edge end spiking neural network compression and deployment method performs forward propagation on an initial spiking neural network model based on acquired multi-modal data, obtaining a memory use state in a forward propagation process based on hardware sensing initialization, and performing dynamic sparsification on the initial pulse neural network model based on the memory use state to construct a sparsified pulse neural network; based on the acquired activation statistical information of the sparse spiking neural network, performing dynamic pruning on the sparse spiking neural network by adopting a pruning strategy based on neuron activeness so as to realize compression of a spiking neural network model; the compressed spiking neural network model is optimized according to the edge end configuration, and the optimized spiking neural network model is deployed to the edge end to execute the reasoning task, so that the storage requirement and the calculation complexity are reduced.

The invention provides a low-energy-consumption three-dimensional point cloud single-target tracking method based on a spiking neural network, and the method comprises the steps: obtaining the three-dimensional point cloud data of a previous frame and a current frame of a target, designing a voxel grid module, and dividing the point cloud into 3D voxels; a feature extraction module is designed, a three-stage pulse convolution block is used as a feature extractor, voxelization point cloud features are extracted, a Transform encoder layer is added, and global context information of the point cloud is captured through a self-attention mechanism; a part-to-part motion modeling module is designed, continuous point cloud features on a time sequence are modeled and coded part by part through a two-dimensional pulse convolution block, and feature information changes of a previous frame and a current frame in corresponding space parts are obtained; and designing a regression prediction module, and inferring the relative motion of the target through a pulse regression prediction module. According to the method, energy consumption is reduced, efficiency is improved, and meanwhile it is guaranteed that the network accurately tracks the target in diversified scenes and motion states.

The invention relates to the crossing field of a federated learning algorithm framework, dynamic sparse training and a spiking neural network, and discloses a federated learning method and architecture for dynamic sparse spiking neural network optimization, and the method comprises the steps: employing a cloud edge end hierarchical architecture, and carrying out the training of local data on an edge device through a spiking neural network; the global model and the sparse mask received by the edge device generate a local sparse model for training; after each round of local training, the edge device uploads new model parameters to the edge server; after the edge server receives the model parameters, the model parameters are aggregated in combination with information such as historical contribution degree and the like, and are uploaded to the central cloud server; on the cloud server, the model parameters are further aggregated to generate a global model, and the global model is broadcasted to each edge server and each edge device before the next round of training. The sparse training and the spiking neural network are introduced into federated learning, so that the computing communication overhead is remarkably reduced, and the performance of the global model is improved.

The invention discloses a pulse neural network image classification method and system based on a space-time adaptive threshold, mainly solving the problems of poor nonlinear expression and limited time sequence and space feature processing ability in the prior art, and the scheme comprises the following steps: obtaining an image data set, and dividing the image data set into a training set and a test set; a spiking neural network main body structure comprising an input layer, a hidden layer and an output layer is selected, an existing neuron model is improved by introducing a space-time joint threshold adjustment mechanism, and improved neurons are placed in each neuron layer in the hidden layer to form a spiking neural network based on a space-time adaptive threshold. The training set is used to carry out iterative training; and inputting the test set into the trained pulse neural network to obtain an image classification result. According to the method, a space-time adaptive threshold mechanism is introduced, the threshold can be dynamically adjusted to adapt to time and space features, the processing capacity of the network on time sequence data and complex features and the classification accuracy of images are remarkably improved, and the method can be widely applied to dynamic visual tasks and event-driven scenes.

State of art techniques, need a decoder following the encoder to encode EEG signals, whose morphology is undefined. Embodiments herein disclose a method and system for a Spiking Neural Network (SNN) based low power cognitive load analysis using electroencephalogram (EEG) signal. The method receives a raw EEG signal from multichannel EEG set up, wherein each of the raw EEG signal is re-referenced and encoded into a spike train using a Light-Weight-Lossless-Decoder less-Peak-based (LWDLP) encoding. Further, the spike trains are processed by the SNN architecture using backpropagation based supervised approach, wherein the spatial information and the temporal information are learnt by the SNN in form of neuronal activity and synaptic weights. Post learning the SNN architecture applies an activation function on the neuronal activity for classifying a cognitive load level experienced by a subject from among a plurality of predefined cognitive load levels using a SNN classifier.

The invention relates to the technical field of robot control, and discloses a spiking neural network-based robot control method, which comprises the following steps of: preprocessing collected multi-modal data to obtain an emotion pulse signal; inputting the emotion pulse signal into a pre-constructed emotion pulse neural network, and outputting a comprehensive emotion pulse; inputting the comprehensive emotion pulse into a central pattern generator, and outputting a behavior rhythm; acquiring an environment feedback signal generated by executing the behavior rhythm, and adjusting a connection weight according to the environment feedback signal; optimizing the comprehensive emotion pulse according to the connection weight, and converting the optimized comprehensive emotion pulse into emotion interaction voice information; and adjusting the emotion intensity according to the optimized comprehensive emotion pulse and the multi-modal data. According to the method, data are collected through the multi-mode sensor to generate emotion pulses, the emotion pulses are input into the central mode generator to generate behavior rhythms after SNN processing, weight optimization, emotional speech generation and emotional steady-state control setting are combined with the STDP algorithm, and the efficiency of a robot service scene is improved.

The invention relates to a multi-degradation scene binocular image adaptive enhancement method based on a spiking neural network, and belongs to the field of image processing. The method comprises the steps that a pulse neural network model used for binocular image enhancement of multiple degradation scenes is constructed, the network model comprises a first branch and a second branch which both adopt encoder-decoder structures, and feature interaction is carried out between encoder blocks and decoder blocks of the first branch and the second branch through a pulse vertical crossing attention module; preparing a data set to train the constructed network model, evaluating the trained network model, judging whether the image restoration effect of the network model meets the performance requirement or not, and if the image restoration effect does not meet the performance requirement, performing training again; and finally, recovering the binocular image of the multi-degradation scene by adopting the trained and evaluated network model. The method can greatly reduce the calculation energy consumption, improves the calculation efficiency of the pulse network, has a good removal effect on rain lines and raindrops in the image, and has universality.

The invention relates to a novel pulse neural network synaptic weight adjustment method, and belongs to the field of artificial intelligence. Firstly, a traditional pulse time sequence dependence plasticity rule is improved based on the distribution trend of a Gaussian function, so that the weight change corresponding to the optimized synaptic plasticity rule when the time interval tends to 0 is larger, and when the time interval is gradually increased, the weight change is in an accelerated descending trend; the method comprises the following steps: firstly, performing synaptic weight local regulation on a neuron model to improve the local regulation capability of the synaptic plasticity of the neuron, then introducing reinforcement learning, associating Q learning with the synaptic plasticity in the neuron model, and realizing the global regulation and control of the plasticity on the basis of the synaptic weight local regulation. According to the above synaptic plasticity rule, the spiking neural network constructed based on Leaky Integrating and Fire is subjected to weight adjustment, so that the spiking neural network has better performance.

The invention relates to the technical field of machine vision and food quality detection, in particular to a food defect real-time detection method and system based on image processing. The method specifically comprises the following steps: integrating multi-modal data, and adaptively adjusting a weight coefficient to improve data quality; multi-stage noise reduction is carried out, key frames are extracted, and a key frame verification mechanism is enhanced through a 2FA control variable; constructing a YOLO-BioNet model, and optimizing the detection efficiency and precision in a complex scene through dynamic convolution kernel allocation and a spiking neural network; integrating Kalman filtering, LSTM (Long Short Term Memory) time sequence modeling and a deep reinforcement learning model, analyzing a food surface change track and predicting a defect state; real-time data and historical information are fused, and a dynamic threshold value is calculated through a Bayesian network; performing linkage response between an automatic sorting instruction and a production line; a distributed storage and graph neural network optimization model is adopted, and defect tracing is supported. The method has remarkable advantages in the aspects of real-time performance, detection accuracy and self-adaptive capability.

The invention belongs to the field of target detection application of image target detection, underwater target detection and the like, and discloses a target detection method based on a spiking neural network and a Transform, which comprises the design of a feature extraction network based on the spiking neural network and the Transform, the design of a novel spiking neuron, the design of a Transform module based on the spiking neuron, and the design of a target detection module based on the novel spiking neuron. The problem that an existing target detection network based on a spiking neural network is too low in performance is mainly solved. According to the rapid and effective target detection method provided by the invention, the spiking neurons are introduced, so that the target detection network achieves high performance, and meanwhile, the network operation power consumption, the parameter quantity and the calculation quantity are reduced; the network can be effectively trained in various target detection scenes, the network weight is deployed to edge small computing power equipment and brain-like chip equipment, and large-scale application of a target detection algorithm is realized.

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

The invention relates to an underwater target detection method based on a pulse neural network, which can realize underwater target detection with high precision and low power consumption. By fusing the cross-stage partial network and the YOLO architecture, the problem of pulse degradation is effectively solved, and the feature extraction capability of the model is enhanced; in order to solve the problem of underwater noise interference, the pulse-based underwater image denoising method is designed, only integer addition is used in the method, a pulse neural network structure can be seamlessly embedded, and the quality of a feature map is enhanced; in order to solve the problem that a traditional normalization method is low in precision in the spiking neural network, separate batch normalization is provided, by independently normalizing a feature map in multiple time steps and optimizing a residual structure, the time dynamic state of the SNN can be effectively captured, and the detection precision is improved. The network shows excellent performance in underwater target detection, and compared with an artificial neural network of the same scale, the network has higher performance and lower energy consumption.

This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNN model having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix. The classifier layer identifies a type of the action performed by the target based on the features.

The invention relates to the technical field of artificial intelligence, and discloses an artificial limb driving method based on a spiking neural network, which comprises the following steps: acquiring a multi-modal signal, and preprocessing the multi-modal signal; converting the preprocessed multi-mode signal into a pulse signal; inputting the pulse signal into a pulse neural network for sensing fusion processing, and outputting a control strategy; dynamically adjusting a control strategy based on pulse reinforcement learning according to the behavior feedback information through a reward function, and generating a learning result; and generating a bionic motion instruction according to the adjusted control strategy and the learning result, and driving an artificial limb execution mechanism to act according to the bionic motion instruction. According to the method, the electromyographic signals, the tactile pressure signals and the inertial measurement data are uniformly coded into the pulse sequence, the pulse neural network is utilized to realize multi-modal sensing fusion, the pulse reinforcement learning algorithm is introduced, the artificial limb control strategy is optimized in real time according to user behavior feedback, and the accurate control of the artificial limb joint is improved.

The invention discloses a small molecule drug dynamic analysis system based on a spiking neural network, and relates to the field of drug analysis, and the system comprises a management module which is used for controlling the editing and sending of control instructions of all function modules and units; the data collection module is used for collecting known drug compound structure data and corresponding in-vivo performance data; the model construction module is used for constructing a pulse graph neural network model architecture, and taking the drug compound structure data collected by the data collection module and the corresponding in-vivo performance data as training samples for training; the pulse calculation of the biological neural network can better simulate the characteristics of a biological system and the dynamic process of the biological system, so that the prediction precision of complex drug molecule behaviors is improved, training is performed on input data distribution through dynamic adjustment, the pulse graph neural network has higher generalization ability, error sources are captured, and the prediction precision is improved. Model structures and parameters are continuously adjusted from model identification output, and dynamic optimization is achieved.