537 results about "Spiking neural network" patented technology

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 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 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 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.

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 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.

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 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 an image classification method and device, equipment, a medium and a product, and relates to the field of image classification, and the method comprises the steps: obtaining to-be-classified image data; according to the to-be-classified image data, performing classification by using a classification model to obtain a classification category; the classification model is a trained pulse neural network; the spiking neural network comprises a plurality of neural layers; each neural layer comprises a plurality of neurons; each neuron comprises an emission threshold value and an input resistance; the training process of the classification model specifically comprises the following steps: taking sample image data as the input of a spiking neural network, taking a sample classification category as the output of the spiking neural network, and determining a total loss function of the spiking neural network according to cross entropy loss and adaptive sparse loss; and optimizing the synaptic weight, the emission threshold and the input resistance of the spiking neural network by using a back propagation algorithm to obtain a classification model. The method can improve the classification precision.

The disclosure regards a spiking neural network device comprising a plurality of cores, wherein each core is configured to generate and process a sequence of spike packets, wherein each of the cores is assigned to a router, said router being configured to transmit sequences of spike packets to one or more other routers or to receive sequences of spike packets from one or more other routers, and a plurality of transmission paths, wherein each transmission path is configured to connect two routers so that sequences of spike packets can be transmitted between the two routers, wherein the topology of the spiking neural network device is represented by a grid, in which the plurality of routers are arranged in a plurality of rows and a plurality of columns, wherein a substantially diamond-shaped or rhomboidal outline of the grid is obtained by letting the number of routers arranged in each row increase gradually by one or two routers between neighbouring rows from a minimum row length, such as one or two routers, in the two outermost rows in the grid, to a maximum row length in one or more central rows in the grid, and by letting the number of routers arranged in each column increase gradually by one or two routers between neighbouring columns from a minimum column height, such as one or two routers, in the two outermost columns in the grid, to a maximum column height in one or more central columns in the grid.

The invention relates to a method for deploying a spiking neural network to a hardware device. The method includes providing the spiking neural network, training the spiking neural network to obtain a trained spiking neural network, mapping neurons and synapses in the trained spiking neural network to corresponding components of the hardware device, simulating deployment of the trained spiking neural network on a hardware device using the obtained mapping, and deploying the trained spiking neural network to the hardware device using the mapping and the simulation. The training, mapping, simulation and deployment steps are executed by using hardware information of the hardware device; wherein the hardware information comprises at least one of hardware resource constraints, hardware connection constraints, dynamic range of hardware design parameters, characterization or statistics of neurons and synapses, reconfigurability, programmability, yield, computing resources, temporal characteristics, constraints on pre-processing, interfaces and peripheral devices, and available encoders and / or decoders.

The invention discloses a mechanical dog indoor autonomous navigation method based on a pulse neural network, and provides an indoor autonomous navigation system capable of increasing endurance and reducing calculation complexity for visual navigation. Comprises: preparing a data set; establishing a pulse neural network obstacle avoidance network model, and training the network model; establishing a pulse neural network self-planning path network model, and training the network model; taking the corrected picture as the input of a pulse neural network self-planning path network model, running the network model, processing the output of the pulse neural network self-planning path network model into a control instruction of a mechanical dog, and transmitting the control instruction to the mechanical dog through a port; the obstacle image data acquired by the RGB-D camera is called as the input of the spiking neural network obstacle avoidance network model, the spiking neural network obstacle avoidance network model is operated, the output of the spiking neural network obstacle avoidance network model is processed into an obstacle avoidance instruction of a mechanical dog, and the obstacle avoidance instruction is transmitted to the mechanical dog through a port; and repeatedly executing the steps 4-5, and continuously planning a path to complete a navigation task.

The invention discloses a spiking neural network deployment method and a deployment tool chain based on intermediate representation, and the method comprises the steps: carrying out the model analysis and structure mapping operation of a trained ANN-ONNX model, generating an SNN-ONNX model with a pulse characteristic, carrying out the optimization of a weight parameter through the combination of transfer learning and a precision fine tuning strategy, and carrying out the optimization of the weight parameter. Therefore, the expression ability and adaptability of the converted model are improved, and the reasoning execution performance of the SNN model on a target platform is improved by adopting an optimization means; meanwhile, an SNN-oriented modular operator component library is constructed, and the portability, maintainability and expandability of operators among different hardware platforms are enhanced by adopting an abstract interface and a design mode of specifically realizing decoupling. According to the method, efficient deployment of the SNN model on a target hardware platform is achieved, performance fidelity of the model under the function equivalent condition is ensured, and the method has wide engineering application prospects in the fields of edge calculation, low-power-consumption intelligent terminals, brain inspiration type artificial intelligence and the like.

The invention discloses a lightweight target trajectory prediction method based on a twin pulse neural network, and belongs to the field of target trajectory prediction. The method comprises the following steps: constructing a sample pair comprising a template frame and a search frame; constructing a twin spiking neural network model comprising a template branch, a search branch, an adaptive mask module, a spiking nerve Transform module and an output prediction layer, wherein the template branch and the search branch share weights; the adaptive mask module is used for aligning output features of two branches and generating a mask and feature enhancement, and the pulse nerve Transform module is used for acquiring a global dependency relationship of the enhanced features and outputting a current position and a tracking path end point of a target through an output prediction layer so as to generate a tracking plan path of the target; and training the constructed model based on the training data constructed by the sample pair to obtain a model used for obtaining the tracking plan path of the target. According to the method, key pulses are dynamically screened, the network structure is optimized, and double improvement of precision and efficiency is achieved.

The invention discloses an intelligent intestinal tract monitoring method and system based on a multispectral sensing technology. The method comprises the following steps: acquiring spectral data of excrement through a multispectral sensor array integrated on a toilet seat; carrying out data processing by adopting segmented trapezoidal architecture mapping and a Spiking neural network, and extracting a light transmittance characteristic matrix; a nonlinear attention mechanism is realized based on a Hopfield network, and a light transmittance dynamic curve is generated; performing intestinal preparation degree evaluation by using a sparse MLP in combination with a motif structure optimization algorithm; and remote monitoring and intelligent feedback are realized through a 5G network. Objective quantitative evaluation of the intestinal preparation process is achieved, the preparation efficiency and quality of enteroscopy and intestinal surgery are improved, and the technical problems that traditional intestinal preparation evaluation is high in subjectivity and lacks real-time monitoring are solved.

The invention relates to the field of mechanical equipment monitoring and intelligent diagnosis, in particular to an equipment real-time fault diagnosis method based on an FPGA and an SNN, and the method comprises the steps: obtaining an original vibration signal, and constructing a local training set; establishing an SNN diagnosis model based on an attention mechanism, and training the model by adopting a local training set; deploying the trained model to an FPGA (Field Programmable Gate Array) end; constructing a mechanical vibration signal real-time acquisition system based on the FPGA; the system obtains real-time vibration data and carries out cross point sliding window preprocessing on the real-time vibration data, the SNN model deployed to the FPGA end carries out reasoning on the preprocessed data, and a diagnosis result is obtained. The pulse neural network model and the FPGA technology are combined, low-energy-consumption and efficient calculation is achieved through edge calculation, the strict requirements for real-time performance and low delay in the industrial environment are met, and the intelligent level of equipment monitoring is effectively improved.

The present invention discloses a node scale-adaptive neuron spike sorting method based on neuromorphic computing, and relates to the field of electroencephalogram signal spike sorting and decoding, the present invention proposes a spiking neural network framework comprising a two-layer spiking neural network and an attention neuron node, by incorporating prior knowledge of spike waveforms, this method automatically guides the addition and removal of network nodes to optimize computational resource allocation according to specific requirements, thereby minimizing hardware resource wastage. This method is characterized by low hardware overhead, high computational speed, and high consistency of results across different datasets. This method enhances the speed of spike sorting processes and shows potential for providing fully automated neuronal classification technology support for wireless implantable brain signal acquisition devices.

The invention discloses a big data analysis system and method for a financial guarantee circle, and relates to the technical field of data analysis, and the method comprises the steps: collecting industrial and commercial, judicial and financial data in real time through an API gateway, and constructing a quantization guarantee network model and a quantum annealing parameter configuration file after preprocessing; a quantum annealing machine is used for executing hidden relation detection, and Bayesian confidence coefficient calibration is combined to generate a high-association guarantee ring list and a quantum calculation log; simulating a risk conduction path based on a synaptic plasticity model of the spiking neural network, and outputting a synaptic weight change table and a dynamic conduction diagram; the risk levels are mapped into a hierarchical blocking strategy matrix through a dynamic blocking decision engine, topology reinforcement is carried out by combining historical data and adopting a minimum spanning tree algorithm, and a stable subnet list and a protection path diagram are generated; and finally, performing weighted fusion on the quantum log, the conduction model and the subnet data, constructing a three-dimensional risk map, and optimizing a real-time decision by using a long and short-term memory network and an attention mechanism network.

The invention relates to the technical field of artificial intelligence, in particular to a method, device and equipment for simulating a finite-state machine based on a spiking neural network and a storage medium. The method comprises the following steps: acquiring an input signal; according to the input signal, a corresponding output signal is output through a DTSRNN model which is trained in advance, the DTSRNN model is a model which simulates the state transition process of an FSM by combining the characteristics of the DTSRNN and an SNN, and the output signal is a response signal generated by the DTSRNN model according to the input signal and internal state transition logic. According to the embodiment of the invention, the DTSRNN model combining the characteristics of the DTRNN and the SNN is provided, and the information can be processed in a discrete and sparse mode, which is highly matched with the discrete state conversion process of the FSM, thereby achieving the efficient simulation of the FSM behavior, and greatly improving the performance of a neural network model for simulating the FSM.